diff --git a/.circleci/config.yml b/.circleci/config.yml index a2d4a5ada7..af1b86e5f6 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -128,6 +128,10 @@ jobs: name: Gene set enrichment analysis to generate GSVA scores command: OPENPBTA_TESTING=1 ./scripts/run_in_ci.sh bash "analyses/gene-set-enrichment-analysis/run-gsea.sh" + - run: + name: Chromosomal instability breakpoints + command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/chromosomal-instability/chromosomal-instability.Rmd', clean = TRUE)" + - run: name: Fusion Summary command: ./scripts/run_in_ci.sh bash "analyses/fusion-summary/run-new-analysis.sh" diff --git a/analyses/README.md b/analyses/README.md index 2233e28f4f..d22f0ea1c8 100644 --- a/analyses/README.md +++ b/analyses/README.md @@ -11,7 +11,8 @@ Note that _nearly all_ modules use the harmonized clinical data file (`pbta-hist | Module | Input Files | Brief Description | Output Files Consumed by Other Analyses | |--------|-------|-------------------|--------------| -| [`cnv-chrom-plot`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/cnv-chrom) | `pbta-cnv-cnvkit-gistic.zip`
`pbta-cnv-cnvkit.seg.gz` | Makes plots from GISTIC output as well as `seg.mean` plots by histology group | N/A +| [`chromosomal-instability`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/chromosomal-instability) | `pbta-histologies.tsv`
`pbta-sv-manta.tsv.gz`
`pbta-cnv-cnvkit.seg.gz` | Evaluates chromosomal instability by calculating chromosomal breakpoint densities | N/A +| [`cnv-chrom-plot`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/cnv-chrom) | `pbta-cnv-cnvkit-gistic.zip`
`pbta-cnv-cnvkit.seg.gz` | Makes plots from GISTIC output as well as `seg.mean` plots by histology group | N/A\ | [`cnv-comparison`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/cnv-comparison) | Earlier version of SEG files | *Deprecated*; compared earlier version of the CNV methods. | N/A | [`collapse-rnaseq`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/collapse-rnaseq) | `pbta-gene-expression-rsem-fpkm.polya.rds`
`pbta-gene-expression-rsem-fpkm.stranded.rds`
`gencode.v27.primary_assembly.annotation.gtf.gz` | Collapses RSEM FPKM matrices such that gene symbols are de-duplicated. | `results/pbta-gene-expression-rsem-fpkm-collapsed.polya.rds`
`results/pbta-gene-expression-rsem-fpkm-collapsed.stranded.rds` (included in data download; too large for tracking via GitHub) | [`comparative-RNASeq-analysis`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/comparative-RNASeq-analysis) | `pbta-gene-expression-rsem-tpm.polya.rds`
`pbta-gene-expression-rsem-tpm.stranded.rds` | *In progress*; will produce expression outlier profiles per [#229](https://github.com/AlexsLemonade/OpenPBTA-analysis/issues/229) | N/A | diff --git a/analyses/chromosomal-instability/README.md b/analyses/chromosomal-instability/README.md index c9ca64b55c..df69d6dfa8 100644 --- a/analyses/chromosomal-instability/README.md +++ b/analyses/chromosomal-instability/README.md @@ -41,5 +41,5 @@ The individual sample plots and grouped by `short_histology` plots are in the `p - `make_granges` : Given a data.frame with chr break coordinates, make a `GenomicRanges` object. - `break_density`: Given data.frame(s) with chr break coordinates, calculate the density of the breaks. -- `map_density_plot`: Given a `GenomicRanges` object, use map the chromosomal coordinates to a `ggplot2` -- `chr_break_plot`: Given a list of `GenomicRanges` objects, plot them in a combined `cowplot`. +- `map_breaks_plot`: Given a `GenomicRanges` object, use map the chromosomal coordinates to a `ggplot2` +- `multipanel_break_plot`: Given a list of `GenomicRanges` objects, plot them in a combined `cowplot`. diff --git a/analyses/chromosomal-instability/chromosomal-instability.Rmd b/analyses/chromosomal-instability/chromosomal-instability.Rmd new file mode 100644 index 0000000000..e73caf2e13 --- /dev/null +++ b/analyses/chromosomal-instability/chromosomal-instability.Rmd @@ -0,0 +1,471 @@ +--- +title: "Chromosomal Instability: Breakpoints" +output: + html_notebook: + toc: true + toc_float: true +author: Candace Savonen for ALSF - CCDL +date: 2020 +--- + +This analysis evaluates chromosomal instability by formatting SV and CNV data +into chromosomal breaks and measuring chromosomal break density. +This notebook returns chromosomal break heatmaps and plots for each tumor type +group in the `plots` directory. + +Code adapted from [svcnvplus](https://github.com/gonzolgarcia/svcnvplus). + +### Usage + +This notebook can be run via the command line from the top directory of the +repository as follows: + +``` +Rscript -e "rmarkdown::render('analyses/chromosomal-instability/chromosomal-instability.Rmd', + clean = TRUE)" +``` + +### Set Up + +```{r} +# Set seed so heatmaps turn out the same +set.seed(2020) + +# This threshold will be used to determine percent of genome changed +# TODO: after updating to consensus CNV data, evaluate this threshold setting. +ch.pct <- 0.2 + +# Magrittr pipe +`%>%` <- dplyr::`%>%` +``` + +Read in the custom functions needed for this analysis. + +```{r} +source(file.path("util", "chr-break-calculate.R")) +source(file.path("util", "chr-break-plot.R")) +``` + +### Directories and Files + +```{r} +# Path to data directory +data_dir <- file.path("..", "..", "data") + +# Path to output directory +plots_dir <- "plots" + +# Path to tumor type plots output directory +hist_plots_dir <- file.path(plots_dir, "tumor-type") + +# Create the hist_plots_dir if it does not exist +if (!dir.exists(hist_plots_dir)) { + dir.create(hist_plots_dir, recursive = TRUE) +} +``` + +### Read in data and format it + +#### Set up metadata + +```{r} +# Read in the metadata +metadata <- readr::read_tsv(file.path(data_dir, "pbta-histologies.tsv")) +``` + +#### Set up CNV data: + +Read in the CNV data and format it to make the chromosome and coordinates easier for +handling later. +TODO: Update to consensus CNV data. + +```{r} +# Read in the segment copy number data +cnv_df <- data.table::fread(file.path(data_dir, "pbta-cnv-cnvkit.seg.gz"), + data.table = FALSE +) %>% + dplyr::mutate( + samples = as.factor(ID), + # Calculate width + width = loc.end - loc.start, + # Make a binary variable that labels whether or not a sequence + # is considered changed by the threshold set + # TODO: after updating to Consensus CNV data, see if sex chromosomes still + # need to be removed and whether these thresholds should be changed. + changed = as.factor(dplyr::case_when( + abs(seg.mean) > log2(1 + ch.pct) ~ "change", + TRUE ~ "no_change" + )) + ) %>% + # Reformat the chromosome variable to drop the "chr" + dplyr::mutate(chrom = factor(gsub("chr", "", chrom), + levels = c(1:22, "X", "Y") + )) %>% + # Changing these so they end up matching the SV data + dplyr::rename(start = loc.start, end = loc.end) +``` + +Filter CNV data to only the changes that are larger than our cutoff `ch.pct`. + +```{r} +cnv_filtered_df <- cnv_df %>% + dplyr::filter(changed == "change") +``` + +#### Set up SV data + +Read in the SV data. + +```{r} +sv_df <- data.table::fread(file.path(data_dir, "pbta-sv-manta.tsv.gz"), + data.table = FALSE +) %>% + # Reformat the 23 and 24 chromosomes so they are X and Y and also factors + dplyr::mutate( + chrom = dplyr::recode(SV.chrom, + `23` = "X", `24` = "Y" + ) + ) +``` + +#### Set up chromosome reference data + +Set up chromosome size data. +It just so happens that this BED file: `WGS.hg38.strelka2.unpadded.bed` is actually +just a list of the chromosome sizes so we are using that for now. + +```{r} +# Set up Chr sizes +chr_sizes <- readr::read_tsv(file.path(data_dir, "WGS.hg38.strelka2.unpadded.bed"), + col_names = FALSE +) %>% + # Reformat the chromosome variable to drop the "chr" + dplyr::mutate(X1 = factor(gsub("chr", "", X1), + levels = c(1:22, "X", "Y", "M") + )) %>% + # Remove sex chromosomes + dplyr::filter(!(X1 %in% c("X", "Y", "M"))) + +# Make chromosome size named vector +chr_sizes_vector <- chr_sizes$X3 +names(chr_sizes_vector) <- chr_sizes$X1 +``` + +## Format the data as chromosomes breaks + +The SV and CNV data comes to us in the form of ranges, but for getting a look +at chromosomal instability, we will want to convert this data into single break +points of the genome. +We'll also remove the sex chromosomes. +TODO: after updating to consensus CNV data, evaluate whether sex chromosomes should still be removed. + +Make an CNV breaks data.frame. + +```{r} +# Make a CNV data.frame that has the breaks +cnv_breaks <- data.frame( + samples = rep(cnv_filtered_df$ID, 2), + chrom = rep(cnv_filtered_df$chrom, 2), + coord = c(cnv_filtered_df$start, cnv_filtered_df$end), + seg.mean = rep(cnv_filtered_df$seg.mean, 2), + copy.num = rep(cnv_filtered_df$copy.num, 2), + stringsAsFactors = FALSE +) %>% + # Remove sex chromosomes + dplyr::filter(!(chrom %in% c("X", "Y"))) +``` + +Make an SV breaks data.frame. + +```{r} +# Make a data.frame that has the breaks +sv_breaks <- data.frame( + samples = rep(sv_df$Kids.First.Biospecimen.ID.Tumor, 2), + chrom = rep(sv_df$chrom, 2), + coord = c(sv_df$SV.start, sv_df$SV.end), + svclass = rep(sv_df$SV.type, 2), + stringsAsFactors = FALSE +) %>% + # Remove sex chromosomes and NAs + dplyr::filter(!(chrom %in% c("X", "Y", "M", NA))) +``` + +Make an common breaks data.frame. + +```{r} +common_breaks <- dplyr::bind_rows(sv_breaks, cnv_breaks) %>% + dplyr::distinct(samples, chrom, coord, .keep_all = TRUE) +``` + +Put all the breaks into a list. + +```{r} +breaks_list <- list( + common_breaks = common_breaks, + cnv_breaks = cnv_breaks, + sv_breaks = sv_breaks +) +``` + +### Co-localization of breakpoints for samples + +For each sample, we will bin the genome and count how many chromosome breaks +occur for each bin, given the given `bin_size`. + +We will run this for each sample and return a list of three `GenomicRanges` objects: +1) `common_breaks` contains the combined break counts for both SV and CNV break data. +2) `cnv_breaks` contains the number of break counts for CNV. +3) `sv_breaks` contains the number of break counts for SV. + +```{r, warning = FALSE, results='hide'} +# Change here and it will change the rest +bin_size <- 1e6 + +# Obtain a list of samples that are common to both datasets. +common_samples <- intersect(sv_breaks$samples, cnv_breaks$samples) + +# Get a big list of break densities for each sample. +sample_densities <- lapply(common_samples, function(sample_id) { + lapply(breaks_list, function(breaks_df) { + break_density(breaks_df, + sample_id = sample_id, + start_col = "coord", + end_col = "coord", + window_size = bin_size, + chr_sizes_vector = chr_sizes_vector + ) + }) +}) + +# Bring along the sample IDs +names(sample_densities) <- common_samples +``` + +### Set up for making heatmaps of the breakpoints + +Given the `GenomicRanges` objects for each sample, create a combined plot for +each. + +Make chromosome labeling `HeatmapAnnotation` object. + +```{r} +# Extract chromosome names from a GenomicRanges object +chr_labels <- sample_densities[[1]]$common_breaks@seqnames +chr_labels <- paste0("chr", as.factor(chr_labels)) + +# Make a key for assigning alternating colors to the chromosomes +chr_colors <- rep(c("grey", "lightblue"), length.out = length(unique(chr_labels))) +names(chr_colors) <- unique(chr_labels) + +# Create the Heatmap annotation object +chr_annot <- ComplexHeatmap::HeatmapAnnotation( + df = data.frame(chr_labels), col = list(chr_labels = c(chr_colors)), + name = "", + show_legend = FALSE, + show_annotation_name = FALSE +) +``` + +Make histology labeling `HeatmapAnnotation` object. + +```{r} +# Get the histologies for the samples in this set and order them by histology +histologies <- + data.frame(Kids_First_Biospecimen_ID = common_samples) %>% + dplyr::inner_join(dplyr::select(metadata, Kids_First_Biospecimen_ID, short_histology)) %>% + dplyr::mutate(short_histology = as.factor(short_histology)) %>% + dplyr::arrange(short_histology) %>% + tibble::column_to_rownames("Kids_First_Biospecimen_ID") + +# Create the Heatmap annotation object +hist_annot <- ComplexHeatmap::rowAnnotation( + df = histologies, + show_annotation_name = FALSE +) +``` + +Make a color function. + +```{r} +col_fun <- circlize::colorRamp2( + c(0, 2, 5, 10, 20), + c("#edf8fb", "#b2e2e2", "#66c2a4", "#2ca25f", "#006d2c") +) +``` + +Make a function for making the heatmaps. + +```{r} +breaks_heatmap <- function(data_name) { + # A wrapper function for making a heatmap from the samples GenomicRanges list. + # + # Args: + # data_name: a character string that matches a name in the list. + + # Returns: + # A heatmap of the chromosomal breaks + + # Pull out the total_counts + total_counts <- lapply(sample_densities, function(granges_list) { + granges_list[[data_name]]@elementMetadata@listData$total_counts + }) + # Make into a data.frame + total_counts <- dplyr::bind_rows(total_counts) %>% + dplyr::select(rownames(histologies)) %>% + t() + # Add chromosome labels + colnames(total_counts) <- chr_labels + # Plot on a heatmap + heatmap <- ComplexHeatmap::Heatmap(total_counts, + col = col_fun, + heatmap_legend_param = list(title = "Count of chr breaks"), + cluster_columns = FALSE, + cluster_rows = FALSE, + show_column_names = FALSE, + show_row_names = FALSE, + bottom_annotation = chr_annot, + left_annotation = hist_annot + ) + # Return plot + return(heatmap) +} +``` + +## Common breaks heatmap + +```{r} +common_heatmap <- breaks_heatmap(data_name = "common_breaks") + +# Save plot as PNG +png(file.path(plots_dir, paste0("common_breaks_heatmap.png")), + width = 1200, height = 900, units = "px" +) +common_heatmap +dev.off() + +# Print out here +common_heatmap +``` + +## CNV breaks heatmap + +```{r} +cnv_heatmap <- breaks_heatmap(data_name = "cnv_breaks") + +# Save plot as PNG +png(file.path(plots_dir, paste0("cnv_breaks_heatmap.png")), + width = 1200, height = 900, units = "px" +) +cnv_heatmap +dev.off() + +# Print out here +cnv_heatmap +``` + +## SV breaks heatmap + +```{r} +sv_heatmap <- breaks_heatmap(data_name = "sv_breaks") + +# Save plot as PNG +png(file.path(plots_dir, paste0("sv_breaks_heatmap.png")), + width = 1200, height = 900, units = "px" +) +sv_heatmap +dev.off() + +# Print out here +sv_heatmap +``` + +### Co-localization of breakpoints for tumor-type groups + +Same as was done for each sample, now we will calculate densities for +each tumor-type group. + +```{r, results='hide'} +# Change bin_size here and it will change the rest +bin_size <- 1e6 + +# Get a list of the tumor_types for which we have DNA-seq data +tumor_types <- metadata %>% + dplyr::filter(!is.na(short_histology), experimental_strategy != "RNA-Seq") %>% + dplyr::distinct(short_histology) %>% + dplyr::pull(short_histology) +``` + +Run the density calculations for the groups. + +```{r} +# Get a big list of break densities for each sample. +group_densities <- lapply(tumor_types, function(tumor_type) { + + # Obtain a list of sample_ids to subset by + sample_ids <- metadata %>% + dplyr::filter(metadata$short_histology == tumor_type) %>% + dplyr::pull(Kids_First_Biospecimen_ID) + + # Double check these samples are in the list + check_samples <- (sample_ids %in% common_samples) + + # If no samples, go to next + if (sum(check_samples) > 1) { + message(paste("Calculating breakpoint density for", tumor_type, "samples")) + lapply(breaks_list, function(breaks_df) { + break_density(breaks_df, + sample_id = sample_ids, + start_col = "coord", + end_col = "coord", + window_size = bin_size, + chr_sizes_vector = chr_sizes_vector + ) + }) + } +}) + +# Bring along the tumor-type labels +names(group_densities) <- tumor_types + +# Remove tumor_types data that didn't have at least two samples +group_densities <- group_densities[!sapply(group_densities, is.null)] +``` + +### Plot the breakpoints for each tumor-type + +Here we will plot median number of break points for the tumor-type group per +each bin. + +```{r} +purrr::imap(group_densities, function(.x, name = .y) { + # Make the combo plot + multipanel_break_plot( + granges_list = .x, + plot_name = name, + y_val = "median_counts", + y_lab = "Median Breaks per Mb", + plot_dir = hist_plots_dir + ) +}) +``` + +Zip up the PNG files into one file. + +```{r} +# Declare name of zip file +zip_file <- paste0(hist_plots_dir, ".zip") + +# Remove any current zip_file of this name so we can overwrite it +if (file.exists(zip_file)) { + file.remove(zip_file) +} +# Zip up the plots +zip(zip_file, hist_plots_dir) +``` + +### Session Info + +```{r} +sessionInfo() +``` + diff --git a/analyses/chromosomal-instability/chromosomal-instability.nb.html b/analyses/chromosomal-instability/chromosomal-instability.nb.html new file mode 100644 index 0000000000..686d1bcfcf --- /dev/null +++ b/analyses/chromosomal-instability/chromosomal-instability.nb.html @@ -0,0 +1,3884 @@ + + + + + + + + + + + + + + + +Chromosomal Instability: Breakpoints + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + + +

This analysis evaluates chromosomal instability by formatting SV and CNV data into chromosomal breaks and measuring chromosomal break density. This notebook returns chromosomal break heatmaps and plots for each tumor type group in the plots directory.

+

Code adapted from svcnvplus.

+
+

Usage

+

This notebook can be run via the command line from the top directory of the repository as follows:

+
Rscript -e "rmarkdown::render('analyses/chromosomal-instability/chromosomal-instability.Rmd', 
+                              clean = TRUE)"
+
+
+

Set Up

+ + + +
# Set seed so heatmaps turn out the same
+set.seed(2020)
+
+# This threshold will be used to determine percent of genome changed
+# TODO: after updating to consensus CNV data, evaluate this threshold setting.
+ch.pct <- 0.2
+
+# Magrittr pipe
+`%>%` <- dplyr::`%>%`
+ + + +

Read in the custom functions needed for this analysis.

+ + + +
source(file.path("util", "chr-break-calculate.R"))
+source(file.path("util", "chr-break-plot.R"))
+ + + +
+
+

Directories and Files

+ + + +
# Path to data directory
+data_dir <- file.path("..", "..", "data")
+
+# Path to output directory
+plots_dir <- "plots"
+
+# Path to tumor type plots output directory
+hist_plots_dir <- file.path(plots_dir, "tumor-type")
+
+# Create the hist_plots_dir  if it does not exist
+if (!dir.exists(hist_plots_dir)) {
+  dir.create(hist_plots_dir, recursive = TRUE)
+}
+ + + +
+
+

Read in data and format it

+
+

Set up metadata

+ + + +
# Read in the metadata
+metadata <- readr::read_tsv(file.path(data_dir, "pbta-histologies.tsv"))
+ + +
Parsed with column specification:
+cols(
+  .default = col_character(),
+  OS_days = col_double(),
+  age_last_update_days = col_double(),
+  normal_fraction = col_double(),
+  tumor_fraction = col_double(),
+  tumor_ploidy = col_double()
+)
+See spec(...) for full column specifications.
+ + + +
+
+

Set up CNV data:

+

Read in the CNV data and format it to make the chromosome and coordinates easier for handling later. TODO: Update to consensus CNV data.

+ + + +
# Read in the segment copy number data
+cnv_df <- data.table::fread(file.path(data_dir, "pbta-cnv-cnvkit.seg.gz"),
+  data.table = FALSE
+) %>%
+  dplyr::mutate(
+    samples = as.factor(ID),
+    # Calculate width
+    width = loc.end - loc.start,
+    # Make a binary variable that labels whether or not a sequence
+    # is considered changed by the threshold set
+    # TODO: after updating to Consensus CNV data, see if sex chromosomes still
+    # need to be removed and whether these thresholds should be changed.
+    changed = as.factor(dplyr::case_when(
+      abs(seg.mean) > log2(1 + ch.pct) ~ "change",
+      TRUE ~ "no_change"
+    ))
+  ) %>%
+  # Reformat the chromosome variable to drop the "chr"
+  dplyr::mutate(chrom = factor(gsub("chr", "", chrom),
+    levels = c(1:22, "X", "Y")
+  )) %>%
+  # Changing these so they end up matching the SV data
+  dplyr::rename(start = loc.start, end = loc.end)
+ + + +

Filter CNV data to only the changes that are larger than our cutoff ch.pct.

+ + + +
cnv_filtered_df <- cnv_df %>%
+  dplyr::filter(changed == "change")
+ + + +
+
+

Set up SV data

+

Read in the SV data.

+ + + +
sv_df <- data.table::fread(file.path(data_dir, "pbta-sv-manta.tsv.gz"),
+  data.table = FALSE
+) %>%
+  # Reformat the 23 and 24 chromosomes so they are X and Y and also factors
+  dplyr::mutate(
+    chrom = dplyr::recode(SV.chrom,
+      `23` = "X", `24` = "Y"
+    )
+  )
+ + +
|--------------------------------------------------|
+|==================================================|
+ + + +
+
+

Set up chromosome reference data

+

Set up chromosome size data. It just so happens that this BED file: WGS.hg38.strelka2.unpadded.bed is actually just a list of the chromosome sizes so we are using that for now.

+ + + +
# Set up Chr sizes
+chr_sizes <- readr::read_tsv(file.path(data_dir, "WGS.hg38.strelka2.unpadded.bed"),
+  col_names = FALSE
+) %>%
+  # Reformat the chromosome variable to drop the "chr"
+  dplyr::mutate(X1 = factor(gsub("chr", "", X1),
+    levels = c(1:22, "X", "Y", "M")
+  )) %>%
+  # Remove sex chromosomes
+  dplyr::filter(!(X1 %in% c("X", "Y", "M")))
+ + +
Parsed with column specification:
+cols(
+  X1 = col_character(),
+  X2 = col_double(),
+  X3 = col_double()
+)
+ + +
# Make chromosome size named vector
+chr_sizes_vector <- chr_sizes$X3
+names(chr_sizes_vector) <- chr_sizes$X1
+ + + +
+
+
+

Format the data as chromosomes breaks

+

The SV and CNV data comes to us in the form of ranges, but for getting a look at chromosomal instability, we will want to convert this data into single break points of the genome. We’ll also remove the sex chromosomes. TODO: after updating to consensus CNV data, evaluate whether sex chromosomes should still be removed.

+

Make an CNV breaks data.frame.

+ + + +
# Make a CNV data.frame that has the breaks
+cnv_breaks <- data.frame(
+  samples = rep(cnv_filtered_df$ID, 2),
+  chrom = rep(cnv_filtered_df$chrom, 2),
+  coord = c(cnv_filtered_df$start, cnv_filtered_df$end),
+  seg.mean = rep(cnv_filtered_df$seg.mean, 2),
+  copy.num = rep(cnv_filtered_df$copy.num, 2),
+  stringsAsFactors = FALSE
+) %>%
+  # Remove sex chromosomes
+  dplyr::filter(!(chrom %in% c("X", "Y")))
+ + + +

Make an SV breaks data.frame.

+ + + +
# Make a data.frame that has the breaks
+sv_breaks <- data.frame(
+  samples = rep(sv_df$Kids.First.Biospecimen.ID.Tumor, 2),
+  chrom = rep(sv_df$chrom, 2),
+  coord = c(sv_df$SV.start, sv_df$SV.end),
+  svclass = rep(sv_df$SV.type, 2),
+  stringsAsFactors = FALSE
+) %>%
+  # Remove sex chromosomes and NAs
+  dplyr::filter(!(chrom %in% c("X", "Y", "M", NA)))
+ + + +

Make an common breaks data.frame.

+ + + +
common_breaks <- dplyr::bind_rows(sv_breaks, cnv_breaks) %>%
+  dplyr::distinct(samples, chrom, coord, .keep_all = TRUE)
+ + +
binding character and factor vector, coercing into character vector
+ + + +

Put all the breaks into a list.

+ + + +
breaks_list <- list(
+  common_breaks = common_breaks,
+  cnv_breaks = cnv_breaks,
+  sv_breaks = sv_breaks
+)
+ + + +
+

Co-localization of breakpoints for samples

+

For each sample, we will bin the genome and count how many chromosome breaks occur for each bin, given the given bin_size.

+

We will run this for each sample and return a list of three GenomicRanges objects: 1) common_breaks contains the combined break counts for both SV and CNV break data.
+2) cnv_breaks contains the number of break counts for CNV.
+3) sv_breaks contains the number of break counts for SV.

+ + + +
# Change here and it will change the rest
+bin_size <- 1e6
+
+# Obtain a list of samples that are common to both datasets.
+common_samples <- intersect(sv_breaks$samples, cnv_breaks$samples)
+
+# Get a big list of break densities for each sample.
+sample_densities <- lapply(common_samples, function(sample_id) {
+  lapply(breaks_list, function(breaks_df) {
+    break_density(breaks_df,
+      sample_id = sample_id,
+      start_col = "coord",
+      end_col = "coord",
+      window_size = bin_size,
+      chr_sizes_vector = chr_sizes_vector
+    )
+  })
+})
+
+# Bring along the sample IDs
+names(sample_densities) <- common_samples
+ + + +
+
+

Set up for making heatmaps of the breakpoints

+

Given the GenomicRanges objects for each sample, create a combined plot for each.

+

Make chromosome labeling HeatmapAnnotation object.

+ + + +
# Extract chromosome names from a GenomicRanges object
+chr_labels <- sample_densities[[1]]$common_breaks@seqnames
+chr_labels <- paste0("chr", as.factor(chr_labels))
+
+# Make a key for assigning alternating colors to the chromosomes
+chr_colors <- rep(c("grey", "lightblue"), length.out = length(unique(chr_labels)))
+names(chr_colors) <- unique(chr_labels)
+
+# Create the Heatmap annotation object
+chr_annot <- ComplexHeatmap::HeatmapAnnotation(
+  df = data.frame(chr_labels), col = list(chr_labels = c(chr_colors)),
+  name = "",
+  show_legend = FALSE,
+  show_annotation_name = FALSE
+)
+ + + +

Make histology labeling HeatmapAnnotation object.

+ + + +
# Get the histologies for the samples in this set and order them by histology
+histologies <-
+  data.frame(Kids_First_Biospecimen_ID = common_samples) %>%
+  dplyr::inner_join(dplyr::select(metadata, Kids_First_Biospecimen_ID, short_histology)) %>%
+  dplyr::mutate(short_histology = as.factor(short_histology)) %>%
+  dplyr::arrange(short_histology) %>%
+  tibble::column_to_rownames("Kids_First_Biospecimen_ID")
+ + +
Joining, by = "Kids_First_Biospecimen_ID"
+Column `Kids_First_Biospecimen_ID` joining factor and character vector, coercing into character vector
+ + +
# Create the Heatmap annotation object
+hist_annot <- ComplexHeatmap::rowAnnotation(
+  df = histologies,
+  show_annotation_name = FALSE
+)
+ + + +

Make a color function.

+ + + +
col_fun <- circlize::colorRamp2(
+  c(0, 2, 5, 10, 20),
+  c("#edf8fb", "#b2e2e2", "#66c2a4", "#2ca25f", "#006d2c")
+)
+ + + +

Make a function for making the heatmaps.

+ + + +
breaks_heatmap <- function(data_name) {
+  # A wrapper function for making a heatmap from the samples GenomicRanges list.
+  #
+  # Args:
+  # data_name: a character string that matches a name in the list.
+
+  # Returns:
+  # A heatmap of the chromosomal breaks
+
+  # Pull out the total_counts
+  total_counts <- lapply(sample_densities, function(granges_list) {
+    granges_list[[data_name]]@elementMetadata@listData$total_counts
+  })
+  # Make into a data.frame
+  total_counts <- dplyr::bind_rows(total_counts) %>%
+    dplyr::select(rownames(histologies)) %>%
+    t()
+  # Add chromosome labels
+  colnames(total_counts) <- chr_labels
+  # Plot on a heatmap
+  heatmap <- ComplexHeatmap::Heatmap(total_counts,
+    col = col_fun,
+    heatmap_legend_param = list(title = "Count of chr breaks"),
+    cluster_columns = FALSE,
+    cluster_rows = FALSE,
+    show_column_names = FALSE,
+    show_row_names = FALSE,
+    bottom_annotation = chr_annot,
+    left_annotation = hist_annot
+  )
+  # Return plot
+  return(heatmap)
+}
+ + + +
+
+
+

Common breaks heatmap

+ + + +
common_heatmap <- breaks_heatmap(data_name = "common_breaks")
+
+# Save plot as PNG
+png(file.path(plots_dir, paste0("common_breaks_heatmap.png")),
+  width = 1200, height = 900, units = "px"
+)
+common_heatmap
+dev.off()
+ + +
null device 
+          1
+ + +
# Print out here
+common_heatmap
+ + +

+ + + +
+
+

CNV breaks heatmap

+ + + +
cnv_heatmap <- breaks_heatmap(data_name = "cnv_breaks")
+
+# Save plot as PNG
+png(file.path(plots_dir, paste0("cnv_breaks_heatmap.png")),
+  width = 1200, height = 900, units = "px"
+)
+cnv_heatmap
+dev.off()
+ + +
null device 
+          1
+ + +
# Print out here
+cnv_heatmap
+ + +

+ + + +
+
+

SV breaks heatmap

+ + + +
sv_heatmap <- breaks_heatmap(data_name = "sv_breaks")
+
+# Save plot as PNG
+png(file.path(plots_dir, paste0("sv_breaks_heatmap.png")),
+  width = 1200, height = 900, units = "px"
+)
+sv_heatmap
+dev.off()
+ + +
null device 
+          1
+ + +
# Print out here
+sv_heatmap
+ + +

+ + + +
+

Co-localization of breakpoints for tumor-type groups

+

Same as was done for each sample, now we will calculate densities for each tumor-type group.

+ + + +
# Change bin_size here and it will change the rest
+bin_size <- 1e6
+
+# Get a list of the tumor_types for which we have DNA-seq data
+tumor_types <- metadata %>%
+  dplyr::filter(!is.na(short_histology), experimental_strategy != "RNA-Seq") %>%
+  dplyr::distinct(short_histology) %>%
+  dplyr::pull(short_histology)
+ + + +

Run the density calculations for the groups.

+ + + +
# Get a big list of break densities for each sample.
+group_densities <- lapply(tumor_types, function(tumor_type) {
+
+  # Obtain a list of sample_ids to subset by
+  sample_ids <- metadata %>%
+    dplyr::filter(metadata$short_histology == tumor_type) %>%
+    dplyr::pull(Kids_First_Biospecimen_ID)
+
+  # Double check these samples are in the list
+  check_samples <- (sample_ids %in% common_samples)
+
+  # If no samples, go to next
+  if (sum(check_samples) > 1) {
+    message(paste("Calculating breakpoint density for", tumor_type, "samples"))
+    lapply(breaks_list, function(breaks_df) {
+      break_density(breaks_df,
+        sample_id = sample_ids,
+        start_col = "coord",
+        end_col = "coord",
+        window_size = bin_size,
+        chr_sizes_vector = chr_sizes_vector
+      )
+    })
+  }
+})
+ + +
Calculating breakpoint density for Ependymoma samples
+Calculating breakpoint density for HGAT samples
+Calculating breakpoint density for LGAT samples
+Calculating breakpoint density for Other samples
+Calculating breakpoint density for CNS sarcoma samples
+Calculating breakpoint density for Medulloblastoma samples
+Calculating breakpoint density for Schwannoma samples
+Calculating breakpoint density for ATRT samples
+Calculating breakpoint density for Choroid plexus tumor samples
+Calculating breakpoint density for Craniopharyngioma samples
+Calculating breakpoint density for DNET samples
+Calculating breakpoint density for Teratoma samples
+Calculating breakpoint density for Hemangioblastoma samples
+Calculating breakpoint density for Meningioma samples
+Calculating breakpoint density for Adenoma samples
+Calculating breakpoint density for Neurofibroma samples
+Calculating breakpoint density for Ganglioglioma samples
+Calculating breakpoint density for Langerhans Cell histiocytosis samples
+Calculating breakpoint density for CNS Embryonal tumor samples
+Calculating breakpoint density for CNS neuroblastoma samples
+Calculating breakpoint density for Chordoma samples
+Calculating breakpoint density for MPNST samples
+Calculating breakpoint density for Glial-neuronal tumor NOS samples
+Calculating breakpoint density for Pineoblastoma samples
+Calculating breakpoint density for CNS EFT-CIC samples
+Calculating breakpoint density for Central neurocytoma samples
+Calculating breakpoint density for Germinoma samples
+Calculating breakpoint density for CNS Rhabdomyosarcoma samples
+Calculating breakpoint density for Dysplasia samples
+Calculating breakpoint density for Oligodendroglioma samples
+Calculating breakpoint density for Hemangioma samples
+Calculating breakpoint density for LGMT samples
+Calculating breakpoint density for CNS ganglioneuroblastoma samples
+ + +
# Bring along the tumor-type labels
+names(group_densities) <- tumor_types
+
+# Remove tumor_types data that didn't have at least two samples
+group_densities <- group_densities[!sapply(group_densities, is.null)]
+ + + +
+
+

Plot the breakpoints for each tumor-type

+

Here we will plot median number of break points for the tumor-type group per each bin.

+ + + +
purrr::imap(group_densities, function(.x, name = .y) {
+  # Make the combo plot
+  multipanel_break_plot(
+    granges_list = .x,
+    plot_name = name,
+    y_val = "median_counts",
+    y_lab = "Median Breaks per Mb",
+    plot_dir = hist_plots_dir
+  )
+})
+ + +
$Ependymoma
+
+$HGAT
+
+$LGAT
+
+$Other
+
+$`CNS sarcoma`
+
+$Medulloblastoma
+
+$Schwannoma
+
+$ATRT
+
+$`Choroid plexus tumor`
+
+$Craniopharyngioma
+
+$DNET
+
+$Teratoma
+
+$Hemangioblastoma
+
+$Meningioma
+
+$Adenoma
+
+$Neurofibroma
+
+$Ganglioglioma
+
+$`Langerhans Cell histiocytosis`
+
+$`CNS Embryonal tumor`
+
+$`CNS neuroblastoma`
+
+$Chordoma
+
+$MPNST
+
+$`Glial-neuronal tumor NOS`
+
+$Pineoblastoma
+
+$`CNS EFT-CIC`
+
+$`Central neurocytoma`
+
+$Germinoma
+
+$`CNS Rhabdomyosarcoma`
+
+$Dysplasia
+
+$Oligodendroglioma
+
+$Hemangioma
+
+$LGMT
+
+$`CNS ganglioneuroblastoma`
+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + + +

Zip up the PNG files into one file.

+ + + +
# Declare name of zip file
+zip_file <- paste0(hist_plots_dir, ".zip")
+
+# Remove any current zip_file of this name so we can overwrite it
+if (file.exists(zip_file)) {
+  file.remove(zip_file)
+}
+ + +
[1] TRUE
+ + +
# Zip up the plots
+zip(zip_file, hist_plots_dir)
+ + +
  adding: plots/tumor-type/ (stored 0%)
+  adding: plots/tumor-type/Ependymoma_breaks.png (deflated 16%)
+  adding: plots/tumor-type/CNS Embryonal tumor_breaks.png (deflated 15%)
+  adding: plots/tumor-type/Glial-neuronal tumor NOS_breaks.png (deflated 14%)
+  adding: plots/tumor-type/ATRT_breaks.png (deflated 15%)
+  adding: plots/tumor-type/Medulloblastoma_breaks.png (deflated 15%)
+  adding: plots/tumor-type/Oligodendroglioma_breaks.png (deflated 12%)
+  adding: plots/tumor-type/LGMT_breaks.png (deflated 11%)
+  adding: plots/tumor-type/.DS_Store (deflated 97%)
+  adding: plots/tumor-type/HGAT_breaks.png (deflated 15%)
+  adding: plots/tumor-type/Hemangioma_breaks.png (deflated 12%)
+  adding: plots/tumor-type/LGAT_breaks.png (deflated 16%)
+  adding: plots/tumor-type/Pineoblastoma_breaks.png (deflated 13%)
+  adding: plots/tumor-type/Dysplasia_breaks.png (deflated 16%)
+  adding: plots/tumor-type/MPNST_breaks.png (deflated 13%)
+  adding: plots/tumor-type/Central neurocytoma_breaks.png (deflated 10%)
+  adding: plots/tumor-type/CNS neuroblastoma_breaks.png (deflated 13%)
+  adding: plots/tumor-type/Neurofibroma_breaks.png (deflated 15%)
+  adding: plots/tumor-type/Chordoma_breaks.png (deflated 16%)
+  adding: plots/tumor-type/Schwannoma_breaks.png (deflated 16%)
+  adding: plots/tumor-type/Hemangioblastoma_breaks.png (deflated 14%)
+  adding: plots/tumor-type/DNET_breaks.png (deflated 16%)
+  adding: plots/tumor-type/Langerhans Cell histiocytosis_breaks.png (deflated 13%)
+  adding: plots/tumor-type/CNS Rhabdomyosarcoma_breaks.png (deflated 11%)
+  adding: plots/tumor-type/Meningioma_breaks.png (deflated 15%)
+  adding: plots/tumor-type/Ganglioglioma_breaks.png (deflated 16%)
+  adding: plots/tumor-type/CNS sarcoma_breaks.png (deflated 11%)
+  adding: plots/tumor-type/Germinoma_breaks.png (deflated 15%)
+  adding: plots/tumor-type/Choroid plexus tumor_breaks.png (deflated 14%)
+  adding: plots/tumor-type/Craniopharyngioma_breaks.png (deflated 15%)
+  adding: plots/tumor-type/Teratoma_breaks.png (deflated 15%)
+  adding: plots/tumor-type/CNS EFT-CIC_breaks.png (deflated 16%)
+  adding: plots/tumor-type/Other_breaks.png (deflated 16%)
+  adding: plots/tumor-type/Adenoma_breaks.png (deflated 12%)
+  adding: plots/tumor-type/CNS ganglioneuroblastoma_breaks.png (deflated 14%)
+ + + +
+
+

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)
+
+Matrix products: default
+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       
+
+attached base packages:
+[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     
+
+other attached packages:
+[1] GenomicRanges_1.38.0 GenomeInfoDb_1.22.0  IRanges_2.20.1       S4Vectors_0.24.1     BiocGenerics_0.32.0 
+
+loaded via a namespace (and not attached):
+  [1] colorspace_1.4-1                         rjson_0.2.20                            
+  [3] rprojroot_1.3-2                          biovizBase_1.34.1                       
+  [5] htmlTable_1.13.1                         circlize_0.4.8                          
+  [7] XVector_0.26.0                           GlobalOptions_0.1.1                     
+  [9] base64enc_0.1-3                          dichromat_2.0-0                         
+ [11] clue_0.3-57                              rstudioapi_0.10                         
+ [13] getopt_1.20.3                            bit64_0.9-7                             
+ [15] AnnotationDbi_1.48.0                     fansi_0.4.0                             
+ [17] splines_3.6.0                            R.methodsS3_1.7.1                       
+ [19] ggbio_1.34.0                             knitr_1.23                              
+ [21] zeallot_0.1.0                            Formula_1.2-3                           
+ [23] jsonlite_1.6                             Rsamtools_2.2.1                         
+ [25] cluster_2.1.0                            dbplyr_1.4.2                            
+ [27] png_0.1-7                                R.oo_1.22.0                             
+ [29] graph_1.62.0                             BiocManager_1.30.4                      
+ [31] readr_1.3.1                              compiler_3.6.0                          
+ [33] httr_1.4.0                               backports_1.1.4                         
+ [35] assertthat_0.2.1                         Matrix_1.2-17                           
+ [37] lazyeval_0.2.2                           cli_1.1.0                               
+ [39] acepack_1.4.1                            htmltools_0.3.6                         
+ [41] prettyunits_1.0.2                        tools_3.6.0                             
+ [43] gtable_0.3.0                             glue_1.3.1                              
+ [45] GenomeInfoDbData_1.2.2                   reshape2_1.4.3                          
+ [47] dplyr_0.8.3                              rappdirs_0.3.1                          
+ [49] Rcpp_1.0.1                               TxDb.Hsapiens.UCSC.hg38.knownGene_3.10.0
+ [51] Biobase_2.46.0                           styler_1.1.1                            
+ [53] vctrs_0.1.0                              Biostrings_2.54.0                       
+ [55] rtracklayer_1.46.0                       optparse_1.6.2                          
+ [57] xfun_0.8                                 stringr_1.4.0                           
+ [59] ensembldb_2.10.2                         XML_3.98-1.20                           
+ [61] org.Hs.eg.db_3.10.0                      zlibbioc_1.32.0                         
+ [63] scales_1.0.0                             BSgenome_1.54.0                         
+ [65] VariantAnnotation_1.32.0                 ProtGenerics_1.18.0                     
+ [67] hms_0.4.2                                RBGL_1.62.1                             
+ [69] SummarizedExperiment_1.16.0              rematch2_2.0.1                          
+ [71] AnnotationFilter_1.10.0                  RColorBrewer_1.1-2                      
+ [73] ComplexHeatmap_2.2.0                     yaml_2.2.0                              
+ [75] curl_3.3                                 memoise_1.1.0                           
+ [77] gridExtra_2.3                            ggplot2_3.2.0                           
+ [79] rpart_4.1-15                             biomaRt_2.42.0                          
+ [81] reshape_0.8.8                            latticeExtra_0.6-28                     
+ [83] stringi_1.4.3                            RSQLite_2.1.1                           
+ [85] checkmate_1.9.4                          GenomicFeatures_1.38.0                  
+ [87] BiocParallel_1.20.0                      shape_1.4.4                             
+ [89] rlang_0.4.0                              pkgconfig_2.0.2                         
+ [91] matrixStats_0.55.0                       bitops_1.0-6                            
+ [93] evaluate_0.14                            lattice_0.20-38                         
+ [95] purrr_0.3.2                              labeling_0.3                            
+ [97] GenomicAlignments_1.22.1                 htmlwidgets_1.3                         
+ [99] cowplot_0.9.4                            bit_1.1-14                              
+[101] tidyselect_0.2.5                         GGally_1.4.0                            
+[103] plyr_1.8.4                               magrittr_1.5                            
+[105] R6_2.4.0                                 Hmisc_4.2-0                             
+[107] DelayedArray_0.12.0                      DBI_1.0.0                               
+[109] withr_2.1.2                              foreign_0.8-71                          
+[111] pillar_1.4.2                             nnet_7.3-12                             
+[113] survival_2.44-1.1                        RCurl_1.95-4.12                         
+[115] tibble_2.1.3                             crayon_1.3.4                            
+[117] utf8_1.1.4                               OrganismDbi_1.28.0                      
+[119] BiocFileCache_1.10.2                     rmarkdown_1.13                          
+[121] GetoptLong_0.1.7                         progress_1.2.2                          
+[123] grid_3.6.0                               data.table_1.12.2                       
+[125] blob_1.1.1                               digest_0.6.20                           
+[127] tidyr_0.8.3                              R.utils_2.9.0                           
+[129] openssl_1.4                              munsell_0.5.0                           
+[131] askpass_1.1
+ + + + +
+
+ +
---
title: "Chromosomal Instability: Breakpoints"
output:   
  html_notebook: 
    toc: true
    toc_float: true
author: Candace Savonen for ALSF - CCDL
date: 2020
---

This analysis evaluates chromosomal instability by formatting SV and CNV data 
into chromosomal breaks and measuring chromosomal break density.
This notebook returns chromosomal break heatmaps and plots for each tumor type 
group in the `plots` directory.

Code adapted from [svcnvplus](https://github.com/gonzolgarcia/svcnvplus).

### Usage

This notebook can be run via the command line from the top directory of the 
repository as follows:

```
Rscript -e "rmarkdown::render('analyses/chromosomal-instability/chromosomal-instability.Rmd', 
                              clean = TRUE)"
```

### Set Up

```{r}
# Set seed so heatmaps turn out the same
set.seed(2020)

# This threshold will be used to determine percent of genome changed
# TODO: after updating to consensus CNV data, evaluate this threshold setting.
ch.pct <- 0.2

# Magrittr pipe
`%>%` <- dplyr::`%>%`
```

Read in the custom functions needed for this analysis. 

```{r}
source(file.path("util", "chr-break-calculate.R"))
source(file.path("util", "chr-break-plot.R"))
```

### Directories and Files

```{r}
# Path to data directory
data_dir <- file.path("..", "..", "data")

# Path to output directory
plots_dir <- "plots"

# Path to tumor type plots output directory
hist_plots_dir <- file.path(plots_dir, "tumor-type")

# Create the hist_plots_dir  if it does not exist
if (!dir.exists(hist_plots_dir)) {
  dir.create(hist_plots_dir, recursive = TRUE)
}
```

### Read in data and format it 

#### Set up metadata

```{r}
# Read in the metadata
metadata <- readr::read_tsv(file.path(data_dir, "pbta-histologies.tsv"))
```

#### Set up CNV data: 

Read in the CNV data and format it to make the chromosome and coordinates easier for
handling later. 
TODO: Update to consensus CNV data. 

```{r}
# Read in the segment copy number data
cnv_df <- data.table::fread(file.path(data_dir, "pbta-cnv-cnvkit.seg.gz"),
  data.table = FALSE
) %>%
  dplyr::mutate(
    samples = as.factor(ID),
    # Calculate width
    width = loc.end - loc.start,
    # Make a binary variable that labels whether or not a sequence
    # is considered changed by the threshold set
    # TODO: after updating to Consensus CNV data, see if sex chromosomes still
    # need to be removed and whether these thresholds should be changed.
    changed = as.factor(dplyr::case_when(
      abs(seg.mean) > log2(1 + ch.pct) ~ "change",
      TRUE ~ "no_change"
    ))
  ) %>%
  # Reformat the chromosome variable to drop the "chr"
  dplyr::mutate(chrom = factor(gsub("chr", "", chrom),
    levels = c(1:22, "X", "Y")
  )) %>%
  # Changing these so they end up matching the SV data
  dplyr::rename(start = loc.start, end = loc.end)
```

Filter CNV data to only the changes that are larger than our cutoff `ch.pct`.

```{r}
cnv_filtered_df <- cnv_df %>%
  dplyr::filter(changed == "change")
```

#### Set up SV data

Read in the SV data.

```{r}
sv_df <- data.table::fread(file.path(data_dir, "pbta-sv-manta.tsv.gz"),
  data.table = FALSE
) %>%
  # Reformat the 23 and 24 chromosomes so they are X and Y and also factors
  dplyr::mutate(
    chrom = dplyr::recode(SV.chrom,
      `23` = "X", `24` = "Y"
    )
  )
```

#### Set up chromosome reference data

Set up chromosome size data. 
It just so happens that this BED file: `WGS.hg38.strelka2.unpadded.bed` is actually 
just a list of the chromosome sizes so we are using that for now. 

```{r}
# Set up Chr sizes
chr_sizes <- readr::read_tsv(file.path(data_dir, "WGS.hg38.strelka2.unpadded.bed"),
  col_names = FALSE
) %>%
  # Reformat the chromosome variable to drop the "chr"
  dplyr::mutate(X1 = factor(gsub("chr", "", X1),
    levels = c(1:22, "X", "Y", "M")
  )) %>%
  # Remove sex chromosomes
  dplyr::filter(!(X1 %in% c("X", "Y", "M")))

# Make chromosome size named vector
chr_sizes_vector <- chr_sizes$X3
names(chr_sizes_vector) <- chr_sizes$X1
```

## Format the data as chromosomes breaks

The SV and CNV data comes to us in the form of ranges, but for getting a look 
at chromosomal instability, we will want to convert this data into single break
points of the genome. 
We'll also remove the sex chromosomes. 
TODO: after updating to consensus CNV data, evaluate whether sex chromosomes should still be removed. 

Make an CNV breaks data.frame. 

```{r}
# Make a CNV data.frame that has the breaks
cnv_breaks <- data.frame(
  samples = rep(cnv_filtered_df$ID, 2),
  chrom = rep(cnv_filtered_df$chrom, 2),
  coord = c(cnv_filtered_df$start, cnv_filtered_df$end),
  seg.mean = rep(cnv_filtered_df$seg.mean, 2),
  copy.num = rep(cnv_filtered_df$copy.num, 2),
  stringsAsFactors = FALSE
) %>%
  # Remove sex chromosomes
  dplyr::filter(!(chrom %in% c("X", "Y")))
```

Make an SV breaks data.frame. 

```{r}
# Make a data.frame that has the breaks
sv_breaks <- data.frame(
  samples = rep(sv_df$Kids.First.Biospecimen.ID.Tumor, 2),
  chrom = rep(sv_df$chrom, 2),
  coord = c(sv_df$SV.start, sv_df$SV.end),
  svclass = rep(sv_df$SV.type, 2),
  stringsAsFactors = FALSE
) %>%
  # Remove sex chromosomes and NAs
  dplyr::filter(!(chrom %in% c("X", "Y", "M", NA)))
```

Make an common breaks data.frame. 

```{r}
common_breaks <- dplyr::bind_rows(sv_breaks, cnv_breaks) %>%
  dplyr::distinct(samples, chrom, coord, .keep_all = TRUE)
```

Put all the breaks into a list. 

```{r}
breaks_list <- list(
  common_breaks = common_breaks,
  cnv_breaks = cnv_breaks,
  sv_breaks = sv_breaks
)
```

### Co-localization of breakpoints for samples 

For each sample, we will bin the genome and count how many chromosome breaks 
occur for each bin, given the given `bin_size`.

We will run this for each sample and return a list of three `GenomicRanges` objects:
1) `common_breaks` contains the combined break counts for both SV and CNV break data.   
2) `cnv_breaks` contains the number of break counts for CNV.   
3) `sv_breaks` contains the number of break counts for SV.  

```{r, warning = FALSE, results='hide'}
# Change here and it will change the rest
bin_size <- 1e6

# Obtain a list of samples that are common to both datasets.
common_samples <- intersect(sv_breaks$samples, cnv_breaks$samples)

# Get a big list of break densities for each sample.
sample_densities <- lapply(common_samples, function(sample_id) {
  lapply(breaks_list, function(breaks_df) {
    break_density(breaks_df,
      sample_id = sample_id,
      start_col = "coord",
      end_col = "coord",
      window_size = bin_size,
      chr_sizes_vector = chr_sizes_vector
    )
  })
})

# Bring along the sample IDs
names(sample_densities) <- common_samples
```

### Set up for making heatmaps of the breakpoints

Given the `GenomicRanges` objects for each sample, create a combined plot for 
each. 

Make chromosome labeling `HeatmapAnnotation` object. 

```{r}
# Extract chromosome names from a GenomicRanges object
chr_labels <- sample_densities[[1]]$common_breaks@seqnames
chr_labels <- paste0("chr", as.factor(chr_labels))

# Make a key for assigning alternating colors to the chromosomes
chr_colors <- rep(c("grey", "lightblue"), length.out = length(unique(chr_labels)))
names(chr_colors) <- unique(chr_labels)

# Create the Heatmap annotation object
chr_annot <- ComplexHeatmap::HeatmapAnnotation(
  df = data.frame(chr_labels), col = list(chr_labels = c(chr_colors)),
  name = "",
  show_legend = FALSE,
  show_annotation_name = FALSE
)
```

Make histology labeling `HeatmapAnnotation` object.

```{r}
# Get the histologies for the samples in this set and order them by histology
histologies <-
  data.frame(Kids_First_Biospecimen_ID = common_samples) %>%
  dplyr::inner_join(dplyr::select(metadata, Kids_First_Biospecimen_ID, short_histology)) %>%
  dplyr::mutate(short_histology = as.factor(short_histology)) %>%
  dplyr::arrange(short_histology) %>%
  tibble::column_to_rownames("Kids_First_Biospecimen_ID")

# Create the Heatmap annotation object
hist_annot <- ComplexHeatmap::rowAnnotation(
  df = histologies,
  show_annotation_name = FALSE
)
```

Make a color function. 

```{r}
col_fun <- circlize::colorRamp2(
  c(0, 2, 5, 10, 20),
  c("#edf8fb", "#b2e2e2", "#66c2a4", "#2ca25f", "#006d2c")
)
```

Make a function for making the heatmaps. 

```{r}
breaks_heatmap <- function(data_name) {
  # A wrapper function for making a heatmap from the samples GenomicRanges list.
  #
  # Args:
  # data_name: a character string that matches a name in the list.

  # Returns:
  # A heatmap of the chromosomal breaks

  # Pull out the total_counts
  total_counts <- lapply(sample_densities, function(granges_list) {
    granges_list[[data_name]]@elementMetadata@listData$total_counts
  })
  # Make into a data.frame
  total_counts <- dplyr::bind_rows(total_counts) %>%
    dplyr::select(rownames(histologies)) %>%
    t()
  # Add chromosome labels
  colnames(total_counts) <- chr_labels
  # Plot on a heatmap
  heatmap <- ComplexHeatmap::Heatmap(total_counts,
    col = col_fun,
    heatmap_legend_param = list(title = "Count of chr breaks"),
    cluster_columns = FALSE,
    cluster_rows = FALSE,
    show_column_names = FALSE,
    show_row_names = FALSE,
    bottom_annotation = chr_annot,
    left_annotation = hist_annot
  )
  # Return plot
  return(heatmap)
}
```

## Common breaks heatmap

```{r}
common_heatmap <- breaks_heatmap(data_name = "common_breaks")

# Save plot as PNG
png(file.path(plots_dir, paste0("common_breaks_heatmap.png")),
  width = 1200, height = 900, units = "px"
)
common_heatmap
dev.off()

# Print out here
common_heatmap
```

## CNV breaks heatmap

```{r}
cnv_heatmap <- breaks_heatmap(data_name = "cnv_breaks")

# Save plot as PNG
png(file.path(plots_dir, paste0("cnv_breaks_heatmap.png")),
  width = 1200, height = 900, units = "px"
)
cnv_heatmap
dev.off()

# Print out here
cnv_heatmap
```

## SV breaks heatmap

```{r}
sv_heatmap <- breaks_heatmap(data_name = "sv_breaks")

# Save plot as PNG
png(file.path(plots_dir, paste0("sv_breaks_heatmap.png")),
  width = 1200, height = 900, units = "px"
)
sv_heatmap
dev.off()

# Print out here
sv_heatmap
```

### Co-localization of breakpoints for tumor-type groups

Same as was done for each sample, now we will calculate densities for 
each tumor-type group. 

```{r, results='hide'}
# Change bin_size here and it will change the rest
bin_size <- 1e6

# Get a list of the tumor_types for which we have DNA-seq data
tumor_types <- metadata %>%
  dplyr::filter(!is.na(short_histology), experimental_strategy != "RNA-Seq") %>%
  dplyr::distinct(short_histology) %>%
  dplyr::pull(short_histology)
```

Run the density calculations for the groups. 

```{r}
# Get a big list of break densities for each sample.
group_densities <- lapply(tumor_types, function(tumor_type) {

  # Obtain a list of sample_ids to subset by
  sample_ids <- metadata %>%
    dplyr::filter(metadata$short_histology == tumor_type) %>%
    dplyr::pull(Kids_First_Biospecimen_ID)

  # Double check these samples are in the list
  check_samples <- (sample_ids %in% common_samples)

  # If no samples, go to next
  if (sum(check_samples) > 1) {
    message(paste("Calculating breakpoint density for", tumor_type, "samples"))
    lapply(breaks_list, function(breaks_df) {
      break_density(breaks_df,
        sample_id = sample_ids,
        start_col = "coord",
        end_col = "coord",
        window_size = bin_size,
        chr_sizes_vector = chr_sizes_vector
      )
    })
  }
})

# Bring along the tumor-type labels
names(group_densities) <- tumor_types

# Remove tumor_types data that didn't have at least two samples
group_densities <- group_densities[!sapply(group_densities, is.null)]
```

### Plot the breakpoints for each tumor-type

Here we will plot median number of break points for the tumor-type group per 
each bin.

```{r}
purrr::imap(group_densities, function(.x, name = .y) {
  # Make the combo plot
  multipanel_break_plot(
    granges_list = .x,
    plot_name = name,
    y_val = "median_counts",
    y_lab = "Median Breaks per Mb",
    plot_dir = hist_plots_dir
  )
})
```

Zip up the PNG files into one file. 

```{r}
# Declare name of zip file
zip_file <- paste0(hist_plots_dir, ".zip")

# Remove any current zip_file of this name so we can overwrite it
if (file.exists(zip_file)) {
  file.remove(zip_file)
}
# Zip up the plots
zip(zip_file, hist_plots_dir)
```

### Session Info

```{r}
sessionInfo()
```


+ + +
+
+ +
+ + + + + + + + diff --git a/analyses/chromosomal-instability/plots/cnv_breaks_heatmap.png b/analyses/chromosomal-instability/plots/cnv_breaks_heatmap.png new file mode 100644 index 0000000000..d4855cce48 Binary files /dev/null and b/analyses/chromosomal-instability/plots/cnv_breaks_heatmap.png differ diff --git a/analyses/chromosomal-instability/plots/common_breaks_heatmap.png b/analyses/chromosomal-instability/plots/common_breaks_heatmap.png new file mode 100644 index 0000000000..97afb826e6 Binary files /dev/null and b/analyses/chromosomal-instability/plots/common_breaks_heatmap.png differ diff --git a/analyses/chromosomal-instability/plots/sv_breaks_heatmap.png b/analyses/chromosomal-instability/plots/sv_breaks_heatmap.png new file mode 100644 index 0000000000..f32bf6d975 Binary files /dev/null and b/analyses/chromosomal-instability/plots/sv_breaks_heatmap.png differ diff --git a/analyses/chromosomal-instability/plots/tumor-type.zip b/analyses/chromosomal-instability/plots/tumor-type.zip new file mode 100644 index 0000000000..59dd6a280d Binary files /dev/null and b/analyses/chromosomal-instability/plots/tumor-type.zip differ diff --git a/analyses/chromosomal-instability/util/chr-break-calculate.R b/analyses/chromosomal-instability/util/chr-break-calculate.R index 88a649a048..ea9f4108cb 100644 --- a/analyses/chromosomal-instability/util/chr-break-calculate.R +++ b/analyses/chromosomal-instability/util/chr-break-calculate.R @@ -80,33 +80,27 @@ make_granges <- function(break_df = NULL, return(granges) } -break_density <- function(sv_breaks = NULL, - cnv_breaks = NULL, +break_density <- function(breaks_df = NULL, sample_id = NULL, window_size = 1e6, - chr_sizes_list = NULL, - samples_col_cnv = "samples", - chrom_col_cnv = "chrom", - start_col_cnv = "start", - end_col_cnv = "end", - samples_col_sv = "samples", - chrom_col_sv = "chrom", - start_col_sv = "start", - end_col_sv = "end") { + chr_sizes_vector = NULL, + samples_col = "samples", + chrom_col = "chrom", + start_col = "start", + end_col = "end") { # For given breaks data.frame(s), calculate the breaks density for a tiled # windows across the genome. Returns the data as a GenomicRanges object for # easy mapping with ggbio. Where the density and counts are stored in # @elementMetadata@listData. # # Args: - # sv_breaks: a data.frame with the breaks for the SV data. - # cnv_breaks: a data.frame with the breaks for the CNV data. + # breaks_df: a data.frame with chromosomal breaks. # sample_id: a character string that designates which data needs to be # extracted and made intoa GenomicRanges object by matching the # information in the designated sample_col. If "all" is designated, # all samples will be kept. Multiple samples can be designated # as a character vector. - # chr_size_list: a named numeric vector of the sizes (bp) of the chromosomes. + # chr_size_vector: a named numeric vector of the sizes (bp) of the chromosomes. # names of the chromosomes must match the format of the input # break data. e.g. "chr1" or "1". # window_size: What size windows to calculate break density. Default is 1Mb. @@ -127,82 +121,24 @@ break_density <- function(sv_breaks = NULL, if (is.null(sample_id)) { stop("No sample ID(s) have been specified. Use the `sample_id` argument.") } - + # Check that a chromosome sizes have been given + if (is.null(chr_sizes_vector)) { + stop("No `chr_sizes_vector` argument has been given. Need that to create bins.") + } # Determine how many samples are in the group n_samples <- length(sample_id) - # If both datasets are given, make them into one and use this - if (!is.null(cnv_breaks) & !is.null(sv_breaks)) { - - # Read in CNV data - cnv_ranges <- make_granges( - break_df = cnv_breaks, - sample_id = sample_id, - samples_col = samples_col_cnv, - chrom_col = chrom_col_cnv, - start_col = start_col_cnv, - end_col = end_col_cnv - ) - - # Read in SV data - sv_ranges <- make_granges( - break_df = sv_breaks, - sample_id = sample_id, - samples_col = samples_col_sv, - chrom_col = chrom_col_sv, - start_col = start_col_sv, - end_col = end_col_sv - ) - # Combine datasets - break_ranges <- GenomicRanges::union( - cnv_ranges, - sv_ranges - ) - - # Carry over list data from sv_ranges - sv_overlaps <- suppressWarnings(GenomicRanges::findOverlaps(sv_ranges, - break_ranges)) - - # Carry over list data from cnv_ranges - cnv_overlaps <- suppressWarnings(GenomicRanges::findOverlaps(cnv_ranges, - break_ranges)) - - # Set up an empty list where we can store what sample each sequence came from - break_ranges@elementMetadata@listData$mcols <- rep(NA, length(break_ranges)) - - # Bring over CNV samples - break_ranges@elementMetadata@listData$mcols[cnv_overlaps@from] <- - cnv_ranges@elementMetadata@listData$mcols[cnv_overlaps@to] - - # Bring over SV samples - break_ranges@elementMetadata@listData$mcols[cnv_overlaps@from] <- - cnv_ranges@elementMetadata@listData$mcols[cnv_overlaps@to] - - } else if (!is.null(cnv_breaks) & is.null(sv_breaks)) { - # If only the CNV data is given, use this data only - break_ranges <- make_granges( - break_df = cnv_breaks, - sample_id = sample_id, - samples_col = samples_col_cnv, - chrom_col = chrom_col_cnv, - start_col = start_col_cnv, - end_col = end_col_cnv - ) - } else if (!is.null(sv_breaks) & is.null(cnv_breaks)) { - break_ranges <- make_granges( - break_df = sv_breaks, - sample_id = sample_id, - samples_col = samples_col_sv, - chrom_col = chrom_col_sv, - start_col = start_col_sv, - end_col = end_col_sv - ) - } else if (is.null(sv_breaks) & is.null(cnv_breaks)) { - stop("No data has been provided in either the `sv_break` or `cnv_break` arguments.") - } - + # Make this into a GenomicRanges object + break_ranges <- make_granges( + break_df = breaks_df, + sample_id = sample_id, + samples_col = samples_col, + chrom_col = chrom_col, + start_col = start_col, + end_col = end_col + ) ######################### Tally breaks by genome bins ######################## - bins <- GenomicRanges::tileGenome(chr_sizes_list, + bins <- GenomicRanges::tileGenome(chr_sizes_vector, tilewidth = window_size ) @@ -235,11 +171,11 @@ break_density <- function(sv_breaks = NULL, t() # Calculate the median breaks per bin - median_counts <- apply(freq_per_bin, 1, median) + median_counts <- apply(freq_per_bin, 1, median, na.rm = TRUE) # Store the median break counts, some bins data may be dropped off so we need - # to start with NAs and then fill in the data based on the indices. - bins@elementMetadata@listData$median_counts <- rep(NA, length(bins)) + # to start with 0s and then fill in the data based on the indices. + bins@elementMetadata@listData$median_counts <- rep(0, length(bins)) bins@elementMetadata@listData$median_counts[as.numeric(names(median_counts))] <- median_counts # Store average count info diff --git a/analyses/chromosomal-instability/util/chr-break-plot.R b/analyses/chromosomal-instability/util/chr-break-plot.R index be2ef1bf91..9773cf6ebf 100644 --- a/analyses/chromosomal-instability/util/chr-break-plot.R +++ b/analyses/chromosomal-instability/util/chr-break-plot.R @@ -4,11 +4,11 @@ # # 2020 -map_density_plot <- function(granges, - y_val, - y_lab, - color, - main_title) { +map_breaks_plot <- function(granges, + y_val, + y_lab, + color, + main_title) { # Given a GRanges object, plot it y value along its chromosomal mappings using # ggbio. # @@ -56,20 +56,20 @@ multipanel_break_plot <- function(granges_list, y_lab, plot_dir) { # A wrapper function to make a 3 row chromosomal map plot for a set of GRanges - # objects that contain common_density, cnv_density, and sv_density. + # objects that contain common_breaks, cnv_breaks, and sv_breaks. # # Args: # granges_list: A list of Granges object to plot as a combination plot # plot_name: a character string specifying the plot - # y_val: to be passed to map_density plot for mapping. - # y_lab: to be passed to map_density plot for y axis label + # y_val: to be passed to map_breaks plot for mapping. + # y_lab: to be passed to map_breaks plot for y axis label # plot_dir: a file path where you would like the plot PNG to be saved. # # Returns: # ggplot of chromosomal mapping of the y value given. # # Make combined SV and CNV plot - common_plot <- map_density_plot(granges_list$common_density, + common_plot <- map_breaks_plot(granges_list$common_breaks, y_val = y_val, y_lab = y_lab, color = "blue", @@ -77,7 +77,7 @@ multipanel_break_plot <- function(granges_list, ) # Make CNV plot - cnv_plot <- map_density_plot(granges_list$cnv_density, + cnv_plot <- map_breaks_plot(granges_list$cnv_breaks, y_val = y_val, y_lab = y_lab, color = "darkgreen", @@ -85,7 +85,7 @@ multipanel_break_plot <- function(granges_list, ) # Make SV plot - sv_plot <- map_density_plot(granges_list$sv_density, + sv_plot <- map_breaks_plot(granges_list$sv_breaks, y_val = y_val, y_lab = y_lab, color = "orange",