diff --git a/.gitignore b/.gitignore index e3a1390..2a1860c 100644 --- a/.gitignore +++ b/.gitignore @@ -901,3 +901,4 @@ FodyWeavers.xsd scratch/ *.jnl *.jar +scripts/Untitled.ipynb diff --git a/notebooks/landscape/README.md b/notebooks/landscape/README.md new file mode 100644 index 0000000..7b18a4e --- /dev/null +++ b/notebooks/landscape/README.md @@ -0,0 +1,41 @@ +# Landscape Analysis + +This folder contains results from a workflow for automating the analysis of the landscape of a given domain, +given a declarative configuration describing the resources in that domain. It includes five landscape analyses: + +1. [Disease](disease/disease-landscape.ipynb) +2. [Cell & Cell Line](cell/cell-landscape.ipynb) +3. [Anatomy](anatomy/anatomy-landscape.ipynb) +4. [Protein Complex](complex/complex-landscape.ipynb) +5. [Gene](gene/gene-landscape.ipynb) + +## Example + +Below, we highlight the disease landscape. Each analysis creates a graph of the processed mappings. + +![](disease/graph.svg) + +We're able to automatically generate an UpSet plot like the one in [How many rare diseases are there? (Haendel *et +al.*, 2020)](https://doi.org/10.1038/d41573-019-00180-y) (a similar plot to the following appears in +the [supplementary info](https://media.nature.com/original/magazine-assets/d41573-019-00180-y/17308594) and an +explanation appears on [zenodo](https://zenodo.org/records/3478576)). Note that our plot is about all diseases, not +specifically rare ones: + +![](disease/landscape_upset.svg) + +The following histogram estimates how many diseases there are. Importantly, it shows how many show up in a single +resource, how many show up in all resources, and how many show up in a few + +![](disease/landscape_histogram.svg) + +## Summary + +A summary chart over all landscapes can be generated with `landscape.py`. + +| name | raw_term_count | unique_term_count | reduction | +|---------|---------------:|------------------:|----------:| +| disease | 410173 | 243730 | 0.405787 | +| anatomy | 37917 | 32108 | 0.153203 | +| complex | 15869 | 7775 | 0.510051 | +| gene | 4.94578e+07 | 4.87886e+07 | 0.013529 | +| cell | 207019 | 166274 | 0.196818 | \ No newline at end of file diff --git a/notebooks/landscape/anatomy/anatomy-landscape.ipynb b/notebooks/landscape/anatomy/anatomy-landscape.ipynb new file mode 100644 index 0000000..f39084a --- /dev/null +++ b/notebooks/landscape/anatomy/anatomy-landscape.ipynb @@ -0,0 +1,4822 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "fa998bc2-775e-479c-9878-e8314f4813a2", + "metadata": {}, + "outputs": [], + "source": [ + "from semra.landscape.anatomy import CONFIGURATION\n", + "from semra.landscape.utils import notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "49221887-4ca0-49b3-9a25-f3b93bb2b79c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Summarize the Resources\n", + "\n", + "We summarize the resources used in the landscape analysis, including their [Bioregistry](https://bioregistry.io)\n", + "prefix, license, current version, and number of terms (i.e., named concepts) they contain.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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meshMedical Subject HeadingsCC0-1.020241796subset
btoBRENDA Tissue OntologyCC-BY-4.02021-10-266511full
caroCommon Anatomy Reference OntologyCC-BY-4.02023-03-1584full
ncitNCI ThesaurusCC-BY-4.024.02d7556subset
umlsUnified Medical Language System Concept Unique...https://www.nlm.nih.gov/research/umls/knowledg...2023AB7490subset
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" + ], + "text/plain": [ + " name \\\n", + "prefix \n", + "uberon Uber Anatomy Ontology \n", + "mesh Medical Subject Headings \n", + "bto BRENDA Tissue Ontology \n", + "caro Common Anatomy Reference Ontology \n", + "ncit NCI Thesaurus \n", + "umls Unified Medical Language System Concept Unique... \n", + "\n", + " license version terms \\\n", + "prefix \n", + "uberon CC-BY-3.0 2024-03-22 14480 \n", + "mesh CC0-1.0 2024 1796 \n", + "bto CC-BY-4.0 2021-10-26 6511 \n", + "caro CC-BY-4.0 2023-03-15 84 \n", + "ncit CC-BY-4.0 24.02d 7556 \n", + "umls https://www.nlm.nih.gov/research/umls/knowledg... 2023AB 7490 \n", + "\n", + " status \n", + "prefix \n", + "uberon full \n", + "mesh subset \n", + "bto full \n", + "caro full \n", + "ncit subset \n", + "umls subset " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "There are a total of 37,917 terms across the 6 resources." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Summarize the Mappings\n", + "\n", + "In order to summarize the mappings, we're going to load them, index them, and count\n", + "the number of mappings between each pair of resources. The self-mapping column is\n", + "the count of terms in the resource. We'll do this to the raw mappings first, then\n", + "to the processed mappings, then compare them.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Indexing mappings: 0%| | 0.00/55.6k [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "target_prefix uberon mesh bto caro ncit umls\n", + "source_prefix \n", + "uberon 14480 1080 1461 52 2526 281\n", + "mesh 1080 1796 0 0 28 160\n", + "bto 1461 0 6511 0 0 0\n", + "caro 52 0 0 84 0 0\n", + "ncit 2526 28 0 0 7556 518\n", + "umls 281 160 0 0 518 7490" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Next, we summarize the processed mappings, which include inference, reasoning, and confidence filtering." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixuberonmeshbtocaroncitumls
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uberon1448010821461522531311
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" + ], + "text/plain": [ + "target_prefix uberon mesh bto caro ncit umls\n", + "source_prefix \n", + "uberon 14480 1082 1461 52 2531 311\n", + "mesh 1082 1796 55 0 74 160\n", + "bto 1461 55 6511 6 805 122\n", + "caro 52 0 6 84 20 9\n", + "ncit 2531 74 805 20 7556 529\n", + "umls 311 160 122 9 529 7490" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Below is an graph-based view on the processed mappings." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "mesh\n", + "1,796\n", + "\n", + "mesh\n", + "1,796\n", + "\n", + "\n", + "\n", + "ncit\n", + "7,556\n", + "\n", + "ncit\n", + "7,556\n", + "\n", + "\n", + "\n", + "mesh\n", + "1,796--ncit\n", + "7,556\n", + "\n", + "74\n", + "\n", + "\n", + "\n", + "uberon\n", + "14,480\n", + "\n", + "uberon\n", + "14,480\n", + "\n", + "\n", + "\n", + "mesh\n", + "1,796--uberon\n", + "14,480\n", + "\n", + "1,082\n", + "\n", + "\n", + "\n", + "bto\n", + "6,511\n", + "\n", + "bto\n", + "6,511\n", + "\n", + "\n", + "\n", + "mesh\n", + "1,796--bto\n", + "6,511\n", + "\n", + "55\n", + "\n", + "\n", + "\n", + "umls\n", + "7,490\n", + "\n", + "umls\n", + "7,490\n", + "\n", + "\n", + "\n", + "mesh\n", + "1,796--umls\n", + "7,490\n", + "\n", + "160\n", + "\n", + "\n", + "\n", + "ncit\n", + "7,556--uberon\n", + "14,480\n", + "\n", + "2,531\n", + "\n", + "\n", + "\n", + "ncit\n", + "7,556--bto\n", + "6,511\n", + "\n", + "805\n", + "\n", + "\n", + "\n", + "ncit\n", + "7,556--umls\n", + "7,490\n", + "\n", + "529\n", + "\n", + "\n", + "\n", + "uberon\n", + "14,480--bto\n", + "6,511\n", + "\n", + "1,461\n", + "\n", + "\n", + "\n", + "uberon\n", + "14,480--umls\n", + "7,490\n", + "\n", + "311\n", + "\n", + "\n", + "\n", + "caro\n", + "84\n", + "\n", + "caro\n", + "84\n", + "\n", + "\n", + "\n", + "uberon\n", + "14,480--caro\n", + "84\n", + "\n", + "52\n", + "\n", + "\n", + "\n", + "bto\n", + "6,511--umls\n", + "7,490\n", + "\n", + "122\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Comparison\n", + "\n", + "The following comparison shows the absolute number of mappings added by processing/inference.\n", + "Across the board, this process adds large numbers of mappings to most resources, especially\n", + "ones that were previously only connected to a small number of other resources.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixuberonmeshbtocaroncitumls
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" + ], + "text/plain": [ + "target_prefix uberon mesh bto caro ncit umls\n", + "source_prefix \n", + "uberon 0 2 0 0 5 30\n", + "mesh 2 0 55 0 46 0\n", + "bto 0 55 0 6 805 122\n", + "caro 0 0 6 0 20 9\n", + "ncit 5 46 805 20 0 11\n", + "umls 30 0 122 9 11 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Here's an alternative view on the number of mappings normalized to show percentage gain.\n", + "\n", + "Note:\n", + "\n", + "- `inf` means that there were no mappings before and now there are a non-zero number of mappings\n", + "- `NaN` means there were no mappings before inference and continue to be no mappings after inference\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixuberonmeshbtocaroncitumls
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mesh0.20.0infNaN164.30.0
bto0.0inf0.0infinfinf
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" + ], + "text/plain": [ + "target_prefix uberon mesh bto caro ncit umls\n", + "source_prefix \n", + "uberon 0.0 0.2 0.0 0.0 0.2 10.7\n", + "mesh 0.2 0.0 inf NaN 164.3 0.0\n", + "bto 0.0 inf 0.0 inf inf inf\n", + "caro 0.0 NaN inf 0.0 inf inf\n", + "ncit 0.2 164.3 inf inf 0.0 2.1\n", + "umls 10.7 0.0 inf inf 2.1 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Landscape Analysis\n", + "\n", + "Before, we looked at the overlaps between each resource. Now, we use that information jointly to\n", + "estimate the number of terms in the landscape itself, and estimate how much of the landscape\n", + "each resource covers.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "This estimates a total of 32,108 unique entities.\n", + "\n", + "- 3,529 (11.0%) have\n", + " at least one mapping.\n", + "- 28,579 (89.0%)\n", + " are unique to a single resource.\n", + "- 1 (0.0%)\n", + " appear in all 6 resources.\n", + "\n", + "This estimate is susceptible to several caveats:\n", + "\n", + "- Missing mappings inflates this measurement\n", + "- Generic resources like MeSH contain irrelevant entities that can't be mapped\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Because there are 6, there are 63 possible overlaps to consider.\n", + "Therefore, a Venn diagram is not possible, so we\n", + "we use an [UpSet plot](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720993) (Lex *et al.*, 2014)\n", + "as a high-dimensional Venn diagram.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-04-11T14:03:06.782330\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.0, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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It can be artificially low because for some vocabularies like SNOMED-CT, it's not possible\n", + " to load a terms list, and therefore it's not possible to account for terms that aren't mapped.\n", + " Therefore, we make a lower bound estimate based on the terms that appear in mappings\n", + "4. It can be artificially high if a vocabulary is used that covers many domains and is not properly\n", + " subset'd. For example, EFO covers many different domains, so when doing disease landscape\n", + " analysis, it should be subset to only terms in the disease hierarchy (i.e., appearing under\n", + " ``efo:0000408``).\n", + "5. It can be affected by terminology issues, such as the confusion between Orphanet and ORDO\n", + "6. It can be affected by the existence of many-to-many mappings, which are filtered out during\n", + " processing, which makes the estimate artificially high since some subset of those entities\n", + " could be mapped, but it's not clear which should.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = notebook(CONFIGURATION, output_directory=\".\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/landscape/anatomy/counts.tsv b/notebooks/landscape/anatomy/counts.tsv new file mode 100644 index 0000000..b5ba82c --- 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793, + "2": 2186, + "5": 63, + "6": 1, + "1": 28579 + } +} \ No newline at end of file diff --git a/notebooks/landscape/anatomy/summary.tsv b/notebooks/landscape/anatomy/summary.tsv new file mode 100644 index 0000000..07d84c5 --- /dev/null +++ b/notebooks/landscape/anatomy/summary.tsv @@ -0,0 +1,7 @@ +prefix name license version terms status +uberon Uber Anatomy Ontology CC-BY-3.0 2024-03-22 14480 full +mesh Medical Subject Headings CC0-1.0 2024 1796 subset +bto BRENDA Tissue Ontology CC-BY-4.0 2021-10-26 6511 full +caro Common Anatomy Reference Ontology CC-BY-4.0 2023-03-15 84 full +ncit NCI Thesaurus CC-BY-4.0 24.02d 7556 subset +umls Unified Medical Language System Concept Unique Identifier https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/license_agreement.html 2023AB 7490 subset diff --git a/notebooks/landscape/cell/cell-landscape.ipynb b/notebooks/landscape/cell/cell-landscape.ipynb new file mode 100644 index 0000000..468379f --- /dev/null +++ b/notebooks/landscape/cell/cell-landscape.ipynb @@ -0,0 +1,6648 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "71688a16-3fcf-4222-a8ec-e5d212feef59", + "metadata": {}, + "outputs": [], + "source": [ + "from semra.landscape.cells import CONFIGURATION\n", + "from semra.landscape.utils import notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0438eacb-3ffd-40aa-9cba-d7df7eb4e42e", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading cl.obo: 0.00B [00:00, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "[cl] parsing obo: 0.00it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Summarize the Resources\n", + "\n", + "We summarize the resources used in the landscape analysis, including their [Bioregistry](https://bioregistry.io)\n", + "prefix, license, current version, and number of terms (i.e., named concepts) they contain.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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namelicenseversiontermsstatus
prefix
meshMedical Subject HeadingsCC0-1.02024636subset
efoExperimental Factor OntologyApache-2.03.64.025subset
cellosaurusCellosaurusCC-BY-4.048.0148681full
ccleCancer Cell Line Encyclopedia CellsODbL-1.0None1061full
depmapDepMap Cell LinesCC-BY-4.023Q41719full
btoBRENDA Tissue OntologyCC-BY-4.02021-10-266511full
clCell OntologyCC-BY-4.02024-04-052746full
cloCell Line OntologyCC-BY-3.02.1.17839080full
ncitNCI ThesaurusCC-BY-4.024.02d503subset
umlsUnified Medical Language System Concept Unique...https://www.nlm.nih.gov/research/umls/knowledg...2023AB6057subset
\n", + "
" + ], + "text/plain": [ + " name \\\n", + "prefix \n", + "mesh Medical Subject Headings \n", + "efo Experimental Factor Ontology \n", + "cellosaurus Cellosaurus \n", + "ccle Cancer Cell Line Encyclopedia Cells \n", + "depmap DepMap Cell Lines \n", + "bto BRENDA Tissue Ontology \n", + "cl Cell Ontology \n", + "clo Cell Line Ontology \n", + "ncit NCI Thesaurus \n", + "umls Unified Medical Language System Concept Unique... \n", + "\n", + " license version \\\n", + "prefix \n", + "mesh CC0-1.0 2024 \n", + "efo Apache-2.0 3.64.0 \n", + "cellosaurus CC-BY-4.0 48.0 \n", + "ccle ODbL-1.0 None \n", + "depmap CC-BY-4.0 23Q4 \n", + "bto CC-BY-4.0 2021-10-26 \n", + "cl CC-BY-4.0 2024-04-05 \n", + "clo CC-BY-3.0 2.1.178 \n", + "ncit CC-BY-4.0 24.02d \n", + "umls https://www.nlm.nih.gov/research/umls/knowledg... 2023AB \n", + "\n", + " terms status \n", + "prefix \n", + "mesh 636 subset \n", + "efo 25 subset \n", + "cellosaurus 148681 full \n", + "ccle 1061 full \n", + "depmap 1719 full \n", + "bto 6511 full \n", + "cl 2746 full \n", + "clo 39080 full \n", + "ncit 503 subset \n", + "umls 6057 subset " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "There are a total of 207,019 terms across the 10 resources." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Summarize the Mappings\n", + "\n", + "In order to summarize the mappings, we're going to load them, index them, and count\n", + "the number of mappings between each pair of resources. The self-mapping column is\n", + "the count of terms in the resource. We'll do this to the raw mappings first, then\n", + "to the processed mappings, then compare them.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Indexing mappings: 0%| | 0.00/633k [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixmeshefocellosaurusccledepmapbtoclcloncitumls
source_prefix
mesh63623000084346433
efo22520010010
cellosaurus3021486811131894241503414900
ccle001131061153520000
depmap0018941535171900000
bto012415206511328500
cl8400003282746060
clo3403414900503908000
ncit61000060503495
umls43300000004956057
\n", + "
" + ], + "text/plain": [ + "target_prefix mesh efo cellosaurus ccle depmap bto cl clo ncit \\\n", + "source_prefix \n", + "mesh 636 2 30 0 0 0 84 34 6 \n", + "efo 2 25 2 0 0 1 0 0 1 \n", + "cellosaurus 30 2 148681 113 1894 2415 0 34149 0 \n", + "ccle 0 0 113 1061 1535 2 0 0 0 \n", + "depmap 0 0 1894 1535 1719 0 0 0 0 \n", + "bto 0 1 2415 2 0 6511 328 5 0 \n", + "cl 84 0 0 0 0 328 2746 0 6 \n", + "clo 34 0 34149 0 0 5 0 39080 0 \n", + "ncit 6 1 0 0 0 0 6 0 503 \n", + "umls 433 0 0 0 0 0 0 0 495 \n", + "\n", + "target_prefix umls \n", + "source_prefix \n", + "mesh 433 \n", + "efo 0 \n", + "cellosaurus 0 \n", + "ccle 0 \n", + "depmap 0 \n", + "bto 0 \n", + "cl 0 \n", + "clo 0 \n", + "ncit 495 \n", + "umls 6057 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Next, we summarize the processed mappings, which include inference, reasoning, and confidence filtering." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixmeshefocellosaurusccledepmapbtoclcloncitumls
source_prefix
mesh636232666284347433
efo22520010011
cellosaurus32214868116551912241513415000
ccle601655106115366410141600
depmap601912153617196680147200
bto62124156416686511328143577
cl8401003282746088
clo3403415014161472143503908000
ncit71000780503495
umls43310007804956057
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" + ], + "text/plain": [ + "target_prefix mesh efo cellosaurus ccle depmap bto cl clo ncit \\\n", + "source_prefix \n", + "mesh 636 2 32 6 6 62 84 34 7 \n", + "efo 2 25 2 0 0 1 0 0 1 \n", + "cellosaurus 32 2 148681 1655 1912 2415 1 34150 0 \n", + "ccle 6 0 1655 1061 1536 641 0 1416 0 \n", + "depmap 6 0 1912 1536 1719 668 0 1472 0 \n", + "bto 62 1 2415 641 668 6511 328 1435 7 \n", + "cl 84 0 1 0 0 328 2746 0 8 \n", + "clo 34 0 34150 1416 1472 1435 0 39080 0 \n", + "ncit 7 1 0 0 0 7 8 0 503 \n", + "umls 433 1 0 0 0 7 8 0 495 \n", + "\n", + "target_prefix umls \n", + "source_prefix \n", + "mesh 433 \n", + "efo 1 \n", + "cellosaurus 0 \n", + "ccle 0 \n", + "depmap 0 \n", + "bto 7 \n", + "cl 8 \n", + "clo 0 \n", + "ncit 495 \n", + "umls 6057 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Below is an graph-based view on the processed mappings." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "bto\n", + "6,511\n", + "\n", + "bto\n", + "6,511\n", + "\n", + "\n", + "\n", + "cl\n", + "2,746\n", + "\n", + "cl\n", + "2,746\n", + "\n", + "\n", + "\n", + "bto\n", + "6,511--cl\n", + "2,746\n", + "\n", + "328\n", + "\n", + "\n", + "\n", + "ccle\n", + "1,061\n", + "\n", + "ccle\n", + "1,061\n", + "\n", + "\n", + "\n", + "bto\n", + "6,511--ccle\n", + "1,061\n", + "\n", + "641\n", + "\n", + "\n", + "\n", + "cellosaurus\n", + "148,681\n", + "\n", + "cellosaurus\n", + "148,681\n", + "\n", + "\n", + "\n", + "bto\n", + "6,511--cellosaurus\n", + "148,681\n", + "\n", + "2,415\n", + "\n", + "\n", + "\n", + "depmap\n", + "1,719\n", + "\n", + "depmap\n", + "1,719\n", + "\n", + "\n", + "\n", + "bto\n", + "6,511--depmap\n", + "1,719\n", + "\n", + "668\n", + "\n", + "\n", + "\n", + "mesh\n", + "636\n", + "\n", + "mesh\n", + "636\n", + "\n", + "\n", + "\n", + "bto\n", + "6,511--mesh\n", + "636\n", + "\n", + "62\n", + "\n", + "\n", + "\n", + "clo\n", + "39,080\n", + "\n", + "clo\n", + "39,080\n", + "\n", + "\n", + "\n", + "bto\n", + "6,511--clo\n", + "39,080\n", + "\n", + "1,435\n", + "\n", + "\n", + "\n", + "cl\n", + "2,746--mesh\n", + "636\n", + "\n", + "84\n", + "\n", + "\n", + "\n", + "ccle\n", + "1,061--cellosaurus\n", + "148,681\n", + "\n", + "1,655\n", + "\n", + "\n", + "\n", + "ccle\n", + "1,061--depmap\n", + "1,719\n", + "\n", + "1,536\n", + "\n", + "\n", + "\n", + "ccle\n", + "1,061--clo\n", + "39,080\n", + "\n", + "1,416\n", + "\n", + "\n", + "\n", + "cellosaurus\n", + "148,681--depmap\n", + "1,719\n", + "\n", + "1,912\n", + "\n", + "\n", + "\n", + "cellosaurus\n", + "148,681--mesh\n", + "636\n", + "\n", + "32\n", + "\n", + "\n", + "\n", + "cellosaurus\n", + "148,681--clo\n", + "39,080\n", + "\n", + "34,150\n", + "\n", + "\n", + "\n", + "depmap\n", + "1,719--clo\n", + "39,080\n", + "\n", + "1,472\n", + "\n", + "\n", + "\n", + "mesh\n", + "636--clo\n", + "39,080\n", + "\n", + "34\n", + "\n", + "\n", + "\n", + "umls\n", + "6,057\n", + "\n", + "umls\n", + "6,057\n", + "\n", + "\n", + "\n", + "mesh\n", + "636--umls\n", + "6,057\n", + "\n", + "433\n", + "\n", + "\n", + "\n", + "ncit\n", + "503\n", + "\n", + "ncit\n", + "503\n", + "\n", + "\n", + "\n", + "umls\n", + "6,057--ncit\n", + "503\n", + "\n", + "495\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Comparison\n", + "\n", + "The following comparison shows the absolute number of mappings added by processing/inference.\n", + "Across the board, this process adds large numbers of mappings to most resources, especially\n", + "ones that were previously only connected to a small number of other resources.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixmeshefocellosaurusccledepmapbtoclcloncitumls
source_prefix
mesh00266620010
efo0000000001
cellosaurus20015421801100
ccle601542016390141600
depmap6018106680147200
bto620063966800143077
cl0010000028
clo0011416147214300000
ncit1000072000
umls0100078000
\n", + "
" + ], + "text/plain": [ + "target_prefix mesh efo cellosaurus ccle depmap bto cl clo ncit \\\n", + "source_prefix \n", + "mesh 0 0 2 6 6 62 0 0 1 \n", + "efo 0 0 0 0 0 0 0 0 0 \n", + "cellosaurus 2 0 0 1542 18 0 1 1 0 \n", + "ccle 6 0 1542 0 1 639 0 1416 0 \n", + "depmap 6 0 18 1 0 668 0 1472 0 \n", + "bto 62 0 0 639 668 0 0 1430 7 \n", + "cl 0 0 1 0 0 0 0 0 2 \n", + "clo 0 0 1 1416 1472 1430 0 0 0 \n", + "ncit 1 0 0 0 0 7 2 0 0 \n", + "umls 0 1 0 0 0 7 8 0 0 \n", + "\n", + "target_prefix umls \n", + "source_prefix \n", + "mesh 0 \n", + "efo 1 \n", + "cellosaurus 0 \n", + "ccle 0 \n", + "depmap 0 \n", + "bto 7 \n", + "cl 8 \n", + "clo 0 \n", + "ncit 0 \n", + "umls 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Here's an alternative view on the number of mappings normalized to show percentage gain.\n", + "\n", + "Note:\n", + "\n", + "- `inf` means that there were no mappings before and now there are a non-zero number of mappings\n", + "- `NaN` means there were no mappings before inference and continue to be no mappings after inference\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixmeshefocellosaurusccledepmapbtoclcloncitumls
source_prefix
mesh0.00.06.7infinfinf0.00.016.70.0
efo0.00.00.0NaNNaN0.0NaNNaN0.0inf
cellosaurus6.70.00.01364.61.00.0inf0.0NaNNaN
ccleinfNaN1364.60.00.131950.0NaNinfNaNNaN
depmapinfNaN1.00.10.0infNaNinfNaNNaN
btoinf0.00.031950.0inf0.00.028600.0infinf
cl0.0NaNinfNaNNaN0.00.0NaN33.3inf
clo0.0NaN0.0infinf28600.0NaN0.0NaNNaN
ncit16.70.0NaNNaNNaNinf33.3NaN0.00.0
umls0.0infNaNNaNNaNinfinfNaN0.00.0
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" + ], + "text/plain": [ + "target_prefix mesh efo cellosaurus ccle depmap bto cl \\\n", + "source_prefix \n", + "mesh 0.0 0.0 6.7 inf inf inf 0.0 \n", + "efo 0.0 0.0 0.0 NaN NaN 0.0 NaN \n", + "cellosaurus 6.7 0.0 0.0 1364.6 1.0 0.0 inf \n", + "ccle inf NaN 1364.6 0.0 0.1 31950.0 NaN \n", + "depmap inf NaN 1.0 0.1 0.0 inf NaN \n", + "bto inf 0.0 0.0 31950.0 inf 0.0 0.0 \n", + "cl 0.0 NaN inf NaN NaN 0.0 0.0 \n", + "clo 0.0 NaN 0.0 inf inf 28600.0 NaN \n", + "ncit 16.7 0.0 NaN NaN NaN inf 33.3 \n", + "umls 0.0 inf NaN NaN NaN inf inf \n", + "\n", + "target_prefix clo ncit umls \n", + "source_prefix \n", + "mesh 0.0 16.7 0.0 \n", + "efo NaN 0.0 inf \n", + "cellosaurus 0.0 NaN NaN \n", + "ccle inf NaN NaN \n", + "depmap inf NaN NaN \n", + "bto 28600.0 inf inf \n", + "cl NaN 33.3 inf \n", + "clo 0.0 NaN NaN \n", + "ncit NaN 0.0 0.0 \n", + "umls NaN 0.0 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Landscape Analysis\n", + "\n", + "Before, we looked at the overlaps between each resource. Now, we use that information jointly to\n", + "estimate the number of terms in the landscape itself, and estimate how much of the landscape\n", + "each resource covers.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "This estimates a total of 166,274 unique entities.\n", + "\n", + "- 35,689 (21.5%) have\n", + " at least one mapping.\n", + "- 130,585 (78.5%)\n", + " are unique to a single resource.\n", + "- 0 (0.0%)\n", + " appear in all 10 resources.\n", + "\n", + "This estimate is susceptible to several caveats:\n", + "\n", + "- Missing mappings inflates this measurement\n", + "- Generic resources like MeSH contain irrelevant entities that can't be mapped\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Because there are 10, there are 1023 possible overlaps to consider.\n", + "Therefore, a Venn diagram is not possible, so we\n", + "we use an [UpSet plot](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720993) (Lex *et al.*, 2014)\n", + "as a high-dimensional Venn diagram.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-04-11T14:07:02.946772\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.0, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "\\\n", + " The landscape of 10 resources has 207,019 total terms.\n", + " After merging redundant nodes based on mappings, inference, and reasoning, there\n", + " are 166,274 unique concepts. Using the reduction formula\n", + " $\\frac{\\text{total terms} - \\text{reduced terms}}{\\text{total terms}}$,\n", + " this is a 19.7% reduction.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "This is only an estimate and is susceptible to a few things:\n", + "\n", + "1. It can be artificially high because there are entities that _should_ be mapped, but are not\n", + "2. It can be artificially low because there are entities that are incorrectly mapped, e.g., as\n", + " a result of inference. The frontend curation interface can help identify and remove these\n", + "3. It can be artificially low because for some vocabularies like SNOMED-CT, it's not possible\n", + " to load a terms list, and therefore it's not possible to account for terms that aren't mapped.\n", + " Therefore, we make a lower bound estimate based on the terms that appear in mappings\n", + "4. It can be artificially high if a vocabulary is used that covers many domains and is not properly\n", + " subset'd. For example, EFO covers many different domains, so when doing disease landscape\n", + " analysis, it should be subset to only terms in the disease hierarchy (i.e., appearing under\n", + " ``efo:0000408``).\n", + "5. It can be affected by terminology issues, such as the confusion between Orphanet and ORDO\n", + "6. It can be affected by the existence of many-to-many mappings, which are filtered out during\n", + " processing, which makes the estimate artificially high since some subset of those entities\n", + " could be mapped, but it's not clear which should.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = notebook(CONFIGURATION, output_directory=\".\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/landscape/cell/counts.tsv b/notebooks/landscape/cell/counts.tsv new file mode 100644 index 0000000..23d3a0f --- 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b/notebooks/landscape/cell/raw_counts.tsv @@ -0,0 +1,11 @@ +source_prefix mesh efo cellosaurus ccle depmap bto cl clo ncit umls +mesh 636 2 30 0 0 0 84 34 6 433 +efo 2 25 2 0 0 1 0 0 1 0 +cellosaurus 30 2 148681 113 1894 2415 0 34149 0 0 +ccle 0 0 113 1061 1535 2 0 0 0 0 +depmap 0 0 1894 1535 1719 0 0 0 0 0 +bto 0 1 2415 2 0 6511 328 5 0 0 +cl 84 0 0 0 0 328 2746 0 6 0 +clo 34 0 34149 0 0 5 0 39080 0 0 +ncit 6 1 0 0 0 0 6 0 503 495 +umls 433 0 0 0 0 0 0 0 495 6057 diff --git a/notebooks/landscape/cell/stats.json b/notebooks/landscape/cell/stats.json new file mode 100644 index 0000000..b0f3b03 --- /dev/null +++ b/notebooks/landscape/cell/stats.json @@ -0,0 +1,14 @@ +{ + "raw_term_count": 207019, + "unique_term_count": 166274, + "reduction": 0.19681768340104047, + "distribution": { + "2": 33314, + "5": 595, + "4": 765, + "7": 14, + "3": 1001, + "1": 130585, + "6": 0 + } +} \ No newline at end of file diff --git a/notebooks/landscape/cell/summary.tsv b/notebooks/landscape/cell/summary.tsv new file mode 100644 index 0000000..fab64f0 --- /dev/null +++ b/notebooks/landscape/cell/summary.tsv @@ -0,0 +1,11 @@ +prefix name license version terms status +mesh Medical Subject Headings CC0-1.0 2024 636 subset +efo Experimental Factor Ontology Apache-2.0 3.64.0 25 subset +cellosaurus Cellosaurus CC-BY-4.0 48.0 148681 full +ccle Cancer Cell Line Encyclopedia Cells ODbL-1.0 1061 full +depmap DepMap Cell Lines CC-BY-4.0 23Q4 1719 full +bto BRENDA Tissue Ontology CC-BY-4.0 2021-10-26 6511 full +cl Cell Ontology CC-BY-4.0 2024-04-05 2746 full +clo Cell Line Ontology CC-BY-3.0 2.1.178 39080 full +ncit NCI Thesaurus CC-BY-4.0 24.02d 503 subset +umls Unified Medical Language System Concept Unique Identifier https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/license_agreement.html 2023AB 6057 subset diff --git a/notebooks/landscape/complex/complex-landscape.ipynb b/notebooks/landscape/complex/complex-landscape.ipynb new file mode 100644 index 0000000..aa4ebf6 --- /dev/null +++ b/notebooks/landscape/complex/complex-landscape.ipynb @@ -0,0 +1,5864 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4c0737c8-d457-42ca-a357-76f87e7d2e21", + "metadata": {}, + "outputs": [], + "source": [ + "from semra.landscape.complexes import CONFIGURATION\n", + "from semra.landscape.utils import notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2c2e3cea-ed83-48f4-bda0-2c3bc5f93bae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Summarize the Resources\n", + "\n", + "We summarize the resources used in the landscape analysis, including their [Bioregistry](https://bioregistry.io)\n", + "prefix, license, current version, and number of terms (i.e., named concepts) they contain.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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intactIntAct protein interaction databaseCC-BY-4.02024-02-143326observed
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" + ], + "text/plain": [ + " name license version \\\n", + "prefix \n", + "complexportal Complex Portal CC0-1.0 2024-02-14 \n", + "fplx FamPlex CC0-1.0 None \n", + "go Gene Ontology CC-BY-4.0 2024-03-28 \n", + "chembl.target ChEMBL target CC-BY-SA-3.0 33 \n", + "wikidata Wikidata CC0-1.0 None \n", + "scomp Selventa Complexes Apache-2.0 None \n", + "signor Signaling Network Open Resource CC-BY-NC-4.0 None \n", + "intact IntAct protein interaction database CC-BY-4.0 2024-02-14 \n", + "\n", + " terms status \n", + "prefix \n", + "complexportal 4623 full \n", + "fplx 782 full \n", + "go 2117 subset \n", + "chembl.target 192 observed \n", + "wikidata 4339 observed \n", + "scomp 135 full \n", + "signor 355 observed \n", + "intact 3326 observed " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "There are a total of 15,869 terms across the 8 resources." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Summarize the Mappings\n", + "\n", + "In order to summarize the mappings, we're going to load them, index them, and count\n", + "the number of mappings between each pair of resources. The self-mapping column is\n", + "the count of terms in the resource. We'll do this to the raw mappings first, then\n", + "to the processed mappings, then compare them.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Indexing mappings: 0%| | 0.00/79.6k [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixcomplexportalfplxgochembl.targetwikidatascompsignorintact
source_prefix
complexportal462350204394602663326
fplx57825100661180
go051211700003
chembl.target204001920000
wikidata39460004339000
scomp06600013500
signor26611800003550
intact33260300003326
\n", + "
" + ], + "text/plain": [ + "target_prefix complexportal fplx go chembl.target wikidata scomp \\\n", + "source_prefix \n", + "complexportal 4623 5 0 204 3946 0 \n", + "fplx 5 782 51 0 0 66 \n", + "go 0 51 2117 0 0 0 \n", + "chembl.target 204 0 0 192 0 0 \n", + "wikidata 3946 0 0 0 4339 0 \n", + "scomp 0 66 0 0 0 135 \n", + "signor 266 118 0 0 0 0 \n", + "intact 3326 0 3 0 0 0 \n", + "\n", + "target_prefix signor intact \n", + "source_prefix \n", + "complexportal 266 3326 \n", + "fplx 118 0 \n", + "go 0 3 \n", + "chembl.target 0 0 \n", + "wikidata 0 0 \n", + "scomp 0 0 \n", + "signor 355 0 \n", + "intact 0 3326 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Next, we summarize the processed mappings, which include inference, reasoning, and confidence filtering." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixcomplexportalfplxgochembl.targetwikidatascompsignorintact
source_prefix
complexportal462374204394822663326
fplx7782540399661185
go454211704628117
chembl.target204001920000
wikidata3948399460433960861941
scomp26628060135142
signor26611811086143550
intact33265701941203326
\n", + "
" + ], + "text/plain": [ + "target_prefix complexportal fplx go chembl.target wikidata scomp \\\n", + "source_prefix \n", + "complexportal 4623 7 4 204 3948 2 \n", + "fplx 7 782 54 0 399 66 \n", + "go 4 54 2117 0 46 28 \n", + "chembl.target 204 0 0 192 0 0 \n", + "wikidata 3948 399 46 0 4339 60 \n", + "scomp 2 66 28 0 60 135 \n", + "signor 266 118 11 0 86 14 \n", + "intact 3326 5 7 0 1941 2 \n", + "\n", + "target_prefix signor intact \n", + "source_prefix \n", + "complexportal 266 3326 \n", + "fplx 118 5 \n", + "go 11 7 \n", + "chembl.target 0 0 \n", + "wikidata 86 1941 \n", + "scomp 14 2 \n", + "signor 355 0 \n", + "intact 0 3326 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Below is an graph-based view on the processed mappings." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "fplx\n", + "782\n", + "\n", + "fplx\n", + "782\n", + "\n", + "\n", + "\n", + "scomp\n", + "135\n", + "\n", + "scomp\n", + "135\n", + "\n", + "\n", + "\n", + "fplx\n", + "782--scomp\n", + "135\n", + "\n", + "66\n", + "\n", + "\n", + "\n", + "signor\n", + "355\n", + "\n", + "signor\n", + "355\n", + "\n", + "\n", + "\n", + "fplx\n", + "782--signor\n", + "355\n", + "\n", + "118\n", + "\n", + "\n", + "\n", + "go\n", + "2,117\n", + "\n", + "go\n", + "2,117\n", + "\n", + "\n", + "\n", + "fplx\n", + "782--go\n", + "2,117\n", + "\n", + "54\n", + "\n", + "\n", + "\n", + "complexportal\n", + "4,623\n", + "\n", + "complexportal\n", + "4,623\n", + "\n", + "\n", + "\n", + "fplx\n", + "782--complexportal\n", + "4,623\n", + "\n", + "7\n", + "\n", + "\n", + "\n", + "intact\n", + "3,326\n", + "\n", + "intact\n", + "3,326\n", + "\n", + "\n", + "\n", + "fplx\n", + "782--intact\n", + "3,326\n", + "\n", + "5\n", + "\n", + "\n", + "\n", + "wikidata\n", + "4,339\n", + "\n", + "wikidata\n", + "4,339\n", + "\n", + "\n", + "\n", + "fplx\n", + "782--wikidata\n", + "4,339\n", + "\n", + "399\n", + "\n", + "\n", + "\n", + "scomp\n", + "135--signor\n", + "355\n", + "\n", + "14\n", + "\n", + "\n", + "\n", + "scomp\n", + "135--go\n", + "2,117\n", + "\n", + "28\n", + "\n", + "\n", + "\n", + "scomp\n", + "135--complexportal\n", + "4,623\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "scomp\n", + "135--intact\n", + "3,326\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "scomp\n", + "135--wikidata\n", + "4,339\n", + "\n", + "60\n", + "\n", + "\n", + "\n", + "signor\n", + "355--go\n", + "2,117\n", + "\n", + "11\n", + "\n", + "\n", + "\n", + "signor\n", + "355--complexportal\n", + "4,623\n", + "\n", + "266\n", + "\n", + "\n", + "\n", + "signor\n", + "355--wikidata\n", + "4,339\n", + "\n", + "86\n", + "\n", + "\n", + "\n", + "go\n", + "2,117--complexportal\n", + "4,623\n", + "\n", + "4\n", + "\n", + "\n", + "\n", + "go\n", + "2,117--intact\n", + "3,326\n", + "\n", + "7\n", + "\n", + "\n", + "\n", + "go\n", + "2,117--wikidata\n", + "4,339\n", + "\n", + "46\n", + "\n", + "\n", + "\n", + "complexportal\n", + "4,623--intact\n", + "3,326\n", + "\n", + "3,326\n", + "\n", + "\n", + "\n", + "complexportal\n", + "4,623--wikidata\n", + "4,339\n", + "\n", + "3,948\n", + "\n", + "\n", + "\n", + "chembl.target\n", + "192\n", + "\n", + "chembl.target\n", + "192\n", + "\n", + "\n", + "\n", + "complexportal\n", + "4,623--chembl.target\n", + "192\n", + "\n", + "204\n", + "\n", + "\n", + "\n", + "intact\n", + "3,326--wikidata\n", + "4,339\n", + "\n", + "1,941\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Comparison\n", + "\n", + "The following comparison shows the absolute number of mappings added by processing/inference.\n", + "Across the board, this process adds large numbers of mappings to most resources, especially\n", + "ones that were previously only connected to a small number of other resources.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixcomplexportalfplxgochembl.targetwikidatascompsignorintact
source_prefix
complexportal02402200
fplx2030399005
go43004628114
chembl.target00000000
wikidata2399460060861941
scomp20280600142
signor00110861400
intact05401941200
\n", + "
" + ], + "text/plain": [ + "target_prefix complexportal fplx go chembl.target wikidata scomp \\\n", + "source_prefix \n", + "complexportal 0 2 4 0 2 2 \n", + "fplx 2 0 3 0 399 0 \n", + "go 4 3 0 0 46 28 \n", + "chembl.target 0 0 0 0 0 0 \n", + "wikidata 2 399 46 0 0 60 \n", + "scomp 2 0 28 0 60 0 \n", + "signor 0 0 11 0 86 14 \n", + "intact 0 5 4 0 1941 2 \n", + "\n", + "target_prefix signor intact \n", + "source_prefix \n", + "complexportal 0 0 \n", + "fplx 0 5 \n", + "go 11 4 \n", + "chembl.target 0 0 \n", + "wikidata 86 1941 \n", + "scomp 14 2 \n", + "signor 0 0 \n", + "intact 0 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Here's an alternative view on the number of mappings normalized to show percentage gain.\n", + "\n", + "Note:\n", + "\n", + "- `inf` means that there were no mappings before and now there are a non-zero number of mappings\n", + "- `NaN` means there were no mappings before inference and continue to be no mappings after inference\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixcomplexportalfplxgochembl.targetwikidatascompsignorintact
source_prefix
complexportal0.040.0inf0.00.1inf0.00.0
fplx40.00.05.9NaNinf0.00.0inf
goinf5.90.0NaNinfinfinf133.3
chembl.target0.0NaNNaN0.0NaNNaNNaNNaN
wikidata0.1infinfNaN0.0infinfinf
scompinf0.0infNaNinf0.0infinf
signor0.00.0infNaNinfinf0.0NaN
intact0.0inf133.3NaNinfinfNaN0.0
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" + ], + "text/plain": [ + "target_prefix complexportal fplx go chembl.target wikidata scomp \\\n", + "source_prefix \n", + "complexportal 0.0 40.0 inf 0.0 0.1 inf \n", + "fplx 40.0 0.0 5.9 NaN inf 0.0 \n", + "go inf 5.9 0.0 NaN inf inf \n", + "chembl.target 0.0 NaN NaN 0.0 NaN NaN \n", + "wikidata 0.1 inf inf NaN 0.0 inf \n", + "scomp inf 0.0 inf NaN inf 0.0 \n", + "signor 0.0 0.0 inf NaN inf inf \n", + "intact 0.0 inf 133.3 NaN inf inf \n", + "\n", + "target_prefix signor intact \n", + "source_prefix \n", + "complexportal 0.0 0.0 \n", + "fplx 0.0 inf \n", + "go inf 133.3 \n", + "chembl.target NaN NaN \n", + "wikidata inf inf \n", + "scomp inf inf \n", + "signor 0.0 NaN \n", + "intact NaN 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Landscape Analysis\n", + "\n", + "Before, we looked at the overlaps between each resource. Now, we use that information jointly to\n", + "estimate the number of terms in the landscape itself, and estimate how much of the landscape\n", + "each resource covers.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "This estimates a total of 7,775 unique entities.\n", + "\n", + "- 4,702 (60.5%) have\n", + " at least one mapping.\n", + "- 3,073 (39.5%)\n", + " are unique to a single resource.\n", + "- 0 (0.0%)\n", + " appear in all 8 resources.\n", + "\n", + "This estimate is susceptible to several caveats:\n", + "\n", + "- Missing mappings inflates this measurement\n", + "- Generic resources like MeSH contain irrelevant entities that can't be mapped\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Because there are 8, there are 255 possible overlaps to consider.\n", + "Therefore, a Venn diagram is not possible, so we\n", + "we use an [UpSet plot](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720993) (Lex *et al.*, 2014)\n", + "as a high-dimensional Venn diagram.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-04-11T10:01:45.640404\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.0, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "\\\n", + " The landscape of 8 resources has 15,869 total terms.\n", + " After merging redundant nodes based on mappings, inference, and reasoning, there\n", + " are 7,775 unique concepts. Using the reduction formula\n", + " $\\frac{\\text{total terms} - \\text{reduced terms}}{\\text{total terms}}$,\n", + " this is a 51.0% reduction.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "This is only an estimate and is susceptible to a few things:\n", + "\n", + "1. It can be artificially high because there are entities that _should_ be mapped, but are not\n", + "2. It can be artificially low because there are entities that are incorrectly mapped, e.g., as\n", + " a result of inference. The frontend curation interface can help identify and remove these\n", + "3. It can be artificially low because for some vocabularies like SNOMED-CT, it's not possible\n", + " to load a terms list, and therefore it's not possible to account for terms that aren't mapped.\n", + " Therefore, we make a lower bound estimate based on the terms that appear in mappings\n", + "4. It can be artificially high if a vocabulary is used that covers many domains and is not properly\n", + " subset'd. For example, EFO covers many different domains, so when doing disease landscape\n", + " analysis, it should be subset to only terms in the disease hierarchy (i.e., appearing under\n", + " ``efo:0000408``).\n", + "5. It can be affected by terminology issues, such as the confusion between Orphanet and ORDO\n", + "6. It can be affected by the existence of many-to-many mappings, which are filtered out during\n", + " processing, which makes the estimate artificially high since some subset of those entities\n", + " could be mapped, but it's not clear which should.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = notebook(CONFIGURATION, output_directory=\".\", minimum_count=1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/landscape/complex/counts.tsv b/notebooks/landscape/complex/counts.tsv new file mode 100644 index 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100644 index 0000000..7f7ece0 --- /dev/null +++ b/notebooks/landscape/complex/raw_counts.tsv @@ -0,0 +1,9 @@ +source_prefix complexportal fplx go chembl.target wikidata scomp signor intact +complexportal 4623 5 0 204 3946 0 266 3326 +fplx 5 782 51 0 0 66 118 0 +go 0 51 2117 0 0 0 0 3 +chembl.target 204 0 0 192 0 0 0 0 +wikidata 3946 0 0 0 4339 0 0 0 +scomp 0 66 0 0 0 135 0 0 +signor 266 118 0 0 0 0 355 0 +intact 3326 0 3 0 0 0 0 3326 diff --git a/notebooks/landscape/complex/stats.json b/notebooks/landscape/complex/stats.json new file mode 100644 index 0000000..b9eaa54 --- /dev/null +++ b/notebooks/landscape/complex/stats.json @@ -0,0 +1,14 @@ +{ + "raw_term_count": 15869, + "unique_term_count": 7775, + "reduction": 0.5100510429138572, + "distribution": { + "2": 1707, + "3": 2706, + "6": 9, + "5": 40, + "4": 235, + "7": 5, + "1": 3073 + } +} \ No newline at end of file diff --git a/notebooks/landscape/complex/summary.tsv b/notebooks/landscape/complex/summary.tsv new file mode 100644 index 0000000..433751f --- /dev/null +++ b/notebooks/landscape/complex/summary.tsv @@ -0,0 +1,9 @@ +prefix name license version terms status +complexportal Complex Portal CC0-1.0 2024-02-14 4623 full +fplx FamPlex CC0-1.0 782 full +go Gene Ontology CC-BY-4.0 2024-03-28 2117 subset +chembl.target ChEMBL target CC-BY-SA-3.0 33 192 observed +wikidata Wikidata CC0-1.0 4339 observed +scomp Selventa Complexes Apache-2.0 135 full +signor Signaling Network Open Resource CC-BY-NC-4.0 355 observed +intact IntAct protein interaction database CC-BY-4.0 2024-02-14 3326 observed diff --git a/notebooks/landscape/disease/counts.tsv b/notebooks/landscape/disease/counts.tsv new file mode 100644 index 0000000..8452e4b --- /dev/null +++ b/notebooks/landscape/disease/counts.tsv @@ -0,0 +1,19 @@ +source_prefix doid mondo efo mesh ncit orphanet orphanet.ordo umls omim omim.ps gard icd10 icd10cm icd10pcs icd11 icd9 icd9cm icdo +doid 11537 11472 1257 671 5097 398 2468 7700 5941 51 2110 11 3592 0 1 0 2237 470 +mondo 11472 24201 1699 880 7233 1854 9609 16368 9898 580 11839 16 1647 0 0 4341 4 732 +efo 1257 1699 2080 364 1235 151 197 1569 327 26 304 503 309 0 0 1167 5 72 +mesh 671 880 364 3125 397 118 127 2205 14 1 0 2 189 0 0 0 0 0 +ncit 5097 7233 1235 397 20095 445 510 19017 158 23 2 9 480 0 0 0 1 0 +orphanet 398 1854 151 118 445 15348 1947 8356 140 21 0 0 127 0 0 0 0 0 +orphanet.ordo 2468 9609 197 127 510 1947 14495 8104 11175 36 1 7623 164 0 5484 0 1 0 +umls 7700 16368 1569 2205 19017 8356 8104 178734 18060 42 1 5769 24997 0 0 0 9109 0 +omim 5941 9898 327 14 158 140 11175 18060 20292 2 2 0 41 0 0 0 1 0 +omim.ps 51 580 26 1 23 21 36 42 2 576 1 0 7 0 0 0 1 0 +gard 2110 11839 304 0 2 0 1 1 2 1 12292 0 1 0 0 0 1 0 +icd10 11 16 503 2 9 0 7623 5769 0 0 0 2345 10 0 0 0 0 0 +icd10cm 3592 1647 309 189 480 127 164 24997 41 7 1 10 21556 0 0 0 1 0 +icd10pcs 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +icd11 1 0 0 0 0 0 5484 0 0 0 0 0 0 0 69477 0 0 0 +icd9 0 4341 1167 0 0 0 0 0 0 0 0 0 0 0 0 3984 0 0 +icd9cm 2237 4 5 0 1 0 1 9109 1 1 1 0 1 0 0 0 9236 0 +icdo 470 732 72 0 0 0 0 0 0 0 0 0 0 0 0 0 0 800 diff --git a/notebooks/landscape/disease/disease-landscape.ipynb b/notebooks/landscape/disease/disease-landscape.ipynb new file mode 100644 index 0000000..d060fd4 --- /dev/null +++ b/notebooks/landscape/disease/disease-landscape.ipynb @@ -0,0 +1,57010 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "02787abf-7bf4-47c3-8794-1f81ff573476", + "metadata": {}, + "outputs": [], + "source": [ + "from semra.landscape.diseases import CONFIGURATION\n", + "from semra.landscape.utils import notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a6c215a0-f559-46c4-b3fe-3aa537406ffe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Summarize the Resources\n", + "\n", + "We summarize the resources used in the landscape analysis, including their [Bioregistry](https://bioregistry.io)\n", + "prefix, license, current version, and number of terms (i.e., named concepts) they contain.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "1 resources were not available through\n", + "[PyOBO](https://github.com/biopragmatics/pyobo). Therefore, we estimate the number\n", + "of terms in that resource based on the ones appearing in mappings. Note that these\n", + "are typically an underestimate.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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doidHuman Disease OntologyCC0-1.02024-03-2811537full
mondoMondo Disease OntologyCC-BY-4.02024-04-0224201full
efoExperimental Factor OntologyApache-2.03.64.02080subset
meshMedical Subject HeadingsCC0-1.020243125subset
ncitNCI ThesaurusCC-BY-4.024.02d20095subset
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omimOnline Mendelian Inheritance in Manhttps://www.omim.org/help/agreementApril 10, 202420292observed
omim.psOMIM Phenotypic Serieshttps://www.omim.org/help/agreementApril 10, 2024576full
gardGenetic and Rare Diseases Information CenterNoneNone12292observed
icd10International Classification of Diseases, 10th...https://cdn.who.int/media/docs/default-source/...20192345full
icd10cmInternational Classification of Diseases, 10th...NoneNone21556observed
icd10pcsInternational Classification of Diseases, 10th...NoneNone0observed
icd11International Classification of Diseases, 11th...CC-BY-ND-3.0-IGO2024-0169477full
icd9International Classification of Diseases, 9th ...NoneNone3984observed
icd9cmInternational Classification of Diseases, 9th ...NoneNone9236observed
icdoInternational Classification of Diseases for O...NoneNone800observed
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" + ], + "text/plain": [ + " name \\\n", + "prefix \n", + "doid Human Disease Ontology \n", + "mondo Mondo Disease Ontology \n", + "efo Experimental Factor Ontology \n", + "mesh Medical Subject Headings \n", + "ncit NCI Thesaurus \n", + "orphanet Orphanet \n", + "orphanet.ordo Orphanet Rare Disease Ontology \n", + "umls Unified Medical Language System Concept Unique... \n", + "omim Online Mendelian Inheritance in Man \n", + "omim.ps OMIM Phenotypic Series \n", + "gard Genetic and Rare Diseases Information Center \n", + "icd10 International Classification of Diseases, 10th... \n", + "icd10cm International Classification of Diseases, 10th... \n", + "icd10pcs International Classification of Diseases, 10th... \n", + "icd11 International Classification of Diseases, 11th... \n", + "icd9 International Classification of Diseases, 9th ... \n", + "icd9cm International Classification of Diseases, 9th ... \n", + "icdo International Classification of Diseases for O... \n", + "\n", + " license \\\n", + "prefix \n", + "doid CC0-1.0 \n", + "mondo CC-BY-4.0 \n", + "efo Apache-2.0 \n", + "mesh CC0-1.0 \n", + "ncit CC-BY-4.0 \n", + "orphanet CC-BY-4.0 \n", + "orphanet.ordo CC-BY-4.0 \n", + "umls https://www.nlm.nih.gov/research/umls/knowledg... \n", + "omim https://www.omim.org/help/agreement \n", + "omim.ps https://www.omim.org/help/agreement \n", + "gard None \n", + "icd10 https://cdn.who.int/media/docs/default-source/... \n", + "icd10cm None \n", + "icd10pcs None \n", + "icd11 CC-BY-ND-3.0-IGO \n", + "icd9 None \n", + "icd9cm None \n", + "icdo None \n", + "\n", + " version terms status \n", + "prefix \n", + "doid 2024-03-28 11537 full \n", + "mondo 2024-04-02 24201 full \n", + "efo 3.64.0 2080 subset \n", + "mesh 2024 3125 subset \n", + "ncit 24.02d 20095 subset \n", + "orphanet None 15348 full \n", + "orphanet.ordo 4.4 14495 observed \n", + "umls 2023AB 178734 subset \n", + "omim April 10, 2024 20292 observed \n", + "omim.ps April 10, 2024 576 full \n", + "gard None 12292 observed \n", + "icd10 2019 2345 full \n", + "icd10cm None 21556 observed \n", + "icd10pcs None 0 observed \n", + "icd11 2024-01 69477 full \n", + "icd9 None 3984 observed \n", + "icd9cm None 9236 observed \n", + "icdo None 800 observed " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "There are a total of 410,173 terms across the 18 resources." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Summarize the Mappings\n", + "\n", + "In order to summarize the mappings, we're going to load them, index them, and count\n", + "the number of mappings between each pair of resources. The self-mapping column is\n", + "the count of terms in the resource. We'll do this to the raw mappings first, then\n", + "to the processed mappings, then compare them.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Indexing mappings: 0%| | 0.00/412k [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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0 1 \n", + "mondo 16368 9898 580 11839 16 1647 0 0 \n", + "efo 1569 327 26 304 503 309 0 0 \n", + "mesh 2205 14 1 0 2 189 0 0 \n", + "ncit 19017 158 23 2 9 480 0 0 \n", + "orphanet 8356 140 21 0 0 127 0 0 \n", + "orphanet.ordo 8104 11175 36 1 7623 164 0 5484 \n", + "umls 178734 18060 42 1 5769 24997 0 0 \n", + "omim 18060 20292 2 2 0 41 0 0 \n", + "omim.ps 42 2 576 1 0 7 0 0 \n", + "gard 1 2 1 12292 0 1 0 0 \n", + "icd10 5769 0 0 0 2345 10 0 0 \n", + "icd10cm 24997 41 7 1 10 21556 0 0 \n", + "icd10pcs 0 0 0 0 0 0 0 0 \n", + "icd11 0 0 0 0 0 0 0 69477 \n", + "icd9 0 0 0 0 0 0 0 0 \n", + "icd9cm 9109 1 1 1 0 1 0 0 \n", + "icdo 0 0 0 0 0 0 0 0 \n", + "\n", + "target_prefix icd9 icd9cm icdo \n", + "source_prefix \n", + "doid 0 2237 470 \n", + "mondo 4341 4 732 \n", + "efo 1167 5 72 \n", + "mesh 0 0 0 \n", + "ncit 0 1 0 \n", + "orphanet 0 0 0 \n", + "orphanet.ordo 0 1 0 \n", + "umls 0 9109 0 \n", + "omim 0 1 0 \n", + "omim.ps 0 1 0 \n", + "gard 0 1 0 \n", + "icd10 0 0 0 \n", + "icd10cm 0 1 0 \n", + "icd10pcs 0 0 0 \n", + "icd11 0 0 0 \n", + "icd9 3984 0 0 \n", + "icd9cm 0 9236 0 \n", + "icdo 0 0 800 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Below is an graph-based view on the processed mappings." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "doid\n", + "11,537\n", + "\n", + "doid\n", + "11,537\n", + "\n", + "\n", + "\n", + "umls\n", + "178,734\n", + "\n", + "umls\n", + "178,734\n", + "\n", + "\n", + "\n", + "doid\n", + "11,537--umls\n", + "178,734\n", + "\n", + "7,700\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080\n", + "\n", + "efo\n", + "2,080\n", + "\n", + "\n", + "\n", + "doid\n", + "11,537--efo\n", + "2,080\n", + "\n", + "1,257\n", + "\n", + "\n", + "\n", + "mesh\n", + "3,125\n", + "\n", + "mesh\n", + "3,125\n", + "\n", + "\n", + "\n", + "doid\n", + "11,537--mesh\n", + 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"\n", + "364\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--ncit\n", + "20,095\n", + "\n", + "1,235\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--mondo\n", + "24,201\n", + "\n", + "1,699\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--icdo\n", + "800\n", + "\n", + "72\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--icd10cm\n", + "21,556\n", + "\n", + "309\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--orphanet.ordo\n", + "14,495\n", + "\n", + "197\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--gard\n", + "12,292\n", + "\n", + "304\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--omim\n", + "20,292\n", + "\n", + "327\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--omim.ps\n", + "576\n", + "\n", + "26\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--orphanet\n", + "15,348\n", + "\n", + "151\n", + "\n", + "\n", + "\n", + "efo\n", + "2,080--icd10\n", + "2,345\n", + "\n", + "503\n", + "\n", + "\n", + "\n", + "icd9\n", + "3,984\n", + "\n", + "icd9\n", + "3,984\n", + "\n", + "\n", 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"\n", + "\n", + "ncit\n", + "20,095--orphanet\n", + "15,348\n", + "\n", + "445\n", + "\n", + "\n", + "\n", + "mondo\n", + "24,201--icdo\n", + "800\n", + "\n", + "732\n", + "\n", + "\n", + "\n", + "mondo\n", + "24,201--icd10cm\n", + "21,556\n", + "\n", + "1,647\n", + "\n", + "\n", + "\n", + "mondo\n", + "24,201--orphanet.ordo\n", + "14,495\n", + "\n", + "9,609\n", + "\n", + "\n", + "\n", + "mondo\n", + "24,201--gard\n", + "12,292\n", + "\n", + "11,839\n", + "\n", + "\n", + "\n", + "mondo\n", + "24,201--omim\n", + "20,292\n", + "\n", + "9,898\n", + "\n", + "\n", + "\n", + "mondo\n", + "24,201--omim.ps\n", + "576\n", + "\n", + "580\n", + "\n", + "\n", + "\n", + "mondo\n", + "24,201--orphanet\n", + "15,348\n", + "\n", + "1,854\n", + "\n", + "\n", + "\n", + "mondo\n", + "24,201--icd9\n", + "3,984\n", + "\n", + "4,341\n", + "\n", + "\n", + "\n", + "icd10cm\n", + "21,556--orphanet.ordo\n", + "14,495\n", + "\n", + "164\n", + "\n", + "\n", + "\n", + "icd10cm\n", + "21,556--omim\n", + "20,292\n", + "\n", + "41\n", + "\n", + "\n", + "\n", + "icd10cm\n", + "21,556--orphanet\n", + "15,348\n", + "\n", + "127\n", + "\n", + "\n", + "\n", + "orphanet.ordo\n", + "14,495--omim\n", + "20,292\n", + "\n", + "11,175\n", + "\n", + "\n", + "\n", + "orphanet.ordo\n", + "14,495--omim.ps\n", + "576\n", + "\n", + "36\n", + "\n", + "\n", + "\n", + "orphanet.ordo\n", + "14,495--orphanet\n", + "15,348\n", + "\n", + "1,947\n", + "\n", + "\n", + "\n", + "orphanet.ordo\n", + "14,495--icd10\n", + "2,345\n", + "\n", + "7,623\n", + "\n", + "\n", + "\n", + "icd11\n", + "69,477\n", + "\n", + "icd11\n", + "69,477\n", + "\n", + "\n", + "\n", + "orphanet.ordo\n", + "14,495--icd11\n", + "69,477\n", + "\n", + "5,484\n", + "\n", + "\n", + "\n", + "omim\n", + "20,292--orphanet\n", + "15,348\n", + "\n", + "140\n", + "\n", + "\n", + "\n", + "omim.ps\n", + "576--orphanet\n", + "15,348\n", + "\n", + "21\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Comparison\n", + "\n", + "The following comparison shows the absolute number of mappings added by processing/inference.\n", + "Across the board, this process adds large numbers of mappings to most resources, especially\n", + "ones that were previously only connected to a small number of other resources.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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efo3.44.50.011.710.7inf447.246.81.98.30.00.28.4NaNNaN0.00.00.0
mesh6.82.911.70.01370.4infinf4.4infinfNaNinfinfNaNNaNNaNNaNNaN
ncit10.53.710.71370.40.0infinf1.3infinfinfinfinfNaNNaNNaNinfNaN
orphanetinfinfinfinfinf0.0inf0.0infinfNaNNaNinfNaNNaNNaNNaNNaN
orphanet.ordo16.80.3447.2infinfinf0.03.20.3infinf0.0infNaN0.0NaNinfNaN
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Now, we use that information jointly to\n", + "estimate the number of terms in the landscape itself, and estimate how much of the landscape\n", + "each resource covers.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "This estimates a total of 243,730 unique entities.\n", + "\n", + "- 44,703 (18.3%) have\n", + " at least one mapping.\n", + "- 199,027 (81.7%)\n", + " are unique to a single resource.\n", + "- 0 (0.0%)\n", + " appear in all 18 resources.\n", + "\n", + "This estimate is susceptible to several caveats:\n", + "\n", + "- Missing mappings inflates this measurement\n", + "- Generic resources like MeSH contain irrelevant entities that can't be mapped\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Because there are 18, there are 262143 possible overlaps to consider.\n", + "Therefore, a Venn diagram is not possible, so we\n", + "we use an [UpSet plot](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720993) (Lex *et al.*, 2014)\n", + "as a high-dimensional Venn diagram.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-04-11T14:08:08.420229\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.0, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "\\\n", + " The landscape of 18 resources has 410,173 total terms.\n", + " After merging redundant nodes based on mappings, inference, and reasoning, there\n", + " are 243,730 unique concepts. Using the reduction formula\n", + " $\\frac{\\text{total terms} - \\text{reduced terms}}{\\text{total terms}}$,\n", + " this is a 40.6% reduction.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "This is only an estimate and is susceptible to a few things:\n", + "\n", + "1. It can be artificially high because there are entities that _should_ be mapped, but are not\n", + "2. It can be artificially low because there are entities that are incorrectly mapped, e.g., as\n", + " a result of inference. The frontend curation interface can help identify and remove these\n", + "3. It can be artificially low because for some vocabularies like SNOMED-CT, it's not possible\n", + " to load a terms list, and therefore it's not possible to account for terms that aren't mapped.\n", + " Therefore, we make a lower bound estimate based on the terms that appear in mappings\n", + "4. It can be artificially high if a vocabulary is used that covers many domains and is not properly\n", + " subset'd. For example, EFO covers many different domains, so when doing disease landscape\n", + " analysis, it should be subset to only terms in the disease hierarchy (i.e., appearing under\n", + " ``efo:0000408``).\n", + "5. It can be affected by terminology issues, such as the confusion between Orphanet and ORDO\n", + "6. It can be affected by the existence of many-to-many mappings, which are filtered out during\n", + " processing, which makes the estimate artificially high since some subset of those entities\n", + " could be mapped, but it's not clear which should.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = notebook(CONFIGURATION, output_directory=\".\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/landscape/disease/graph.svg b/notebooks/landscape/disease/graph.svg new file mode 100644 index 0000000..b0d14dd --- 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b/notebooks/landscape/disease/raw_counts.tsv @@ -0,0 +1,19 @@ +source_prefix doid mondo efo mesh ncit orphanet orphanet.ordo umls omim omim.ps gard icd10 icd10cm icd10pcs icd11 icd9 icd9cm icdo +doid 11537 11394 1216 628 4613 0 2113 6732 5924 0 2110 1 3528 0 1 0 2237 470 +mondo 11394 24201 1626 855 6974 0 9576 13894 9895 580 11838 15 1636 0 0 4341 2 732 +efo 1216 1626 2080 326 1116 0 36 1069 321 24 304 502 285 0 0 1167 5 72 +mesh 628 855 326 3125 27 0 0 2112 0 0 0 0 0 0 0 0 0 0 +ncit 4613 6974 1116 27 20095 0 0 18777 0 0 0 0 0 0 0 0 0 0 +orphanet 0 0 0 0 0 15348 0 8356 0 0 0 0 0 0 0 0 0 0 +orphanet.ordo 2113 9576 36 0 0 0 14495 7854 11146 0 0 7623 0 0 5484 0 0 0 +umls 6732 13894 1069 2112 18777 8356 7854 178734 18049 0 0 5762 24877 0 0 0 9109 0 +omim 5924 9895 321 0 0 0 11146 18049 20292 0 0 0 0 0 0 0 0 0 +omim.ps 0 580 24 0 0 0 0 0 0 576 0 0 0 0 0 0 0 0 +gard 2110 11838 304 0 0 0 0 0 0 0 12292 0 0 0 0 0 0 0 +icd10 1 15 502 0 0 0 7623 5762 0 0 0 2345 0 0 0 0 0 0 +icd10cm 3528 1636 285 0 0 0 0 24877 0 0 0 0 21556 0 0 0 0 0 +icd10pcs 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +icd11 1 0 0 0 0 0 5484 0 0 0 0 0 0 0 69477 0 0 0 +icd9 0 4341 1167 0 0 0 0 0 0 0 0 0 0 0 0 3984 0 0 +icd9cm 2237 2 5 0 0 0 0 9109 0 0 0 0 0 0 0 0 9236 0 +icdo 470 732 72 0 0 0 0 0 0 0 0 0 0 0 0 0 0 800 diff --git a/notebooks/landscape/disease/stats.json b/notebooks/landscape/disease/stats.json new file mode 100644 index 0000000..5cd2986 --- /dev/null +++ b/notebooks/landscape/disease/stats.json @@ -0,0 +1,24 @@ +{ + "raw_term_count": 410173, + "unique_term_count": 243730, + "reduction": 0.4057873141333047, + "distribution": { + "5": 1973, + "2": 33154, + "11": 100, + "8": 335, + "3": 4506, + "4": 2926, + "17": 1, + "6": 611, + "12": 90, + "14": 95, + "15": 27, + "7": 393, + "13": 98, + "10": 167, + "9": 224, + "16": 3, + "1": 199027 + } +} \ No newline at end of file diff --git a/notebooks/landscape/disease/summary.tsv b/notebooks/landscape/disease/summary.tsv new file mode 100644 index 0000000..6bcacf4 --- /dev/null +++ b/notebooks/landscape/disease/summary.tsv @@ -0,0 +1,19 @@ +prefix name license version terms status +doid Human Disease Ontology CC0-1.0 2024-03-28 11537 full +mondo Mondo Disease Ontology CC-BY-4.0 2024-04-02 24201 full +efo Experimental Factor Ontology Apache-2.0 3.64.0 2080 subset +mesh Medical Subject Headings CC0-1.0 2024 3125 subset +ncit NCI Thesaurus CC-BY-4.0 24.02d 20095 subset +orphanet Orphanet CC-BY-4.0 15348 full +orphanet.ordo Orphanet Rare Disease Ontology CC-BY-4.0 4.4 14495 observed +umls Unified Medical Language System Concept Unique Identifier https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/license_agreement.html 2023AB 178734 subset +omim Online Mendelian Inheritance in Man https://www.omim.org/help/agreement April 10, 2024 20292 observed +omim.ps OMIM Phenotypic Series https://www.omim.org/help/agreement April 10, 2024 576 full +gard Genetic and Rare Diseases Information Center 12292 observed +icd10 International Classification of Diseases, 10th Revision https://cdn.who.int/media/docs/default-source/publishing-policies/copyright/who-faq-licensing-icd-10.pdf 2019 2345 full +icd10cm International Classification of Diseases, 10th Revision, Clinical Modification 21556 observed +icd10pcs International Classification of Diseases, 10th Revision, Procedure Coding System 0 observed +icd11 International Classification of Diseases, 11th Revision CC-BY-ND-3.0-IGO 2024-01 69477 full +icd9 International Classification of Diseases, 9th Revision 3984 observed +icd9cm International Classification of Diseases, 9th Revision, Clinical Modification 9236 observed +icdo International Classification of Diseases for Oncology 800 observed diff --git a/notebooks/landscape/gene/counts.tsv b/notebooks/landscape/gene/counts.tsv new file mode 100644 index 0000000..57e26e3 --- /dev/null +++ b/notebooks/landscape/gene/counts.tsv @@ -0,0 +1,14 @@ +source_prefix ncbigene hgnc mgi rgd cgnc wormbase flybase sgd omim civic.gid umls ncit wikidata +ncbigene 47782242 44191 76239 48324 25931 0 0 0 18255 528 0 5761 788874 +hgnc 44191 43865 0 0 0 0 0 0 17534 491 0 5785 43557 +mgi 76239 0 682164 0 0 0 0 0 0 0 0 0 0 +rgd 48324 0 0 61339 0 0 0 0 0 0 0 0 0 +cgnc 25931 0 0 0 25936 0 0 0 0 0 0 0 0 +wormbase 0 0 0 0 0 0 0 0 0 0 0 0 0 +flybase 0 0 0 0 0 0 17883 0 0 0 0 0 0 +sgd 0 0 0 0 0 0 0 6600 0 0 0 0 0 +omim 18255 17534 0 0 0 0 0 0 26954 491 0 5636 28144 +civic.gid 528 491 0 0 0 0 0 0 491 506 0 457 491 +umls 0 0 0 0 0 0 0 0 0 0 1 0 0 +ncit 5761 5785 0 0 0 0 0 0 5636 457 0 12311 5761 +wikidata 788874 43557 0 0 0 0 0 0 28144 491 0 5761 797966 diff --git a/notebooks/landscape/gene/gene-landscape.ipynb b/notebooks/landscape/gene/gene-landscape.ipynb new file mode 100644 index 0000000..492281c --- /dev/null +++ b/notebooks/landscape/gene/gene-landscape.ipynb @@ -0,0 +1,5725 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "df43c150-06ba-4ba5-bb3e-d07159cc596e", + "metadata": {}, + "outputs": [], + "source": [ + "from semra.landscape.genes import CONFIGURATION\n", + "from semra.landscape.utils import notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b83901f2-4649-4c89-9cf7-1ec61a434e5e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Summarize the Resources\n", + "\n", + "We summarize the resources used in the landscape analysis, including their [Bioregistry](https://bioregistry.io)\n", + "prefix, license, current version, and number of terms (i.e., named concepts) they contain.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "1 resources were not available through\n", + "[PyOBO](https://github.com/biopragmatics/pyobo). Therefore, we estimate the number\n", + "of terms in that resource based on the ones appearing in mappings. Note that these\n", + "are typically an underestimate.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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namelicenseversiontermsstatus
prefix
ncbigeneEntrez GeneNonedaily47782242full
hgncHUGO Gene Nomenclature CommitteeCC0-1.02024-03-0143865full
mgiMouse Genome InformaticsCC-BY-4.06.23682164full
rgdRat Genome DatabaseCC-BY-4.02024-03-2261339full
cgncChicken Gene Nomenclature ConsortiumNoneNone25936full
wormbaseWormbase GeneCC0-1.0None0observed
flybaseFlyBase GeneCC-BY-4.02024_0117883full
sgdSaccharomyces Genome DatabaseCC-BY-4.0R64-3-16600full
omimOnline Mendelian Inheritance in Manhttps://www.omim.org/help/agreementApril 10, 202426954observed
civic.gidCIViC geneCC0-1.001-Apr-2024506full
umlsUnified Medical Language System Concept Unique...https://www.nlm.nih.gov/research/umls/knowledg...2023AB1subset
ncitNCI ThesaurusCC-BY-4.024.02d12311subset
wikidataWikidataCC0-1.0None797966observed
\n", + "
" + ], + "text/plain": [ + " name \\\n", + "prefix \n", + "ncbigene Entrez Gene \n", + "hgnc HUGO Gene Nomenclature Committee \n", + "mgi Mouse Genome Informatics \n", + "rgd Rat Genome Database \n", + "cgnc Chicken Gene Nomenclature Consortium \n", + "wormbase Wormbase Gene \n", + "flybase FlyBase Gene \n", + "sgd Saccharomyces Genome Database \n", + "omim Online Mendelian Inheritance in Man \n", + "civic.gid CIViC gene \n", + "umls Unified Medical Language System Concept Unique... \n", + "ncit NCI Thesaurus \n", + "wikidata Wikidata \n", + "\n", + " license version \\\n", + "prefix \n", + "ncbigene None daily \n", + "hgnc CC0-1.0 2024-03-01 \n", + "mgi CC-BY-4.0 6.23 \n", + "rgd CC-BY-4.0 2024-03-22 \n", + "cgnc None None \n", + "wormbase CC0-1.0 None \n", + "flybase CC-BY-4.0 2024_01 \n", + "sgd CC-BY-4.0 R64-3-1 \n", + "omim https://www.omim.org/help/agreement April 10, 2024 \n", + "civic.gid CC0-1.0 01-Apr-2024 \n", + "umls https://www.nlm.nih.gov/research/umls/knowledg... 2023AB \n", + "ncit CC-BY-4.0 24.02d \n", + "wikidata CC0-1.0 None \n", + "\n", + " terms status \n", + "prefix \n", + "ncbigene 47782242 full \n", + "hgnc 43865 full \n", + "mgi 682164 full \n", + "rgd 61339 full \n", + "cgnc 25936 full \n", + "wormbase 0 observed \n", + "flybase 17883 full \n", + "sgd 6600 full \n", + "omim 26954 observed \n", + "civic.gid 506 full \n", + "umls 1 subset \n", + "ncit 12311 subset \n", + "wikidata 797966 observed " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "There are a total of 49,457,767 terms across the 13 resources." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Summarize the Mappings\n", + "\n", + "In order to summarize the mappings, we're going to load them, index them, and count\n", + "the number of mappings between each pair of resources. The self-mapping column is\n", + "the count of terms in the resource. We'll do this to the raw mappings first, then\n", + "to the processed mappings, then compare them.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Indexing mappings: 0%| | 0.00/1.61M [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "target_prefix ncbigene hgnc mgi rgd cgnc wormbase flybase sgd \\\n", + "source_prefix \n", + "ncbigene 47782242 43796 76239 48324 25931 0 0 0 \n", + "hgnc 43796 43865 0 0 0 0 0 0 \n", + "mgi 76239 0 682164 0 0 0 0 0 \n", + "rgd 48324 0 0 61339 0 0 0 0 \n", + "cgnc 25931 0 0 0 25936 0 0 0 \n", + "wormbase 0 0 0 0 0 0 0 0 \n", + "flybase 0 0 0 0 0 0 17883 0 \n", + "sgd 0 0 0 0 0 0 0 6600 \n", + "omim 18234 17153 0 0 0 0 0 0 \n", + "civic.gid 506 0 0 0 0 0 0 0 \n", + "umls 0 0 0 0 0 0 0 0 \n", + "ncit 0 5785 0 0 0 0 0 0 \n", + "wikidata 788061 43557 0 0 0 0 0 0 \n", + "\n", + "target_prefix omim civic.gid umls ncit wikidata \n", + "source_prefix \n", + "ncbigene 18234 506 0 0 788061 \n", + "hgnc 17153 0 0 5785 43557 \n", + "mgi 0 0 0 0 0 \n", + "rgd 0 0 0 0 0 \n", + "cgnc 0 0 0 0 0 \n", + "wormbase 0 0 0 0 0 \n", + "flybase 0 0 0 0 0 \n", + "sgd 0 0 0 0 0 \n", + "omim 26954 0 0 0 25906 \n", + "civic.gid 0 506 0 0 491 \n", + "umls 0 0 1 0 0 \n", + "ncit 0 0 0 12311 0 \n", + "wikidata 25906 491 0 0 797966 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Next, we summarize the processed mappings, which include inference, reasoning, and confidence filtering." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixncbigenehgncmgirgdcgncwormbaseflybasesgdomimcivic.gidumlsncitwikidata
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ncbigene47782242441917623948324259310001825552805761788874
hgnc4419143865000000175344910578543557
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" + ], + "text/plain": [ + "target_prefix ncbigene hgnc mgi rgd cgnc wormbase flybase sgd \\\n", + "source_prefix \n", + "ncbigene 47782242 44191 76239 48324 25931 0 0 0 \n", + "hgnc 44191 43865 0 0 0 0 0 0 \n", + "mgi 76239 0 682164 0 0 0 0 0 \n", + "rgd 48324 0 0 61339 0 0 0 0 \n", + "cgnc 25931 0 0 0 25936 0 0 0 \n", + "wormbase 0 0 0 0 0 0 0 0 \n", + "flybase 0 0 0 0 0 0 17883 0 \n", + "sgd 0 0 0 0 0 0 0 6600 \n", + "omim 18255 17534 0 0 0 0 0 0 \n", + "civic.gid 528 491 0 0 0 0 0 0 \n", + "umls 0 0 0 0 0 0 0 0 \n", + "ncit 5761 5785 0 0 0 0 0 0 \n", + "wikidata 788874 43557 0 0 0 0 0 0 \n", + "\n", + "target_prefix omim civic.gid umls ncit wikidata \n", + "source_prefix \n", + "ncbigene 18255 528 0 5761 788874 \n", + "hgnc 17534 491 0 5785 43557 \n", + "mgi 0 0 0 0 0 \n", + "rgd 0 0 0 0 0 \n", + "cgnc 0 0 0 0 0 \n", + "wormbase 0 0 0 0 0 \n", + "flybase 0 0 0 0 0 \n", + "sgd 0 0 0 0 0 \n", + "omim 26954 491 0 5636 28144 \n", + "civic.gid 491 506 0 457 491 \n", + "umls 0 0 1 0 0 \n", + "ncit 5636 457 0 12311 5761 \n", + "wikidata 28144 491 0 5761 797966 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Below is an graph-based view on the processed mappings." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "hgnc\n", + "43,865\n", + "\n", + "hgnc\n", + "43,865\n", + "\n", + "\n", + "\n", + "ccds\n", + "\n", + "ccds\n", + "\n", + "\n", + "\n", + "hgnc\n", + "43,865--ccds\n", + "\n", + "35,511\n", + "\n", + "\n", + "\n", + "ena.embl\n", + "\n", + "ena.embl\n", + "\n", + "\n", + "\n", + "hgnc\n", + "43,865--ena.embl\n", + "\n", + "20,710\n", + "\n", + "\n", + "\n", + "ensembl\n", + "\n", + "ensembl\n", + "\n", + "\n", + "\n", + "hgnc\n", + "43,865--ensembl\n", + "\n", + "44,363\n", + "\n", + "\n", + "\n", + "iuphar.receptor\n", + "\n", + "iuphar.receptor\n", + "\n", + "\n", + "\n", + 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"43,865--civic.gid\n", + "506\n", + "\n", + "491\n", + "\n", + "\n", + "\n", + "ncit\n", + "12,311\n", + "\n", + "ncit\n", + "12,311\n", + "\n", + "\n", + "\n", + "hgnc\n", + "43,865--ncit\n", + "12,311\n", + "\n", + "5,785\n", + "\n", + "\n", + "\n", + "wikidata\n", + "797,966\n", + "\n", + "wikidata\n", + "797,966\n", + "\n", + "\n", + "\n", + "hgnc\n", + "43,865--wikidata\n", + "797,966\n", + "\n", + "43,557\n", + "\n", + "\n", + "\n", + "ensembl--ncbigene\n", + "47,782,242\n", + "\n", + "155,882\n", + "\n", + "\n", + "\n", + "ensembl--omim\n", + "26,954\n", + "\n", + "19,094\n", + "\n", + "\n", + "\n", + "ensembl--civic.gid\n", + "506\n", + "\n", + "543\n", + "\n", + "\n", + "\n", + "ensembl--ncit\n", + "12,311\n", + "\n", + "6,653\n", + "\n", + "\n", + "\n", + "ensembl--wikidata\n", + "797,966\n", + "\n", + "156,630\n", + "\n", + "\n", + "\n", + "mgi\n", + "682,164\n", + "\n", + "mgi\n", + "682,164\n", + "\n", + "\n", + "\n", + "ensembl--mgi\n", + "682,164\n", + "\n", + "56,682\n", + "\n", + "\n", + "\n", + "rgd\n", + "61,339\n", + "\n", + "rgd\n", + "61,339\n", + "\n", + "\n", + "\n", + "ensembl--rgd\n", + "61,339\n", + "\n", + "52,906\n", + "\n", + "\n", + "\n", + "cgnc\n", + "25,936\n", + "\n", + "cgnc\n", + "25,936\n", + "\n", + "\n", + "\n", + "ensembl--cgnc\n", + "25,936\n", + "\n", + "15,259\n", + "\n", + "\n", + "\n", + "ncbigene\n", + "47,782,242--omim\n", + "26,954\n", + "\n", + "18,255\n", + "\n", + "\n", + "\n", + "ncbigene\n", + "47,782,242--civic.gid\n", + "506\n", + "\n", + "528\n", + "\n", + "\n", + "\n", + "ncbigene\n", + "47,782,242--ncit\n", + "12,311\n", + "\n", + "5,761\n", + "\n", + "\n", + "\n", + "ncbigene\n", + "47,782,242--wikidata\n", + "797,966\n", + "\n", + "788,874\n", + "\n", + "\n", + "\n", + "ncbigene\n", + "47,782,242--mgi\n", + "682,164\n", + "\n", + "76,239\n", + "\n", + "\n", + "\n", + "ncbigene\n", + "47,782,242--rgd\n", + "61,339\n", + "\n", + "48,324\n", + "\n", + "\n", + "\n", + "ncbigene\n", + "47,782,242--cgnc\n", + "25,936\n", + "\n", + "25,931\n", + "\n", + "\n", + "\n", + "omim\n", + "26,954--civic.gid\n", + "506\n", + "\n", + "491\n", + "\n", + "\n", + "\n", + "omim\n", + "26,954--ncit\n", + "12,311\n", + "\n", + "5,636\n", + "\n", + "\n", + "\n", + "omim\n", + "26,954--wikidata\n", + "797,966\n", + "\n", + "28,144\n", + "\n", + "\n", + "\n", + "civic.gid\n", + "506--ncit\n", + "12,311\n", + "\n", + "457\n", + "\n", + "\n", + "\n", + "civic.gid\n", + "506--wikidata\n", + "797,966\n", + "\n", + "491\n", + "\n", + "\n", + "\n", + "ncit\n", + "12,311--wikidata\n", + "797,966\n", + "\n", + "5,761\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Comparison\n", + "\n", + "The following comparison shows the absolute number of mappings added by processing/inference.\n", + "Across the board, this process adds large numbers of mappings to most resources, especially\n", + "ones that were previously only connected to a small number of other resources.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixncbigenehgncmgirgdcgncwormbaseflybasesgdomimcivic.gidumlsncitwikidata
source_prefix
ncbigene0395000000212205761813
hgnc3950000000381491000
mgi0000000000000
rgd0000000000000
cgnc0000000000000
wormbase0000000000000
flybase0000000000000
sgd0000000000000
omim213810000000491056362238
civic.gid22491000000491004570
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ncit576100000005636457005761
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\n", + "
" + ], + "text/plain": [ + "target_prefix ncbigene hgnc mgi rgd cgnc wormbase flybase sgd omim \\\n", + "source_prefix \n", + "ncbigene 0 395 0 0 0 0 0 0 21 \n", + "hgnc 395 0 0 0 0 0 0 0 381 \n", + "mgi 0 0 0 0 0 0 0 0 0 \n", + "rgd 0 0 0 0 0 0 0 0 0 \n", + "cgnc 0 0 0 0 0 0 0 0 0 \n", + "wormbase 0 0 0 0 0 0 0 0 0 \n", + "flybase 0 0 0 0 0 0 0 0 0 \n", + "sgd 0 0 0 0 0 0 0 0 0 \n", + "omim 21 381 0 0 0 0 0 0 0 \n", + "civic.gid 22 491 0 0 0 0 0 0 491 \n", + "umls 0 0 0 0 0 0 0 0 0 \n", + "ncit 5761 0 0 0 0 0 0 0 5636 \n", + "wikidata 813 0 0 0 0 0 0 0 2238 \n", + "\n", + "target_prefix civic.gid umls ncit wikidata \n", + "source_prefix \n", + "ncbigene 22 0 5761 813 \n", + "hgnc 491 0 0 0 \n", + "mgi 0 0 0 0 \n", + "rgd 0 0 0 0 \n", + "cgnc 0 0 0 0 \n", + "wormbase 0 0 0 0 \n", + "flybase 0 0 0 0 \n", + "sgd 0 0 0 0 \n", + "omim 491 0 5636 2238 \n", + "civic.gid 0 0 457 0 \n", + "umls 0 0 0 0 \n", + "ncit 457 0 0 5761 \n", + "wikidata 0 0 5761 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Here's an alternative view on the number of mappings normalized to show percentage gain.\n", + "\n", + "Note:\n", + "\n", + "- `inf` means that there were no mappings before and now there are a non-zero number of mappings\n", + "- `NaN` means there were no mappings before inference and continue to be no mappings after inference\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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target_prefixncbigenehgncmgirgdcgncwormbaseflybasesgdomimcivic.gidumlsncitwikidata
source_prefix
ncbigene0.00.90.00.00.0NaNNaNNaN0.14.3NaNinf0.1
hgnc0.90.0NaNNaNNaNNaNNaNNaN2.2infNaN0.00.0
mgi0.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
rgd0.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
cgnc0.0NaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN
wormbaseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
flybaseNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaN
sgdNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaN
omim0.12.2NaNNaNNaNNaNNaNNaN0.0infNaNinf8.6
civic.gid4.3infNaNNaNNaNNaNNaNNaNinf0.0NaNinf0.0
umlsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN
ncitinf0.0NaNNaNNaNNaNNaNNaNinfinfNaN0.0inf
wikidata0.10.0NaNNaNNaNNaNNaNNaN8.60.0NaNinf0.0
\n", + "
" + ], + "text/plain": [ + "target_prefix ncbigene hgnc mgi rgd cgnc wormbase flybase sgd omim \\\n", + "source_prefix \n", + "ncbigene 0.0 0.9 0.0 0.0 0.0 NaN NaN NaN 0.1 \n", + "hgnc 0.9 0.0 NaN NaN NaN NaN NaN NaN 2.2 \n", + "mgi 0.0 NaN 0.0 NaN NaN NaN NaN NaN NaN \n", + "rgd 0.0 NaN NaN 0.0 NaN NaN NaN NaN NaN \n", + "cgnc 0.0 NaN NaN NaN 0.0 NaN NaN NaN NaN \n", + "wormbase NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "flybase NaN NaN NaN NaN NaN NaN 0.0 NaN NaN \n", + "sgd NaN NaN NaN NaN NaN NaN NaN 0.0 NaN \n", + "omim 0.1 2.2 NaN NaN NaN NaN NaN NaN 0.0 \n", + "civic.gid 4.3 inf NaN NaN NaN NaN NaN NaN inf \n", + "umls NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "ncit inf 0.0 NaN NaN NaN NaN NaN NaN inf \n", + "wikidata 0.1 0.0 NaN NaN NaN NaN NaN NaN 8.6 \n", + "\n", + "target_prefix civic.gid umls ncit wikidata \n", + "source_prefix \n", + "ncbigene 4.3 NaN inf 0.1 \n", + "hgnc inf NaN 0.0 0.0 \n", + "mgi NaN NaN NaN NaN \n", + "rgd NaN NaN NaN NaN \n", + "cgnc NaN NaN NaN NaN \n", + "wormbase NaN NaN NaN NaN \n", + "flybase NaN NaN NaN NaN \n", + "sgd NaN NaN NaN NaN \n", + "omim inf NaN inf 8.6 \n", + "civic.gid 0.0 NaN inf 0.0 \n", + "umls NaN 0.0 NaN NaN \n", + "ncit inf NaN 0.0 inf \n", + "wikidata 0.0 NaN inf 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Landscape Analysis\n", + "\n", + "Before, we looked at the overlaps between each resource. Now, we use that information jointly to\n", + "estimate the number of terms in the landscape itself, and estimate how much of the landscape\n", + "each resource covers.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "This estimates a total of 48,788,619 unique entities.\n", + "\n", + "- 859,853 (1.8%) have\n", + " at least one mapping.\n", + "- 47,928,766 (98.2%)\n", + " are unique to a single resource.\n", + "- 0 (0.0%)\n", + " appear in all 13 resources.\n", + "\n", + "This estimate is susceptible to several caveats:\n", + "\n", + "- Missing mappings inflates this measurement\n", + "- Generic resources like MeSH contain irrelevant entities that can't be mapped\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Because there are 13, there are 8191 possible overlaps to consider.\n", + "Therefore, a Venn diagram is not possible, so we\n", + "we use an [UpSet plot](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720993) (Lex *et al.*, 2014)\n", + "as a high-dimensional Venn diagram.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-04-11T14:18:16.217420\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.0, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "\\\n", + " The landscape of 13 resources has 49,457,767 total terms.\n", + " After merging redundant nodes based on mappings, inference, and reasoning, there\n", + " are 48,788,619 unique concepts. Using the reduction formula\n", + " $\\frac{\\text{total terms} - \\text{reduced terms}}{\\text{total terms}}$,\n", + " this is a 1.4% reduction.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "This is only an estimate and is susceptible to a few things:\n", + "\n", + "1. It can be artificially high because there are entities that _should_ be mapped, but are not\n", + "2. It can be artificially low because there are entities that are incorrectly mapped, e.g., as\n", + " a result of inference. The frontend curation interface can help identify and remove these\n", + "3. It can be artificially low because for some vocabularies like SNOMED-CT, it's not possible\n", + " to load a terms list, and therefore it's not possible to account for terms that aren't mapped.\n", + " Therefore, we make a lower bound estimate based on the terms that appear in mappings\n", + "4. It can be artificially high if a vocabulary is used that covers many domains and is not properly\n", + " subset'd. For example, EFO covers many different domains, so when doing disease landscape\n", + " analysis, it should be subset to only terms in the disease hierarchy (i.e., appearing under\n", + " ``efo:0000408``).\n", + "5. It can be affected by terminology issues, such as the confusion between Orphanet and ORDO\n", + "6. It can be affected by the existence of many-to-many mappings, which are filtered out during\n", + " processing, which makes the estimate artificially high since some subset of those entities\n", + " could be mapped, but it's not clear which should.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = notebook(CONFIGURATION, output_directory=\".\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/landscape/gene/graph.svg b/notebooks/landscape/gene/graph.svg new file mode 100644 index 0000000..3af1aa4 --- /dev/null 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@@ +source_prefix ncbigene hgnc mgi rgd cgnc wormbase flybase sgd omim civic.gid umls ncit wikidata +ncbigene 47782242 43796 76239 48324 25931 0 0 0 18234 506 0 0 788061 +hgnc 43796 43865 0 0 0 0 0 0 17153 0 0 5785 43557 +mgi 76239 0 682164 0 0 0 0 0 0 0 0 0 0 +rgd 48324 0 0 61339 0 0 0 0 0 0 0 0 0 +cgnc 25931 0 0 0 25936 0 0 0 0 0 0 0 0 +wormbase 0 0 0 0 0 0 0 0 0 0 0 0 0 +flybase 0 0 0 0 0 0 17883 0 0 0 0 0 0 +sgd 0 0 0 0 0 0 0 6600 0 0 0 0 0 +omim 18234 17153 0 0 0 0 0 0 26954 0 0 0 25906 +civic.gid 506 0 0 0 0 0 0 0 0 506 0 0 491 +umls 0 0 0 0 0 0 0 0 0 0 1 0 0 +ncit 0 5785 0 0 0 0 0 0 0 0 0 12311 0 +wikidata 788061 43557 0 0 0 0 0 0 25906 491 0 0 797966 diff --git a/notebooks/landscape/gene/stats.json b/notebooks/landscape/gene/stats.json new file mode 100644 index 0000000..5660be9 --- /dev/null +++ b/notebooks/landscape/gene/stats.json @@ -0,0 +1,13 @@ +{ + "raw_term_count": 49457767, + "unique_term_count": 48788619, + "reduction": 0.013529684831909212, + "distribution": { + "4": 11853, + "5": 5166, + "6": 490, + "3": 134617, + "1": 47955104, + "2": 681389 + } +} \ No newline at end of file diff --git a/notebooks/landscape/gene/summary.tsv b/notebooks/landscape/gene/summary.tsv new file mode 100644 index 0000000..2ded8cd --- /dev/null +++ b/notebooks/landscape/gene/summary.tsv @@ -0,0 +1,14 @@ +prefix name license version terms status +ncbigene Entrez Gene daily 47782242 full +hgnc HUGO Gene Nomenclature Committee CC0-1.0 2024-03-01 43865 full +mgi Mouse Genome Informatics CC-BY-4.0 6.23 682164 full +rgd Rat Genome Database CC-BY-4.0 2024-03-22 61339 full +cgnc Chicken Gene Nomenclature Consortium 25936 full +wormbase Wormbase Gene CC0-1.0 0 observed +flybase FlyBase Gene CC-BY-4.0 2024_01 17883 full +sgd Saccharomyces Genome Database CC-BY-4.0 R64-3-1 6600 full +omim Online Mendelian Inheritance in Man https://www.omim.org/help/agreement April 10, 2024 26954 observed +civic.gid CIViC gene CC0-1.0 01-Apr-2024 506 full +umls Unified Medical Language System Concept Unique Identifier https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/license_agreement.html 2023AB 1 subset +ncit NCI Thesaurus CC-BY-4.0 24.02d 12311 subset +wikidata Wikidata CC0-1.0 797966 observed diff --git a/notebooks/landscape/landscape.py b/notebooks/landscape/landscape.py new file mode 100644 index 0000000..1283810 --- /dev/null +++ b/notebooks/landscape/landscape.py @@ -0,0 +1,29 @@ +"""Generate a summary over the landscape analyses.""" + +import json +from pathlib import Path + +import pandas as pd + +HERE = Path(__file__).parent.resolve() + + +def main() -> None: + """Generate a summary over the landscape analyses.""" + rows = [] + for directory in HERE.iterdir(): + if not directory.is_dir(): + continue + path = directory.joinpath("stats.json") + if not path.is_file(): + continue + row = json.loads(path.read_text()) + row["name"] = directory.name + rows.append(row) + df = pd.DataFrame(rows).set_index("name") + df = df[["raw_term_count", "unique_term_count", "reduction"]] + print(df.to_markdown(tablefmt="github")) + + +if __name__ == "__main__": + main() diff --git a/src/semra/api.py b/src/semra/api.py index a0d21ef..9a9271a 100644 --- a/src/semra/api.py +++ b/src/semra/api.py @@ -54,6 +54,7 @@ "infer_reversible", "flip", "to_digraph", + "to_multidigraph", "from_digraph", "infer_chains", "tabulate_index", @@ -78,11 +79,14 @@ "validate_mappings", "summarize_prefixes", "filter_minimum_confidence", + "filter_subsets", + "count_component_sizes", ] logger = logging.getLogger(__name__) +DATA_KEY = "data" PREDICATE_KEY = "predicate" EVIDENCE_KEY = "evidence" @@ -90,12 +94,13 @@ Index = t.Dict[Triple, t.List[Evidence]] -def _tqdm(mappings, desc: str | None = None, *, progress: bool = True): +def _tqdm(mappings, desc: str | None = None, *, progress: bool = True, leave: bool = True): return tqdm( mappings, unit_scale=True, unit="mapping", desc=desc, + leave=leave, disable=not progress, ) @@ -189,10 +194,10 @@ def print_source_target_counts(mappings: Iterable[Mapping], minimum: int = 0) -> print(str_source_target_counts(mappings=mappings, minimum=minimum)) # noqa:T201 -def get_index(mappings: Iterable[Mapping], *, progress: bool = True) -> Index: +def get_index(mappings: Iterable[Mapping], *, progress: bool = True, leave: bool = False) -> Index: """Aggregate and deduplicate evidences for each core triple.""" dd: t.DefaultDict[Triple, t.List[Evidence]] = defaultdict(list) - for mapping in _tqdm(mappings, desc="Indexing mappings", progress=progress): + for mapping in _tqdm(mappings, desc="Indexing mappings", progress=progress, leave=leave): dd[mapping.triple].extend(mapping.evidence) return {triple: deduplicate_evidence(evidence) for triple, evidence in dd.items()} @@ -250,11 +255,10 @@ def infer_reversible(mappings: t.Iterable[Mapping], *, progress: bool = True) -> >>> r2 = get_test_reference(prefix="p2") >>> e1 = get_test_evidence() >>> m1 = Mapping(s=r1, p=EXACT_MATCH, o=r2, evidence=[e1]) - >>> mappings = infer_reversible([m1]) + >>> mappings = list(infer_reversible([m1])) >>> len(mappings) 2 >>> mappings[0] == m1 - >>> mappings[1] == .. warning:: @@ -270,7 +274,7 @@ def infer_reversible(mappings: t.Iterable[Mapping], *, progress: bool = True) -> >>> e1, e2 = get_test_evidence(2) >>> m1 = Mapping(s=r1, p=EXACT_MATCH, o=r2, evidence=[e1]) >>> m2 = Mapping(s=r2, p=EXACT_MATCH, o=r1, evidence=[e2]) - >>> mappings = infer_reversible([m1, m2]) + >>> mappings = list(infer_reversible([m1, m2])) >>> len(mappings) 3 >>> mappings = assemble_evidences(mappings) @@ -312,7 +316,7 @@ def flip(mapping: Mapping) -> Mapping | None: ) -def to_digraph(mappings: t.List[Mapping]) -> nx.DiGraph: +def to_digraph(mappings: t.Iterable[Mapping]) -> nx.DiGraph: """Convert mappings into a simple directed graph data model. :param mappings: An iterable of mappings @@ -334,23 +338,60 @@ def to_digraph(mappings: t.List[Mapping]) -> nx.DiGraph: ``graph.add_edge(mappings.s, mapping.o, key=mapping.p, **{EVIDENCE_KEY: mapping.evidence})`` """ graph = nx.DiGraph() + edges: t.DefaultDict[t.Tuple[Reference, Reference], t.DefaultDict[Reference, t.List[Evidence]]] = defaultdict( + lambda: defaultdict(list) + ) for mapping in mappings: - graph.add_edge( - mapping.s, - mapping.o, - **{PREDICATE_KEY: mapping.p, EVIDENCE_KEY: mapping.evidence}, - ) + edges[mapping.s, mapping.o][mapping.p].extend(mapping.evidence) + for (s, o), data in edges.items(): + graph.add_edge(s, o, **{DATA_KEY: data}) return graph def from_digraph(graph: nx.DiGraph) -> t.List[Mapping]: """Extract mappings from a simple directed graph data model.""" - return [_from_digraph_edge(graph, s, o) for s, o in graph.edges()] + return [mapping for s, o in graph.edges() for mapping in _from_digraph_edge(graph, s, o)] -def _from_digraph_edge(graph: nx.Graph, s: Reference, o: Reference) -> Mapping: +def _from_digraph_edge(graph: nx.Graph, s: Reference, o: Reference) -> t.Iterable[Mapping]: data = graph[s][o] - return Mapping(s=s, p=data[PREDICATE_KEY], o=o, evidence=data[EVIDENCE_KEY]) + for p, evidence in data[DATA_KEY].items(): + yield Mapping(s=s, p=p, o=o, evidence=evidence) + + +def iter_components(mappings: t.Iterable[Mapping]) -> t.Iterable[t.Set[Reference]]: + """Iterate over connected components in the multidigraph view over the mappings.""" + graph = to_digraph(mappings) + return nx.weakly_connected_components(graph) + + +def to_multidigraph(mappings: t.Iterable[Mapping], *, progress: bool = False) -> nx.MultiDiGraph: + """Convert mappings into a multi directed graph data model. + + :param mappings: An iterable of mappings + :param progress: Should a progress bar be shown? + :returns: A directed graph in which the nodes are + :class:`curies.Reference` objects. The predicate + is put under the :data:`PREDICATE_KEY` key in the + edge data and the evidences are put under the + :data:`EVIDENCE_KEY` key in the edge data. + + .. warning:: + + This function makes the following assumptions: + + 1. The graph has already been assembled using :func:`assemble_evidences` + + """ + graph = nx.MultiDiGraph() + for mapping in _tqdm(mappings, progress=progress): + graph.add_edge( + mapping.s, + mapping.o, + key=mapping.p, + **{EVIDENCE_KEY: mapping.evidence}, + ) + return graph def _reason_multiple_predicates(predicates: t.Iterable[Reference]) -> Reference | None: @@ -375,7 +416,13 @@ def _reason_multiple_predicates(predicates: t.Iterable[Reference]) -> Reference def infer_chains( - mappings: t.List[Mapping], *, backwards: bool = True, progress: bool = True, cutoff: int = 5 + mappings: t.List[Mapping], + *, + backwards: bool = True, + progress: bool = True, + cutoff: int = 5, + minimum_component_size: int = 2, + maximum_component_size: int = 100, ) -> t.List[Mapping]: """Apply graph-based reasoning over mapping chains to infer new mappings. @@ -383,51 +430,84 @@ def infer_chains( :param backwards: Should inference be done in reverse? :param progress: Should a progress bar be shown? Defaults to true. :param cutoff: What's the maximum length path to infer over? + :param minimum_component_size: The smallest size of a component to consider, defaults to 2 + :param maximum_component_size: The smallest size of a component to consider, defaults to 100. + Components that are very large (i.e., much larger than the number of target prefixes) + likely are the result of many broad/narrow mappings :return: The list of input mappings _plus_ inferred mappings """ mappings = assemble_evidences(mappings, progress=progress) - # FIXME to_digraph requires a single predicate for each s/o pair, - # which isn't necessarily true, so there should be some kind of reasoning - # step that picks which is "best" - graph = to_digraph(mappings) + graph = to_multidigraph(mappings) new_mappings = [] components = sorted( - (c for c in nx.weakly_connected_components(graph) if len(c) > 2), # noqa: PLR2004 + ( + component + for component in nx.weakly_connected_components(graph) + if minimum_component_size < len(component) <= maximum_component_size + ), key=len, reverse=True, ) it = tqdm(components, unit="component", desc="Inferring chains", unit_scale=True, disable=not progress) for _i, component in enumerate(it): - sg: nx.DiGraph = graph.subgraph(component).copy() - it.set_postfix(size=sg.number_of_nodes()) - for s, o in itt.combinations(sg, 2): + sg: nx.MultiDiGraph = graph.subgraph(component).copy() + sg_len = sg.number_of_nodes() + it.set_postfix(size=sg_len) + inner_it = tqdm( + itt.combinations(sg, 2), + total=sg_len * (sg_len - 1) // 2, + unit_scale=True, + disable=not progress, + unit="edge", + leave=False, + ) + for s, o in inner_it: if sg.has_edge(s, o): # do not overwrite existing mappings continue # TODO there has to be a way to reimplement transitive closure to handle this # nx.shortest_path(sg, s, o) + predicate_evidence_dict: t.DefaultDict[Reference, t.List[Evidence]] = defaultdict(list) for path in nx.all_simple_edge_paths(sg, s, o, cutoff=cutoff): - predicates = [sg[u][v][PREDICATE_KEY] for u, v in path] + if _path_has_prefix_duplicates(path): + continue + predicates = [k for _u, _v, k in path] p = _reason_multiple_predicates(predicates) - if p: + if p is not None: evidence = ReasonedEvidence( justification=CHAIN_MAPPING, mappings=[ Mapping( s=path_s, o=path_o, - p=graph[path_s][path_o][PREDICATE_KEY], - evidence=graph[path_s][path_o][EVIDENCE_KEY], + p=path_p, + evidence=graph[path_s][path_o][path_p][EVIDENCE_KEY], ) - for path_s, path_o in path + for path_s, path_o, path_p in path ], + # TODO add confidence that's inversely proportional to sg_len, i.e. + # larger components should return less confident mappings ) - new_mappings.append(Mapping(s=s, p=p, o=o, evidence=[evidence])) - if backwards: - new_mappings.append(Mapping(o=s, s=o, p=FLIP[p], evidence=[evidence])) + predicate_evidence_dict[p].append(evidence) + + for p, evidences in predicate_evidence_dict.items(): + new_mappings.append(Mapping(s=s, p=p, o=o, evidence=evidences)) + if backwards: + new_mappings.append(Mapping(o=s, s=o, p=FLIP[p], evidence=evidences)) + return [*mappings, *new_mappings] +def _path_has_prefix_duplicates(path) -> bool: + """Return if the path has multiple unique.""" + elements = set() + for u, v, _ in path: + elements.add(u) + elements.add(v) + counter = Counter(element.prefix for element in elements) + return any(v > 1 for v in counter.values()) + + def tabulate_index(index: Index) -> str: """Create a table of all mappings contained in an index. @@ -482,7 +562,7 @@ def infer_mutual_dbxref_mutations( >>> from semra import DB_XREF, EXACT_MATCH, Reference, NARROW_MATCH >>> curies = "DOID:0050577", "mesh:C562966", "umls:C4551571" - >>> r1, r2, r3, r4 = (Reference.from_curie(c) for c in curies) + >>> r1, r2, r3, r4 = map(Reference.from_curie, curies) >>> m1 = Mapping.from_triple((r1, DB_XREF, r2)) >>> m2 = Mapping.from_triple((r2, DB_XREF, r3)) >>> mappings = [m1, m2, m3] @@ -823,12 +903,13 @@ def prioritize(mappings: t.List[Mapping], priority: t.List[str]) -> t.List[Mappi if o is None: continue rv.extend( - _from_digraph_edge(graph, s, o) + mapping # TODO should this work even if s-o edge not exists? # can also do "inference" here, but also might be # because of negative edge filtering for s in component if s != o and graph.has_edge(s, o) + for mapping in _from_digraph_edge(graph, s, o) ) # sort such that the mappings are ordered by object by priority order @@ -962,3 +1043,172 @@ def filter_minimum_confidence(mappings: Iterable[Mapping], cutoff: float = 0.7) continue if confidence >= cutoff: yield mapping + + +def hydrate_subsets(subset_configuration: t.Mapping[str, t.Collection[str]]) -> t.Mapping[str, t.Collection[str]]: + """Convert a subset configuration dictionary into a subset artifact. + + :param subset_configuration: A dictionary of prefixes to sets of parent terms + :return: A dictionary that uses the is-a hierarchy within the resources to get full term lists + :raises ValueError: If a prefix can't be looked up with PyOBO + + To get all the cells from MeSH: + + .. code-block:: python + + from semra.api import hydrate_subsets, filter_subsets + + configuration = {"mesh": ["mesh:D002477"], ...} + prefix_to_identifiers = hydrate_subsets(configuration) + + It's also possible to use parents outside the vocabulary, such as when search for entity + type in UMLS: + + .. code-block:: python + + from semra.api import hydrate_subsets, filter_subsets + + configuration = { + "umls": [ + # all children of https://uts.nlm.nih.gov/uts/umls/semantic-network/Pathologic%20Function + "sty:T049", # cell or molecular dysfunction + "sty:T047", # disease or syndrome + "sty:T191", # neoplastic process + "sty:T050", # experimental model of disease + "sty:T048", # mental or behavioral dysfunction + ], + ... + } + prefix_to_identifiers = hydrate_subsets(configuration) + + """ + import pyobo + from pyobo.getters import NoBuild + + rv: t.Dict[str, t.Set[str]] = {} + # do lookup of the hierarchy and lookup of ancestors in 2 steps to allow for + # querying parents inside a resource that aren't defined by it (e.g., sty terms in umls) + for prefix, parent_curies in subset_configuration.items(): + try: + hierarchy = pyobo.get_hierarchy(prefix, include_part_of=False, include_has_member=False) + except NoBuild: + rv[prefix] = set() + except Exception as e: + raise ValueError(f"Failed on {prefix}") from e + else: + _pp = f"{prefix}:" + rv[prefix] = { + descendant_curie[len(_pp) :] + for parent_curie in parent_curies + for descendant_curie in nx.ancestors(hierarchy, parent_curie) or [] + if descendant_curie.startswith(_pp) + } + for parent_curie in parent_curies: + if parent_curie.startswith(_pp): + rv[prefix].add(parent_curie[len(_pp) :]) + return rv + + +def filter_subsets( + mappings: t.Iterable[Mapping], prefix_to_identifiers: t.Mapping[str, t.Collection[str]] +) -> t.List[Mapping]: + """Filter mappings that don't appear in the given subsets. + + :param mappings: An iterable of semantic mappings + :param prefix_to_identifiers: A dictionary whose keys are prefixes and whose values are collections + of local unique identifiers strings for a subset of terms in the resource to keep. + + In situations where a mapping's subject or object's prefix does not appear in this dictionary, the check + is skipped. + :return: A list that has been filtered based on the prefix_to_identifiers dict + :raises ValueError: If CURIEs are given instead of identifiers + + If you have a simple configuration dictionary that contains the parent terms, like + ``{"mesh": ["mesh:D002477"]}``, you'll want to do the following first: + + .. code-block:: python + + from semra.api import hydrate_subsets, filter_subsets + + mappings = [ ... ] + configuration = {"mesh": ["mesh:D002477"]} + prefix_to_identifiers = hydrate_subsets(configuration) + filter_subsets(mappings, prefix_to_identifiers) + """ + clean_prefix_to_identifiers = {} + for prefix, identifiers in prefix_to_identifiers.items(): + if not identifiers: + # skip empty lists + continue + problems = {identifier for identifier in identifiers if ":" in identifier} + if problems: + raise ValueError("value list should be local unique ids, not curies") + clean_prefix_to_identifiers[prefix] = identifiers + + rv = [] + for mapping in mappings: + if ( + mapping.s.prefix in clean_prefix_to_identifiers + and mapping.s.identifier not in clean_prefix_to_identifiers[mapping.s.prefix] + ): + continue + if ( + mapping.o.prefix in clean_prefix_to_identifiers + and mapping.o.identifier not in clean_prefix_to_identifiers[mapping.o.prefix] + ): + continue + rv.append(mapping) + return rv + + +def aggregate_components( + mappings: t.Iterable[Mapping], + prefix_allowlist: t.Optional[t.Collection[str]] = None, +) -> t.Mapping[t.FrozenSet[str], t.Set[t.FrozenSet[Reference]]]: + """Get a counter where the keys are the set of all prefixes in a weakly connected component. + + :param mappings: Mappings to aggregate + :param prefix_allowlist: An optional prefix filter - only keeps prefixes in this list + :returns: A dictionary mapping from a frozenset of prefixes to a set of frozensets of references + """ + dd: t.DefaultDict[t.FrozenSet[str], t.Set[t.FrozenSet[Reference]]] = defaultdict(set) + components = iter_components(mappings) + + if prefix_allowlist is not None: + prefix_set = set(prefix_allowlist) + for component in components: + # subset to the priority prefixes + subcomponent: t.FrozenSet[Reference] = frozenset(r for r in component if r.prefix in prefix_set) + key = frozenset(r.prefix for r in subcomponent) + dd[key].add(subcomponent) + else: + for component in components: + subcomponent = frozenset(component) + key = frozenset(r.prefix for r in subcomponent) + dd[key].add(subcomponent) + + return dict(dd) + + +def count_component_sizes( + mappings: t.Iterable[Mapping], prefix_allowlist: t.Optional[t.Collection[str]] = None +) -> t.Counter[t.FrozenSet[str]]: + """Get a counter where the keys are the set of all prefixes in a weakly connected component.""" + xx = aggregate_components(mappings, prefix_allowlist) + return Counter({k: len(v) for k, v in xx.items()}) + + +def count_coverage_sizes( + mappings: t.Iterable[Mapping], prefix_allowlist: t.Optional[t.Collection[str]] = None +) -> t.Counter[int]: + """Get a counter of the number of prefixes in which each entity appears based on the mappings.""" + xx = count_component_sizes(mappings, prefix_allowlist=prefix_allowlist) + counter: t.Counter[int] = Counter() + for prefixes, count in xx.items(): + counter[len(prefixes)] += count + # Back-fill any intermediate counts with zero + max_key = max(counter) + for i in range(1, max_key): + if i not in counter: + counter[i] = 0 + return counter diff --git a/src/semra/client.py b/src/semra/client.py index 7da49b1..fbb0868 100644 --- a/src/semra/client.py +++ b/src/semra/client.py @@ -18,6 +18,7 @@ import semra from semra import Evidence, MappingSet, Reference from semra.io import _get_name_by_curie +from semra.rules import RELATIONS __all__ = [ "Node", @@ -30,6 +31,16 @@ ReferenceHint: TypeAlias = t.Union[str, Reference] +DEFAULT_MAX_LENGTH = 3 + + +def _safe_curie(curie_or_luid: ReferenceHint, prefix: str) -> str: + if isinstance(curie_or_luid, Reference): + return curie_or_luid.curie + if curie_or_luid.startswith(prefix): + return curie_or_luid + return f"{prefix}:{curie_or_luid}" + class Neo4jClient: """A client to Neo4j.""" @@ -54,6 +65,12 @@ def __init__( self.driver = neo4j.GraphDatabase.driver(uri=uri, auth=(user, password), max_connection_lifetime=180) + query = "CALL db.relationshipTypes() YIELD relationshipType RETURN relationshipType" + self._all_relations = {curie for curie, in self.read_query(query)} + self._rel_q = "|".join( + f"`{reference.curie}`" for reference in RELATIONS if reference.curie in self._all_relations + ) + def __del__(self): """Ensure driver is shut down when client is destroyed.""" if self.driver is not None: @@ -115,20 +132,19 @@ def get_mapping(self, curie: ReferenceHint) -> semra.Mapping: unique identifier representing a SeMRA mapping. :return: A semantic mapping object """ - if isinstance(curie, Reference): - curie = curie.curie - if not curie.startswith("semra.mapping:"): - curie = f"semra.mapping:{curie}" + curie = _safe_curie(curie, "semra.mapping") query = """\ MATCH - (mapping {curie: $curie})-[:`owl:annotatedSource`]->(source) , - (mapping {curie: $curie})-[:`owl:annotatedTarget`]->(target) , - (mapping {curie: $curie})-[:hasEvidence]->(evidence) + (mapping {curie: $curie}) , + (mapping)-[:`owl:annotatedSource`]->(source) , + (mapping)-[:`owl:annotatedTarget`]->(target) , + (mapping)-[:hasEvidence]->(evidence) OPTIONAL MATCH (evidence)-[:fromSet]->(mset) OPTIONAL MATCH (evidence)-[:hasAuthor]->(author) RETURN mapping, source.curie, target.curie, collect([evidence, mset, author.curie]) + LIMIT 1 """ mapping, source_curie, target_curie, evidence_pairs = self.read_query(query, curie=curie)[0] evidence: list[Evidence] = [] @@ -167,10 +183,7 @@ def get_mapping_set(self, curie: ReferenceHint) -> MappingSet: For example, use ``semra.mappingset:7831d5bc95698099fb6471667e5282cd`` for biomappings :return: A mapping set object """ - if isinstance(curie, Reference): - curie = curie.curie - if not curie.startswith("semra.mappingset:"): - curie = f"semra.mappingset:{curie}" + curie = _safe_curie(curie, "semra.mappingset") node = self._get_node_by_curie(curie) return MappingSet.parse_obj(node) @@ -180,8 +193,7 @@ def get_evidence(self, curie: ReferenceHint) -> Evidence: :param curie: The CURIE for a mapping set, using ``semra.evidence`` as a prefix. :return: An evidence object """ - if isinstance(curie, Reference): - curie = curie.curie + curie = _safe_curie(curie, "semra.evidence") query = "MATCH (n {curie: $curie}) RETURN n" res = self.read_query(query, curie=curie) return res[0][0] @@ -237,7 +249,7 @@ def get_highest_exact_matches(self, limit: int = 10) -> t.Counter[tuple[str, str :return: A counter with keys that are CURIE/name pairs """ query = """\ - MATCH (a)-[:`skos:exactMatch`]-(b) + MATCH (a:concept)-[:`skos:exactMatch`]-(b:concept) WHERE a.priority RETURN a.curie, a.name, count(distinct b) as c ORDER BY c DESCENDING @@ -248,19 +260,26 @@ def get_highest_exact_matches(self, limit: int = 10) -> t.Counter[tuple[str, str def _count_with_name(self, query: str, **kwargs: Any) -> t.Counter[tuple[str, str]]: return Counter({(curie, name): count for curie, name, count in self.read_query(query, **kwargs)}) - def get_exact_matches(self, curie: ReferenceHint) -> dict[Reference, str]: + def get_exact_matches(self, curie: ReferenceHint, *, max_distance: t.Optional[int] = None) -> dict[Reference, str]: """Get a mapping of references->name for all concepts equivalent to the given concept.""" if isinstance(curie, Reference): curie = curie.curie - query = "MATCH (a {curie: $curie})-[:`skos:exactMatch`]-(b) RETURN a.curie, a.name" + if max_distance is None: + max_distance = DEFAULT_MAX_LENGTH + query = f"""\ + MATCH (a:concept {{curie: $curie}})-[:`skos:exactMatch`*1..{max_distance}]-(b:concept) + WHERE a.curie <> b.curie + RETURN b.curie, b.name + """ return {Reference.from_curie(n_curie): name for n_curie, name in self.read_query(query, curie=curie)} def get_connected_component( - self, curie: ReferenceHint + self, curie: ReferenceHint, max_distance: t.Optional[int] = None ) -> tuple[list[neo4j.graph.Node], list[neo4j.graph.Relationship]]: """Get the nodes and relations in the connected component of mappings around the given CURIE. :param curie: A CURIE string or reference + :param max_distance: The maximum number of hops to consider :return: A pair of: 1. The nodes in the connected component, as Neo4j node objects @@ -268,13 +287,16 @@ def get_connected_component( """ if isinstance(curie, Reference): curie = curie.curie - query = """\ - MATCH (:concept {curie: $curie})-[r *..3 {hasPrimary: true}]-(n:concept) + if max_distance is None: + max_distance = DEFAULT_MAX_LENGTH + query = f"""\ + MATCH (:concept {{curie: $curie}})-[r:{self._rel_q} *..{max_distance}]-(n:concept) + WHERE ALL(p IN r WHERE p.primary or p.secondary) RETURN collect(DISTINCT n) AS nodes, collect(DISTINCT r) AS relations """ res = self.read_query(query, curie=curie) nodes = res[0][0] - relations = sorted({r for relations in res[0][1] for r in relations}) + relations = sorted({r for relations in res[0][1] for r in relations}, key=lambda r: r.type) return nodes, relations def get_connected_component_graph(self, curie: ReferenceHint) -> nx.MultiDiGraph: @@ -303,6 +325,23 @@ def get_concept_name(self, curie: ReferenceHint) -> str | None: curie = curie.curie return _get_name_by_curie(curie) + def sample_mappings_from_set(self, curie: ReferenceHint, n: int = 10) -> t.List: + """Get n mappings from a given set (by CURIE).""" + if isinstance(curie, Reference): + curie = curie.curie + query = f"""\ + MATCH + (:mappingset {{curie: $curie}})<-[:fromSet]-()<-[:hasEvidence]-(n:mapping) + MATCH + (n)-[:`owl:annotatedSource`]->(s:concept) + MATCH + (n)-[:`owl:annotatedTarget`]->(t:concept) + WHERE s.name IS NOT NULL and t.name IS NOT NULL and s.curie < t.curie + RETURN n.curie, n.predicate, s.curie, s.name, t.curie, t.name + LIMIT {n} + """ + return list(self.read_query(query, curie=curie)) + # Follows example here: # https://neo4j.com/docs/python-manual/current/session-api/#python-driver-simple-transaction-fn diff --git a/src/semra/io.py b/src/semra/io.py index 40f3e65..5682106 100644 --- a/src/semra/io.py +++ b/src/semra/io.py @@ -38,7 +38,7 @@ logger = logging.getLogger(__name__) -#: The precision for confidences used before exporing to the graph data model +#: The precision for confidences used before exporting to the graph data model CONFIDENCE_PRECISION = 5 #: The predicate used in the graph data model connecting a mapping node to an evidence node HAS_EVIDENCE_PREDICATE = "hasEvidence" @@ -235,7 +235,7 @@ def _from_pyobo_df( df[df.columns[0]] = df[df.columns[0]].map(lambda s: bioregistry.standardize_identifier(source_prefix, s)) df[df.columns[2]] = [ bioregistry.standardize_identifier(target_prefix, target_id) - for target_prefix, target_id in df[[1, 2]].values + for target_prefix, target_id in df[[df.columns[1], df.columns[2]]].values ] rv = [ Mapping( @@ -461,7 +461,14 @@ def _get_name_by_curie(curie: str) -> str | None: orcid = curie[len("orcid:") :] res = requests.get(f"https://orcid.org/{orcid}", headers={"Accept": "application/json"}, timeout=5).json() - return res["person"]["name"]["given-names"]["value"] + " " + res["person"]["name"]["family-name"]["value"] + name = res["person"]["name"] + if name is None: + return None + if credit_name := name.get("credit-name"): + return credit_name["value"] + if (given_names := name.get("given-names")) and (family_name := name.get("family-name")): + return f"{given_names['value']} {family_name['value']}" + return None return pyobo.get_name_by_curie(curie) @@ -828,12 +835,13 @@ def write_neo4j( #!/bin/bash docker build --tag {docker_name} . # -t means allocate a pseudo-TTY, necessary to keep it running in the background - docker run -t --detach -p 7474:7474 -p 7687:7687 -p 8773:8773 {docker_name} + docker run -t --detach -p 7474:7474 -p 7687:7687 -p 8773:8773 --name {docker_name} {docker_name}:latest """ ) run_path.write_text(run_command) click.secho("Run Neo4j with the following:", fg="green") - click.secho(f" sh {run_path.absolute()}") + click.secho(f" cd {run_path.parent.absolute()}") + click.secho(f" sh {run_script_name}") # shell_command = dedent(f"""\ # neo4j-admin database import full \\ diff --git a/src/semra/landscape/__init__.py b/src/semra/landscape/__init__.py new file mode 100644 index 0000000..14daa20 --- /dev/null +++ b/src/semra/landscape/__init__.py @@ -0,0 +1 @@ +"""Landscape Analysis.""" diff --git a/src/semra/landscape/__main__.py b/src/semra/landscape/__main__.py new file mode 100644 index 0000000..740689b --- /dev/null +++ b/src/semra/landscape/__main__.py @@ -0,0 +1,30 @@ +"""Run all landscape builds.""" + +import click + +import semra.landscape.anatomy +import semra.landscape.cells +import semra.landscape.complexes +import semra.landscape.diseases +import semra.landscape.genes + +FUNCTIONS = [ + ("Complexes", semra.landscape.complexes.main), + ("Anatomy", semra.landscape.anatomy.main), + ("Cells and Cell Lines", semra.landscape.cells.main), + ("Diseases", semra.landscape.diseases.main), + ("Genes", semra.landscape.genes.main), +] + + +@click.command() +@click.pass_context +def main(ctx: click.Context): + """Run all landscape builds.""" + for label, func in FUNCTIONS: + click.secho(label, bold=True, fg="green") + ctx.invoke(func) + + +if __name__ == "__main__": + main() diff --git a/src/semra/landscape/anatomy.py b/src/semra/landscape/anatomy.py new file mode 100644 index 0000000..47f889f --- /dev/null +++ b/src/semra/landscape/anatomy.py @@ -0,0 +1,77 @@ +"""A configuration for assembling mappings for anatomical terms.""" + +import click +import pystow +from pyobo.sources.mesh import get_mesh_category_curies + +import semra + +__all__ = [ + "MODULE", + "CONFIGURATION", +] + +MODULE = pystow.module("semra", "case-studies", "anatomy") +PRIORITY = [ + "uberon", + "mesh", + "bto", + "caro", + "ncit", + "umls", +] +# some resources are generic, so we want to cut to a relevant subset +SUBSETS = { + "mesh": get_mesh_category_curies("A", skip=["A11"]), + "ncit": ["ncit:C12219"], + "umls": [ + # see https://uts.nlm.nih.gov/uts/umls/semantic-network/root + "sty:T024", # tissue + "sty:T017", # anatomical structure + ], +} + +CONFIGURATION = semra.Configuration( + name="Anatomy mappings", + inputs=[ + semra.Input(source="biomappings"), + semra.Input(source="gilda"), + semra.Input(prefix="uberon", source="pyobo", confidence=0.99), + semra.Input(prefix="bto", source="pyobo", confidence=0.99), + semra.Input(prefix="caro", source="pyobo", confidence=0.99), + semra.Input(prefix="mesh", source="pyobo", confidence=0.99), + semra.Input(prefix="ncit", source="pyobo", confidence=0.99), + semra.Input(prefix="umls", source="pyobo", confidence=0.99), + ], + subsets=SUBSETS, + add_labels=False, + priority=PRIORITY, + keep_prefixes=PRIORITY, + remove_imprecise=False, + mutations=[ + semra.Mutation(source="uberon", confidence=0.8), + semra.Mutation(source="bto", confidence=0.65), + semra.Mutation(source="caro", confidence=0.8), + semra.Mutation(source="ncit", confidence=0.7), + semra.Mutation(source="umls", confidence=0.7), + ], + raw_pickle_path=MODULE.join(name="raw.pkl"), + raw_sssom_path=MODULE.join(name="raw.sssom.tsv"), + # raw_neo4j_path=MODULE.join("neo4j_raw"), + processed_pickle_path=MODULE.join(name="processed.pkl"), + processed_sssom_path=MODULE.join(name="processed.sssom.tsv"), + processed_neo4j_path=MODULE.join("neo4j"), + processed_neo4j_name="semra-anatomy", + priority_pickle_path=MODULE.join(name="priority.pkl"), + priority_sssom_path=MODULE.join(name="priority.sssom.tsv"), +) + + +@click.command() +def main(): + """Build the mapping database for anatomical terms.""" + CONFIGURATION.get_mappings(refresh_raw=True, refresh_processed=True) + + +if __name__ == "__main__": + main() diff --git a/scripts/cancer_cell_reproduction.py b/src/semra/landscape/cells.py similarity index 74% rename from scripts/cancer_cell_reproduction.py rename to src/semra/landscape/cells.py index b15cf4c..d0fef7f 100644 --- a/scripts/cancer_cell_reproduction.py +++ b/src/semra/landscape/cells.py @@ -1,5 +1,7 @@ -""" -Reproduce the scenario from the Biomappings paper on cancer cell lines +"""A configuration for assembling mappings for cell and cell line terms. + +This configuration can be used to reproduce the results from the Biomappings paper +by doing the following: 1. Load positive mappings - PyOBO: EFO, DepMap, CCLE @@ -20,23 +22,32 @@ from semra.io import write_sssom from semra.pipeline import Configuration, Input, Mutation, get_mappings_from_config -MODULE = pystow.module("semra", "case-studies", "cancer-cell-lines") -PRIORITY_SSSOM_PATH = MODULE.join(name="priority.sssom.tsv") +__all__ = [ + "MODULE", + "CONFIGURATION", +] -PREFIXES = { +MODULE = pystow.module("semra", "case-studies", "cells") +PREFIXES = PRIORITY = [ + "mesh", "efo", "cellosaurus", - "depmap", "ccle", - "clo", - "cl", + "depmap", "bto", - "mesh", + "cl", + "clo", + "ncit", + "umls", +] + +# some resources are generic, so we want to cut to a relevant subset +SUBSETS = { + "mesh": ["mesh:D002477"], + "efo": ["efo:0000324"], + "ncit": ["ncit:C12508"], + "umls": ["sty:T025"], # see https://uts.nlm.nih.gov/uts/umls/semantic-network/root } -PRIORITY = ["mesh", "efo", "cellosaurus", "ccle", "depmap", "bto", "cl", "clo"] -for prefix in PREFIXES: - if prefix not in PRIORITY: - raise ValueError(f"Missing prioirty order for {prefix}") CONFIGURATION = Configuration( name="Cell and Cell Line Mappings", @@ -45,22 +56,10 @@ inputs=[ Input(source="biomappings"), Input(source="gilda"), - # Cellosaurus removed its xrefs to depmap after v43 - # FIXME need to upgrade these mappings to SSSOM and trash this old source file - # Input( - # source="custom", - # extras={ - # "path": "/Users/cthoyt/dev/biomappings/notebooks/cellosaurus_43_xrefs.tsv", - # "source_prefix": "cellosaurus", - # "prefixes": PREFIXES, - # "version": "43", - # "license": "CC-BY-4.0", - # }, - # ), Input(prefix="cellosaurus", source="pyobo", confidence=0.99), Input(prefix="bto", source="bioontologies", confidence=0.99), Input(prefix="cl", source="bioontologies", confidence=0.99), - Input(prefix="clo", source="custom", confidence=0.99), + Input(prefix="clo", source="custom", confidence=0.65), Input(prefix="efo", source="pyobo", confidence=0.99), Input( prefix="depmap", @@ -69,8 +68,10 @@ extras={"version": "22Q4", "standardize": True, "license": "CC-BY-4.0"}, ), Input(prefix="ccle", source="pyobo", confidence=0.99, extras={"version": "2019"}), + Input(prefix="ncit", source="pyobo", confidence=0.99), + Input(prefix="umls", source="pyobo", confidence=0.99), ], - add_labels=False, + subsets=SUBSETS, priority=PRIORITY, keep_prefixes=PREFIXES, remove_imprecise=False, @@ -82,22 +83,25 @@ Mutation(source="depmap", confidence=0.7), Mutation(source="ccle", confidence=0.7), Mutation(source="cellosaurus", confidence=0.7), + Mutation(source="ncit", confidence=0.7), + Mutation(source="umls", confidence=0.7), ], raw_pickle_path=MODULE.join(name="raw.pkl"), raw_sssom_path=MODULE.join(name="raw.sssom.tsv"), - raw_neo4j_path=MODULE.join("neo4j_raw"), + # raw_neo4j_path=MODULE.join("neo4j_raw"), + add_labels=True, processed_pickle_path=MODULE.join(name="processed.pkl"), processed_sssom_path=MODULE.join(name="processed.sssom.tsv"), processed_neo4j_path=MODULE.join("neo4j"), processed_neo4j_name="semra-cell", priority_pickle_path=MODULE.join(name="priority.pkl"), - priority_sssom_path=PRIORITY_SSSOM_PATH, + priority_sssom_path=MODULE.join(name="priority.sssom.tsv"), ) @click.command() def main(): - # 1. load mappings + """Build the mapping database for cell and cell line terms.""" mappings = get_mappings_from_config(CONFIGURATION, refresh_raw=True, refresh_processed=True) click.echo(f"Processing returned {len(mappings):,} mappings") diff --git a/src/semra/landscape/complexes.py b/src/semra/landscape/complexes.py new file mode 100644 index 0000000..2724b52 --- /dev/null +++ b/src/semra/landscape/complexes.py @@ -0,0 +1,71 @@ +"""A configuration for assembling mappings for protein complex terms.""" + +import click +import pystow + +from semra.pipeline import Configuration, Input, Mutation + +__all__ = [ + "MODULE", + "CONFIGURATION", +] + +MODULE = pystow.module("semra", "case-studies", "complex") +PREFIXES = PRIORITY = [ + "complexportal", + "fplx", + "go", + "chembl.target", + "wikidata", + "scomp", + # "reactome", # TODO need a subset in Reactome to make this useful + "signor", + "intact", +] +SUBSETS = { + "go": ["go:0032991"], +} + +CONFIGURATION = Configuration( + name="Protein Complex Landscape Analysis", + description="Analyze the landscape of protein complex nomenclature resources, species-agnostic.", + inputs=[ + Input(source="gilda"), + Input(source="biomappings"), + Input(prefix="fplx", source="pyobo", confidence=0.99), + Input(prefix="fplx", source="custom", confidence=0.99), + Input(prefix="intact_complexportal", source="custom", confidence=0.99), + Input(prefix="complexportal", source="pyobo", confidence=0.99), + Input(prefix="go", source="pyobo", confidence=0.99), + # Wikidata has mappings as well + Input(prefix="complexportal", source="wikidata", confidence=0.99), + Input(prefix="reactome", source="wikidata", confidence=0.99), + ], + add_labels=True, + subsets=SUBSETS, + priority=PRIORITY, + post_keep_prefixes=PREFIXES, + remove_imprecise=False, + mutations=[ + Mutation(source="go", confidence=0.95), + ], + raw_pickle_path=MODULE.join(name="raw.pkl"), + raw_sssom_path=MODULE.join(name="raw.sssom.tsv"), + # raw_neo4j_path=MODULE.join("neo4j_raw"), + processed_pickle_path=MODULE.join(name="processed.pkl"), + processed_sssom_path=MODULE.join(name="processed.sssom.tsv"), + processed_neo4j_path=MODULE.join("neo4j"), + processed_neo4j_name="semra-complex", + priority_pickle_path=MODULE.join(name="priority.pkl"), + priority_sssom_path=MODULE.join(name="priority.sssom.tsv"), +) + + +@click.command() +def main(): + """Build the mapping database for protein complex terms.""" + CONFIGURATION.get_mappings(refresh_raw=True, refresh_processed=True) + + +if __name__ == "__main__": + main() diff --git a/src/semra/landscape/diseases.py b/src/semra/landscape/diseases.py new file mode 100644 index 0000000..33ae4d5 --- /dev/null +++ b/src/semra/landscape/diseases.py @@ -0,0 +1,98 @@ +"""A configuration for assembling mappings for disease terms.""" + +import bioregistry +import click +import pystow +from pyobo.sources.mesh import get_mesh_category_curies + +from semra.pipeline import Configuration, Input, Mutation + +__all__ = [ + "MODULE", + "CONFIGURATION", +] + +ICD_PREFIXES = bioregistry.get_collection("0000004").resources +MODULE = pystow.module("semra", "case-studies", "disease") +PREFIXES = PRIORITY = [ + "doid", + "mondo", + "efo", + "mesh", + "ncit", + "orphanet", + "orphanet.ordo", + "umls", + "omim", + "omim.ps", + # "snomedct", + "gard", + *ICD_PREFIXES, +] +# some resources are generic, so we want to cut to a relevant subset +SUBSETS = { + "mesh": [*get_mesh_category_curies("C"), *get_mesh_category_curies("F")], + "efo": ["efo:0000408"], + "ncit": ["ncit:C2991"], + "umls": [ + # all children of https://uts.nlm.nih.gov/uts/umls/semantic-network/Pathologic%20Function + "sty:T049", # cell or molecular dysfunction + "sty:T047", # disease or syndrome + "sty:T191", # neoplastic process + "sty:T050", # experimental model of disease + "sty:T048", # mental or behavioral dysfunction + ], +} + +CONFIGURATION = Configuration( + name="Disease Landscape Analysis", + description="", + inputs=[ + Input(source="biomappings"), + Input(source="gilda"), + Input(prefix="doid", source="bioontologies", confidence=0.99), + Input(prefix="mondo", source="bioontologies", confidence=0.99), + Input(prefix="efo", source="bioontologies", confidence=0.99), + Input(prefix="mesh", source="pyobo", confidence=0.99), + Input(prefix="ncit", source="bioontologies", confidence=0.85), + Input(prefix="umls", source="pyobo", confidence=0.9), + Input(prefix="orphanet.ordo", source="bioontologies", confidence=0.9), + # Input(prefix="orphanet", source="bioontologies", confidence=0.9), + # Input(prefix="hp", source="bioontologies", confidence=0.99), + ], + subsets=SUBSETS, + add_labels=True, + priority=PRIORITY, + keep_prefixes=PREFIXES, + remove_imprecise=False, + mutations=[ + Mutation(source="doid", confidence=0.95), + Mutation(source="mondo", confidence=0.95), + Mutation(source="efo", confidence=0.90), + Mutation(source="ncit", confidence=0.7), + Mutation(source="umls", confidence=0.7), + Mutation(source="orphanet.ordo", confidence=0.7), + Mutation(source="orphanet", confidence=0.7), + # Mutation(source="hp", confidence=0.7), + ], + raw_pickle_path=MODULE.join(name="raw.pkl"), + raw_sssom_path=MODULE.join(name="raw.sssom.tsv"), + # raw_neo4j_path=MODULE.join("neo4j_raw"), + processed_pickle_path=MODULE.join(name="processed.pkl"), + processed_sssom_path=MODULE.join(name="processed.sssom.tsv"), + processed_neo4j_path=MODULE.join("neo4j"), + processed_neo4j_name="semra-disease", + priority_pickle_path=MODULE.join(name="priority.pkl"), + priority_sssom_path=MODULE.join(name="priority.sssom.tsv"), +) + + +@click.command() +def main(): + """Build the mapping database for disease terms.""" + # Takes about 2 hours + CONFIGURATION.get_mappings(refresh_raw=True, refresh_processed=True) + + +if __name__ == "__main__": + main() diff --git a/src/semra/landscape/genes.py b/src/semra/landscape/genes.py new file mode 100644 index 0000000..6e75cc8 --- /dev/null +++ b/src/semra/landscape/genes.py @@ -0,0 +1,80 @@ +"""A configuration for assembling mappings for gene terms.""" + +import click +import pystow + +from semra.pipeline import Configuration, Input, Mutation + +__all__ = [ + "MODULE", + "CONFIGURATION", +] + +MODULE = pystow.module("semra", "case-studies", "gene") +PREFIXES = PRIORITY = [ + "ncbigene", + "hgnc", + "mgi", + "rgd", + "cgnc", + "wormbase", + "flybase", + "sgd", + # + "omim", + "civic.gid", + # + "umls", + "ncit", + "wikidata", +] + +CONFIGURATION = Configuration( + name="Gene Landscape Analysis", + description="Analyze the landscape of gene nomenclature resources, species-agnostic.", + inputs=[ + Input(prefix="hgnc", source="pyobo", confidence=0.99), + Input(prefix="mgi", source="pyobo", confidence=0.99), + Input(prefix="rgd", source="pyobo", confidence=0.99), + Input(prefix="cgnc", source="pyobo", confidence=0.99), + Input(prefix="sgd", source="pyobo", confidence=0.99), + Input(prefix="civic.gid", source="pyobo", confidence=0.99), + # Input(prefix="wormbase", source="pyobo", confidence=0.99), + Input(prefix="flybase", source="pyobo", confidence=0.99), + Input(prefix="ncit_hgnc", source="custom", confidence=0.99), + Input(prefix="omim_gene", source="custom", confidence=0.99), + Input(source="wikidata", prefix="ncbigene", confidence=0.99), + Input(source="wikidata", prefix="civic.gid", confidence=0.99), + Input(source="wikidata", prefix="ensembl", confidence=0.99), + Input(source="wikidata", prefix="hgnc", confidence=0.99), + Input(source="wikidata", prefix="omim", confidence=0.99), + Input(source="wikidata", prefix="umls", confidence=0.99), + ], + subsets={"umls": ["umls:C0017337"], "ncit": ["ncit:C16612"]}, + add_labels=True, + priority=PRIORITY, + remove_imprecise=False, + mutations=[ + Mutation(source="umls", confidence=0.8), + Mutation(source="ncit", confidence=0.8), + ], + raw_pickle_path=MODULE.join(name="raw.pkl"), + raw_sssom_path=MODULE.join(name="raw.sssom.tsv"), + # raw_neo4j_path=MODULE.join("neo4j_raw"), + processed_pickle_path=MODULE.join(name="processed.pkl"), + processed_sssom_path=MODULE.join(name="processed.sssom.tsv"), + processed_neo4j_path=MODULE.join("neo4j"), + processed_neo4j_name="semra-gene", + priority_pickle_path=MODULE.join(name="priority.pkl"), + priority_sssom_path=MODULE.join(name="priority.sssom.tsv"), +) + + +@click.command() +def main(): + """Build the mapping database for gene terms.""" + CONFIGURATION.get_mappings(refresh_raw=True, refresh_processed=True) + + +if __name__ == "__main__": + main() diff --git a/src/semra/landscape/utils.py b/src/semra/landscape/utils.py new file mode 100644 index 0000000..4127d2f --- /dev/null +++ b/src/semra/landscape/utils.py @@ -0,0 +1,709 @@ +"""Landscape analysis utilities.""" + +import json +import typing as t +from collections import Counter, defaultdict +from dataclasses import dataclass, field +from pathlib import Path +from textwrap import dedent + +import bioregistry +import matplotlib.pyplot as plt +import networkx as nx +import pandas as pd +import pyobo +import seaborn as sns +import upsetplot +from IPython.display import SVG, Markdown, display +from matplotlib_inline.backend_inline import set_matplotlib_formats +from pyobo.sources.mesh import get_mesh_category_curies + +from semra.api import ( + aggregate_components, + count_component_sizes, + filter_subsets, + get_index, + hydrate_subsets, +) +from semra.io import _safe_get_version +from semra.pipeline import Configuration, SubsetConfiguration +from semra.rules import DB_XREF, EXACT_MATCH +from semra.struct import Mapping + +__all__ = [ + "notebook", + "draw_counter", + "counter_to_df", + "landscape_analysis", + "get_symmetric_counts_df", + "overlap_analysis", + "get_mesh_category_curies", +] + + +DirectedIndex = t.Dict[t.Tuple[str, str], t.Set[str]] +XXCounter = t.Counter[t.Tuple[str, str]] +XXTerms = t.Mapping[str, t.Mapping[str, str]] +XXObservedTerms = t.Dict[str, t.Set[str]] +XXSubsets = t.Mapping[str, t.Collection[str]] + + +def _markdown(x): + return display(Markdown(dedent(x))) + + +def notebook( + configuration: Configuration, + *, + output_directory: t.Union[str, Path, None] = None, + matplotlib_formats: t.Optional[str] = "svg", + show: bool = True, + minimum_count: t.Optional[int] = None, +) -> t.Tuple["OverlapResults", "LandscapeResult"]: + """Run the landscape analysis inside a Jupyter notebook.""" + if not configuration.raw_pickle_path: + raise ValueError + if matplotlib_formats: + set_matplotlib_formats(matplotlib_formats) + if output_directory is None: + output_directory = configuration.raw_pickle_path.parent + output_directory = Path(output_directory).expanduser().resolve() + terms = get_terms(configuration.priority, configuration.subsets) + + hydrated_subsets = configuration.get_hydrated_subsets() + + raw_mappings = configuration.read_raw_mappings() + if configuration.subsets: + raw_mappings = filter_subsets(raw_mappings, hydrated_subsets) + + processed_mappings = configuration.read_processed_mappings() + if configuration.subsets: + processed_mappings = filter_subsets(processed_mappings, hydrated_subsets) + + terms_observed = get_observed_terms(processed_mappings) + summary_df = get_summary_df( + prefixes=configuration.priority, subsets=configuration.subsets, terms=terms, terms_observed=terms_observed + ) + summary_df.to_csv(output_directory.joinpath("summary.tsv"), sep="\t") + number_pyobo_unavailable = (summary_df["terms"] == 0).sum() + _markdown( + """\ + ## Summarize the Resources + + We summarize the resources used in the landscape analysis, including their [Bioregistry](https://bioregistry.io) + prefix, license, current version, and number of terms (i.e., named concepts) they contain. + """ + ) + if number_pyobo_unavailable > 0: + _markdown( + f"""\ + {number_pyobo_unavailable} resources were not available through + [PyOBO](https://github.com/biopragmatics/pyobo). Therefore, we estimate the number + of terms in that resource based on the ones appearing in mappings. Note that these + are typically an underestimate. + """ + ) + + display(summary_df) + + _summary_total = summary_df["terms"].sum() + _markdown(f"There are a total of {_summary_total:,} terms across the {len(summary_df.index):,} resources.") + + _markdown( + """\ + ## Summarize the Mappings + + In order to summarize the mappings, we're going to load them, index them, and count + the number of mappings between each pair of resources. The self-mapping column is + the count of terms in the resource. We'll do this to the raw mappings first, then + to the processed mappings, then compare them. + """ + ) + overlap_results = overlap_analysis( + configuration, + terms, + minimum_count=minimum_count, + mappings=processed_mappings, + raw_mappings=raw_mappings, + terms_observed=terms_observed, + ) + overlap_results.write(output_directory) + _markdown("First, we summarize the raw mappings, i.e., the mappings that are directly available from the sources") + display(overlap_results.raw_counts_df) + _markdown( + "Next, we summarize the processed mappings, which include inference, reasoning, and confidence filtering." + ) + display(overlap_results.counts_df) + _markdown("Below is an graph-based view on the processed mappings.") + display(SVG(overlap_results.counts_drawing)) + _markdown( + """\ + ## Comparison + + The following comparison shows the absolute number of mappings added by processing/inference. + Across the board, this process adds large numbers of mappings to most resources, especially + ones that were previously only connected to a small number of other resources. + """ + ) + display(overlap_results.gains_df) + _markdown( + """\ + Here's an alternative view on the number of mappings normalized to show percentage gain. + + Note: + + - `inf` means that there were no mappings before and now there are a non-zero number of mappings + - `NaN` means there were no mappings before inference and continue to be no mappings after inference + """ + ) + display(overlap_results.percent_gains_df.round(1)) + _markdown( + """\ + ## Landscape Analysis + + Before, we looked at the overlaps between each resource. Now, we use that information jointly to + estimate the number of terms in the landscape itself, and estimate how much of the landscape + each resource covers. + """ + ) + + # note we're using the sliced counts dataframe index instead of the + # original priority since we threw a couple prefixes away along the way + landscape_results = landscape_analysis( + overlap_results.mappings, + prefix_to_identifiers=terms, + priority=overlap_results.counts_df.index, + prefix_to_observed_identifiers=terms_observed, + ) + + _markdown(landscape_results.get_description_markdown()) + + n_prefixes = len(overlap_results.counts_df.index) + number_overlaps = 2**n_prefixes - 1 + _markdown( + f"""\ + Because there are {n_prefixes}, there are {number_overlaps} possible overlaps to consider. + Therefore, a Venn diagram is not possible, so we + we use an [UpSet plot](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720993) (Lex *et al.*, 2014) + as a high-dimensional Venn diagram. + """ + ) + landscape_results.plot_upset() + plt.savefig(output_directory.joinpath("landscape_upset.svg")) + if show: + plt.show() + + _markdown( + """\ + We now aggregate the mappings together to estimate the number of unique entities and number + that appear in each group of resources. + """ + ) + landscape_results.plot_distribution() + plt.tight_layout() + plt.savefig(output_directory.joinpath("landscape_histogram.svg")) + if show: + plt.show() + + reduced = landscape_results.total_entity_estimate + total_terms = 0 + for prefix in configuration.priority: + count, _exact = _count_terms(prefix, terms=terms, terms_observed=terms_observed) + total_terms += count + reduction_percent = (total_terms - reduced) / total_terms + _markdown( + rf"""\ + The landscape of {len(configuration.priority)} resources has {total_terms:,} total terms. + After merging redundant nodes based on mappings, inference, and reasoning, there + are {reduced:,} unique concepts. Using the reduction formula + $\frac{{\text{{total terms}} - \text{{reduced terms}}}}{{\text{{total terms}}}}$, + this is a {reduction_percent:.1%} reduction. + """ + ) + _markdown( + """\ + This is only an estimate and is susceptible to a few things: + + 1. It can be artificially high because there are entities that _should_ be mapped, but are not + 2. It can be artificially low because there are entities that are incorrectly mapped, e.g., as + a result of inference. The frontend curation interface can help identify and remove these + 3. It can be artificially low because for some vocabularies like SNOMED-CT, it's not possible + to load a terms list, and therefore it's not possible to account for terms that aren't mapped. + Therefore, we make a lower bound estimate based on the terms that appear in mappings + 4. It can be artificially high if a vocabulary is used that covers many domains and is not properly + subset'd. For example, EFO covers many different domains, so when doing disease landscape + analysis, it should be subset to only terms in the disease hierarchy (i.e., appearing under + ``efo:0000408``). + 5. It can be affected by terminology issues, such as the confusion between Orphanet and ORDO + 6. It can be affected by the existence of many-to-many mappings, which are filtered out during + processing, which makes the estimate artificially high since some subset of those entities + could be mapped, but it's not clear which should. + """ + ) + + stats = { + "raw_term_count": total_terms, + "unique_term_count": reduced, + "reduction": reduction_percent, + "distribution": landscape_results.distribution, + } + stats_path = output_directory.joinpath("stats.json") + stats_path.write_text(json.dumps(stats, indent=2)) + + return overlap_results, landscape_results + + +@dataclass +class OverlapResults: + """Results from mapping analysis.""" + + raw_mappings: t.List[Mapping] + raw_counts_df: pd.DataFrame + mappings: t.List[Mapping] + counts: XXCounter + counts_df: pd.DataFrame + gains_df: pd.DataFrame + percent_gains_df: pd.DataFrame + minimum_count: t.Optional[int] = None + counts_drawing: bytes = field(init=False) + + def __post_init__(self) -> None: + """Initialize the object by creating a drawing of the counter.""" + if self.minimum_count is None: + self.minimum_count = 20 + self.counts_drawing = draw_counter(self.counts, cls=nx.Graph, minimum_count=self.minimum_count) + + def write(self, directory: t.Union[str, Path]) -> None: + """Write the tables and charts to a directory.""" + directory = Path(directory).resolve() + self.counts_df.to_csv(directory / "counts.tsv", sep="\t", index=True) + self.raw_counts_df.to_csv(directory / "raw_counts.tsv", sep="\t", index=True) + directory.joinpath("graph.svg").write_bytes(self.counts_drawing) + + +def overlap_analysis( + configuration: Configuration, + terms: XXTerms, + *, + terms_observed: XXObservedTerms, + mappings: t.List[Mapping], + raw_mappings: t.List[Mapping], + minimum_count: t.Optional[int] = None, +) -> OverlapResults: + """Run overlap analysis.""" + if not configuration.raw_pickle_path: + raise ValueError("No raw pickle path available") + raw_index = _get_summary_index(raw_mappings) + _, raw_counts_df = get_symmetric_counts_df( + raw_index, terms, priority=configuration.priority, terms_observed=terms_observed + ) + + directed = _get_summary_index(mappings) + counts, counts_df = get_symmetric_counts_df( + directed, terms, priority=configuration.priority, terms_observed=terms_observed + ) + + gains_df = counts_df - raw_counts_df + percent_gains_df = 100.0 * (counts_df - raw_counts_df) / raw_counts_df + + return OverlapResults( + raw_mappings=raw_mappings, + raw_counts_df=raw_counts_df, + mappings=mappings, + counts=counts, + counts_df=counts_df, + gains_df=gains_df, + percent_gains_df=percent_gains_df, + minimum_count=minimum_count, + ) + + +def get_terms(prefixes: t.List[str], subset_configuration: t.Optional[SubsetConfiguration] = None) -> XXTerms: + """Get the set of identifiers for each of the resources.""" + prefix_to_identifier_to_name = {} + if subset_configuration is None: + subset_configuration = {} + sss = hydrate_subsets(subset_configuration) + for prefix in prefixes: + id_to_name = pyobo.get_id_name_mapping(prefix) + subset = sss.get(prefix) or set() + if subset: + prefix_to_identifier_to_name[prefix] = { + identifier: name for identifier, name in id_to_name.items() if identifier in subset + } + else: + prefix_to_identifier_to_name[prefix] = id_to_name + return prefix_to_identifier_to_name + + +def _count_terms(prefix: str, terms: XXTerms, terms_observed: XXObservedTerms) -> t.Tuple[int, bool]: + terms_exact = terms.get(prefix) + if terms_exact: + exact = True + count = len(terms_exact) + elif prefix in terms_observed: + exact = False + count = len(terms_observed[prefix]) + else: + exact = False + count = 0 + return count, exact + + +def get_summary_df( + prefixes: t.List[str], *, subsets=None, terms: XXTerms, terms_observed: XXObservedTerms +) -> pd.DataFrame: + """Create a summary dataframe for the prefixes in a landscape analysis. + + :param prefixes: The list of prefixes + :param subsets: The subset configuration + :param terms: The dictionary of prefix -> collection of identifiers from :mod:`pyobo` + :param terms_observed: The dictionary of prefix -> collection of identifiers encountered in the mappings + appearing in the landscape analysis. This should be calculated from raw mappings to make sure that + it accounts for any that might be filtered out during processing. + :return: A pandas dataframe with the following columns: + + 1. Prefix + 2. Name + 3. License + 4. Version + 5. Terms - the number of terms in the resource. If the full term list can be looked up with :mod:`pyobo`, then + this is considered as exact. Otherwise, this will be estimated based on the number of unique terms appearing + in the mappings. This is typically an underestimate since not necessarily all terms appear in mappings. + 6. Exact. Will be "true" if :mod:`pyobo` supports looking up all terms from the resource. Otherwise, + will be "false" + """ + summary_rows = [] + if subsets is None: + subsets = {} + for prefix in prefixes: + count, exact = _count_terms(prefix, terms, terms_observed) + if not exact: + status = "observed" + elif prefix in subsets: + status = "subset" + else: + status = "full" + row = ( + prefix, + bioregistry.get_name(prefix), + bioregistry.get_license(prefix), + _safe_get_version(prefix), + count, + status, + ) + summary_rows.append(row) + + df = pd.DataFrame(summary_rows, columns=["prefix", "name", "license", "version", "terms", "status"]) + df = df.set_index("prefix") + return df + + +def _get_summary_index(mappings: t.Iterable[Mapping]) -> DirectedIndex: + """Index which entities in each vocabulary have been mapped. + + :param mappings: An iterable of mappings to be indexed + :return: A directed index + + For example, if we have the triple P1:1 skos:exactMatch P2:A and P1:1 skos:exactMatch P3:X, we + would have the following index: + + .. code-block:: + + { + ("P1", "P2"): {"1"}, + ("P2", "P1"): {"A"}, + ("P1", "P3"): {"1"}, + ("P3", "P1"): {"X"}, + } + """ + index = get_index(mappings, progress=True, leave=False) + directed: t.DefaultDict[t.Tuple[str, str], t.Set[str]] = defaultdict(set) + target_predicates = {EXACT_MATCH, DB_XREF} + for s, p, o in index: + if p in target_predicates: + directed[s.prefix, o.prefix].add(s.identifier) + directed[o.prefix, s.prefix].add(o.identifier) + return dict(directed) + + +def get_symmetric_counts_df( + directed: DirectedIndex, + terms: XXTerms, + priority: t.List[str], + *, + terms_observed: XXObservedTerms, +) -> t.Tuple[XXCounter, pd.DataFrame]: + """Create a symmetric mapping counts dataframe from a directed index.""" + counter: XXCounter = Counter() + + for left_prefix, right_prefix in directed: + left_observed_terms = directed[left_prefix, right_prefix] + left_all_terms: t.Collection[str] = terms.get(left_prefix, []) + if left_all_terms: + left_observed_terms.intersection_update(left_all_terms) + + right_observed_terms = directed[right_prefix, left_prefix] + right_all_terms: t.Collection[str] = terms.get(right_prefix, []) + if right_all_terms: + right_observed_terms.intersection_update(right_all_terms) + + counter[left_prefix, right_prefix] = max(len(left_observed_terms), len(right_observed_terms)) + + for prefix in priority: + count, _exact = _count_terms(prefix, terms, terms_observed) + counter[prefix, prefix] = count + + df = counter_to_df(counter, priority=priority).fillna(0).astype(int) + return counter, df + + +def draw_counter( + counter: XXCounter, + scaling_factor: float = 3.0, + count_format=",", + cls: t.Type[nx.Graph] = nx.DiGraph, + minimum_count: float = 0.0, + prog: str = "dot", + output_format: str = "svg", + direction: str = "LR", +) -> bytes: + """Draw a source/target prefix pair counter as a network.""" + graph = cls() + renames = {} + for (source_prefix, target_prefix), count in counter.items(): + if not count: + continue + if source_prefix == target_prefix: + renames[source_prefix] = f"{source_prefix}\n{count:,}" + continue + if count <= minimum_count: + continue + graph.add_edge(source_prefix, target_prefix, label=f"{count:{count_format}}") + + # rename from prefix -> prefix + count + graph = nx.relabel_nodes(graph, renames) + + agraph = nx.nx_agraph.to_agraph(graph) + agraph.graph_attr["rankdir"] = direction + + values = [v for v in counter.values() if v is not None and v > 0] + bottom, top = min(values), max(values) + rr = top - bottom + + for edge, weight in counter.items(): + if not weight: + continue + x = 1 + scaling_factor * (weight - bottom) / rr + if agraph.has_edge(*edge): + agraph.get_edge(*edge).attr["penwidth"] = x + return agraph.draw(prog=prog, format=output_format) + + +def counter_to_df(counter: XXCounter, priority: t.List[str], drop_missing: bool = True) -> pd.DataFrame: + """Get a dataframe from a counter.""" + rows = [[counter.get((p1, p2), None) for p2 in priority] for p1 in priority] + df = pd.DataFrame(rows, columns=priority, index=priority) + if drop_missing: + df = df.dropna(axis=1, how="all") + df = df.dropna(axis=0, how="all") + + df.index.name = "source_prefix" + df.columns.name = "target_prefix" + return df + + +def get_observed_terms(mappings: t.Iterable[Mapping]) -> t.Dict[str, t.Set[str]]: + """Get the set of terms appearing in each prefix.""" + entities = defaultdict(set) + for mapping in mappings: + for reference in (mapping.s, mapping.o): + entities[reference.prefix].add(reference.identifier) + return dict(entities) + + +def count_unobserved( + *, + prefixes: t.Collection[str], + prefix_to_identifiers: XXTerms, + prefix_to_observed_identifiers: XXObservedTerms, +) -> t.Counter[t.FrozenSet[str]]: + """Count the number of unobserved entities for each prefix.""" + rv: t.Counter[t.FrozenSet[str]] = Counter() + for prefix in prefixes: + observed_identifiers = prefix_to_observed_identifiers.get(prefix) + if not observed_identifiers: + # This situation doesn't really make sense - if the prefix is in the given list, and + # it doesn't appear at all in the mappings, don't count it + continue + identifiers = prefix_to_identifiers.get(prefix) + if not identifiers: + # The term list for the resource corresponding to the prefix is unavailable. + # There might still be observed terms for this prefix, but they are all appearing + # in mappings. Therefore, the count for this resource is zero, since all entities + # are observed in mappings + count = 0 + else: + # Get the terms appearing in mappings for this vocabulary, then get the difference + # between the whole term list and the ones appearing in mappings to count how + # many are unique to the specific resource represented by the prefix + unmapped_terms = set(identifiers).difference(observed_identifiers) + count = len(unmapped_terms) + rv[frozenset([prefix])] = count + return rv + + +def landscape_analysis( + mappings: t.List[Mapping], + prefix_to_identifiers: XXTerms, + priority: t.List[str], + *, + prefix_to_observed_identifiers: XXObservedTerms, +) -> "LandscapeResult": + """Run the landscape analysis.""" + mapped_counter = count_component_sizes(mappings=mappings, prefix_allowlist=priority) + + xx = aggregate_components(mappings, priority) + mapped_counter = Counter({k: len(v) for k, v in xx.items()}) + + #: A count of the number of entities that have at least mapping. + #: This is calculated by the appearance of a weakly connected component + #: in :func:`_get_counter`. This needs to be calculated before enriching + #: the resulting counter object with entities that don't appear in mappings + at_least_1_mapping = sum(mapped_counter.values()) + + #: A count of the number of entities appearing in _all_ resources. + #: Also calculated the same way with :func:`_get_counter` + conserved = mapped_counter[frozenset(priority)] + + single_counter = count_unobserved( + prefixes=priority, + prefix_to_identifiers=prefix_to_identifiers, + prefix_to_observed_identifiers=prefix_to_observed_identifiers, + ) + + #: A count of the number of entities that have no mappings. + #: This is calculated based on the set difference between entities + #: appearing in mappings and the preloaded terms lists + only_1_mapping = sum(single_counter.values()) + + counter = mapped_counter + single_counter + + total_entity_estimate = sum(counter.values()) + + return LandscapeResult( + at_least_1_mapping=at_least_1_mapping, + only_1_mapping=only_1_mapping, + total_entity_estimate=total_entity_estimate, + conserved=conserved, + priority=priority, + mapped_counter=mapped_counter, + single_counter=single_counter, + ) + + +@dataclass +class LandscapeResult: + """Describes results of landscape analysis.""" + + priority: t.List[str] + at_least_1_mapping: int + only_1_mapping: int + conserved: int + total_entity_estimate: int + mapped_counter: t.Counter[t.FrozenSet[str]] + single_counter: t.Counter[t.FrozenSet[str]] + counter: t.Counter[t.FrozenSet[str]] = field(init=False) + distribution: t.Counter[int] = field(init=False) + + def __post_init__(self): + """Post initialize the landscape result object.""" + self.counter = self.mapped_counter + self.single_counter + self.distribution = self.get_distribution() + + def get_description_markdown(self) -> str: + """Describe the results in English prose.""" + return f"""\ + This estimates a total of {self.total_entity_estimate:,} unique entities. + + - {self.at_least_1_mapping:,} ({self.at_least_1_mapping / self.total_entity_estimate:.1%}) have + at least one mapping. + - {self.only_1_mapping:,} ({self.only_1_mapping / self.total_entity_estimate:.1%}) + are unique to a single resource. + - {self.conserved:,} ({self.conserved / self.total_entity_estimate:.1%}) + appear in all {len(self.priority)} resources. + + This estimate is susceptible to several caveats: + + - Missing mappings inflates this measurement + - Generic resources like MeSH contain irrelevant entities that can't be mapped + """ + + def get_upset_df(self) -> pd.DataFrame: + """Format the landscape analysis's result counter into an :mod:`upsetplot`-compatible dataframe.""" + return upsetplot.from_memberships(*zip(*self.counter.most_common())) + + def plot_upset(self): + """Plot the results with an UpSet plot.""" + upset_df = self.get_upset_df() + """Here's what the output from upsetplot.plot looks like: + + {'matrix': , + 'shading': , + 'totals': , + 'intersections': } + """ + plot_result = upsetplot.plot( + upset_df, + # show_counts=True, + ) + plot_result["intersections"].set_yscale("log") + plot_result["intersections"].set_ylim([1, plot_result["intersections"].get_ylim()[1]]) + # plot_result["totals"].set_xlabel("Size") + mm, _ = plot_result["totals"].get_xlim() + plot_result["totals"].set_xlim([mm, 1]) + plot_result["totals"].set_xscale("log") # gets domain error + + def get_distribution(self) -> t.Counter[int]: + """Get the distribution of component sizes.""" + counter: t.Counter[int] = Counter() + for prefixes, count in self.counter.items(): + counter[len(prefixes)] += count + + # Back-fill any intermediate counts with zero + max_key = max(counter) + for i in range(1, max_key): + if i not in counter: + counter[i] = 0 + return counter + + def plot_distribution( + self, + height: float = 2.7, + width_ratio: float = 0.7, + top_ratio: float = 20.0, + ) -> None: + """Plot the distribution of component sizes.""" + fig, ax = plt.subplots(figsize=(1 + len(self.distribution) * width_ratio, height)) + sns.barplot(self.distribution, ax=ax) + + for index, value in self.distribution.items(): + if value == 0: + # don't bother writing labels for zero + continue + plt.text( + index - 1, + value + 0.2, + f"{value:,}\n({value / self.total_entity_estimate:.1%})", + ha="center", + va="bottom", + ) + + ax.set_xlabel("# Resources a Concept Appears in") + ax.set_ylabel("Count") + ax.set_title(f"Landscape of {self.total_entity_estimate:,} Unique Concepts") + ax.set_yscale("log") + # since we're in a log scale, pad half of the max value to the top to make sure + # the counts fit in the box + a, b = ax.get_ylim() + ax.set_ylim([a, b + top_ratio * max(self.distribution.values())]) diff --git a/src/semra/pipeline.py b/src/semra/pipeline.py index 78a30df..7c9514d 100644 --- a/src/semra/pipeline.py +++ b/src/semra/pipeline.py @@ -9,13 +9,15 @@ from typing import Any, Literal, Optional from pydantic import BaseModel, Field, root_validator -from tqdm.autonotebook import tqdm +from tqdm.auto import tqdm from semra.api import ( assemble_evidences, filter_mappings, filter_prefixes, filter_self_matches, + filter_subsets, + hydrate_subsets, infer_chains, infer_mutual_dbxref_mutations, infer_reversible, @@ -41,11 +43,13 @@ get_biomappings_positive_mappings, ) from semra.sources.gilda import get_gilda_mappings +from semra.sources.wikidata import get_wikidata_mappings_by_prefix from semra.struct import Mapping, Reference __all__ = [ # Configuration model "Configuration", + "SubsetConfiguration", "Input", "Mutation", # Functions @@ -60,7 +64,7 @@ class Input(BaseModel): """Represents the input to a mapping assembly.""" - source: Literal["pyobo", "bioontologies", "biomappings", "custom", "sssom", "gilda"] + source: Literal["pyobo", "bioontologies", "biomappings", "custom", "sssom", "gilda", "wikidata"] prefix: Optional[str] = None confidence: float = 1.0 extras: t.Dict[str, Any] = Field(default_factory=dict) @@ -75,6 +79,9 @@ class Mutation(BaseModel): new: Reference = Field(default=EXACT_MATCH) +SubsetConfiguration = t.Mapping[str, t.Collection[str]] + + class Configuration(BaseModel): """Represents the steps taken during mapping assembly.""" @@ -88,14 +95,26 @@ class Configuration(BaseModel): default_factory=list, description="If no priority is given, is inferred from the order of inputs" ) mutations: t.List[Mutation] = Field(default_factory=list) + subsets: t.Optional[t.Mapping[str, t.List[str]]] = Field( + None, + description="A field to put restrictions on the subhierarchies from each resource. For example, if " + "you want to assemble cell mappings from MeSH, you don't need all possible mesh mappings, but only " + "ones that have to do with terms in the cell hierchy under the mesh:D002477 term. Therefore, this " + "dictionary allows for specifying such restrictions", + examples=[ + {"mesh": ["mesh:D002477"]}, + ], + ) exclude_pairs: t.List[t.Tuple[str, str]] = Field( default_factory=list, description="A list of pairs of prefixes. Remove all mappings whose source " "prefix is the first in a pair and target prefix is second in a pair. Order matters.", ) - remove_prefixes: Optional[t.List[str]] = None - keep_prefixes: Optional[t.List[str]] = None + remove_prefixes: Optional[t.List[str]] = Field(None, description="Prefixes to remove before processing") + keep_prefixes: Optional[t.List[str]] = Field(None, description="Prefixes to keep before processing") + post_remove_prefixes: Optional[t.List[str]] = Field(None, description="Prefixes to remove after processing") + post_keep_prefixes: Optional[t.List[str]] = Field(None, description="Prefixes to keep after processing") remove_imprecise: bool = True validate_raw: bool = Field( default=False, @@ -148,6 +167,28 @@ def get_mappings( """Run assembly based on this configuration.""" return get_mappings_from_config(self, refresh_raw=refresh_raw, refresh_processed=refresh_processed) + def read_raw_mappings(self) -> t.List[Mapping]: + """Read raw mappings from pickle, if already cached.""" + if self.raw_pickle_path is None: + raise ValueError + if not self.raw_pickle_path.is_file(): + raise FileNotFoundError + return from_pickle(self.raw_pickle_path) + + def read_processed_mappings(self) -> t.List[Mapping]: + """Read processed mappings from pickle, if already cached.""" + if self.processed_pickle_path is None: + raise ValueError + if not self.processed_pickle_path.is_file(): + raise FileNotFoundError + return from_pickle(self.processed_pickle_path) + + def get_hydrated_subsets(self) -> t.Mapping[str, t.Collection[str]]: + """Get the full subset filter lists based on the parent configuration.""" + if not self.subsets: + return {} + return hydrate_subsets(self.subsets) + def get_mappings_from_config( configuration: Configuration, @@ -179,13 +220,13 @@ def get_mappings_from_config( if configuration.raw_pickle_path: write_pickle(raw_mappings, configuration.raw_pickle_path) if configuration.raw_sssom_path: - write_sssom(raw_mappings, configuration.raw_sssom_path, add_labels=configuration.add_labels) + write_sssom(raw_mappings, configuration.raw_sssom_path) # , add_labels=configuration.add_labels) if configuration.raw_neo4j_path: write_neo4j( raw_mappings, configuration.raw_neo4j_path, docker_name=configuration.raw_neo4j_name, - add_labels=configuration.add_labels, + add_labels=False, # configuration.add_labels, ) # click.echo(semra.api.str_source_target_counts(mappings, minimum=20)) @@ -196,7 +237,10 @@ def get_mappings_from_config( ], remove_prefix_set=configuration.remove_prefixes, keep_prefix_set=configuration.keep_prefixes, + post_remove_prefixes=configuration.post_remove_prefixes, + post_keep_prefixes=configuration.post_keep_prefixes, remove_imprecise=configuration.remove_imprecise, + subsets=configuration.get_hydrated_subsets(), ) prioritized_mappings = prioritize(processed_mappings, configuration.priority) @@ -260,6 +304,8 @@ def get_raw_mappings(configuration: Configuration) -> t.List[Mapping]: elif inp.source == "custom": func = SOURCE_RESOLVER.make(inp.prefix, inp.extras) mappings.extend(func()) + elif inp.source == "wikidata": + mappings.extend(get_wikidata_mappings_by_prefix(inp.prefix, **inp.extras)) elif inp.source == "sssom": mappings.extend(from_sssom(inp.prefix, **inp.extras)) elif inp.source == "cache": @@ -272,8 +318,11 @@ def get_raw_mappings(configuration: Configuration) -> t.List[Mapping]: def process( mappings: t.List[Mapping], upgrade_prefixes=None, - remove_prefix_set=None, - keep_prefix_set=None, + remove_prefix_set: t.Optional[t.Collection[str]] = None, + keep_prefix_set: t.Optional[t.Collection[str]] = None, + post_remove_prefixes: t.Optional[t.Collection[str]] = None, + post_keep_prefixes: t.Optional[t.Collection[str]] = None, + subsets: t.Optional[t.Mapping[str, t.Collection[str]]] = None, *, remove_imprecise: bool = True, ) -> t.List[Mapping]: @@ -286,6 +335,9 @@ def process( if remove_prefix_set: mappings = filter_prefixes(mappings, remove_prefix_set) + if subsets: + mappings = list(filter_subsets(mappings, subsets)) + start = time.time() negatives = from_biomappings_negative() logger.info(f"Loaded {len(negatives):,} negative mappings in %.2f seconds", time.time() - start) @@ -353,6 +405,12 @@ def process( # filter out self mappings again, just in case mappings = filter_self_matches(mappings) + if post_keep_prefixes: + mappings = keep_prefixes(mappings, post_keep_prefixes) + + if post_remove_prefixes: + mappings = filter_prefixes(mappings, post_remove_prefixes) + return mappings diff --git a/src/semra/rules.py b/src/semra/rules.py index 51bc7c3..1d44c18 100644 --- a/src/semra/rules.py +++ b/src/semra/rules.py @@ -12,6 +12,8 @@ EQUIVALENT_TO = Reference(prefix="owl", identifier="equivalentTo") REPLACED_BY = Reference(prefix="iao", identifier="0100001") +RELATIONS = [EXACT_MATCH, DB_XREF, BROAD_MATCH, NARROW_MATCH, CLOSE_MATCH, EQUIVALENT_TO, REPLACED_BY] + IMPRECISE = {DB_XREF, CLOSE_MATCH} FLIP = { BROAD_MATCH: NARROW_MATCH, diff --git a/src/semra/sources/__init__.py b/src/semra/sources/__init__.py index e60852f..a2c31a1 100644 --- a/src/semra/sources/__init__.py +++ b/src/semra/sources/__init__.py @@ -12,7 +12,7 @@ ) from semra.sources.chembl import get_chembl_compound_mappings, get_chembl_protein_mappings from semra.sources.clo import get_clo_mappings -from semra.sources.famplex import get_famplex_mappings +from semra.sources.famplex import get_fplx_mappings from semra.sources.gilda import get_gilda_mappings from semra.sources.intact import get_intact_complexportal_mappings, get_intact_reactome_mappings from semra.sources.ncit import ( @@ -21,10 +21,13 @@ get_ncit_hgnc_mappings, get_ncit_uniprot_mappings, ) +from semra.sources.omim import get_omim_gene_mappings from semra.sources.pubchem import get_pubchem_mesh_mappings +from semra.sources.wikidata import get_wikidata_mappings from semra.struct import Mapping __all__ = [ + "SOURCE_RESOLVER", "get_chembl_compound_mappings", "get_ncit_chebi_mappings", "get_ncit_uniprot_mappings", @@ -32,7 +35,7 @@ "get_ncit_hgnc_mappings", "get_pubchem_mesh_mappings", "get_chembl_protein_mappings", - "get_famplex_mappings", + "get_fplx_mappings", "get_intact_complexportal_mappings", "get_intact_reactome_mappings", "get_custom", @@ -41,6 +44,8 @@ "from_biomappings_negative", "get_gilda_mappings", "get_clo_mappings", + "get_wikidata_mappings", + "get_omim_gene_mappings", ] SOURCE_RESOLVER: FunctionResolver[t.Callable[[], t.List[Mapping]]] = FunctionResolver( @@ -49,7 +54,7 @@ get_chembl_protein_mappings, get_intact_reactome_mappings, get_intact_complexportal_mappings, - get_famplex_mappings, + get_fplx_mappings, get_pubchem_mesh_mappings, get_ncit_chebi_mappings, get_ncit_hgnc_mappings, @@ -60,6 +65,8 @@ get_biomappings_positive_mappings, get_gilda_mappings, get_clo_mappings, + get_wikidata_mappings, + get_omim_gene_mappings, ] ) diff --git a/src/semra/sources/clo.py b/src/semra/sources/clo.py index 23fa552..b3ec0fd 100644 --- a/src/semra/sources/clo.py +++ b/src/semra/sources/clo.py @@ -59,7 +59,7 @@ def get_clo_mappings(confidence: float = 0.8) -> list[Mapping]: # noqa:C901 if p.predicate_raw != "http://www.w3.org/2000/01/rdf-schema#seeAlso": continue for raw_curie in _split(p.value_raw): - curie = _removeprefix(_removeprefix(raw_curie, "rrid:"), "RRID") + curie = _removeprefix(_removeprefix(raw_curie, "rrid:"), "RRID:") prefix: Optional[str] identifier: Optional[str] if curie.startswith("Sanger:COSMICID:"): diff --git a/src/semra/sources/famplex.py b/src/semra/sources/famplex.py index fca349a..c4512c5 100644 --- a/src/semra/sources/famplex.py +++ b/src/semra/sources/famplex.py @@ -13,16 +13,16 @@ from semra.struct import Mapping, MappingSet, SimpleEvidence __all__ = [ - "get_famplex_mappings", + "get_fplx_mappings", ] logger = logging.getLogger(__name__) URL = "https://github.com/sorgerlab/famplex/raw/master/equivalences.csv" -MAPPING_SET = MappingSet(name="famplex", confidence=0.99, license="CC0") +MAPPING_SET = MappingSet(name="fplx", confidence=0.99, license="CC0") -def get_famplex_mappings() -> list[Mapping]: +def get_fplx_mappings() -> list[Mapping]: """Get xrefs from FamPlex.""" df = pd.read_csv(URL, header=None, names=["target_prefix", "target_id", "source_id"], sep=",") df = df[df["target_prefix"] != "MEDSCAN"] @@ -47,4 +47,4 @@ def get_famplex_mappings() -> list[Mapping]: if __name__ == "__main__": - get_famplex_mappings() + get_fplx_mappings() diff --git a/src/semra/sources/gilda.py b/src/semra/sources/gilda.py index 6ea402e..26b602d 100644 --- a/src/semra/sources/gilda.py +++ b/src/semra/sources/gilda.py @@ -37,9 +37,9 @@ def get_gilda_mappings(confidence: float = 0.95) -> list[Mapping]: if not sp or not tp: continue m = Mapping( - s=Reference(prefix=sp, identifier=bioregistry.standardize_identifier(sp, si)), + s=Reference(prefix=bioregistry.normalize_prefix(sp), identifier=bioregistry.standardize_identifier(sp, si)), p=EXACT_MATCH, - o=Reference(prefix=tp, identifier=bioregistry.standardize_identifier(tp, ti)), + o=Reference(prefix=bioregistry.normalize_prefix(tp), identifier=bioregistry.standardize_identifier(tp, ti)), evidence=[ SimpleEvidence( justification=LEXICAL_MAPPING, diff --git a/src/semra/sources/omim.py b/src/semra/sources/omim.py new file mode 100644 index 0000000..47847cb --- /dev/null +++ b/src/semra/sources/omim.py @@ -0,0 +1,40 @@ +"""Get OMIM gene mappings.""" + +import typing as t + +import pandas as pd +from curies import Reference + +from semra.rules import EXACT_MATCH, UNSPECIFIED_MAPPING +from semra.struct import Mapping, MappingSet, SimpleEvidence + +__all__ = ["get_omim_gene_mappings"] + +URL = "https://omim.org/static/omim/data/mim2gene.txt" + + +def get_omim_gene_mappings() -> t.List[Mapping]: + """Get gene mappings from OMIM.""" + df = pd.read_csv(URL, sep="\t", dtype=str, skiprows=4) + mapping_set = MappingSet(name="OMIM", confidence=0.99) + rv = [] + for identifier, _type, entrez_id, _hgnc_symbol, ensembl_id in df.values: + s = Reference(prefix="omim", identifier=identifier) + if pd.notna(entrez_id): + mapping = Mapping( + s=s, + p=EXACT_MATCH, + o=Reference(prefix="ncbigene", identifier=entrez_id), + evidence=[SimpleEvidence(justification=UNSPECIFIED_MAPPING, mapping_set=mapping_set)], + ) + rv.append(mapping) + # TODO handle dependencies for mapping gene symbol + if pd.notna(ensembl_id): + mapping = Mapping( + s=s, + p=EXACT_MATCH, + o=Reference(prefix="ensembl", identifier=ensembl_id), + evidence=[SimpleEvidence(justification=UNSPECIFIED_MAPPING, mapping_set=mapping_set)], + ) + rv.append(mapping) + return rv diff --git a/src/semra/sources/wikidata.py b/src/semra/sources/wikidata.py new file mode 100644 index 0000000..9f18994 --- /dev/null +++ b/src/semra/sources/wikidata.py @@ -0,0 +1,54 @@ +"""Get arbitrary Wikidata mappings.""" + +import typing as t + +from curies import Reference + +from semra.rules import EXACT_MATCH, UNSPECIFIED_MAPPING +from semra.struct import Mapping, MappingSet, SimpleEvidence + +__all__ = [ + "get_wikidata_mappings", + "get_wikidata_mappings_by_prefix", +] + + +def get_wikidata_mappings(prop: str, predicate: t.Optional[Reference] = None) -> t.List[Mapping]: + """Get mappings from Wikidata.""" + import bioregistry + + prop_to_prefix = bioregistry.get_registry_invmap("wikidata") + target_prefix = prop_to_prefix[prop] + + return _help(target_prefix=target_prefix, prop=prop, predicate=predicate) + + +def get_wikidata_mappings_by_prefix(prefix: str, predicate: t.Optional[Reference] = None) -> t.List[Mapping]: + """Get mappings from Wikidata.""" + import bioregistry + + prefix_to_prop = bioregistry.get_registry_map("wikidata") + prop = prefix_to_prop[prefix] + + return _help(target_prefix=prefix, prop=prop, predicate=predicate) + + +def _help( + target_prefix: str, prop: str, *, predicate: t.Optional[Reference] = None, cache: bool = True +) -> t.List[Mapping]: + """Get mappings from Wikidata.""" + from pyobo.xrefdb.sources.wikidata import iter_wikidata_mappings + + if predicate is None: + predicate = EXACT_MATCH + + mapping_set = MappingSet(name="Wikidata", license="CC0", confidence=0.99) + return [ + Mapping( + s=Reference(prefix="wikidata", identifier=wikidata_id), + p=predicate, + o=Reference(prefix=target_prefix, identifier=xref_id), + evidence=[SimpleEvidence(justification=UNSPECIFIED_MAPPING, mapping_set=mapping_set)], + ) + for wikidata_id, xref_id in iter_wikidata_mappings(prop, cache=cache) + ] diff --git a/src/semra/templates/home.html b/src/semra/templates/home.html index 8fc96dc..9d0361f 100644 --- a/src/semra/templates/home.html +++ b/src/semra/templates/home.html @@ -1,6 +1,7 @@ {% extends "base.html" %} -{% import "bootstrap5/utils.html" as util %} +{% import "bootstrap5/utils.html" as bs_util %} +{% import "utils.html" as util %} {% block title %}SeMRA{% endblock %} @@ -22,7 +23,9 @@
{{ label }} Summary
{{ label }} - {% if has_names %}Name{% endif %} + {% if has_names %} + Name + {% endif %} Count @@ -30,7 +33,7 @@
{{ label }} Summary
{% for key, count in counter.most_common() %} - {%- if is_concept -%} + {%- if is_concept -%} {% if has_names %} {{ key[0] }} {% else %} @@ -41,7 +44,9 @@
{{ label }} Summary
{% if has_names %}{{ key[0] }}{% else %}{{ key }}{% endif %} {%- endif -%} - {% if has_names %}{{ key[1] }}{% endif %} + {% if has_names %} + {{ key[1] }} + {% endif %} {{ "{:,}".format(count) }} {% endfor %} @@ -51,7 +56,7 @@
{{ label }} Summary
{% block content %}
- {{ util.render_messages(dismissible=True, container=False) }} + {{ bs_util.render_messages(dismissible=True, container=False) }}
@@ -66,6 +71,7 @@
{{ count_column(node_counter, "Evidences", "book") }} {{ count_column(node_counter, "Mapping Sets", "puzzle-piece") }}
+

See the API Documentation.

Mapping Sets
@@ -108,18 +114,9 @@
Mapping Sets
Example Mappings
- + {{ util.mapping_table(example_mappings) }} - {% endblock %} diff --git a/src/semra/templates/mapping.html b/src/semra/templates/mapping.html index ca85644..fadcfb7 100644 --- a/src/semra/templates/mapping.html +++ b/src/semra/templates/mapping.html @@ -16,11 +16,11 @@
Subject
-
{{ mapping.s.curie }}
+
{{ mapping.s.curie }}
Predicate
-
{{ mapping.p.curie }}
+
{{ mapping.p.curie }}
Object
-
{{ mapping.o.curie }}
+
{{ mapping.o.curie }}
Evidence
{%- for evidence in mapping.evidence -%}
diff --git a/src/semra/templates/mapping_set.html b/src/semra/templates/mapping_set.html index 7ca260b..13064d9 100644 --- a/src/semra/templates/mapping_set.html +++ b/src/semra/templates/mapping_set.html @@ -1,13 +1,15 @@ {% extends "base.html" %} -{% import "bootstrap5/utils.html" as util %} +{% import "bootstrap5/utils.html" as bs_util %} +{% import "utils.html" as util %} + {% block title %}SeMRA{% endblock %} {% block content %}
- {{ util.render_messages(dismissible=True, container=False) }} + {{ bs_util.render_messages(dismissible=True, container=False) }}
@@ -22,6 +24,9 @@
Confidence
{{ mapping_set.confidence }}
+ +
Example Mappings
+ {{ util.mapping_table(mapping_examples) }} diff --git a/src/semra/templates/utils.html b/src/semra/templates/utils.html new file mode 100644 index 0000000..01e9312 --- /dev/null +++ b/src/semra/templates/utils.html @@ -0,0 +1,26 @@ +{% macro mapping_table(mappings) -%} +
+ + + + + + + + + + {%- for curie, predicate, subject_curie, subject_name, target_curie, target_name in mappings -%} + + + + + + + {%- endfor -%} + +
HashSubjectPredicateObject
+ + {{ curie[14:22]}} + + {% if subject_name %}{{subject_name}} ({{subject_curie}}){% else %}{{subject_curie}}{% endif %}{{predicate}}{% if target_name %}{{target_name}} ({{target_curie}}){% else %}{{target_curie}}{% endif %}
+{% endmacro %} diff --git a/src/semra/wsgi.py b/src/semra/wsgi.py index ba5fb31..5df003a 100644 --- a/src/semra/wsgi.py +++ b/src/semra/wsgi.py @@ -9,7 +9,7 @@ import flask import networkx as nx from curies import Reference -from fastapi import Path +from fastapi import Path, Query from fastapi.responses import JSONResponse from flask import Flask, render_template from flask_bootstrap import Bootstrap5 @@ -33,9 +33,18 @@ flask_app.secret_key = os.urandom(8) Bootstrap5(flask_app) - EXAMPLE_CONCEPTS = ["efo:0002142"] -EXAMPLE_MAPPINGS = ["25b67912bc720127a43a06ce4688b672", "5a56bf7ac409d8de84c3382a99e17715"] +EXAMPLE_MAPPINGS = list( + client.read_query( + """\ + MATCH + (t:concept)<-[`owl:annotatedTarget`]-(n:mapping)-[`owl:annotatedSource`]->(s:concept) + WHERE n.predicate = 'skos:exactMatch' + RETURN n.curie, n.predicate, s.curie, s.name, t.curie, t.name + LIMIT 5 + """ + ) +) PREDICATE_COUNTER = client.summarize_predicates() @@ -146,18 +155,18 @@ def mark_exact_incorrect(source: str, target: str): return flask.redirect(flask.url_for(view_concept.__name__, curie=source)) -@flask_app.get("/mapping_set/") -def view_mapping_set(curie: str): - """View a mapping set by its CURIE.""" - mapping_set = client.get_mapping_set(curie) - # TODO sample 10 mappings - return render_template("mapping_set.html", mapping_set=mapping_set) +@flask_app.get("/mapping_set/{mapping_set_id}") +def view_mapping_set(mapping_set_id: str): + """View a mapping set by its ID.""" + mapping_set = client.get_mapping_set(mapping_set_id) + examples = client.sample_mappings_from_set(mapping_set_id, n=10) + return render_template("mapping_set.html", mapping_set=mapping_set, mapping_examples=examples) -@api_router.get("/evidence/{curie}", response_model=Evidence) -def get_evidence(curie: str = Path(description="An evidence's MD5 hex digest.")): +@api_router.get("/evidence/{evidence_id}", response_model=Evidence) +def get_evidence(evidence_id: str = Path(description="An evidence's MD5 hex digest.")): """Get an evidence by its MD5 hex digest.""" - return client.get_evidence(curie) + return client.get_evidence(evidence_id) @api_router.get("/cytoscape/{curie}") @@ -170,20 +179,31 @@ def get_concept_cytoscape( return JSONResponse(cytoscape_json) -@api_router.get("/mapping/{mapping}", response_model=Mapping) -def get_mapping(mapping: str = Path(description="A mapping's MD5 hex digest.", examples=EXAMPLE_MAPPINGS)): +@api_router.get("/exact/{curie}", response_model=t.List[Reference]) +def get_exact_matches( + curie: str = Path(description="the compact URI (CURIE) for a concept", examples=EXAMPLE_CONCEPTS), + max_distance: int = Query(None, description="the distance in the mapping graph to traverse. Defaults to 7"), +): + """Get the exact matches to the concept.""" + return list(client.get_exact_matches(curie, max_distance=max_distance)) + + +@api_router.get("/mapping/{mapping_id}", response_model=Mapping) +def get_mapping( + mapping_id: str = Path(description="A mapping's MD5 hex digest.", examples=[t[0] for t in EXAMPLE_MAPPINGS]) +): """Get the mapping by its MD5 hex digest.""" - return client.get_mapping(mapping) + return client.get_mapping(mapping_id) -@api_router.get("/mapping_set/{mapping_set}", response_model=MappingSet) +@api_router.get("/mapping_set/{mapping_set_id}", response_model=MappingSet) def get_mapping_set( - mapping_set: str = Path( + mapping_set_id: str = Path( description="A mapping set's MD5 hex digest.", examples=["7831d5bc95698099fb6471667e5282cd"] ) ): """Get a mapping set by its MD5 hex digest.""" - return client.get_mapping_set(mapping_set) + return client.get_mapping_set(mapping_set_id) @api_router.get("/mapping_set/", response_model=t.List[MappingSet]) diff --git a/tests/test_api.py b/tests/test_api.py index a19371c..777e298 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -12,9 +12,12 @@ EXACT_MATCH, NARROW_MATCH, Index, + count_component_sizes, + count_coverage_sizes, filter_mappings, filter_self_matches, flip, + from_digraph, get_index, get_many_to_many, infer_chains, @@ -22,6 +25,7 @@ infer_reversible, keep_prefixes, project, + to_digraph, ) from semra.rules import KNOWLEDGE_MAPPING, MANUAL_MAPPING from semra.struct import ( @@ -35,8 +39,13 @@ ) -def _get_references(n: int, prefix: str = "test") -> t.List[Reference]: - return [Reference(prefix=prefix, identifier=str(i)) for i in range(1, n + 1)] +def _get_references(n: int, *, prefix: str = "test", different_prefixes: bool = False) -> t.List[Reference]: + if different_prefixes: + prefixes = [f"{prefix}{i + 1}" for i in range(n)] + else: + prefixes = [prefix for _ in range(n)] + identifiers = [str(i + 1) for i in range(n)] + return [Reference(prefix=prefix, identifier=identifier) for prefix, identifier in zip(prefixes, identifiers)] def _exact(s, o, evidence: t.Optional[t.List[SimpleEvidence]] = None) -> Mapping: @@ -138,7 +147,7 @@ def _clean_index(index: Index) -> t.List[str]: def test_infer_exact_match(self): """Test inference through the transitivity of SKOS exact matches.""" - r1, r2, r3, r4 = _get_references(4) + r1, r2, r3, r4 = _get_references(4, different_prefixes=True) m1, m2, m3 = line(r1, EXACT_MATCH, r2, EXACT_MATCH, r3, EXACT_MATCH, r4) m4 = _exact(r1, r3) m5 = _exact(r1, r4) @@ -180,7 +189,7 @@ def test_no_infer(self): def test_infer_broad_match_1(self): """Test inferring broad matches.""" - r1, r2, r3, r4 = _get_references(4) + r1, r2, r3, r4 = _get_references(4, different_prefixes=True) m1, m2, m3 = line(r1, EXACT_MATCH, r2, BROAD_MATCH, r3, EXACT_MATCH, r4) m4 = Mapping(s=r1, p=BROAD_MATCH, o=r3, evidence=[EV]) m5 = Mapping(s=r1, p=BROAD_MATCH, o=r4, evidence=[EV]) @@ -214,7 +223,7 @@ def test_infer_broad_match_1(self): def test_infer_broad_match_2(self): """Test inferring broad matches.""" - r1, r2, r3, r4 = _get_references(4) + r1, r2, r3, r4 = _get_references(4, different_prefixes=True) m1, m2, m3 = line(r1, BROAD_MATCH, r2, EXACT_MATCH, r3, BROAD_MATCH, r4) m4 = Mapping(s=r1, p=BROAD_MATCH, o=r3) m5 = Mapping(s=r1, p=BROAD_MATCH, o=r4) @@ -235,7 +244,7 @@ def test_infer_broad_match_2(self): def test_infer_narrow_match(self): """Test inferring narrow matches.""" - r1, r2, r3 = _get_references(3) + r1, r2, r3 = _get_references(3, different_prefixes=True) m1, m2 = line(r1, EXACT_MATCH, r2, NARROW_MATCH, r3) m3 = Mapping(s=r1, p=NARROW_MATCH, o=r3) m3_i = Mapping(o=r1, p=BROAD_MATCH, s=r3) @@ -244,7 +253,7 @@ def test_infer_narrow_match(self): def test_mixed_inference_1(self): """Test inferring over a mix of narrow, broad, and exact matches.""" - r1, r2, r3 = _get_references(3) + r1, r2, r3 = _get_references(3, different_prefixes=True) m1 = Mapping(s=r1, p=EXACT_MATCH, o=r2) m2 = Mapping(s=r2, p=NARROW_MATCH, o=r3) m3 = Mapping(s=r1, p=NARROW_MATCH, o=r3) @@ -326,6 +335,60 @@ def test_filter_confidence(self): mmm = list(api.filter_minimum_confidence([m1, m2], cutoff=0.7)) self.assertEqual([m1], mmm) + def test_filter_subsets(self): + """Test filtering by subsets.""" + a1, a2 = _get_references(2, prefix="a") + b1, b2 = _get_references(2, prefix="b") + c1, c2 = _get_references(2, prefix="c") + ev = SimpleEvidence(confidence=0.95, mapping_set=MS) + m1 = Mapping(s=a1, p=EXACT_MATCH, o=b1, evidence=[ev]) + m2 = Mapping(s=b1, p=EXACT_MATCH, o=a1, evidence=[ev]) + m3 = Mapping(s=a2, p=EXACT_MATCH, o=b2, evidence=[ev]) + m4 = Mapping(s=b2, p=EXACT_MATCH, o=a2, evidence=[ev]) + m5 = Mapping(s=b1, p=EXACT_MATCH, o=c1, evidence=[ev]) + m6 = Mapping(s=c1, p=EXACT_MATCH, o=b1, evidence=[ev]) + + terms = {"a": ["1", "2"], "b": ["1"]} + mmm = list(api.filter_subsets([m1, m2, m3, m4, m5, m6], terms)) + self.assertEqual( + [m1, m2, m5, m6], mmm, msg="Mappings 3 and 4 should not pass since b2 is not in the term filter" + ) + + terms = {"a": ["1", "2"], "b": ["1"], "c": []} + # the fact that c has an empty dictionary will get ignored + mmm = list(api.filter_subsets([m1, m2, m3, m4, m5, m6], terms)) + self.assertEqual( + [m1, m2, m5, m6], mmm, msg="Mappings 3 and 4 should not pass since b2 is not in the term filter" + ) + + def test_count_component_sizes(self): + """Test counting component sizes.""" + priority = "abc" + a1, a2 = _get_references(2, prefix="a") + b1, b2 = _get_references(2, prefix="b") + c1, _ = _get_references(2, prefix="c") + ev = SimpleEvidence(confidence=0.95, mapping_set=MS) + m1 = Mapping(s=a1, p=EXACT_MATCH, o=b1, evidence=[ev]) + m2 = Mapping(s=b1, p=EXACT_MATCH, o=c1, evidence=[ev]) + m3 = Mapping(s=a2, p=EXACT_MATCH, o=b2, evidence=[ev]) + m4 = Mapping(s=a2, p=DB_XREF, o=b2, evidence=[ev]) # this shouldn't hae an effect + mappings = [m1, m2, m3, m4] + self.assertEqual({frozenset("ab"): 1, frozenset("abc"): 1}, dict(count_component_sizes(mappings, priority))) + self.assertEqual({1: 0, 2: 1, 3: 1}, dict(count_coverage_sizes(mappings, priority))) + + def test_digraph_roundtrip(self): + """Test I/O roundtrip through a directed graph.""" + a1, a2 = _get_references(2, prefix="a") + b1, b2 = _get_references(2, prefix="b") + c1, _ = _get_references(2, prefix="c") + ev = SimpleEvidence(confidence=0.95, mapping_set=MS) + m1 = Mapping(s=a1, p=EXACT_MATCH, o=b1, evidence=[ev]) + m2 = Mapping(s=b1, p=EXACT_MATCH, o=c1, evidence=[ev]) + m3 = Mapping(s=a2, p=EXACT_MATCH, o=b2, evidence=[ev]) + m4 = Mapping(s=a2, p=DB_XREF, o=b2, evidence=[ev]) # this shouldn't hae an effect + mappings = [m1, m2, m3, m4] + self.assertEqual(mappings, from_digraph(to_digraph(mappings))) + class TestUpgrades(unittest.TestCase): """Test inferring mutations."""