From 0985505eb18402ecac925b2038764fb2ddc2a712 Mon Sep 17 00:00:00 2001 From: Candace Savonen Date: Thu, 21 May 2020 09:06:20 -0400 Subject: [PATCH] Remove R script/notebook package installs (Part 1 of N) (#698) * update the for clinical data of version 6 * Get rid of cnv-chrom-plot installs * Refresh focal-cn and push resulting results * Rerun script and add files again * Add note about how much RAM you need in README --- analyses/cnv-chrom-plot/gistic_plot.Rmd | 5 - analyses/cnv-chrom-plot/gistic_plot.nb.html | 200 +++++------------- .../02-add-ploidy-consensus.nb.html | 10 +- .../03-add-cytoband-status-consensus.nb.html | 84 ++++---- .../04-prepare-cn-file.R | 33 --- .../05-define-most-focal-cn-units.nb.html | 144 +++++++------ analyses/focal-cn-file-preparation/README.md | 11 +- ...ensus_seg_with_ucsc_cytoband_status.tsv.gz | Bin 4577216 -> 4578754 bytes 8 files changed, 192 insertions(+), 295 deletions(-) diff --git a/analyses/cnv-chrom-plot/gistic_plot.Rmd b/analyses/cnv-chrom-plot/gistic_plot.Rmd index 6e6a2376f4..75aaf826f9 100644 --- a/analyses/cnv-chrom-plot/gistic_plot.Rmd +++ b/analyses/cnv-chrom-plot/gistic_plot.Rmd @@ -21,11 +21,6 @@ Rscript -e "rmarkdown::render('analyses/cnv-chrom-plot/gistic_plot.Rmd', ### Set Up ```{r} -if (!("ggbio" %in% installed.packages())) { - install.packages("BiocManager") - BiocManager::install("ggbio") -} - # Magrittr pipe `%>%` <- dplyr::`%>%` ``` diff --git a/analyses/cnv-chrom-plot/gistic_plot.nb.html b/analyses/cnv-chrom-plot/gistic_plot.nb.html index 8efd0c597c..058c3741b8 100644 --- a/analyses/cnv-chrom-plot/gistic_plot.nb.html +++ b/analyses/cnv-chrom-plot/gistic_plot.nb.html @@ -2949,15 +2949,15 @@

Usage

Set Up

- -
if (!("ggbio" %in% installed.packages())) {
-  install.packages("BiocManager")
-  BiocManager::install("ggbio")
-}
-
-# Magrittr pipe
+
+
# Magrittr pipe
 `%>%` <- dplyr::`%>%`
+ +
Registered S3 method overwritten by 'dplyr':
+  method           from
+  print.rowwise_df     
+ @@ -3052,55 +3052,40 @@

Read in data

) )
+ +
Registered S3 method overwritten by 'data.table':
+  method           from
+  print.data.table     
+Error in data.table::fread(file.path(gistic_dir, "scores.gistic"), data.table = FALSE) : 
+  File '../../data/pbta-cnv-consensus-gistic/scores.gistic' does not exist or is non-readable. getwd()=='/home/rstudio/kitematic/OpenPBTA-analysis/analyses/cnv-chrom-plot'
+

Read in the seg file so we can plot data by histology.

- -
# TODO: update file path when the consensus file has been added to the data release. 
-# Read in the segment copy number data
-seg_data <- data.table::fread(file.path(
-  "..", 
-  "copy_number_consensus_call", 
-  "results", 
-  "pbta-cnv-consensus.seg.gz"),
-  data.table = FALSE
-) %>%
-  # Join the histology column to this data
-  dplyr::inner_join(dplyr::select(
-    metadata,
-    "Kids_First_Biospecimen_ID",
-    "short_histology"
-  ),
-  by = c("ID" = "Kids_First_Biospecimen_ID")
-  ) %>% 
-  # Reformat the chromosome variable to drop the "chr"
-  dplyr::mutate(chrom = factor(gsub("chr", "", chrom), 
-                               levels = c(1:22, "X", "Y")))
+ +

rr gistic_plot <- ggbio::autoplot(gistic_ranges, ggplot2::aes(y = score, fill = mcols.Type), geom = , scales = _x, space = _x
+) + ggplot2::theme_classic() + ggplot2::theme(axis.text.x = ggplot2::element_text(size = 3, angle = 45, hjust = 1)) + colorblindr::scale_fill_OkabeIto(name = ) + ggplot2::ylab(scores)

+ +

Make GISTIC data into GRanges object

- -
gistic_ranges <- GenomicRanges::GRanges(
-  seqnames = gistic_scores$Chromosome,
-  ranges = IRanges::IRanges(
-    start = gistic_scores$Start,
-    end = gistic_scores$End
-  ),
-  score = gistic_scores$gscore,
-  mcols = gistic_scores
-)
+ +

rr ggplot2::ggsave( plot = , filename = file.path(plots_dir, _plot.png), width = 10, height = 3, units =
+)

+
@@ -3109,21 +3094,17 @@

Make GISTIC data into GRanges object

Plot the GISTIC scores

- -
gistic_plot <- ggbio::autoplot(gistic_ranges, ggplot2::aes(y = score, fill = mcols.Type),
-  geom = "bar", scales = "free_x", space = "free_x"
-) +
-  ggplot2::theme_classic() +
-  ggplot2::theme(axis.text.x = ggplot2::element_text(size = 3, angle = 45, hjust = 1)) +
-  colorblindr::scale_fill_OkabeIto(name = "Type") +
-  ggplot2::ylab("G-scores")
-
-# Print out plot here
-gistic_plot@ggplot
+ +

rr # Make a plot for each histology group lapply(unique(seg_data$short_histology), function(histology_group) {

+

# How many samples are of this histology label? sample_num <- seg_data %>% dplyr::filter(short_histology == histology_group) %>% dplyr::pull(ID) %>% unique() %>% length()

+

# Isolate the data to the histology group hist_df <- seg_data %>% dplyr::filter(short_histology == histology_group) %>% dplyr::group_by(short_histology, chrom, loc.start, loc.end) %>% dplyr::summarize(hist.mean = mean(seg.mean)) %>% # Make Del/Amp variable dplyr::mutate(Type = dplyr::case_when( hist.mean < 0 ~ , hist.mean > 0 ~ , hist.mean == 0 ~
+))

+

# Turn into a GRanges for easier mapping hist_ranges <- GenomicRanges::GRanges( seqnames = hist_df\(chrom, ranges = IRanges::IRanges( start = hist_df\)loc.start, end = hist_df\(loc.end ), score = hist_df\)hist.mean, mcols = hist_df$Type ) # Map this on a plot hist_plot <- ggbio::autoplot(hist_ranges, ggplot2::aes(y = score, fill = mcols), geom = , scales = _x, space = _x
+) + ggplot2::theme_classic() + ggplot2::theme(axis.text.x = ggplot2::element_text(size = 3, angle = 45, hjust = 1)) + colorblindr::scale_fill_OkabeIto(name = ) + ggplot2::labs( title = histology_group, y = segment mean, subtitle = paste(=, sample_num) )

+

# Save the plot to a png # Have to use @ggplot to extract the portion of the ggbio object with the plot ggplot2::ggsave( plot = , filename = file.path(plots_dir, paste0(histology_group, _plot.png)), width = 10, height = 3, units =
+) })

+
- -

-
@@ -3131,89 +3112,9 @@

Plot the GISTIC scores

Save the plot as PNG.

- -
ggplot2::ggsave(
-  plot = gistic_plot@ggplot,
-  filename = file.path(plots_dir, "gistic_plot.png"),
-  width = 10,
-  height = 3,
-  units = "in"
-)
- - - - -
-

Segment mean plots by histology

- - - -
# Make a plot for each histology group
-lapply(unique(seg_data$short_histology), function(histology_group) {
-
-  # How many samples are of this histology label?
-  sample_num <- seg_data %>%
-    dplyr::filter(short_histology == histology_group) %>%
-    dplyr::pull(ID) %>%
-    unique() %>%
-    length()
-
-  # Isolate the data to the histology group
-  hist_df <- seg_data %>%
-    dplyr::filter(short_histology == histology_group) %>%
-    dplyr::group_by(short_histology, chrom, loc.start, loc.end) %>%
-    dplyr::summarize(hist.mean = mean(seg.mean)) %>%
-    # Make Del/Amp variable
-    dplyr::mutate(Type = dplyr::case_when(
-      hist.mean < 0 ~ "Del",
-      hist.mean > 0 ~ "Amp",
-      hist.mean == 0 ~ "Neutral"
-    ))
-
-  # Turn into a GRanges for easier mapping
-  hist_ranges <- GenomicRanges::GRanges(
-    seqnames = hist_df$chrom,
-    ranges = IRanges::IRanges(
-      start = hist_df$loc.start,
-      end = hist_df$loc.end
-    ),
-    score = hist_df$hist.mean,
-    mcols = hist_df$Type
-  )
-  # Map this on a plot
-  hist_plot <- ggbio::autoplot(hist_ranges,
-    ggplot2::aes(y = score, fill = mcols),
-    geom = "bar", scales = "free_x", space = "free_x"
-  ) +
-    ggplot2::theme_classic() +
-    ggplot2::theme(axis.text.x = ggplot2::element_text(size = 3, angle = 45, hjust = 1)) +
-    colorblindr::scale_fill_OkabeIto(name = "Type") +
-    ggplot2::labs(
-      title = histology_group,
-      y = "Average segment mean",
-      subtitle = paste("n =", sample_num)
-    )
-
-  # Save the plot to a png
-  # Have to use @ggplot to extract the portion of the ggbio object with the plot
-  ggplot2::ggsave(
-    plot = hist_plot@ggplot,
-    filename = file.path(plots_dir, paste0(histology_group, "_plot.png")),
-    width = 10,
-    height = 3,
-    units = "in"
-  )
-})
- - - -
-
-

Session Info

- - - -
sessionInfo()
+ +

rr sessionInfo()

+
R version 3.6.0 (2019-04-26)
@@ -3280,10 +3181,25 @@ 

Session Info

+
+
+

Segment mean plots by histology

+ + + + +
+ +
+

Session Info

+ + + +
-
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+
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
diff --git a/analyses/focal-cn-file-preparation/02-add-ploidy-consensus.nb.html b/analyses/focal-cn-file-preparation/02-add-ploidy-consensus.nb.html index 723380dc69..e226d33a64 100644 --- a/analyses/focal-cn-file-preparation/02-add-ploidy-consensus.nb.html +++ b/analyses/focal-cn-file-preparation/02-add-ploidy-consensus.nb.html @@ -2955,8 +2955,8 @@

Libraries and functions

library(tidyverse)
- -
── Attaching packages ────────────────────────────────────────────── tidyverse 1.2.1 ──
+ +
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.2.0     ✔ purrr   0.3.2
@@ -2964,8 +2964,8 @@ 

Libraries and functions

✔ tidyr 0.8.3 ✔ stringr 1.4.0 ✔ readr 1.3.1 ✔ forcats 0.4.0
- -
── Conflicts ───────────────────────────────────────────────── tidyverse_conflicts() ──
+
+
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
 ✖ dplyr::filter() masks stats::filter()
 ✖ dplyr::lag()    masks stats::lag()
@@ -3117,7 +3117,7 @@

Does seg.mean agree with status?

geom_jitter()
-

+

diff --git a/analyses/focal-cn-file-preparation/03-add-cytoband-status-consensus.nb.html b/analyses/focal-cn-file-preparation/03-add-cytoband-status-consensus.nb.html index b9965a8a3d..2b4b52d742 100644 --- a/analyses/focal-cn-file-preparation/03-add-cytoband-status-consensus.nb.html +++ b/analyses/focal-cn-file-preparation/03-add-cytoband-status-consensus.nb.html @@ -2951,9 +2951,23 @@

Set up

Libraries and functions

- +
library(tidyverse)
+ +
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
+ + +
✔ ggplot2 3.2.0     ✔ purrr   0.3.2
+✔ tibble  2.1.3     ✔ dplyr   0.8.3
+✔ tidyr   0.8.3     ✔ stringr 1.4.0
+✔ readr   1.3.1     ✔ forcats 0.4.0
+ + +
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
+✖ dplyr::filter() masks stats::filter()
+✖ dplyr::lag()    masks stats::lag()
+ @@ -2961,7 +2975,7 @@

Libraries and functions

Files and directories

- +
scratch_dir <- file.path("..", "..", "scratch")
@@ -2972,7 +2986,7 @@

Read in files

The files we are reading in here are the bed files prepared in the shell script using bedtools coverage.

- +
all_callable_cytoband_status_df <-
   read_tsv(file.path(scratch_dir, "intersect_with_cytoband_callable.bed"),
                   col_names = FALSE)
@@ -2995,7 +3009,7 @@ 

Wrangle and merge consensus cytoband status data

Filter each of the cytoband status data.frames

- +
all_callable_cytoband_status_df <-
   all_callable_cytoband_status_df %>%
   select(
@@ -3032,7 +3046,7 @@ 

Filter each of the cytoband status data.frames

Join all data.frames together

- +
final_df <- all_callable_cytoband_status_df %>%
   left_join(gain_cytoband_status_df,
             by = c("chr", "cytoband", "Kids_First_Biospecimen_ID")) %>%
@@ -3046,7 +3060,7 @@ 

Join all data.frames together

Add dominant_status field to final data.frame

- +
# Create a dominant status column
 final_df <- final_df %>%
   replace_na(list(
@@ -3070,7 +3084,7 @@ 

Add dominant_status field to final data.frame

Add chromosome arm column

- +
# Add a column that tells us the position of the p or q and then use this to
 # split the cytoband column
 final_df <- final_df %>%
@@ -3095,20 +3109,17 @@ 

Add chromosome arm column

Display and save final table

- +
# Display final table with `uncallable` value added to `dominant_status` column
 final_df
-
- - -

-# Write to file
+
+
# Write to file
 write_tsv(final_df, file.path("results", "consensus_seg_with_ucsc_cytoband_status.tsv.gz"))
@@ -3119,10 +3130,10 @@

Display and save final table

Session Info

- +
sessionInfo()
- +
R version 3.6.0 (2019-04-26)
 Platform: x86_64-pc-linux-gnu (64-bit)
 Running under: Debian GNU/Linux 9 (stretch)
@@ -3131,33 +3142,34 @@ 

Session Info

BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.19.so locale: - [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 - [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C - [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C -[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C + [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C + [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 + [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C + [7] LC_PAPER=en_US.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: - [1] forcats_0.4.0 stringr_1.4.0 purrr_0.3.2 readr_1.3.1 - [5] tidyr_0.8.3 tibble_2.1.3 tidyverse_1.2.1 ggplot2_3.2.0 - [9] patchwork_1.0.0.9000 treemapify_2.5.3 scales_1.0.0 colorspace_1.4-1 -[13] dplyr_0.8.3 +[1] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2 +[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.3 ggplot2_3.2.0 +[9] tidyverse_1.2.1 loaded via a namespace (and not attached): - [1] Rcpp_1.0.1 cellranger_1.1.0 pillar_1.4.2 compiler_3.6.0 - [5] base64enc_0.1-3 tools_3.6.0 ggfittext_0.8.0 digest_0.6.20 - [9] evaluate_0.14 lubridate_1.7.4 jsonlite_1.6 gtable_0.3.0 -[13] nlme_3.1-140 lattice_0.20-38 pkgconfig_2.0.2 rlang_0.4.0 -[17] cli_1.1.0 rstudioapi_0.10 haven_2.1.1 xfun_0.8 -[21] xml2_1.2.0 httr_1.4.0 withr_2.1.2 knitr_1.23 -[25] hms_0.4.2 generics_0.0.2 htmlwidgets_1.3 grid_3.6.0 -[29] tidyselect_0.2.5 glue_1.3.1 data.table_1.12.2 R6_2.4.0 -[33] readxl_1.3.1 rmarkdown_1.13 modelr_0.1.4 magrittr_1.5 -[37] backports_1.1.4 htmltools_0.3.6 rvest_0.3.4 assertthat_0.2.1 -[41] stringi_1.4.3 lazyeval_0.2.2 munsell_0.5.0 broom_0.5.2 -[45] crayon_1.3.4
+ [1] Rcpp_1.0.1 cellranger_1.1.0 pillar_1.4.2 compiler_3.6.0 + [5] base64enc_0.1-3 tools_3.6.0 digest_0.6.20 lubridate_1.7.4 + [9] jsonlite_1.6 evaluate_0.14 nlme_3.1-140 gtable_0.3.0 +[13] lattice_0.20-38 pkgconfig_2.0.2 rlang_0.4.0 cli_1.1.0 +[17] rstudioapi_0.10 yaml_2.2.0 haven_2.1.1 xfun_0.8 +[21] withr_2.1.2 xml2_1.2.0 httr_1.4.0 knitr_1.23 +[25] generics_0.0.2 hms_0.4.2 grid_3.6.0 tidyselect_0.2.5 +[29] glue_1.3.1 R6_2.4.0 readxl_1.3.1 rmarkdown_1.13 +[33] modelr_0.1.4 magrittr_1.5 backports_1.1.4 scales_1.0.0 +[37] htmltools_0.3.6 rvest_0.3.4 assertthat_0.2.1 colorspace_1.4-1 +[41] stringi_1.4.3 lazyeval_0.2.2 munsell_0.5.0 broom_0.5.2 +[45] crayon_1.3.4
diff --git a/analyses/focal-cn-file-preparation/04-prepare-cn-file.R b/analyses/focal-cn-file-preparation/04-prepare-cn-file.R index d3e6429c5e..3b769ceaf2 100644 --- a/analyses/focal-cn-file-preparation/04-prepare-cn-file.R +++ b/analyses/focal-cn-file-preparation/04-prepare-cn-file.R @@ -20,39 +20,6 @@ #### Set Up -------------------------------------------------------------------- -# We require bioconductor -if (!("BiocManager" %in% installed.packages())) { - install.packages("BiocManager") -} - -# Install GenomicRanges -if (!("GenomicRanges" %in% installed.packages())) { - BiocManager::install("GenomicRanges", update = FALSE) -} - -# Install IRanges -if (!("IRanges" %in% installed.packages())) { - BiocManager::install("IRanges", update = FALSE) -} - -# Install annotatr -if (!("annotatr" %in% installed.packages())) { - BiocManager::install("annotatr", update = FALSE) -} - -# hg38 genome annotations -if (!("TxDb.Hsapiens.UCSC.hg38.knownGene" %in% installed.packages())) { - BiocManager::install("TxDb.Hsapiens.UCSC.hg38.knownGene", update = FALSE) -} - -if (!("org.Hs.eg.db" %in% installed.packages())) { - BiocManager::install("org.Hs.eg.db", update = FALSE) -} - -if (!("AnnotationDbi" %in% installed.packages())) { - BiocManager::install("AnnotationDbi", update = FALSE) -} - # Get `magrittr` pipe `%>%` <- dplyr::`%>%` diff --git a/analyses/focal-cn-file-preparation/05-define-most-focal-cn-units.nb.html b/analyses/focal-cn-file-preparation/05-define-most-focal-cn-units.nb.html index e32ce36acd..9ec41f039f 100644 --- a/analyses/focal-cn-file-preparation/05-define-most-focal-cn-units.nb.html +++ b/analyses/focal-cn-file-preparation/05-define-most-focal-cn-units.nb.html @@ -2952,7 +2952,7 @@

Usage

Cutoffs:

- +
# The fraction of calls a particular status needs to be
 # above to be called the majority status -- the decision
 # for a cutoff of 90% here was made to ensure that the status
@@ -2977,9 +2977,23 @@ 

Set up

Libraries and functions

- +
library(tidyverse)
+ +
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
+ + +
✔ ggplot2 3.2.0     ✔ purrr   0.3.2
+✔ tibble  2.1.3     ✔ dplyr   0.8.3
+✔ tidyr   0.8.3     ✔ stringr 1.4.0
+✔ readr   1.3.1     ✔ forcats 0.4.0
+ + +
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
+✖ dplyr::filter() masks stats::filter()
+✖ dplyr::lag()    masks stats::lag()
+ @@ -2987,7 +3001,7 @@

Libraries and functions

Files and directories

- +
results_dir <- "results"
@@ -2998,7 +3012,7 @@

Read in files

Read in cytoband status file and format it for what we will need in this notebook.

- +
# Read in the file with consensus CN status data and the UCSC cytoband data --
 # generated in `03-add-cytoband-status-consensus.Rmd`
 consensus_seg_cytoband_status_df <-
@@ -3020,44 +3034,44 @@ 

Read in files

callable_fraction )
- +
Parsed with column specification:
 cols(
-  Kids_First_Biospecimen_ID = col_character(),
-  chr = col_character(),
-  cytoband = col_character(),
-  dominant_status = col_character(),
-  band_length = col_double(),
-  callable_fraction = col_double(),
-  gain_fraction = col_double(),
-  loss_fraction = col_double(),
-  chromosome_arm = col_character()
+  Kids_First_Biospecimen_ID = col_character(),
+  chr = col_character(),
+  cytoband = col_character(),
+  dominant_status = col_character(),
+  band_length = col_double(),
+  callable_fraction = col_double(),
+  gain_fraction = col_double(),
+  loss_fraction = col_double(),
+  chromosome_arm = col_character()
 )
- +

Read in the gene-level data.

- +
# Read in the annotated gene level CN file
 consensus_seg_autosomes_df <-
   read_tsv(file.path(results_dir, "consensus_seg_annotated_cn_autosomes.tsv.gz")) %>%
   mutate(chromosome_arm = gsub("(p|q).*", "\\1", cytoband))
- +
Parsed with column specification:
 cols(
-  biospecimen_id = col_character(),
-  status = col_character(),
-  copy_number = col_double(),
-  ploidy = col_double(),
-  ensembl = col_character(),
-  gene_symbol = col_character(),
-  cytoband = col_character()
+  biospecimen_id = col_character(),
+  status = col_character(),
+  copy_number = col_double(),
+  ploidy = col_double(),
+  ensembl = col_character(),
+  gene_symbol = col_character(),
+  cytoband = col_character()
 )
- - + +
# Rename "amplification" status calls to be "gain" for the purpose of this script
 consensus_seg_autosomes_df$status <-
   gsub("amplification", "gain", consensus_seg_autosomes_df$status)
@@ -3072,7 +3086,7 @@

Define most focal units

Determine chromosome arm status

- +
consensus_seg_arm_status <- consensus_seg_cytoband_status_df %>%
     # Group by biospecimen ID and region
     group_by(Kids_First_Biospecimen_ID, chromosome_arm) %>%
@@ -3097,13 +3111,11 @@ 

Determine chromosome arm status

# Display table consensus_seg_arm_status
-
- @@ -3112,7 +3124,7 @@

Determine cytoband status

We want to include cytoband and gene-level calls for chromosome arms that have not been defined as a gain or loss to make the cytoband-level majority calls.

- +
# Now define the cytoband as that status if more than the `status_threshold` 
 # fraction value of the total counts are for that particular status
 consensus_seg_cytoband_status <-
@@ -3158,13 +3170,11 @@ 

Determine cytoband status

# Display table filtered_consensus_cytoband_status
-
- @@ -3172,7 +3182,7 @@

Determine cytoband status

Determine gene-level status

- +
# Create separate rows for genes span multiple cytobands
 # CAUTION: This will require addition of a distinct() statment later to resolve duplicates
 # Filtering to only multiband genes first because regexes are slow.
@@ -3231,13 +3241,11 @@ 

Determine gene-level status

# Display table filtered_consensus_gene_status
-
- @@ -3246,7 +3254,7 @@

Determine gene-level status

Combine arm, cytoband, and gene-level status data

- +
# Rename each the dominant status columns of the data.frames
 # to be uniform for the binding rows step and filter out "neutral" calls
 consensus_seg_arm_status <- consensus_seg_arm_status %>%
@@ -3270,7 +3278,7 @@ 

Combine arm, cytoband, and gene-level status data

Combine into one long data frame

- +
# Combine the arm, cytoband, and gene status count data
 final_df <- bind_rows(
   arm = select(consensus_seg_arm_status,
@@ -3294,13 +3302,11 @@ 

Combine arm, cytoband, and gene-level status data

# Print out preview final_df
-
- @@ -3308,7 +3314,7 @@

Combine arm, cytoband, and gene-level status data

Write to a TSV file

- +
# Write final long status table to file
 write_tsv(
   final_df,
@@ -3319,13 +3325,11 @@ 

Write to a TSV file

final_df %>% arrange(region_type)
-
- @@ -3333,10 +3337,10 @@

Write to a TSV file

Session Info

- +
sessionInfo()
- +
R version 3.6.0 (2019-04-26)
 Platform: x86_64-pc-linux-gnu (64-bit)
 Running under: Debian GNU/Linux 9 (stretch)
@@ -3345,32 +3349,34 @@ 

Session Info

BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.19.so locale: - [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 - [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C LC_PAPER=en_US.UTF-8 LC_NAME=C - [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C + [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C + [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 + [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C + [7] LC_PAPER=en_US.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: -[1] parallel stats4 stats graphics grDevices utils datasets methods base +[1] stats graphics grDevices utils datasets methods base other attached packages: - [1] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2 readr_1.3.1 - [6] tidyr_0.8.3 tibble_2.1.3 ggplot2_3.2.0 tidyverse_1.2.1 GenomicRanges_1.38.0 -[11] GenomeInfoDb_1.22.1 IRanges_2.20.2 S4Vectors_0.24.3 BiocGenerics_0.32.0 +[1] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2 +[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.3 ggplot2_3.2.0 +[9] tidyverse_1.2.1 loaded via a namespace (and not attached): - [1] Rcpp_1.0.1 lubridate_1.7.4 lattice_0.20-38 circlize_0.4.8 png_0.1-7 - [6] assertthat_0.2.1 digest_0.6.20 R6_2.4.0 cellranger_1.1.0 backports_1.1.4 -[11] evaluate_0.14 httr_1.4.0 pillar_1.4.2 GlobalOptions_0.1.1 zlibbioc_1.32.0 -[16] rlang_0.4.0 lazyeval_0.2.2 readxl_1.3.1 rstudioapi_0.10 data.table_1.12.2 -[21] GetoptLong_0.1.8 rmarkdown_1.13 labeling_0.3 RCurl_1.95-4.12 munsell_0.5.0 -[26] broom_0.5.2 compiler_3.6.0 modelr_0.1.4 xfun_0.8 pkgconfig_2.0.2 -[31] base64enc_0.1-3 shape_1.4.4 htmltools_0.3.6 tidyselect_0.2.5 GenomeInfoDbData_1.2.2 -[36] withr_2.1.2 crayon_1.3.4 bitops_1.0-6 grid_3.6.0 nlme_3.1-140 -[41] jsonlite_1.6 gtable_0.3.0 magrittr_1.5 scales_1.0.0 cli_1.1.0 -[46] stringi_1.4.3 XVector_0.26.0 xml2_1.2.0 generics_0.0.2 rjson_0.2.20 -[51] RColorBrewer_1.1-2 tools_3.6.0 glue_1.3.1 hms_0.4.2 rsconnect_0.8.13 -[56] yaml_2.2.0 clue_0.3-57 colorspace_1.4-1 cluster_2.1.0 rvest_0.3.4 -[61] ComplexHeatmap_2.2.0 knitr_1.23 haven_2.1.1
+ [1] Rcpp_1.0.1 cellranger_1.1.0 pillar_1.4.2 compiler_3.6.0 + [5] base64enc_0.1-3 tools_3.6.0 digest_0.6.20 lubridate_1.7.4 + [9] jsonlite_1.6 evaluate_0.14 nlme_3.1-140 gtable_0.3.0 +[13] lattice_0.20-38 pkgconfig_2.0.2 rlang_0.4.0 cli_1.1.0 +[17] rstudioapi_0.10 yaml_2.2.0 haven_2.1.1 xfun_0.8 +[21] withr_2.1.2 xml2_1.2.0 httr_1.4.0 knitr_1.23 +[25] hms_0.4.2 generics_0.0.2 grid_3.6.0 tidyselect_0.2.5 +[29] glue_1.3.1 R6_2.4.0 readxl_1.3.1 rmarkdown_1.13 +[33] modelr_0.1.4 magrittr_1.5 backports_1.1.4 scales_1.0.0 +[37] htmltools_0.3.6 rvest_0.3.4 assertthat_0.2.1 colorspace_1.4-1 +[41] stringi_1.4.3 lazyeval_0.2.2 munsell_0.5.0 broom_0.5.2 +[45] crayon_1.3.4
diff --git a/analyses/focal-cn-file-preparation/README.md b/analyses/focal-cn-file-preparation/README.md index ff53e3d281..1fca5a3fe9 100644 --- a/analyses/focal-cn-file-preparation/README.md +++ b/analyses/focal-cn-file-preparation/README.md @@ -6,6 +6,7 @@ The copy number data from OpenPBTA are provided as ranges or segments. The purpose of this module is to map from those ranges to gene identifiers for consumption by downstream analyses (e.g., OncoPrint plotting). ### Running this analysis +*This analysis requires at least ~24 GB of RAM to run to completion* To run this analysis _only on consensus SEG file_, use the following (from the root directory of the repository): @@ -23,7 +24,7 @@ RUN_ORIGINAL=1 bash analyses/focal-cn-file-preparation/run-prepare-cn.sh ### Scripts and notebooks -* `01-add-ploidy-cnvkit.Rmd` - The two CNV callers, CNVkit and ControlFreeC, do not handle ploidy in the same way ([A Note on Ploidy](https://github.com/AlexsLemonade/OpenPBTA-analysis/blob/de661fbe740717472fcf01c7d9b74fe1b946aece/doc/data-formats.md#a-note-on-ploidy) in the Data Formats documentation). +* `01-add-ploidy-cnvkit.Rmd` - The two CNV callers, CNVkit and ControlFreeC, do not handle ploidy in the same way ([A Note on Ploidy](https://github.com/AlexsLemonade/OpenPBTA-analysis/blob/de661fbe740717472fcf01c7d9b74fe1b946aece/doc/data-formats.md#a-note-on-ploidy) in the Data Formats documentation). This notebook adds the ploidy inferred via ControlFreeC to the CNVkit data and adds a status column that defines gain and loss broadly. Specifically, segments with copy number fewer than ploidy are losses, segments with copy number greater than ploidy are marked as a gain, and segments where copy number is equal to ploidy are marked as neutral. (Note that [the logic around sex chromosomes in males when ploidy = 3 leaves something to be desired](https://github.com/AlexsLemonade/OpenPBTA-analysis/pull/259#discussion_r345354403)). @@ -36,16 +37,16 @@ See the notebook for more information. This notebook also prepares lists of copy | `Kids_First_Biospecimen_ID` | chr | cytoband | dominant_status | band_length | callable_fraction | gain_fraction | loss_fraction | chromosome_arm | |----------------|--------|-------------|--------|---------|----------|-------------|---------|---------------| - + * `04-prepare-cn-file.R` - This script performs the ranges to annotation mapping using the GENCODE v27 GTF included via the data download step; it takes the ControlFreeC file or a SEG (e.g., CNVkit, consensus SEG) file prepared with `01-add-ploidy-cnvkit.Rmd` and `02-add-ploidy-cnvkit.Rmd` as input. **The mapping is limited to _exons_.** Mapping to cytobands is performed with the [`org.Hs.eg.db`](https://doi.org/doi:10.18129/B9.bioc.org.Hs.eg.db) package. A table with the following columns is returned: - + | biospecimen_id | status | copy_number | ploidy | ensembl | gene_symbol | cytoband | |----------------|--------|-------------|--------|---------|-------------|---------| Any segment that is copy neutral is filtered out of this table. In addition, [any segments with copy number > (2 * ploidy) are marked as amplifications](https://github.com/AlexsLemonade/OpenPBTA-analysis/blob/e2058dd43d9b1dd41b609e0c3429c72f79ff3be6/analyses/focal-cn-file-preparation/03-prepare-cn-file.R#L275) in the `status` column. - + * `05-define-most-focal-cn-units.Rmd` - This notebook defines the _most focal_ recurrent copy number units by removing focal changes that are within entire chromosome arm losses and gains. _Most focal_ here meaning if a chromosome arm is not clearly defined as a gain or loss (and is callable) we look to define the cytoband level status. Similarly, if a cytoband is not clearly defined as a gain or loss (and is callable) we then look to define the gene level status. @@ -60,7 +61,7 @@ It produces loss/neutral and zero/neutral correlation plots, as well as stacked _Note: The shell script's default behavior is to produce these plots using the annotated consensus SEG autosome and sex chromsome files found in this module's `results` directory and listed below._ -### Output files for downstream consumption +### Output files for downstream consumption **Note:** The output files from `03-prepare-cn-file.R` have neutral calls filtered out to reduce file size. diff --git a/analyses/focal-cn-file-preparation/results/consensus_seg_with_ucsc_cytoband_status.tsv.gz b/analyses/focal-cn-file-preparation/results/consensus_seg_with_ucsc_cytoband_status.tsv.gz index ab8809844f2828e698191011b2663713a64b8e39..066b176a6d99c30d4a1433724b0c8bef765b7970 100644 GIT binary patch delta 2951244 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