From 2da4eb4bb805dbc3c165866f197f83f25cf4f50e Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Sun, 8 Oct 2023 11:40:08 +0100 Subject: [PATCH 01/13] minor edits to the overview for omics 1 --- omics/week-3/overview.qmd | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/omics/week-3/overview.qmd b/omics/week-3/overview.qmd index 76d916a..4a5bc8d 100644 --- a/omics/week-3/overview.qmd +++ b/omics/week-3/overview.qmd @@ -24,7 +24,7 @@ The successful student will be able to: 1. [Prepare](study_before_workshop.qmd) - i. πŸ“– Read how the data were generated and how they have been processed so far, install + i. πŸ“– Read how the data were generated and how they have been processed so far 2. [Workshop](workshop.qmd) @@ -35,9 +35,7 @@ The successful student will be able to: iii. πŸ’» Explore the distribution of values across samples/cells and across genes/species - iv. πŸ’» Perform quality control - - v. πŸ’» Look after future you! + iv. πŸ’» Look after future you! 3. [Consolidate](study_after_workshop.qmd) From e8c622e893a0ac3ffcf976951f5ed6b070bbfad4 Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Sun, 8 Oct 2023 11:41:13 +0100 Subject: [PATCH 02/13] omics 2 overview framework added --- omics/week-4/overview.qmd | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/omics/week-4/overview.qmd b/omics/week-4/overview.qmd index 76d916a..6f08bcc 100644 --- a/omics/week-4/overview.qmd +++ b/omics/week-4/overview.qmd @@ -1,12 +1,12 @@ --- title: "Overview" -subtitle: "Omics 1: πŸ‘‹ Hello data!" +subtitle: "Omics 2: Statistical Analysis" toc: true toc-location: right --- -This week you will meet your data. The independent study will concisely cover how these data were generated and how they have been processed before being given to you. There will also be an overview of the analysis we will carry out over three workshops. -In the workshop, you will learn what steps to take to get a good understanding of ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control. +This week you will .... The independent study will ... +In the workshop, you will learn ... We suggest you sit together with your group in the workshop. @@ -14,28 +14,28 @@ We suggest you sit together with your group in the workshop. The successful student will be able to: -- explore 'omics data to find the number of rows and columns and know how these correspond to samples and variables -- explore the distribution of expression measures across whole data sets, across variables and across samples by summarising and plotting -- explain what distributions are expected and interpret the distributions they have -- explain on what basis we might filter out variables or samples -- import, explore and filter 'omics data reproducibly so they can understand and reuse their code in the future +- xxx +- xxx +- xxx +- xxx + ### Instructions 1. [Prepare](study_before_workshop.qmd) - i. πŸ“– Read how the data were generated and how they have been processed so far, install + i. πŸ“– Read xxx 2. [Workshop](workshop.qmd) - i. πŸ’» Set up a Project + i. πŸ’» xx - ii. πŸ’» Import data + ii. πŸ’» xx - iii. πŸ’» Explore the distribution of values across samples/cells and across genes/species + iii. πŸ’» xx - iv. πŸ’» Perform quality control + iv. πŸ’» xx v. πŸ’» Look after future you! From 26f919638a79acd25a08f4555838dc6b0326a966 Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Sun, 8 Oct 2023 11:43:36 +0100 Subject: [PATCH 03/13] omics 2 prepare framework prepared --- omics/week-4/study_before_workshop.qmd | 252 +------------------------ 1 file changed, 7 insertions(+), 245 deletions(-) diff --git a/omics/week-4/study_before_workshop.qmd b/omics/week-4/study_before_workshop.qmd index 89d226b..b96fa49 100644 --- a/omics/week-4/study_before_workshop.qmd +++ b/omics/week-4/study_before_workshop.qmd @@ -1,10 +1,10 @@ --- title: "Independent Study to prepare for workshop" -subtitle: "Omics 1: πŸ‘‹ Hello data!" +subtitle: "Omics 2: Statistical Analysis" author: "Emma Rand" format: revealjs: - footer: "πŸ”— [About Omics 1: Hello data!](https://3mmarand.github.io/BIO00088H-data/omics/week-3/overview.html)" + footer: "πŸ”— [About Omics 2: Statistical Analysis](https://3mmarand.github.io/BIO00088H-data/omics/week-4/overview.html)" slide-number: true chalkboard: true code-link: true @@ -18,254 +18,16 @@ editor: ## Overview ::: incremental -- Concise summary of the experimental design and aims +- xx -- What the raw data consist of +- xx -- What has been done to the data so far +- xx -- What steps we will take in the workshop +- xx ::: -## The Data +## xxx -There are three datasets - -- 🐸 transcriptomic data (bulk RNA-seq) from frog embryos. - -- 🐭 transcriptomic data (single cell RNA-seq) from stemcells - -- πŸ‚ ??????? Metabolomic / Metagenomic data from anaerobic digesters - -# Experimental design - -## 🐸 Experimental design {auto-animate="true"} - -![Schematic of frog development -experiment](images/88H-exp-design-betsy.png){fig-align="center" -width="700"} - -## 🐸 Experimental design {auto-animate="true"} - -![Schematic of frog development -experiment](images/88H-exp-design-betsy.png){fig-align="left" -width="200"} - -::: incremental -- 3 fertilisations - -- two siblings from each fertilisation one control, on FGF treated - -- sequenced at three time points - -- 3 x 2 x 3 = 18 groups -::: - -## 🐸 Experimental design {auto-animate="true"} - -![Schematic of frog development -experiment](images/88H-exp-design-betsy.png){fig-align="left" -width="200"} - -::: incremental -- 3 fertilisations. [These are the replicates, `.5`, `.6`, - `A`]{style="color:#009900"} - -- two siblings from each fertilisation one control, one FGF treated. - [The treatments are paired]{style="color:#009900"} - -- sequenced at three time points. [S14, S20, - S30]{style="color:#009900"} - -- 3 x 2 x 3 = 18 groups -::: - -## 🐸 Aim - -::: incremental -- find genes important in frog development - -- Important means the genes that are differentially expressed between - the control-treated and the FGF-treated siblings - -- Differentially expressed means the expression in one group is - significantly higher than in the other -::: - -## 🐸 Guided analysis - -::: incremental -- The workshops will take you through comparing the control and FGF - treated sibling at S30 - -- This is the "least interesting" comparison - -- You will be guided to carefully document your work so you can apply - the same methods to other comparisons -::: - -## 🐭 Experimental design {auto-animate="true"} - -![Schematic of stem cell -experiment](images/88H-exp-design-jillian.png){fig-align="center" -width="700"} - -## 🐭 Experimental design {auto-animate="true"} - -![Schematic of stem cell -experiment](images/88H-exp-design-jillian.png){fig-align="left" -width="200"} - -::: incremental -- Cells were sorted using flow cytometry on the basis of cell surface - markers - -- There are three cell types: LT-HSCs, HSPCs, Progs - -- Many cells of each cell type were sequenced -::: - -## 🐭 Experimental design {auto-animate="true"} - -![Schematic of stem cell -experiment](images/88H-exp-design-jillian.png){fig-align="left" -width="200"} - -::: incremental -- There are three cell types: LT-HSCs, HSPCs, Progs [These are the - "treaments"]{style="color:#009900"} - -- Many cells of each type were sequenced: [These are the - replicates]{style="color:#009900"} - -- [155 LT-HSCs, 701 HSPCs, 798 Progs]{style="color:#009900"} -::: - -## 🐭 Aim - -::: incremental -- find genes for cell surface proteins that are important in stem cell - identity - -- Important means genes that are differentially expressed between at - least two cell types - -- Differentially expressed means the expression in one group is - significantly higher than in the other -::: - -## 🐭 Guided analysis - -::: incremental -- The workshops will take you through comparing the HSPC and Prog - cells - -- This is the "least interesting" comparison - -- You will be guided to carefully document your work so you can apply - the same methods to other comparisons -::: - -# The raw data - -## Raw Sequence data - -::: incremental -- The raw data are "reads" from a sequencing machine. - -- A read is sequence of DNA or RNA shorter than the whole genome or - transcriptome - -- The length of the reads depends on the type of sequencing machine - - - Short-read technologies e.g. Illumina have higher base accuracy - but are harder to align - - Long-read technologies e.g. Nanopore have lower base accuracy - but are easier to align -::: - -# The raw data - -## Raw Sequence data - -::: incremental -- Sequencing technology is constantly improving - -- Optional: You can read more about Sequencing technologies in - [Statistically useful experimental - design](https://cloud-span.github.io/experimental_design00-overview/) - [@rand_statistically_2022] -::: - -## Raw Sequence data - -::: incremental -- The RNA-seq data are from an Illumina machine 150-300bp; Metagenomic - data are often Nanopore 10,000 - 30000bp - -- Reads are in FASTQ files - -- FASTQ files contain the sequence of each read and a quality score - for each base -::: - -# What has been done to the data so far - -## General steps - -::: incremental -- Reads are filtered and trimmed on the basis of the quality score - -- They are then aligned/pseudo-aligned to a reference - genome/transcriptome or, in metagenomics, assembled de novo. - -- Reads are then counted to quantify the expression or number of - genomes in metagenomics - -- Counts are normalised to account for differences in sequencing depth - and gene/transcript/genome length before statistical analysis -::: - -## 🐸 Data - -- Unpublished (so far!) - -- Expression for the whole transcriptome [*X. laevis* v10.1 genome - assembly](https://www.xenbase.org/xenbase/static-xenbase/ftpDatafiles.jsp) - -- Values are raw counts - -- The statistical analysis method we will use `DESeq2` [@DESeq2] - requires raw counts and performs the normalisation itself - -## 🐭 Data - -- Published in @nestorowa2016 - -- Expression for a subset of genes, the surfaceome - -- Values are log2 normalised values - -- The statistical analysis method we will use `scran` [@scran] - requires normalised values - -# Workshops - -## Workshops - -- Omics 1: Hello data Getting to know the data. Checking the - distributions of values overall, across samples and across genes to - check things are as we expect and detect genes/samples that need to - be removed - -- Omics 2: Statistical Analysis Identifying which genes are - differentially expressed between treatments. This is the main - analysis step. We will use different methods for bulk and single - cell data. - -- Omics 3: Visualising and Interpreting Production of volcano plots - and heatmaps to visualise the results of the statistical analysis. - We will also look at how to interpret the results and how to find - out more about the genes of interest. ## References From 0d439db42b041396fbe3153f0de05f3e8a9738bd Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Sun, 8 Oct 2023 11:45:07 +0100 Subject: [PATCH 04/13] consolidate framework added --- omics/week-4/study_after_workshop.qmd | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/omics/week-4/study_after_workshop.qmd b/omics/week-4/study_after_workshop.qmd index d88995e..fda7265 100644 --- a/omics/week-4/study_after_workshop.qmd +++ b/omics/week-4/study_after_workshop.qmd @@ -1,6 +1,6 @@ --- title: "Independent Study to consolidate this week" -subtitle: "Core 1" +subtitle: Omics 2: Statistical Analysis" toc: true toc-location: right format: @@ -14,17 +14,14 @@ You need only do the section for your own project data 🐸 Frogs -🎬 Open your `frogs-88H` Project. Make a new script and, using `cont-fgf-s30.R` as a template, repeat the analysis on one of the other comparisons. - +🎬 Open your `frogs-88H` Project. xxx 🐭 Mice -🎬 Open your `mice-88H` Project. Open your `hspc-prog.R` script and, using you code working with the hspc cells as a template, repeat the analysis on the prog cells. - +🎬 Open your `mice-88H` Project. xxx πŸ‚ xxxx -🎬 Open your `xxxx-88H` Project. Make a new script and, using `xxxxxxx.R` as a template, repeat the analysis on xxxxxxxx - +🎬 Open your `xxxx-88H` Project. xxx From ef01e60b940ff495b6c4a96d2c31ae0e4770bb82 Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Sun, 8 Oct 2023 11:54:04 +0100 Subject: [PATCH 05/13] minor tweeks to omics 1 for Kelly's project in the absence of data --- omics/week-3/study_after_workshop.qmd | 4 +++- omics/week-3/workshop.qmd | 8 +++++--- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/omics/week-3/study_after_workshop.qmd b/omics/week-3/study_after_workshop.qmd index d88995e..33da2bc 100644 --- a/omics/week-3/study_after_workshop.qmd +++ b/omics/week-3/study_after_workshop.qmd @@ -24,7 +24,9 @@ You need only do the section for your own project data πŸ‚ xxxx -🎬 Open your `xxxx-88H` Project. Make a new script and, using `xxxxxxx.R` as a template, repeat the analysis on xxxxxxxx +Follow one of the other examples. + + diff --git a/omics/week-3/workshop.qmd b/omics/week-3/workshop.qmd index b4c51a2..f87f659 100644 --- a/omics/week-3/workshop.qmd +++ b/omics/week-3/workshop.qmd @@ -757,9 +757,11 @@ whole script again. ### πŸ‚ xxxx and future you -🎬 Create a new Project, `xxxx-88H`, populated with folders and your -data. Make a script file called `xxxx.R`. This will a be commented -analysis of t xxxxxxxxxxxx. +**Do one of the other two examples.** + + + + # πŸ₯³ Finished From ea456ef2bd0994534de03835a3f098df5664a6ba Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Sun, 8 Oct 2023 12:27:03 +0100 Subject: [PATCH 06/13] workshop framework added --- omics/week-4/workshop.qmd | 729 +------------------------------------- 1 file changed, 9 insertions(+), 720 deletions(-) diff --git a/omics/week-4/workshop.qmd b/omics/week-4/workshop.qmd index b4c51a2..50d1ade 100644 --- a/omics/week-4/workshop.qmd +++ b/omics/week-4/workshop.qmd @@ -1,6 +1,6 @@ --- title: "Workshop" -subtitle: "Omics 1: Hello data!" +subtitle: "Omics 2: Statistical Analysis" author: "Emma Rand" toc: true toc-depth: 4 @@ -19,714 +19,20 @@ editor: ## Session overview -In this workshop you will learn what steps to take to get a good -understanding of your 'omics data before you consider any statistical -analysis. This is an often overlooked, but very valuable and -informative, part of any data pipeline. It gives you the deep -understanding of the data structures and values that you will need to -code and trouble-shoot code, allows you to spot failed or problematic -samples and informs your decisions on quality control. +In this workshop you will lxxxx -You should examine **all three data sets** because the comparisons will -give you a stronger understanding of your own project data. +# 🐸 Frog development -# Exercises +🎬 Open your `frogs-88H` Project and the `cont-fgf-s30.R` script. -## Set up a Project -🎬 Start RStudio from the Start menu -🎬 Make an RStudio project. Be deliberate about where you create it so -that it is a good place for you +# 🐭 Mouse Stem cells -🎬 Use the Files pane to make new folders for the data. I suggest -`data-raw` and `data-processed` +🎬 Open your `mice-88H` Project and the `hspc-prog.R` script. -🎬 Make a new script called `workshop-1.R` to carry out the rest of the -work. -🎬 Record what you do and what you find out. All of it! -🎬 Load `tidyverse` [@tidyverse] for importing, summarising, plotting -and filtering. - -```{r} -library(tidyverse) -``` - -## Examine the data in a spreadsheet - -These are the three datasets. Each set compromises several files. - -🐸 Frog development data: - -- [xlaevis_counts_S14.csv](data-raw/xlaevis_counts_S14.csv) -- [xlaevis_counts_S20.csv](data-raw/xlaevis_counts_S20.csv) -- [xlaevis_counts_S30.csv](data-raw/xlaevis_counts_S30.csv) -- [meta_data.txt](data-raw/meta_data.txt) - -🐭 Stem cell data: - -- [surfaceome_hspc.csv](data-raw/surfaceome_hspc.csv) -- [surfaceome_prog.csv](data-raw/surfaceome_prog.csv) -- [surfaceome_lthsc.csv](data-raw/surfaceome_lthsc.csv) - -πŸ‚ xxxx data: - -- xxx -- xxx - -🎬 Save the files to `data-raw` and open them in Excel - -🎬 Answer the following questions: - -- Describe how the sets of data are similar and how they are - different. -- What is in the rows and columns of each file? -- How many rows and columns are there in each file? Are these the - same? In all cases or some cases? Why? -- Google an id. Where does your search take you? How much information - is available? - -🎬 Did you record all that?? - -## Import - -Now let's get the data into R and visualise it. - -🎬 Import [xlaevis_counts_S30.csv](data-raw/xlaevis_counts_S30.csv), -[surfaceome_hspc.csv](data-raw/surfaceome_hspc.csv) and xxxxxxxx - -```{r} -# 🐸 import the s30 data -s30 <- read_csv("data-raw/xlaevis_counts_S30.csv") -``` - -```{r} -# 🐭 import the hspc data -hspc <- read_csv("data-raw/surfaceome_hspc.csv") -``` - -```{r} -# πŸ‚ xxxx import the xxxx data -# prog <- read_csv("") -``` - -🎬 Check these have the number of rows and column you were expecting and -that column types and names are as expected. - -## Explore - -The first task is to get an overview. We want to know - -- are there any missing values? If so, how many and how are they - distributed? -- how may zeros are there and how are they distributed -- does it look as tough all the samples/cells were equally - "successful"? Can we spot any problematic anomalies? -- what is the distribution of values? - -If our data collection has gone well we would hope to see approximately -the same average expression in each sample or cell of the same type. -That is replicates should be similar. We would also expect to see that -the average expression of genes varies. We might have genes which are -zero in every cell/sample. We will want to to filter those out. - -We get this overview by looking at: - -- The distribution of values across the whole dataset - -- The distribution of values across the sample/cells (i.e., averaged - across genes). This allows us to see variation between - samples/cells: - -- The distribution of values across the genes (i.e., averaged across - samples/cells). This allows us to see variation between genes. - -### Distribution of values across the whole dataset - -In all data sets, the values are spread over multiple columns so in -order to plot the distribution as a whole, we will need to first use -`pivot_longer()` to put the data in ['tidy' -format](https://3mmarand.github.io/BIO00017C-Data-Analysis-in-R-2020/workshops/02TestingDataTypesReadingInData.html#Tidy_format) -[@Wickham2014-nl] by stacking the columns. We *could* save a copy of the -stacked data and then plot it, but here, I have just piped the stacked -data straight into `ggplot()`. - -#### 🐸 Frogs - -🎬 Pivot the counts (stack the columns) so all the counts are in a -single column (`count`) and pipe into `ggplot()` to create a histogram: - -```{r} -s30 |> - pivot_longer(cols = -xenbase_gene_id, - names_to = "sample", - values_to = "count") |> - ggplot(aes(x = count)) + - geom_histogram() -``` - -This data is very skewed - there are so many low values that we can't -see the tiny bars for the higher values. Logging the counts is a way to -make the distribution more visible. - -🎬 Repeat the plot on log of the counts. - -```{r} -s30 |> - pivot_longer(cols = -xenbase_gene_id, - names_to = "sample", - values_to = "count") |> - ggplot(aes(x = log10(count))) + - geom_histogram() -``` - -I've used base 10 only because it easy to convert to the original scale -(1 is 10, 2 is 100, 3 is 1000 etc). The warning about rows being removed -is expected - these are the counts of 0 since you can't log a value of -0. The peak at zero suggests quite a few counts of 1. We would expect we -would expect the distribution of counts to be roughly log normal because -this is expression of *all* the genes in the genome[^1]. That small peak -near the low end suggests that these lower counts might be anomalies. - -[^1]: This a result of the [Central limit - theorem](https://en.wikipedia.org/wiki/Central_limit_theorem),one - consequence of which is that adding together lots of distributions - - whatever distributions they are - will tend to a normal - distribution. - -The excess number of low counts indicates we might want to create a cut -off for quality control. The removal of low counts is a common -processing step in 'omic data. We will revisit this after we have -considered the distribution of counts across samples and genes. - -#### 🐭 Mice - -🎬 Pivot the expression values (stack the columns) so all the counts are -in a single column (`expr`) and pipe into `ggplot()` to create a -histogram: - -```{r} -hspc |> - pivot_longer(cols = -ensembl_gene_id, - names_to = "cell", - values_to = "expr") |> - ggplot(aes(x = expr)) + - geom_histogram() -``` - -This is a very striking distribution. Is it what we are expecting? -Again,the excess number of low values is almost certainly anomalous. -They will be inaccurate measure and we will want to exclude expression -values below (about) 1. We will revisit this after we have considered -the distribution of expression across cells and genes. - -What about the bimodal appearance of the the 'real' values? If we had -the whole genome we would not expect to see such a pattern - we'd expect -to see a roughly normal distribution[^2]. However, this is a subset of -the genome and the nature of the subsetting has had an influence here. -These are a subset of cell surface proteins that show a significant -difference between at least two of twelve cell subtypes. That is, all of -these genes are either high or low. - -[^2]: This a result of the [Central limit - theorem](https://en.wikipedia.org/wiki/Central_limit_theorem),one - consequence of which is that adding together lots of distributions - - whatever distributions they are - will tend to a normal - distribution. - -### Distribution of values across the sample/cells - -#### 🐸 Frog samples - -Summary statistics including the the number of NAs can be seen using the -`summary()`. It is most helpful which you have up to about 30 columns. -There is nothing special about the number 30, it is just that text -summaries of a larger number of columns are difficult to grasp. - -🎬 Get a quick overview of the columns: - -```{r} -# examine all the columns quickly -# works well with smaller numbers of column -summary(s30) -``` - -Notice that: - the minimum count is 0 and the maximums are very high in -all the columns - the medians are quite a lot lower than the means so -the data are skewed (hump to the left, tail to the right) - there must -be quite a lot of zeros - the columns are roughly similar and it doesn't -look like there is an anomalous replicate. - -To find out how may zeros there are in a column we can make use of the -fact that `TRUE` evaluates to 1 and `FALSE` evaluates to 0. This means -`sum(S30_C_5 == 0)` gives the number of 0 in the `S30_C_5` column - -🎬 Find the number of zeros in all six columns: - -```{r} - -s30 |> - summarise(sum(S30_C_5 == 0), - sum(S30_C_6 == 0), - sum(S30_C_A == 0), - sum(S30_F_5 == 0), - sum(S30_F_6 == 0), - sum(S30_F_A == 0)) -``` - -There is a better way of doing this that saves you having to repeat so -much code - especially useful if you have a lot more than 6 columns. We -can use `pivot_longer()` to put the data in tidy format and then use the -`group_by()` and `summarise()` approach we have used extensively before. - -🎬 Find the number of zeros in all columns: - -```{r} -s30 |> - pivot_longer(cols = -xenbase_gene_id, - names_to = "sample", - values_to = "count") |> - group_by(sample) |> - summarise(n_zero = sum(count == 0)) -``` - -You could expand to get all the summary information - -🎬 Summarise all the samples: - -```{r} -s30 |> - pivot_longer(cols = -xenbase_gene_id, - names_to = "sample", - values_to = "count") |> - group_by(sample) |> - summarise(min = min(count), - lowerq = quantile(count, 0.25), - mean = mean(count), - median = median(count), - upperq = quantile(count, 0.75), - max = max(count), - n_zero = sum(count == 0)) -``` - -The mean count ranges from 260 to 426. - -One advantage this has over using `summary()` is that the output is a -dataframe. For results, this is useful, and makes it easier to: - -- write to file -- use in `ggplot()` -- format in a Quarto report - -🎬 Save the summary as a dataframe, `s30_summary_samp`. - -```{r} -#| echo: false -s30_summary_samp <- s30 |> - pivot_longer(cols = -xenbase_gene_id, - names_to = "sample", - values_to = "count") |> - group_by(sample) |> - summarise(min = min(count), - lowerq = quantile(count, 0.25), - mean = mean(count), - median = median(count), - upperq = quantile(count, 0.75), - max = max(count), - n_zero = sum(count == 0)) -``` - -We can write to file using `write_csv()` - -🎬 Write `s30_summary_samp` to a file called "s30_summary_samp.csv": - -```{r} -write_csv(s30_summary_samp, - file = "data-processed/s30_summary_samp.csv") -``` - -Plotting the distribution of values is perhaps the easiest way to -understand the data. We could plot each column separately or we can pipe -the tidy format of data into `ggplot()` and make use of `facet_wrap()` - -🎬 Pivot the data and pipe into `ggplot`: - -```{r} -s30 |> - pivot_longer(cols = -xenbase_gene_id, - names_to = "sample", - values_to = "count") |> - ggplot(aes(count)) + - geom_density() + - facet_wrap(. ~ sample, nrow = 3) - -``` - -We have many values (`r length(s30$xenbase_gene_id)`) so we are not -limited to using `geom_histogram()`. `geom_density()` gives us a smooth -distribution. - -We have many low values and a few very high ones which makes it tricky -to see the distributions. Logging the counts will make these clearer. - -🎬 Repeat the graph but taking the base 10 log of the counts: - -```{r} -s30 |> - pivot_longer(cols = -xenbase_gene_id, - names_to = "sample", - values_to = "count") |> - ggplot(aes(log10(count))) + - geom_density() + - facet_wrap(. ~ sample, nrow = 3) -``` - -The key information to take from these plots is: - -- the distributions are roughly similar in width, height, location and - overall shape so it doesn't look as though we have any suspect - samples -- the peak at zero suggests quite a few counts of 1. -- since we would expect the distribution of counts in each sample to - be roughly log normal so that the small rise near the low end, even - before the peak at zero, suggests that these lower counts might be - anomalies. - -The excess number of low counts indicates we might want to create a cut -off for quality control. The removal of low counts is a common -processing step in 'omic data. We will revisit this after we have -considered the distribution of counts across genes (averaged over the -samples). - -#### 🐭 Mouse cells - -We used the `summary()` function to get an overview of the columns in -the frog data. Let's try that here. - -🎬 Get a quick overview of the columns: - -```{r} -#| class: outputscroll -summary(hspc) -``` - -Hmmmm, did you get all that? Nope, me neither! We have 701 cells but we -only have 6 samples for the frogs. We will need a different approach to -get an overview but I find it is still useful to look at the few columns - -🎬 Get a quick overview the first 20 columns: - -```{r} -#| class: outputscroll -summary(hspc[1:20]) -``` - -Notice that: - -- the maximum value is much less high than for the frogs and has - decimals. That is because the mouse data are logged (to base 2) - normalised counts, not raw counts as they are in the frog data set. -- a minimum value of 0 appears in all 20 columns - perhaps that is - true across the whole dataset (or at least common) -- at least some of the medians are zeros so there must be quite a lot - of zeros -- the few columns we can see are roughly similar -- it would not be very practical to plot the distributions of values - in cell cell using `facet_wrap()`. - -In this data set, there is even more of an advantage of using the -`pivot_longer()`, `group_by()` and `summarise()` approach. We will be -able to open the dataframe in the Viewer and make plots to examine -whether the distributions are similar across cells. - -🎬 Summarise all the cells: - -```{r} -hspc_summary_samp <- hspc |> - pivot_longer(cols = -ensembl_gene_id, - names_to = "cell", - values_to = "expr") |> - group_by(cell) |> - summarise(min = min(expr), - lowerq = quantile(expr, 0.25), - mean = mean(expr), - median = median(expr), - sd = sd(expr), - upperq = quantile(expr, 0.75), - max = max(expr), - n_zero = sum(expr == 0)) -``` - -Notice that I have used `cell` as the column name rather than `sample` -and `expr` (expression) rather than `count`. I've also added the -standard deviation. - -🎬 View the `hspc_summary_samp` dataframe (click on it in the -environment). - -All cells have quite a few zeros and the lower quartile is 0 for all -cells, *i.e.*, every cell has many genes with zero expression. - -To get a better understanding of the distribution of expressions in -cells we can create a ggplot using the pointrange geom. Pointrange puts -a dot at the mean and a line between a minimum and a maximum such as +/- -one s.d. Not unlike a boxplot, but when you need the boxes too be very -narrow! - -🎬 Create a pointrange plot. - -```{r} -hspc_summary_samp |> - ggplot(aes(x = cell, y = mean)) + - geom_pointrange(aes(ymin = mean - sd, - ymax = mean + sd ), - size = 0.1) -``` - -You will need to use the Zoom button to pop the plot window out so you -can make it as wide as possible - -The things to notice are: - -- the average expression in cells is similar for all cells. This is - good to know - if some cells had much lower expression perhaps there - is something wrong with them, or their sequencing, and they should - be excluded. -- the distributions are roughly similar in width too - -The default order of `cell` is alphabetical. It can be easier to see -these (non-) effects if we order the lines by the size of the mean. - -🎬 Order a pointrange plot with `reorder(variable_to_order, order_by)`. - -```{r} -hspc_summary_samp |> - ggplot(aes(x = reorder(cell, mean), y = mean)) + - geom_pointrange(aes(ymin = mean - sd, - ymax = mean + sd ), - size = 0.1) -``` - -`reorder()` arranges `cell` in increasing size of `mean` - -🎬 Write `hspc_summary_samp` to a file called "hspc_summary_samp.csv": - -```{r} -#| echo: false -write_csv(hspc_summary_samp, - file = "data-processed/hspc_summary_samp.csv") -``` - -### Distribution of values across the genes - -#### 🐸 Frog genes - -There are lots of genes in this dataset therefore we will take the same -approach as that we took for the distributions across mouse cells. We -will pivot the data to tidy and then summarise the counts for each gene. - -🎬 Summarise the counts for each genes: - -```{r} -s30_summary_gene <- s30 |> - pivot_longer(cols = -xenbase_gene_id, - names_to = "sample", - values_to = "count") |> - group_by(xenbase_gene_id) |> - summarise(min = min(count), - lowerq = quantile(count, 0.25), - sd = sd(count), - mean = mean(count), - median = median(count), - upperq = quantile(count, 0.75), - max = max(count), - total = sum(count), - n_zero = sum(count == 0)) -``` - -I have calculated the values we used before with one addition: the sum -of the counts (`total`). - -🎬 View the `s30_summary_gene` dataframe. - -Notice that we have: - -- a lot of genes with counts of zero in every sample -- a lot of genes with zero counts in several of the samples -- some very very low counts. - -These should be filtered out because they are unreliable - or, at the -least, uninformative. The goal of our downstream analysis will be to see -if there is a signifcance difference in gene expression between the -control and FGF-treated sibling. Since we have only three replicates in -each group, having one or two unreliable, missing or zero values, makes -such a determination impossible for a particular gene. We will use the -total counts and the number of samples with non-zero values to filter -our genes later. - -As we have a lot of genes, it is again helpful to plot the mean counts -with pointrange to get an overview. We will plot the log of the counts - -we saw earlier that logging made it easier to understand the -distribution of counts over such a wide range. We will also order the -genes from lowest to highest mean count. - -🎬 Plot the logged mean counts for each gene in order of size using -`geom_pointrange()`: - -```{r} -s30_summary_gene |> - ggplot(aes(x = reorder(xenbase_gene_id, mean), y = log10(mean))) + - geom_pointrange(aes(ymin = log10(mean - sd), - ymax = log10(mean + sd )), - size = 0.1) -``` - -(Remember, the warning is expected since we have zeros). - -You can see we also have quite a few genes with means less than 1 (log -below zero). Note that the variability between genes (average counts -between 0 and 102586) is far greater than between samples (average -counts from 260 to 426) which is exactly what we would expect to see. - -🎬 Write `s30_summary_gene` to a file called "s30_summary_gene.csv": - -```{r} -#| echo: false -write_csv(s30_summary_gene, - file = "data-processed/s30_summary_gene.csv") -``` - -#### 🐭 Mouse genes - -There are fewer genes in this dataset, but still more than you can -understand without the overview provided by a plot. We will again pivot -the data to tidy and then summarise the expression for each gene. - -🎬 Summarise the expression for each genes: - -```{r} -hspc_summary_gene <- hspc |> - pivot_longer(cols = -ensembl_gene_id, - names_to = "cell", - values_to = "expr") |> - group_by(ensembl_gene_id) |> - summarise(min = min(expr), - lowerq = quantile(expr, 0.25), - sd = sd(expr), - mean = mean(expr), - median = median(expr), - upperq = quantile(expr, 0.75), - max = max(expr), - total = sum(expr), - n_zero = sum(expr == 0)) -``` - -🎬 View the `hspc_summary_gene` dataframe. Remember these are normalised -and logged (base 2) so we should not see very large values. - -Notice that we have: - -- no genes with 0 in every cell -- very few genes (9) with no zeros at all -- quite a few genes with zero in many cells but this matters less than - zeros in the frog samples because we had just 6 samples and we have - 701 cells. - -As we have a lot of genes, it is again helpful to plot the mean -expression with pointrange to get an overview. We do not need to log the -values but ordering the genes will help. - -🎬 Plot the logged mean counts for each gene in order of size using -`geom_pointrange()`: - -```{r} -hspc_summary_gene |> - ggplot(aes(x = reorder(ensembl_gene_id, mean), y = mean)) + - geom_pointrange(aes(ymin = mean - sd, - ymax = mean + sd), - size = 0.1) -``` - -Note again that the variability between genes (average expression -between 0.02 and and 10.03) is far greater than between cells (average -expression from1.46 to 3.18) which is expected. - -🎬 Write `s30_summary_gene` to a file called "s30_summary_gene.csv": - -```{r} -#| echo: false -write_csv(hspc_summary_gene, - file = "data-processed/hspc_summary_gene.csv") -``` - -## Filtering for QC - -### 🐸 Frog filtering - -Our samples look to be similarly well sequenced. There are no samples we -should remove. However, some genes are not express or the expression -values are so low in for a gene that they are uninformative. We will -filter the `s30_summary_gene` dataframe to obtain a list of -`xenbase_gene_id` we can use to filter `s30`. - -My suggestion is to include only the genes with counts in at least 3 -samples[^3] and those with total counts above 20. - -[^3]: I chose three because that would keep \[0, 0, 0\] \[#,#,#\]. This - is difference we cannot test statistically, but which would matter - biologically. - -🎬 Filter the summary by gene dataframe: - -```{r} -s30_summary_gene_filtered <- s30_summary_gene |> - filter(total > 20) |> - filter(n_zero < 4) -``` - -🎬 Write the filtered summary by gene to file: - -```{r} -write_csv(s30_summary_gene_filtered, - file = "data-processed/s30_summary_gene_filtered.csv") -``` - -🎬 Use the list of `xenbase_gene_id` in the filtered summary to filter -the original dataset: - -```{r} -s30_filtered <- s30 |> - filter(xenbase_gene_id %in% s30_summary_gene_filtered$xenbase_gene_id) -``` - -🎬 Write the filtered original to file: - -```{r} -write_csv(s30_filtered, - file = "data-processed/s30_filtered.csv") -``` - -### 🐭 Mouse filtering - -We will take a different approach to filtering the single cell data. For -the Frog samples we are examining the control and the FGF treated -samples. This means have a low number of counts overall means the gene -is not really expressed (detected) in any condition, and filtering out -those genes is removing things that definitely are not interesting. For -the mice, we have examined only one cell type but will be making -comparisons between cells types. It may be that low expression of a gene -in this cell type tells us something if that gene is highly expressed in -another cell type. Instead, we will make statistical comparisons between -the cell types and then filter based on overall expression, the -difference in expression between cell types and whether that difference -is significant. - -The number of "replicates" is also important. When you have only three -in each group it is not possible to make statistical comparisons when -several replicates are zero. This is less of an issue with single cell -data. ## πŸ€— Look after future you! @@ -734,32 +40,15 @@ data. ### 🐸 Frogs and future you -🎬 Create a new Project, `frogs-88H`, populated with folders and your -data. Make a script file called `cont-fgf-s30.R`. This will a be -commented analysis of the control vs FGF at S30 comparison. You will -build on this each workshop and be able to use it as a template to -examine other comparisons. Copy in the appropriate code and comments -from `workshop-1.R`. Edit to improve your comments where your -understanding has developed since you made them. Make sure you can close -down RStudio, reopen it and run your whole script again. +🎬 xxx ### 🐭 Mice and future you -🎬 Create a new Project, `mice-88H`, populated with folders and your -data. Make a script file called `hspc-prog.R`. This will a be commented -analysis of the hspc cells vs the prog cells. At this point you will -have only code for the hspc cells. You will build on this each workshop -and be able to use it as a template to examine other comparisons. Copy -in the appropriate code and comments from `workshop-1.R`. Edit to -improve your comments where your understanding has developed since you -made them. Make sure you can close down RStudio, reopen it and run your -whole script again. +🎬 xxx ### πŸ‚ xxxx and future you -🎬 Create a new Project, `xxxx-88H`, populated with folders and your -data. Make a script file called `xxxx.R`. This will a be commented -analysis of t xxxxxxxxxxxx. +🎬 xxx # πŸ₯³ Finished From 867469bf0a04901d0269b79181b7c7500c347018 Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Sun, 8 Oct 2023 12:27:26 +0100 Subject: [PATCH 07/13] where you should be addedd to prepare --- omics/week-4/study_before_workshop.qmd | 36 ++++++++++++++++++++++++-- 1 file changed, 34 insertions(+), 2 deletions(-) diff --git a/omics/week-4/study_before_workshop.qmd b/omics/week-4/study_before_workshop.qmd index b96fa49..c5d6c4e 100644 --- a/omics/week-4/study_before_workshop.qmd +++ b/omics/week-4/study_before_workshop.qmd @@ -18,7 +18,8 @@ editor: ## Overview ::: incremental -- xx + +- Check where you are. - xx @@ -27,7 +28,38 @@ editor: - xx ::: -## xxx +# Where should you be? + +## 🐸 Frogs + +After the Omics 1 Workshop, Look after future you and the Independent Study to consolidate, you should have: + +- An RStudio Project called `frogs-88H` which contains + + - Raw data (S14, S20 and S30) + - Processed data (s20_filtered.csv, s20_summary_gene.csv, s20_summary_gene_filtered.csv, s20_summary_samp.csv, s30_filtered.csv, s30_summary_gene.csv, s30_summary_gene_filtered.csv, s30_summary_samp.csv) + - Two scripts called cont-fgf-s30.R and cont-fgf-s20.R + +These should be organised into folders. Code should well commented and easy to read. + +Note: you may have done S14 instead of S20. + + +## 🐭 Mice + +After the Omics 1 Workshop, Look after future you and the Independent Study to consolidate, you should have: + +- An RStudio Project called `mice-88H` which contains + + - Raw data (hspc, prog, lthsc) + - Processed data (hspc_summary_gene.csv, hspc_summary_samp.csv, prog_summary_gene.csv, prog_summary_samp.csv) + - One script called hspc-prog.R + +These should be organised into folders. Code should well commented and easy to read. + +## πŸ‚ xxxx + +Either of the other examples. ## References From 6f7399448d426907ef90f5686510e1976ea57f5c Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Thu, 12 Oct 2023 11:29:38 +0100 Subject: [PATCH 08/13] frogs DE and saving of normalised counts and DE results --- omics/crib/cont-fgf-s30.R | 252 ++++++++++++++++++- omics/week-4/meta/frog_meta_data.txt | 19 ++ omics/week-4/study_before_workshop.qmd | 79 +++++- omics/week-4/workshop.qmd | 332 ++++++++++++++++++++++++- 4 files changed, 671 insertions(+), 11 deletions(-) create mode 100644 omics/week-4/meta/frog_meta_data.txt diff --git a/omics/crib/cont-fgf-s30.R b/omics/crib/cont-fgf-s30.R index 77d4a4f..b7a8a6b 100644 --- a/omics/crib/cont-fgf-s30.R +++ b/omics/crib/cont-fgf-s30.R @@ -8,7 +8,7 @@ library(tidyverse) # Import ------------------------------------------------------------------ # 🐸 import the s30 data -s30 <- read_csv("data-raw/xlaevis_counts_S30.csv") +s30 <- read_csv("data-raw/xlaevis_s30_filtered.csv") # Explore ----------------------------------------------------------------- @@ -192,10 +192,258 @@ write_csv(s30_filtered, # OMICS 2 STATISTICAL ANALYSIS -------------------------------------------- +library(tidyverse) +library(conflicted) +library(DESeq2) + +# import the filtered s30 +s30_filtered <- read_csv("data-processed/s30_filtered.csv") + +# number of genes and samples. remember we filtered out any genes +# with 4, 5 or 6 zeros and those where the total count was less than 20 + + +# first find genes that are expressed only in one group. that is those +# only zeros in one group but values in all of the others. + +# Find the genes that are expressed only in the FGF-treated group: + +s30_fgf_only <- s30_filtered |> + filter(S30_C_5 == 0, + S30_C_6 == 0, + S30_C_A == 0, + S30_F_5 > 0, + S30_F_6 > 0, + S30_F_A > 0) +# there are 26 genes expressed in the FGF-treated group but not in the control group + +# genes that are expressed only in the control group. + +s30_con_only <- s30_filtered |> + filter(S30_C_5 > 0, + S30_C_6 > 0, + S30_C_A > 0, + S30_F_5 == 0, + S30_F_6 == 0, + S30_F_A == 0) +# there are no genes expressed in the control group but not in the FGF-treated group + +# Write to file +write_csv(s30_fgf_only, "results/s30_fgf_only.csv") + + +## Import metadata that maps the sample names to treatments +meta <- read_table("meta/frog_meta_data.txt") +row.names(meta) <- meta$sample_id + +# We only need the s30 +meta_S30 <- meta |> + dplyr::filter(stage == "stage_30") + + + +# 1. check the names match: rownames of meta and column names of counts +names(s30_filtered) +row.names(meta_S30) + +# verfiy with equality +names(s30_filtered[2:7]) == row.names(meta_S30) + + + +# 2. Create a DESeq2 object +# bioconductor has custom data types +# to make a DESeqDataSet object we need +# a) count matrix, +# b) metadata and +# c) a design matrix + +# The count matrix must contain only the counts +# the genes are not inside the matrix, but are the row names +s30_count_mat <- s30_filtered |> + select(-xenbase_gene_id) |> + as.matrix() +# add the genes as row names +row.names(s30_count_mat) <- s30_filtered$xenbase_gene_id + +View(s30_count_mat) +knitr::kable(head(s30_count_mat)) + + +# creates the DESeqDataSet (dds) object +dds <- DESeqDataSetFromMatrix(s30_count_mat, + colData = meta_S30, + design = ~ treatment + sibling_rep) +# design matrix is a formula that describes the experimental design using the +# column names of the metadata dataframe + + +# This is fine: +# converting counts to integer mode +# Warning message: + +# some variables in design formula are characters, converting to factors + + + +# To see the counts in the DESeqDataSet object, we can use the counts() function +counts(dds) |> View() +# This should be what is in s30_count_mat + +# The normalisation is done automatically with the DE +# However, we need the normalised counts for data viz. + + +# The normalised counts can be generated using: +# estimateSizeFactors() for estimating the factors for normalisation. +dds <- estimateSizeFactors(dds) +# By assigning the results back to the dds object, we are filling in the +# slots of the DESeqDataSet object with the appropriate information. + +# We can take a look at the normalization factors of each sample using: +sizeFactors(dds) +# S30_C_5 S30_C_6 S30_C_A S30_F_5 S30_F_6 S30_F_A +# 0.8812200 0.9454600 1.2989886 1.0881870 1.0518961 0.8322894 + +# Notice there is a relationship between the total number of +# counts and the size factors which we can see with a quick scatter plot +plot(colSums(s30_count_mat), sizeFactors(dds)) + +# Then to get the normalized counts as a matrix, we can use: +normalised_counts <- counts(dds, normalized = TRUE) + + +# Save this normalized data matrix to file for later use: +data.frame(normalised_counts, + xenbase_gene_id = row.names(normalised_counts)) |> + write_csv(file = "results/S30_normalised_counts.csv") + +# These normalized counts will be useful for downstream visualization +# of results, but cannot be used as input to DESeq2 or any other tools that +# perform differential expression analysis that use the negative binomial model. + + +# PCA AND CLUSTERING ----------------------- + +# we do this on the log2 transformed normalised counts or the regularized the +# log transformed counts +# rlog is a method to bias from low count genes. +# https://hbctraining.github.io/DGE_workshop_salmon_online/lessons/03_DGE_QC_analysis.html gives a good explanation of regularized the log transform (rlog) + + +# The rlog transformation of the normalized counts is only necessary +# for these visualization methods during this quality assessment. +# They are not used for DE because DESeq2 takes care of that + +### Transform counts for data visualization +rld <- rlog(dds, blind = TRUE) +# transformed counts are accessed with +# assay(rld) +rlogged <- assay(rld) |> t() +# These techniques want cells (samples in rows) and genes in columns. + + +# perform PCA using standard functions +pca <- rlogged |> + prcomp(scale. = TRUE) + +summary(pca) +# PC1 proportion 0.3857, PC2 proportion 0.2112 + +pca_labelled <- data.frame(pca$x, + sample_id = row.names(rlogged)) + + +# merge with metadata +pca_labelled <- pca_labelled |> + left_join(meta_S30, + by = "sample_id") + +pca_labelled |> ggplot(aes(x = PC1, y = PC2, + colour = treatment, + shape = sibling_rep)) + + geom_point(size = 3) + + scale_colour_viridis_d(end = 0.95) + + theme_classic() + +# they don't cluster that well, by treatment or sib group + + +# ########## TO COPY TO OTHER COMPARISONS FROM HERE +# # Hierarchical Clustering +# # +# # pheatmap need a matrix or dataframe so use SummarizedExperiment::assay() +# # which is loaded with DESeq2 +# rld_mat <- assay(rld) +# +# # Compute pairwise correlation values +# rld_cor <- cor(rld_mat) ## cor() is a base R function +# +# meta_S30 <- as.data.frame(meta_S30) +# row.names(meta_S30) <- meta_S30$sample_id +# # Plot heatmap using the correlation matrix and the metadata object +# pheatmap(rld_cor, +# annotation = meta_S30[3:4]) + + +# DE WITH DESeq2 ---------------------------------------------------------- + +# run the actual differential expression analysis, +# we use a single call to the function DESeq(). +# Run analysis +dds <- DESeq(dds) + +# check dispersion estimates, checks the assumption of the DESeq2 model +plotDispEsts(dds) +# look fine + + +## Define contrasts for FGF overexpression +contrast_fgf <- c("treatment", "FGF", "control") +results_fgf <- results(dds, + contrast = contrast_fgf, + alpha = 0.01) +results_fgf |> + data.frame() |> head() + + +data.frame(results_fgf, + xenbase_gene_id = row.names(results_fgf)) |> + write_csv(file = "results/S30_results.csv") + + +# MAYBE WEEK 3 +# plotMA(results_fgf) +# +# resultsNames(dds) +# # "Intercept", "treatment_FGF_vs_control" "sibling_rep_five_vs_A" "sibling_rep_six_vs_A" +# +# +# +# # results_fgf_shrunken <- lfcShrink(dds, +# # coef = "treatment_FGF_vs_control", +# # type = "apeglm") +# # +# # plotMA(results_fgf_shrunken) + + + + + + + + +# OMICS 3 VISUALISE AND INTERPRET and chek the assumptions?? ----------------------------------------- +## Principle Components Analysis (PCA) + +# explore data further - at the sample and gene level check reps cluster +# together do on the normalised counts -# OMICS 3 VISUALISE AND INTERPRET ----------------------------------------- +# venn diagram for frogs, use original dataset +# look up information about the genes +# volcano plots \ No newline at end of file diff --git a/omics/week-4/meta/frog_meta_data.txt b/omics/week-4/meta/frog_meta_data.txt new file mode 100644 index 0000000..fb451e7 --- /dev/null +++ b/omics/week-4/meta/frog_meta_data.txt @@ -0,0 +1,19 @@ +sample_id stage treatment sibling_rep +S14_C_5 stage_14 control five +S14_C_6 stage_14 control six +S14_C_A stage_14 control A +S14_F_5 stage_14 FGF five +S14_F_6 stage_14 FGF six +S14_F_A stage_14 FGF A +S20_C_5 stage_20 control five +S20_C_6 stage_20 control six +S20_C_A stage_20 control A +S20_F_5 stage_20 FGF five +S20_F_6 stage_20 FGF six +S20_F_A stage_20 FGF A +S30_C_5 stage_30 control five +S30_C_6 stage_30 control six +S30_C_A stage_30 control A +S30_F_5 stage_30 FGF five +S30_F_6 stage_30 FGF six +S30_F_A stage_30 FGF A diff --git a/omics/week-4/study_before_workshop.qmd b/omics/week-4/study_before_workshop.qmd index c5d6c4e..2f1dc31 100644 --- a/omics/week-4/study_before_workshop.qmd +++ b/omics/week-4/study_before_workshop.qmd @@ -21,11 +21,16 @@ editor: - Check where you are. -- xx - -- xx - -- xx +- Basis of comparisons + - normalisation + - statistical model + - Multiple correction + +- Methods vary + - DESeq2 + - edgeR + - limma + - scran ::: # Where should you be? @@ -62,4 +67,68 @@ These should be organised into folders. Code should well commented and easy to r Either of the other examples. +## + +further explore with PCA + +what is pca + + +## DESeq2 + +the Negative Binomial is a good approximation for data where the +mean < variance i.e., more clumped + +Goal of DE is to identify and correct for sources of variation that are not +due to treatments + +explain normalisation # https://hbctraining.github.io/DGE_workshop_salmon_online/lessons/02_DGE_count_normalization.html +# this is a good account of normalisation methods + +To normalize for sequencing depth and RNA composition, + +DESeq2 uses the median of ratios method. This happens automatically +when we do the DE. models the normalization inside the Generalized Linear Model + +data must be raw counts + +## DESeq2 + +DESeq2 needs +- a metadata file which contain information about the samples and the treatments +- a matrix of the counts +- a design formula which describes the model to be fitted. it must use the columns in the metadat file. + + + +## + +```{r} +#| echo: false +library(tidyverse) +## Import metadata that maps the sample names to treatments +meta <- read_table("meta/frog_meta_data.txt") +knitr::kable(meta) + +``` + + +## DESeq2 + + +differential expression results + +log2 foldhcange - explain why log2 +a tests statistic, + +baseMean, log2FoldChange, lfcSE, stat, pvalue and padj, and also includes metadata columns of variable information. The lfcSE gives the standard error of the log2FoldChange. For the Wald test, stat is the Wald statistic: + + +a p-value and a padj + +explain the adjusted p value. + + + + ## References diff --git a/omics/week-4/workshop.qmd b/omics/week-4/workshop.qmd index 50d1ade..e2e9a02 100644 --- a/omics/week-4/workshop.qmd +++ b/omics/week-4/workshop.qmd @@ -21,18 +21,342 @@ editor: In this workshop you will lxxxx -# 🐸 Frog development +# Set up -🎬 Open your `frogs-88H` Project and the `cont-fgf-s30.R` script. +Either: +🎬 Open 🐸 `frogs-88H` Project and the `cont-fgf-s30.R` script. +Or -# 🐭 Mouse Stem cells +🎬 Open 🐭 `mice-88H` Project and the `hspc-prog.R` script. -🎬 Open your `mice-88H` Project and the `hspc-prog.R` script. +🎬 Make a new folder `results` in the project directory. This is where +we will save our results. +```{r} +#| eval: false +fs::dir_create("results") +``` +🎬 Load **`tidyverse`** You most likely have this code at the top of +`cont-fgf-s30.R` or `hspc-prog.R` already. +```{r} +library(tidyverse) +``` + +``` +── Attaching core tidyverse packages ─────────────────────────────────────────────── tidyverse 2.0.0 ── +βœ” dplyr 1.1.3 βœ” readr 2.1.4 +βœ” forcats 1.0.0 βœ” stringr 1.5.0 +βœ” ggplot2 3.4.3 βœ” tibble 3.2.1 +βœ” lubridate 1.9.3 βœ” tidyr 1.3.0 +βœ” purrr 1.0.2 +── Conflicts ───────────────────────────────────────────────────────────────── tidyverse_conflicts() ── +βœ– dplyr::filter() masks stats::filter() +βœ– dplyr::lag() masks stats::lag() +β„Ή Use the conflicted package to force all conflicts to become errors +``` + +Have you ever stopped to think about this message? It is telling us that +there are functions in the `dplyr` package that have the same name as +functions in the `stats` package and that R will use the `dplyr` +version. As this is what you want, this has always been fine. It still +is fine in this case. However, as you start to load more packages, you +will want to know if you are using a function from a package that has +the same name as a function in another loaded package. This is where the +**`conflicted`** package comes in. **`Conflicted`** will warn you when +you are using a function that has the same name as a function in another +package. You can then choose which function to use. + +🎬 Load the **`conflicted`** package. + +```{r} +library(conflicted) +``` + +Perfer to use dplyr's version of filter + +```{r} +conflict_prefer("filter", "dplyr") +``` + +# 🐸 Analysis + +We will carry out several steps + +1. Import the data +2. Find the genes that are expressed in only one treatment (the control + or the FGF-treated) +3. Create a DESeqDataSet object. This is a special object that is used + by the **`DESeq2`** package +4. Prepare the normalised counts from the DESeqDataSet object. +5. Do differential expression analysis on the genes. This needs to be + done on the raw counts. + + +The creation of the DESeqDataSet object, the preparation of the normalised counts and the differential expression analysis are all done with the **`DESeq2`** package + + +## Import + +We need to import the S30 data that were filtered to remove genes with 4, 5 +or 6 zeros and those where the total counts was less than 20. + +🎬 Import the data from the `data-processed` folder. + +```{r} +s30_filtered <- read_csv("data-processed/s30_filtered.csv") +``` + +## Genes expressed in one treatment + +The genes expressed in only one treatment group are those with zeros in +all three replicates in one group and non-zero values in all three +replicates in the other group. For example, those shown here: + +```{r} +#| echo: false +# sort the data by the values in the control column +s30_filtered |> + arrange(S30_C_A) |> + head(3) + +``` + +We will use `filter()` to find these genes. + +🎬 Find the genes that are expressed only in the FGF-treated group: + +```{r} +s30_fgf_only <- s30_filtered |> + filter(S30_C_5 == 0, + S30_C_6 == 0, + S30_C_A == 0, + S30_F_5 > 0, + S30_F_6 > 0, + S30_F_A > 0) +``` + +❓ How many genes are expressed only in the FGF-treated group? + + + +🎬 Now you find any genes that are expressed only in the control group. + +```{r} +#| echo: false +#---CODING ANSWER--- +s30_con_only <- s30_filtered |> + filter(S30_C_5 > 0, + S30_C_6 > 0, + S30_C_A > 0, + S30_F_5 == 0, + S30_F_6 == 0, + S30_F_A == 0) + +``` + +❓ Do the results make sense to you in light of what you know about the biology? + + + + + + + + + + + + + +🎬 Write to file (saved in `results`) all the genes that are expressed +one group only. + +```{r} +#| echo: false +#---CODING ANSWER--- +write_csv(s30_fgf_only, "results/s30_fgf_only.csv") +``` + + + +## Create DESeqDataSet object + +🎬 Load the DESeq2 package + +```{r} +#| echo: false +#---CODING ANSWER--- +library(DESeq2) +``` + +A DEseqDataSet object is a custom data type that is used by the DESeq2. Custom data types are common in the Bioconductor packages. They are used to store data in a way that is useful for the analysis. These data types typically have data, transformed data, metadata and experimental designs within them. + +To create a DESeqDataSet object, we need to provide three things: + +1. The raw counts - these are what we imported into `s30_filtered` +2. The meta data which gives information about the samples and which treatment groups they belong to +3. A design matrix which captures the design of the statistical model. + +The counts must in a matrix rather than a dataframe. Unlike a dataframe, a matrix has columns of all the same type. That is, it will contain only the counts. The gene ids are given as row names rather than a column. The `matrix()` function will create a matrix from a dataframe of columns of the same type and the `select()` function can be used to remove the gene ids column. + +🎬 Create a matrix of the counts: +```{r} +s30_count_mat <- s30_filtered |> + select(-xenbase_gene_id) |> + as.matrix() +``` + + +🎬 Add the gene ids as row names to the matrix: +```{r} +# add the row names to the matrix +rownames(s30_count_mat) <- s30_filtered$xenbase_gene_id + +``` + +You might want to view the matrix. + +The metadata are in a file, [frog_meta_data.txt](meta_data/frog_meta_data.txt). This is a tab-delimited file. The first column is the sample name and the second column is the treatment group. + +🎬 Make a folder called `meta` and save the file to it. + +🎬 Read the metadata into a dataframe: + +```{r} +meta <- read_table("meta/frog_meta_data.txt") +``` + +🎬 Examine the resulting dataframe + +We need to add the sample names as row names to the metadata dataframe. This is because the DESeqDataSet object will use the row names to match the samples in the metadata to the samples in the counts matrix. + +🎬 Add the sample names as row names to the metadata dataframe: + +```{r} +row.names(meta) <- meta$sample_id +``` +(you will get a warning message but you can ignore it) + +We are dealing only with the S30 data so we need to remove the samples that are not in the S30 data. + +🎬 Filter the metadata to keep only the S30 information: + +```{r} +meta_S30 <- meta |> + dplyr::filter(stage == "stage_30") +``` + + +```{r} +#| echo: false +meta_S30 +``` + +We can now create the DESeqDataSet object. The design formula describes the statistical model You should notice that it is the same sort of formula we used in `t.test()`, `lm()`,` glm()` etc. The `~` indicates that the left hand side is the response variable (in this case counts) and the right hand side are the explanatory variables. We are interested in the difference between the treatments but we include `sibling_rep` to account for the fact that the data are paired. +The names of the columns in the count matrix have to match the names in the metadata dataframe and the names of the explanatory variables in the design formula have to match the names of columns in the metadata. + +🎬 Create the DESeqDataSet object: + +```{r} +dds <- DESeqDataSetFromMatrix(countData = s30_count_mat, + colData = meta_S30, + design = ~ treatment + sibling_rep) +``` + + + +The warning "Warning: some variables in design formula are characters, converting to factors" just means that the variable type of treatment and sibling_rep in the metadata dataframe are char. This is not a as `DESeqDataSetFromMatrix()` has made them into the factors it needs. + +🎬 Examine the DESeqDataSet object. + +The counts are in `dds@assays@data@listData[["counts"]]` and the metadata are in `dds@colData` but the easiest way to see tham is to use the `counts()` and `colData()` functions from the **`DESeq2`** package. + +🎬 View the counts: +```{r} +counts(dds) |> View() +``` +You should be able to see that this is the same as in `s30_count_mat`. + + +```{r} +colData(dds) + +``` + + + +## Prepare the normalised counts + +The normalised counts are the counts that have been transformed to account for the library size (ie., the total number of reads in a sample) and the gene length. We have to first estimate the normalisation factors and store them in the DESeqDataSet object and then we can get the normalised counts. + +🎬 Estimate the factors for normalisation and store them in the DESeqDataSet object: + +```{r} +dds <- estimateSizeFactors(dds) +``` + +🎬 Look at the factors (just for information) +```{r} +sizeFactors(dds) +``` +To get the normalised counts we again used the counts() function but this time we use the `normalized=TRUE` argument.` + +🎬 Save the normalised to a matrix: +```{r} +normalised_counts <- counts(dds, normalized = TRUE) +``` + +We will write the normalised counts to a file so that we can use them in the future. + +🎬 Make a dataframe of the normalised counts, add a column for the gene ids and write to file: + +```{r} +data.frame(normalised_counts, + xenbase_gene_id = row.names(normalised_counts)) |> + write_csv(file = "results/S30_normalised_counts.csv") +``` + + +## Differential expression analysis + +We used the `DESeq()` function to do the differential expression analysis. This function fits the statistical model to the data and then uses the model to calculate the significance of the difference between the treatments. It again stored the results in the DESseqDataSet object. Note that the differential expression needs the raw (unnormalised counts) as it does its own normalisation as part of the process. + +🎬 Run the differential expression analysis: +```{r} +dds <- DESeq(dds) +``` +The function will take only a few moments to run on this data but can take longer for bigger datasets. + +We need to define the contrasts we want to test. We want to test the difference between the treatments so we will define the contrast as `FGF` and `control`. + +🎬 Define the contrast: +```{r} +contrast_fgf <- c("treatment", "FGF", "control") +``` + +Notes that `treatment` is the name of the column in the metadata dataframe and `FGF` and `control` are the names of the levels in the `treatment` column. By putting them in the order `FGF` , `control` we are saying the fold change will be FGF / control. If we had put them in the order `control`, `FGF` we would have got the fold change as control / FGF. This means positive log fold changes indicate FGF > control and negative log fold changes indicates control > FGF. + + +🎬 Extract the results from the DESseqDataSet object: + +```{r} +results_fgf <- results(dds, + contrast = contrast_fgf, + alpha = 0.1) + +results_fgf2 <- results(dds, + contrast = contrast_fgf, + alpha = 0.01) +``` + +This will give us the log2 fold change and p-value for the contrast + + +# 🐭 Analysis ## πŸ€— Look after future you! From 4d85a1e9bbef5ea538b523f7e15ebde763211f8d Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Thu, 12 Oct 2023 11:33:56 +0100 Subject: [PATCH 09/13] whoops, forgot to save last edit on workshop --- omics/week-4/workshop.qmd | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/omics/week-4/workshop.qmd b/omics/week-4/workshop.qmd index e2e9a02..0317034 100644 --- a/omics/week-4/workshop.qmd +++ b/omics/week-4/workshop.qmd @@ -345,16 +345,18 @@ Notes that `treatment` is the name of the column in the metadata dataframe and ` ```{r} results_fgf <- results(dds, - contrast = contrast_fgf, - alpha = 0.1) - -results_fgf2 <- results(dds, - contrast = contrast_fgf, - alpha = 0.01) + contrast = contrast_fgf) ``` This will give us the log2 fold change and p-value for the contrast +🎬 Save the results to a file: +```{r} +data.frame(results_fgf, + xenbase_gene_id = row.names(results_fgf)) |> View() + write_csv(file = "results/S30_results.csv") +``` + # 🐭 Analysis From 0ab3824df80f44126380efb74d022e25a4b9a8e0 Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Fri, 13 Oct 2023 10:16:59 +0100 Subject: [PATCH 10/13] DE added to workshop for mouse data; some notes added to prior study --- omics/crib/cont-fgf-s30.R | 91 +- omics/crib/hspc-prog.R | 144 +- .../week-4/results/S30_normalised_counts.csv | 10137 ++++++++++++++++ omics/week-4/results/S30_results.csv | 10137 ++++++++++++++++ omics/week-4/results/prog_hspc_results.csv | 281 + omics/week-4/study_before_workshop.qmd | 8 +- omics/week-4/workshop.qmd | 203 +- renv.lock | 459 +- update-notes.txt | 11 + 9 files changed, 21342 insertions(+), 129 deletions(-) create mode 100644 omics/week-4/results/S30_normalised_counts.csv create mode 100644 omics/week-4/results/S30_results.csv create mode 100644 omics/week-4/results/prog_hspc_results.csv diff --git a/omics/crib/cont-fgf-s30.R b/omics/crib/cont-fgf-s30.R index b7a8a6b..aeaf95f 100644 --- a/omics/crib/cont-fgf-s30.R +++ b/omics/crib/cont-fgf-s30.R @@ -323,6 +323,46 @@ data.frame(normalised_counts, # perform differential expression analysis that use the negative binomial model. +# DE WITH DESeq2 ---------------------------------------------------------- + +# run the actual differential expression analysis, +# we use a single call to the function DESeq(). +# Run analysis +dds <- DESeq(dds) + +# check dispersion estimates, checks the assumption of the DESeq2 model +plotDispEsts(dds) +# look fine + + +## Define contrasts for FGF overexpression +contrast_fgf <- c("treatment", "FGF", "control") +results_fgf <- results(dds, + contrast = contrast_fgf, + alpha = 0.01) +results_fgf |> + data.frame() |> head() + + +data.frame(results_fgf, + xenbase_gene_id = row.names(results_fgf)) |> + write_csv(file = "results/S30_results.csv") + + + + + + + + + + + + +# OMICS 3 VISUALISE AND INTERPRET and chek the assumptions?? ----------------------------------------- + + + # PCA AND CLUSTERING ----------------------- # we do this on the log2 transformed normalised counts or the regularized the @@ -386,31 +426,18 @@ pca_labelled |> ggplot(aes(x = PC1, y = PC2, # annotation = meta_S30[3:4]) -# DE WITH DESeq2 ---------------------------------------------------------- - -# run the actual differential expression analysis, -# we use a single call to the function DESeq(). -# Run analysis -dds <- DESeq(dds) - -# check dispersion estimates, checks the assumption of the DESeq2 model -plotDispEsts(dds) -# look fine +## Principle Components Analysis (PCA) + +# explore data further - at the sample and gene level check reps cluster +# together do on the normalised counts -## Define contrasts for FGF overexpression -contrast_fgf <- c("treatment", "FGF", "control") -results_fgf <- results(dds, - contrast = contrast_fgf, - alpha = 0.01) -results_fgf |> - data.frame() |> head() +# venn diagram for frogs, use original dataset -data.frame(results_fgf, - xenbase_gene_id = row.names(results_fgf)) |> - write_csv(file = "results/S30_results.csv") +# look up information about the genes +# volcano plots # MAYBE WEEK 3 # plotMA(results_fgf) @@ -424,26 +451,4 @@ data.frame(results_fgf, # # coef = "treatment_FGF_vs_control", # # type = "apeglm") # # -# # plotMA(results_fgf_shrunken) - - - - - - - - - -# OMICS 3 VISUALISE AND INTERPRET and chek the assumptions?? ----------------------------------------- - -## Principle Components Analysis (PCA) - -# explore data further - at the sample and gene level check reps cluster -# together do on the normalised counts - - -# venn diagram for frogs, use original dataset - -# look up information about the genes - -# volcano plots \ No newline at end of file +# # plotMA(results_fgf_shrunken) \ No newline at end of file diff --git a/omics/crib/hspc-prog.R b/omics/crib/hspc-prog.R index e507b63..a06e09d 100644 --- a/omics/crib/hspc-prog.R +++ b/omics/crib/hspc-prog.R @@ -1,8 +1,9 @@ -# OMICS 1 HELLO DATA ------------------------------------------------------ - -# Load tidyverse +# Load pckages ------------------------------------------------------------ library(tidyverse) +library(scran) +library(conflicted) +# OMICS 1 HELLO DATA ------------------------------------------------------ # HSPC -------------------------------------------------------------------- @@ -250,7 +251,142 @@ write_csv(prog_summary_gene, # OMICS 2 STATISTICAL ANALYSIS -------------------------------------------- +# We will carry out several steps +# +# 1. the data should be imported already +# 2. Combine the two datasets ready for analysis +# 3. Filter the data to remove genes that are not expressed in any cell +# 4. Find the genes that are expressed in only one cell type +# (the prog or the hspc) +# 5. Do differential expression analysis on the genes using the **`scran`** package. +# This needs to be done on the logged normalised counts. + +# 2. Combine the two datasets ready for analysis --------------------------- + +# We need to combine the two datasets of 701 and 798 cells into one dataset +# of 1499 cells, i.e., 1499 columns. The number of rows is the number of genes, +# 280. Before combining, we must make sure genes in the same order in +# both dataframes or we would be comparing the expression of one gene +# in one cell type to the expression of a different gene in the other cell type! + +# Check the gene ids are in the same order in both dataframes: +identical(prog$ensembl_gene_id, hspc$ensembl_gene_id) +# the names are the same and in the same order + +# **`scran`** can use a matrix or a dataframe of counts but theses must be +# log normalised counts. If using a dataframe, the columns must only +# contain the expression values (not the gene ids). + +# Combine the two dataframes (minus the gene ids) into one dataframe +# called `prog_hspc`: +prog_hspc <- bind_cols(prog[-1], hspc[-1]) + +# Now add the gene ids as the row names: +row.names(prog_hspc) <- prog$ensembl_gene_id + +# Filter to remove unexpressed genes -------------------------------------- +# In this dataset, we will not see and genes that are not expressed in any of the +# cells because we are using a specific subset of the transcriptome that was +# deliberately selected. However, we will go through how to do this because +# it is an important step in most analyses. +# +# For the 🐸 frog data you should remember that we were able to filter +# out our unexpressed genes in [Omics 1](../week-3/workshop.html) because +# we were examining both groups to be compared. In that workshop, +# we discussed that we could not filter out unexpressed genes in +# the 🐭 mouse data because we only had one cell types at that time. +# During the Consolidate Independent Study you examined the hspc cells. + +# Where the sum of all the values in the rows is zero, all the entries must be +# zero. We can use this to find the filter the genes that are not expressed +# in any of the cells. To do row wise aggregates such as the sum across rows +# we can use the `rowwise()` function. `c_across()` allows us to use the +# colon notation `Prog_001:HSPC_852` in `sum()` rather than having to list all +# the column names: `sum(Prog_001, Prog_002, Prog_002, Prog_004,.....)` + +# Find the genes that are 0 in every column of the prog_hspc dataframe: + +prog_hspc |> + rowwise() |> + filter(sum(c_across(Prog_001:HSPC_852)) == 0) +# There are no genes that are completely unexpressed in this set of 280 genes + +# We might also examine the genes which are least expressed. +# Find ten least expressed genes: +rowSums(prog_hspc) |> sort() |> head(10) + + +# When you consider that there are 1499 cells, a values of 30 are low even +# considering these are already logged and normalised (ie., the range of +# values is less that it would be for raw counts) + +# Find the genes that are expressed in only one cell type ----------------- +# To find the genes that are expressed in only one cell type, +# we can use the same approach as above but only sum the columns for one cell type. +# +# Find the genes that are 0 in every column for the Prog cells: + +prog_hspc |> + rowwise() |> + filter(sum(c_across(HSPC_001:HSPC_852)) == 0) + +# Note that if we knew there were some rows that were all zero across both +# cell types, we would need to add +# |> filter(sum(c_across(Prog_001:Prog_852)) != 0) + +# Genes that are 0 in every column for the HSPC cells: +prog_hspc |> + rowwise() |> + filter(sum(c_across(HSPC_001:HSPC_852)) == 0) +# there are no genes that are expressed in only one cell type + +# Differential expression analysis ---------------------------------------- + +# Like **`DESeq2`**, **`scran`** uses a statistical model to calculate the +# significance of the difference between the treatments and needs metadata +# to define the treatments. + +# The meta data needed for the frog data was information about which columns +# were in which treatment group and which sibling group and we had that +# information in a file. Similarly, here we need information on which columns +# are from which cell type. Instead of having this is a file, we will create +# a vector that indicates which column belongs to which cell type. + +# Create a vector that indicates which column belongs to which cell type: + +cell_type <- rep(c("prog","hspc"), + times = c(length(prog) - 1, + length(hspc) - 1)) +# The number of times each cell type is repeated is the number of columns +# in that cell type minus 1. This is because we have removed the column with +# the gene ids. Do check that the length of the `cell_type` vector is the +# same as the number of columns in the `prog_hspc` dataframe. + +# Run the differential expression analysis: +res_prog_hspc <- findMarkers(prog_hspc, + cell_type) + + +# The dataframe `res_prog_hspc$prog` is log prog - log hspc (i.e.,Prog/HSPC). +# This means +# - Positive fold change: prog is higher than hspc +# - Negative fold change: hspc is higher than prog +# +# The dataframe `res_prog_hspc$hspc` is log hspc - log prog (i.e., HSPC/Prog). +# This means +# - Positive fold change: hspc is higher than prog +# - Negative fold change: prog is higher than hspc + + +# Write the results to file: +data.frame(res_prog_hspc$prog, + ensembl_gene_id = row.names(res_prog_hspc$prog)) |> + write_csv("results/prog_hspc_results.csv") + + + + +# OMICS 3 VISUALISE AND INTERPRET ----------------------------------------- -# OMICS 3 VISUALISE AND INTERPRET ----------------------------------------- \ No newline at end of file diff --git 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Code should well commented and easy to r Either of the other examples. -## +what we have done: explore +what we will do this week DE + +what we will do next week +further explore with dimension reduction and clustering +plot results with volcanoplots +look up the gene information further explore with PCA diff --git a/omics/week-4/workshop.qmd b/omics/week-4/workshop.qmd index 0317034..177646d 100644 --- a/omics/week-4/workshop.qmd +++ b/omics/week-4/workshop.qmd @@ -107,6 +107,8 @@ or 6 zeros and those where the total counts was less than 20. 🎬 Import the data from the `data-processed` folder. ```{r} +#| echo: false +#---CODING ANSWER--- s30_filtered <- read_csv("data-processed/s30_filtered.csv") ``` @@ -141,7 +143,8 @@ s30_fgf_only <- s30_filtered |> ❓ How many genes are expressed only in the FGF-treated group? - + + 🎬 Now you find any genes that are expressed only in the control group. @@ -160,16 +163,12 @@ s30_con_only <- s30_filtered |> ❓ Do the results make sense to you in light of what you know about the biology? + - - - - - 🎬 Write to file (saved in `results`) all the genes that are expressed @@ -353,28 +352,202 @@ This will give us the log2 fold change and p-value for the contrast 🎬 Save the results to a file: ```{r} data.frame(results_fgf, - xenbase_gene_id = row.names(results_fgf)) |> View() + xenbase_gene_id = row.names(results_fgf)) |> write_csv(file = "results/S30_results.csv") ``` # 🐭 Analysis -## πŸ€— Look after future you! -**You need only do the section for your own project data** +We will carry out several steps + +1. Import the prog and hspc data +2. Combine the two datasets ready for analysis +3. Filter the data to remove genes that are not expressed in any cell +4. Find the genes that are expressed in only one cell type (the prog + or the hspc) +5. Do differential expression analysis on the genes using the **`scran`** package. This needs to be done on the logged normalised counts. + +## Import + +🎬 Import surfaceome_hspc.csv and surfaceome_prog.csv into dataframes called hspc and prog respectively. + + +```{r} +#| echo: false +#---CODING ANSWER--- +hspc <- read_csv("data-raw/surfaceome_hspc.csv") +prog <- read_csv("data-raw/surfaceome_prog.csv") +``` + + +## Combine the two datasets + +We need to combine the two datasets of 701 and 798 cells into one dataset of 1499 cells, i.e., 1499 columns. The number of rows is the number of genes, 280. Before combining, we must make sure genes in the same order in both dataframes or we would be comparing the expression of one gene in one cell type to the expression of a different gene in the other cell type! + +🎬 Check the gene ids are in the same order in both dataframes: +```{r} +identical(prog$ensembl_gene_id, hspc$ensembl_gene_id) +``` +**`scran`** can use a matrix or a dataframe of counts but theses must be log normalised counts. If using a dataframe, the columns must only contain the expression values (not the gene ids). + +🎬 Combine the two dataframes (minus the gene ids) into one dataframe called `prog_hspc`: +```{r} +prog_hspc <- bind_cols(prog[-1], hspc[-1]) + +``` + +🎬 Now add the gene ids as the row names: +```{r} +row.names(prog_hspc) <- prog$ensembl_gene_id +``` + + +## Filter to remove unexpressed genes + +In this dataset, we will not see and genes that are not expressed in any of the cells because we are using a specific subset of the transcriptome that was deliberately selected. However, we will go through how to do this because it is an important step in most analyses. + +For the 🐸 frog data you should remember that we were able to filter out our unexpressed genes in [Omics 1](../week-3/workshop.html) because we were examining both groups to be compared. In that workshop, [we discussed](../week-3/workshop.html#frog-filtering) that we could not filter out unexpressed genes in the 🐭 mouse data because we only had one cell types at that time. During the [Consolidate Independent Study](../week-3/study_after_workshop.html) you examined the hspc cells. + +Where the sum of all the values in the rows is zero, all the entries must be zero. We can use this to find the filter the genes that are not expressed in any of the cells. To do row wise aggregates such as the sum across rows we can use the `rowwise()` function. `c_across()` allows us to use the colon notation `Prog_001:HSPC_852` in `sum()` rather than having to list all the column names: `sum(Prog_001, Prog_002, Prog_002, Prog_004,.....)` + +🎬 Find the genes that are 0 in every column of the prog_hspc dataframe: +```{r} +prog_hspc |> + rowwise() |> + filter(sum(c_across(Prog_001:HSPC_852)) == 0) + +``` + + +❓ What do you conclude? + + + + +We might also examine the genes which are least expressed. + +🎬 Find ten least expressed genes: +```{r} +rowSums(prog_hspc) |> sort() |> head(10) +``` + +❓ What do you conclude? + + + + + + + +## Find the genes that are expressed in only one cell type + +To find the genes that are expressed in only one cell type, we can use the same approach as above but only sum the columns for one cell type. + +🎬 Find the genes that are 0 in every column for the Prog cells: + +```{r} +prog_hspc |> + rowwise() |> + filter(sum(c_across(HSPC_001:HSPC_852)) == 0) + +``` + +Note that if we knew there were some rows that were all zero across both cerll types, we would need to add `|> filter(sum(c_across(Prog_001:Prog_852)) != 0)` + +🎬 Npw you find the genes that are 0 in every column for the HSPC cells: + +```{r} +#| echo: false +#---CODING ANSWER--- +prog_hspc |> + rowwise() |> + filter(sum(c_across(HSPC_001:HSPC_852)) == 0) + +``` + +❓ What do you conclude? + + + + + +## Differential expression analysis + +Like **`DESeq2`**, **`scran`** uses a statistical model to calculate the significance of the difference between the treatments and needs metadata to define the treatments. + +🎬 Load the **`scran`** package: +```{r} +#| echo: false +library(scran) +``` + +The meta data needed for the frog data was information about which columns were in which treatment group and which sibling group and we had that information in a file. Similarly, here we need information on which columns are from which cell type. Instead of having this is a file, we will create a vector that indicates which column belongs to which cell type. + +🎬 Create a vector that indicates which column belongs to which cell type: + +```{r} +cell_type <- rep(c("prog","hspc"), + times = c(length(prog) - 1, + length(hspc) - 1)) +``` + +The number of times each cell type is repeated is the number of columns in that cell type minus 1. This is because we have removed the column with the gene ids. Do check that the length of the `cell_type` vector is the same as the number of columns in the `prog_hspc` dataframe. + +🎬 Run the differential expression analysis: + +```{r} +res_prog_hspc <- findMarkers(prog_hspc, + cell_type) +``` + +`findMarkers()` is the function that runs the differential expression analysis. The first argument is the dataframe containing the data. The second argument is the vector indicating which columns are in which cell type. It gives us two dataframes of the results - rather unnecessarily. One is the results with fold changes that are Prog/HSPC and the other is the results with fold changes that are HSPC/Prog. These have the same magnitude, just a different sign + +The dataframe `res_prog_hspc$prog` is log prog - log hspc (i.e.,Prog/HSPC). This means +- Positive fold change: prog is higher than hspc +- Negative fold change: hspc is higher than prog + +The dataframe `res_prog_hspc$hspc` is log hspc - log prog (i.e., HSPC/Prog). . This means +- Positive fold change: hspc is higher than prog +- Negative fold change: prog is higher than hspc + + +```{r} +#| echo: false +data.frame(res_prog_hspc$prog, + ensembl_gene_id = row.names(res_prog_hspc$prog)) |> + head() |> + knitr::kable(cap = "The res_prog_hspc$prog dataframe") +``` + + +```{r} +#| echo: false +data.frame(res_prog_hspc$hspc, + ensembl_gene_id = row.names(res_prog_hspc$hspc)) |> + head() |> + knitr::kable(cap = "The res_prog_hspc$hspc dataframe. Notice the sign of the fold change is the other way") +``` + +🎬 Write the results to file: + +```{r} +data.frame(res_prog_hspc$prog, + ensembl_gene_id = row.names(res_prog_hspc$prog)) |> + write_csv("results/prog_hspc_results.csv") +``` + -### 🐸 Frogs and future you -🎬 xxx +# πŸ€— Look after future you! -### 🐭 Mice and future you -🎬 xxx +🎬 tidy up, collect together library statements, +make sure don't have repeat code (e.g import) +edit and rationalise you comments -### πŸ‚ xxxx and future you -🎬 xxx # πŸ₯³ Finished diff --git a/renv.lock b/renv.lock index 84a18e2..18f7ed3 100644 --- a/renv.lock +++ b/renv.lock @@ -8,7 +8,79 @@ } ] }, + "Bioconductor": { + "Version": "3.17" + }, "Packages": { + "BH": { + "Package": "BH", + "Version": "1.81.0-1", + "Source": "Repository", + "Repository": "RSPM", + "Hash": "68122010f01c4dcfbe58ce7112f2433d" + }, + "Biobase": { + "Package": "Biobase", + "Version": "2.60.0", + "Source": "Bioconductor", + "Requirements": [ + "BiocGenerics", + "R", + "methods", + "utils" + ], + "Hash": "ed269b250f5844d54dfdc7e749f901aa" + }, + "BiocGenerics": { + "Package": "BiocGenerics", + "Version": "0.46.0", + "Source": "Bioconductor", + "Requirements": [ + "R", + "graphics", + "methods", + "stats", + "utils" + ], + "Hash": "c179ae59955c36f5d0068ed29ce832f7" + }, + "BiocManager": { + "Package": "BiocManager", + "Version": "1.30.22", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "utils" + ], + "Hash": "d57e43105a1aa9cb54fdb4629725acb1" + }, + "BiocParallel": { + "Package": "BiocParallel", + "Version": "1.34.2", + "Source": "Bioconductor", + "Requirements": [ + "BH", + "R", + "codetools", + "cpp11", + "futile.logger", + "methods", + "parallel", + "snow", + "stats", + "utils" + ], + "Hash": "84347b6a8118ba2182b148298b118f0e" + }, + "BiocVersion": { + "Package": "BiocVersion", + "Version": "3.17.1", + "Source": "Bioconductor", + "Requirements": [ + "R" + ], + "Hash": "f7c0d5521799b7b0d0a211143ed0bfcb" + }, "DBI": { "Package": "DBI", "Version": "1.1.3", @@ -20,6 +92,106 @@ ], "Hash": "b2866e62bab9378c3cc9476a1954226b" }, + "DESeq2": { + "Package": "DESeq2", + "Version": "1.40.2", + "Source": "Bioconductor", + "Requirements": [ + "Biobase", + "BiocGenerics", + "BiocParallel", + "GenomicRanges", + "IRanges", + "Rcpp", + "RcppArmadillo", + "S4Vectors", + "SummarizedExperiment", + "ggplot2", + "locfit", + "matrixStats", + "methods", + "stats4" + ], + "Hash": "867008cb7070cf00bb5912218f8600e3" + }, + "DelayedArray": { + "Package": "DelayedArray", + "Version": "0.26.7", + "Source": "Bioconductor", + "Requirements": [ + "BiocGenerics", + "IRanges", + "Matrix", + "MatrixGenerics", + "R", + "S4Arrays", + "S4Vectors", + "methods", + "stats", + "stats4" + ], + "Hash": "ef6ff3e15ce624118e6cf8151e58e38c" + }, + "GenomeInfoDb": { + "Package": "GenomeInfoDb", + "Version": "1.36.4", + "Source": "Bioconductor", + "Requirements": [ + "BiocGenerics", + "GenomeInfoDbData", + "IRanges", + "R", + "RCurl", + "S4Vectors", + "methods", + "stats", + "stats4", + "utils" + ], + "Hash": "1c6756527d78e8135d34662d2e1d54ec" + }, + "GenomeInfoDbData": { + "Package": "GenomeInfoDbData", + "Version": "1.2.10", + "Source": "Bioconductor", + "Requirements": [ + "R" + ], + "Hash": "56294b21068b8cb5db1c47d0a42f307b" + }, + "GenomicRanges": { + "Package": "GenomicRanges", + "Version": "1.52.0", + "Source": "Bioconductor", + "Requirements": [ + "BiocGenerics", + "GenomeInfoDb", + "IRanges", + "R", + "S4Vectors", + "XVector", + "methods", + "stats", + "stats4", + "utils" + ], + "Hash": "dc2970e434666341650a7983435597bf" + }, + "IRanges": { + "Package": "IRanges", + "Version": "2.34.1", + "Source": "Bioconductor", + "Requirements": [ + "BiocGenerics", + "R", + "S4Vectors", + "methods", + "stats", + "stats4", + "utils" + ], + "Hash": "18939552437a335b59fb381e508275d6" + }, "MASS": { "Package": "MASS", "Version": "7.3-60", @@ -37,12 +209,11 @@ }, "Matrix": { "Package": "Matrix", - 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"Version": "1.9-0", + "Version": "1.8-42", "Source": "Repository", - "Repository": "RSPM", + "Repository": "CRAN", "Requirements": [ "Matrix", "R", @@ -935,7 +1269,7 @@ "stats", "utils" ], - "Hash": "086028ca0460d0c368028d3bda58f31b" + "Hash": "3460beba7ccc8946249ba35327ba902a" }, "mime": { "Package": "mime", @@ -978,9 +1312,9 @@ }, "nlme": { "Package": "nlme", - "Version": "3.1-163", + "Version": "3.1-162", "Source": "Repository", - "Repository": "RSPM", + "Repository": "CRAN", "Requirements": [ "R", "graphics", @@ -988,7 +1322,7 @@ "stats", "utils" ], - "Hash": "8d1938040a05566f4f7a14af4feadd6b" + "Hash": "0984ce8da8da9ead8643c5cbbb60f83e" }, "openssl": { "Package": "openssl", @@ -1314,22 +1648,16 @@ ], "Hash": "3838071b66e0c566d55cc26bd6e27bf4" }, - "strex": { - "Package": "strex", - "Version": "1.6.0", + "snow": { + "Package": "snow", + "Version": "0.4-4", "Source": "Repository", "Repository": "RSPM", "Requirements": [ "R", - "checkmate", - "magrittr", - "rlang", - "stats", - "stringi", - "stringr", "utils" ], - "Hash": "eb6a3d055dcf8d21e9fccd2b823706bb" + "Hash": "40b74690debd20c57d93d8c246b305d4" }, "stringi": { "Package": "stringi", @@ -1653,12 +1981,11 @@ "Repository": "RSPM", "Hash": "0d0056cc5383fbc240ccd0cb584bf436" }, - "zeallot": { - "Package": "zeallot", - "Version": "0.1.0", - "Source": "Repository", - "Repository": "RSPM", - "Hash": "ee9b643aa8331c45d8d82eb3a137c9bc" + "zlibbioc": { + "Package": "zlibbioc", + "Version": "1.46.0", + "Source": "Bioconductor", + "Hash": "20158ef5adb641f0b4e8d63136f0e870" } } } diff --git a/update-notes.txt b/update-notes.txt index 4be17ac..5226f1e 100644 --- a/update-notes.txt +++ b/update-notes.txt @@ -41,6 +41,17 @@ fs::dir_create("data-raw") fs::dir_create("data-processed") +week 3 + + + + + + +Further explore the data with Principle Components Analysis (PCA) on the normalised counts. +6. + + for workshop 2 they willneed biocmanager From f1b1c9804a6c78ff4b2c6af338edadce36449515 Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Fri, 13 Oct 2023 10:32:23 +0100 Subject: [PATCH 11/13] fix typo in yaml --- omics/week-4/study_after_workshop.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/omics/week-4/study_after_workshop.qmd b/omics/week-4/study_after_workshop.qmd index fda7265..56a6870 100644 --- a/omics/week-4/study_after_workshop.qmd +++ b/omics/week-4/study_after_workshop.qmd @@ -1,6 +1,6 @@ --- title: "Independent Study to consolidate this week" -subtitle: Omics 2: Statistical Analysis" +subtitle: "Omics 2: Statistical Analysis" toc: true toc-location: right format: From c982efe3f5e4f2639f164d661d4974337d098adb Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Fri, 13 Oct 2023 10:56:22 +0100 Subject: [PATCH 12/13] fix: conflicts_prefer(GenomicRanges::setdiff) --- omics/week-4/results/s30_fgf_only.csv | 27 +++++++++++++++++++++++++++ omics/week-4/workshop.qmd | 7 ++++++- 2 files changed, 33 insertions(+), 1 deletion(-) create mode 100644 omics/week-4/results/s30_fgf_only.csv diff --git a/omics/week-4/results/s30_fgf_only.csv b/omics/week-4/results/s30_fgf_only.csv new file mode 100644 index 0000000..b90d268 --- /dev/null +++ b/omics/week-4/results/s30_fgf_only.csv @@ -0,0 +1,27 @@ +xenbase_gene_id,S30_C_5,S30_C_6,S30_C_A,S30_F_5,S30_F_6,S30_F_A +XB-GENE-1018260,0,0,0,10,2,16 +XB-GENE-17330117,0,0,0,13,4,17 +XB-GENE-17332184,0,0,0,6,19,6 +XB-GENE-17342281,0,0,0,42,10,35 +XB-GENE-485208,0,0,0,73,17,56 +XB-GENE-485796,0,0,0,51,10,44 +XB-GENE-486919,0,0,0,108,18,80 +XB-GENE-6252072,0,0,0,35,7,38 +XB-GENE-6252388,0,0,0,35,7,35 +XB-GENE-6252857,0,0,0,21,1,22 +XB-GENE-6253886,0,0,0,2,1,20 +XB-GENE-6254394,0,0,0,73,6,63 +XB-GENE-6466680,0,0,0,21,2,22 +XB-GENE-6488002,0,0,0,10,1,26 +XB-GENE-6488182,0,0,0,11,5,20 +XB-GENE-864885,0,0,0,14,5,14 +XB-GENE-865003,0,0,0,68,49,43 +XB-GENE-865180,0,0,0,19,5,32 +XB-GENE-865309,0,0,0,56,9,66 +XB-GENE-865376,0,0,0,39,3,32 +XB-GENE-865554,0,0,0,43,8,35 +XB-GENE-865614,0,0,0,28,4,70 +XB-GENE-866599,0,0,0,14,9,9 +XB-GENE-920126,0,0,0,11,4,15 +XB-GENE-971141,0,0,0,15,1,17 +XB-GENE-972091,0,0,0,5,5,29 diff --git a/omics/week-4/workshop.qmd b/omics/week-4/workshop.qmd index 177646d..7b04f8a 100644 --- a/omics/week-4/workshop.qmd +++ b/omics/week-4/workshop.qmd @@ -191,6 +191,11 @@ write_csv(s30_fgf_only, "results/s30_fgf_only.csv") #---CODING ANSWER--- library(DESeq2) ``` +```{r} +#| echo: false +conflicts_prefer(GenomicRanges::setdiff) +``` + A DEseqDataSet object is a custom data type that is used by the DESeq2. Custom data types are common in the Bioconductor packages. They are used to store data in a way that is useful for the analysis. These data types typically have data, transformed data, metadata and experimental designs within them. @@ -456,7 +461,7 @@ prog_hspc |> Note that if we knew there were some rows that were all zero across both cerll types, we would need to add `|> filter(sum(c_across(Prog_001:Prog_852)) != 0)` -🎬 Npw you find the genes that are 0 in every column for the HSPC cells: +🎬 Now you find the genes that are 0 in every column for the HSPC cells: ```{r} #| echo: false From bb64ffdd7f5e188cacbd2978141c843859aa5eb5 Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Fri, 13 Oct 2023 11:14:45 +0100 Subject: [PATCH 13/13] fix attempt 2 by adding conflict preference sooner --- omics/week-4/workshop.qmd | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/omics/week-4/workshop.qmd b/omics/week-4/workshop.qmd index 7b04f8a..8966743 100644 --- a/omics/week-4/workshop.qmd +++ b/omics/week-4/workshop.qmd @@ -189,11 +189,12 @@ write_csv(s30_fgf_only, "results/s30_fgf_only.csv") ```{r} #| echo: false #---CODING ANSWER--- +conflicts_prefer(GenomicRanges::setdiff) library(DESeq2) ``` ```{r} #| echo: false -conflicts_prefer(GenomicRanges::setdiff) + ```