diff --git a/.circleci/config.yml b/.circleci/config.yml
index 70bbcec3c6..7265b5f786 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -108,10 +108,6 @@ jobs:
name: Mutational Signatures
command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/mutational-signatures/mutational_signatures.Rmd', clean = TRUE)"
- - run:
- name: Tumor mutation burden with TCGA
- command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/tmb-compare-tcga/compare-tmb.Rmd', clean = TRUE)"
-
- run:
name: Chromosomal instability breakpoints
command: OPENPBTA_TESTING=1 ./scripts/run_in_ci.sh bash analyses/chromosomal-instability/run_breakpoint_analysis.sh
@@ -180,7 +176,11 @@ jobs:
- run:
name: SNV Caller Analysis
command: OPENPBTA_VAF_CUTOFF=0.5 ./scripts/run_in_ci.sh bash analyses/snv-callers/run_caller_consensus_analysis-pbta.sh
-
+
+ - run:
+ name: Tumor mutation burden with TCGA
+ command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/tmb-compare-tcga/compare-tmb.Rmd', clean = TRUE)"
+
- run:
name: Lancet WXS vs WGS test
command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/snv-callers/lancet-wxs-tests/lancet-paired-WXS-WGS.Rmd', clean = TRUE)"
diff --git a/analyses/snv-callers/compare_snv_callers_plots-tcga.Rmd b/analyses/snv-callers/compare_snv_callers_plots-tcga.Rmd
new file mode 100755
index 0000000000..f40268bf2f
--- /dev/null
+++ b/analyses/snv-callers/compare_snv_callers_plots-tcga.Rmd
@@ -0,0 +1,397 @@
+---
+title: "TCGA Plot Caller Comparisons"
+output:
+ html_notebook:
+ toc: TRUE
+ toc_float: TRUE
+author: C. Savonen for ALSF CCDL
+date: 2020
+---
+
+Purpose: After creating a TCGA consensus file with `02-merge_callers.R`, this notebook
+plots these data in a series of plots to compare the consensus calls to the individual
+callers.
+
+### Summary of Findings:
+Lancet does not appear to be doing well with the WXS data.
+See the further analyses on this in the `lancet-wxs-tests` folder in this analysis folder.
+
+#### Individual Caller's Reports
+
+### Outline of analyses completed:
+
+- [Upset graph](#upset-graph)
+- [VAF of combinations of callers](#vaf-for-combination-sets-of-callers)
+- [Base changes by caller](#base-changes)
+- [VAF distributions by caller](#vaf-distributions)
+- [VAF correlations between callers](#vaf-correlations)
+
+#### Usage
+
+To run this from the command line, use:
+```
+Rscript -e "rmarkdown::render('analyses/snv-callers/compare_snv_callers_plots-tcga.Rmd',
+ clean = TRUE)"
+```
+
+_This assumes you are in the top directory of the repository._
+
+## Setup
+
+#### Packages and functions
+
+Read in set up script.
+
+```{r}
+if (!("ggupset" %in% installed.packages())) {
+ install.packages("ggupset", repos = "http://cran.us.r-project.org")
+}
+if (!("UpSetR" %in% installed.packages())) {
+ install.packages("UpSetR", repos = "http://cran.us.r-project.org")
+}
+if (!("GGally" %in% installed.packages())) {
+ install.packages("GGally", repos = "http://cran.us.r-project.org")
+}
+# Magrittr pipe
+`%>%` <- dplyr::`%>%`
+```
+
+Set up directories.
+
+```{r}
+scratch_dir <- file.path("..", "..", "scratch")
+results_dir <- file.path("results", "tcga-comparison")
+plots_dir <- file.path("plots", "tcga-comparison")
+
+if (!dir.exists(results_dir)) {
+ dir.create(results_dir)
+}
+if (!dir.exists(plots_dir)) {
+ dir.create(plots_dir)
+}
+```
+
+```{r}
+upset_plot <- function(callers_per_mutation_df) {
+callers_per_mutation_df %>%
+ dplyr::group_by(caller_combo) %>%
+ dplyr::tally() %>%
+ ggplot2::ggplot(ggplot2::aes(x = reorder(caller_combo, -n), y = n)) +
+ ggplot2::geom_bar(
+ position = "dodge", stat = "identity") +
+ ggplot2::theme_classic() +
+ ggupset::scale_x_mergelist(sep = "-") +
+ ggupset::axis_combmatrix(sep = "-") +
+ ggplot2::xlab("") +
+ ggplot2::ylab("")
+}
+```
+
+## Connect to TCGA database
+
+Connect to SQLite database.
+
+```{r}
+# Start up connection
+con <- DBI::dbConnect(RSQLite::SQLite(),
+ file.path(scratch_dir, "tcga_snv_db.sqlite"))
+```
+
+Note what columns we will join by.
+
+```{r}
+join_cols = c("Chromosome",
+ "Start_Position",
+ "Reference_Allele",
+ "Allele",
+ "Tumor_Sample_Barcode",
+ "Variant_Classification")
+```
+
+Set up tables from the database but only call the columns we need.
+
+```{r}
+strelka <- dplyr::tbl(con, "strelka") %>%
+ dplyr::select(join_cols, "VAF") %>%
+ as.data.frame()
+
+lancet <- dplyr::tbl(con, "lancet") %>%
+ dplyr::select(join_cols, "VAF") %>%
+ as.data.frame()
+
+mutect <- dplyr::tbl(con, "mutect") %>%
+ dplyr::select(join_cols, "VAF") %>%
+ as.data.frame()
+```
+
+Full join to get the summary of mutations called by each caller.
+
+```{r}
+# Bring out the data.frame
+all_caller_df <- strelka %>%
+ dplyr::full_join(mutect, by = join_cols,
+ suffix = c("_strelka", "_mutect")) %>%
+ dplyr::full_join(lancet,
+ by = join_cols) %>%
+ dplyr::rename(VAF_lancet = VAF)
+
+# Take a peek at what this looks like
+head(all_caller_df)
+```
+
+```{r}
+# Set up as data.frame with each column a caller's VAF
+vaf_df <- all_caller_df %>%
+ dplyr::select("VAF_lancet", "VAF_strelka", "VAF_mutect",
+ "Variant_Classification") %>%
+ dplyr::mutate(VAF_consensus = VAF_strelka)
+
+# Determine which mutations are not part of the consensus
+consensus_index <- vaf_df %>%
+ dplyr::select(-c(VAF_consensus, Variant_Classification)) %>%
+ rowSums(is.na(.)) > 0
+
+# Make non consensus VAF into an NA
+vaf_df$VAF_consensus[consensus_index] <- NA
+
+# Make this long form for plotting
+long_vaf_df <- vaf_df %>%
+ # This index needs to be a factor so tapply step in the next chunk works more seamlessly
+ dplyr::mutate(index = factor(1:nrow(.))) %>%
+ tidyr::gather(key = "caller", value = "vaf", -index, -Variant_Classification) %>%
+ dplyr::mutate(caller = gsub("VAF_", "", caller)) %>%
+ dplyr::filter(!is.na(vaf))
+```
+
+Get median vaf for each mutation with each caller combination.
+
+```{r}
+# Take out the consensus VAF for this combination
+long_caller_df <- long_vaf_df %>%
+ dplyr::filter(caller != "consensus")
+
+# Determine the combination of callers for each mutation
+callers_per_mutation <- tapply(
+ long_caller_df$caller,
+ long_caller_df$index,
+ paste0,
+ collapse = "-"
+ ) %>%
+ as.data.frame() %>%
+ tibble::rownames_to_column("index")
+
+# Determine Median VAF for each mutation
+vaf_med <- tapply(
+ long_caller_df$vaf,
+ long_caller_df$index,
+ median
+ ) %>%
+ as.data.frame() %>%
+ tibble::rownames_to_column("index")
+
+# Join the median VAF and the callers that call that mutation into one data.frame
+callers_per_mutation <- callers_per_mutation %>%
+ dplyr::inner_join(vaf_med, by = "index") %>%
+ dplyr::left_join(long_caller_df %>%
+ dplyr::select(Variant_Classification, index),
+ by = "index") %>%
+ # Make column names more sensible
+ dplyr::rename(caller_combo = "..x", median_vaf = "..y")
+```
+
+## Upset graph
+
+Make the upset plot.
+
+```{r}
+upsettr_plot <- callers_per_mutation %>%
+ upset_plot()
+
+# Print this out here
+upsettr_plot
+```
+
+Save this plot to a png
+
+```{r}
+# We can save the plot like a normal ggplot
+png(file = file.path(plots_dir, "tcga-upset_plot.png"))
+upsettr_plot
+dev.off()
+```
+
+Transcripts only upset plot.
+
+```{r}
+callers_per_mutation %>%
+ dplyr::filter(!(Variant_Classification %in% c("5'Flank", "3'Flank", "IGR", "Intron"))) %>%
+ upset_plot()
+```
+
+Non transcript upset plot.
+
+```{r}
+callers_per_mutation %>%
+ dplyr::filter(Variant_Classification %in% c("5'Flank", "3'Flank", "IGR", "Intron")) %>%
+ upset_plot()
+```
+
+## VAF for combination sets of callers
+
+Graph mutations by combinations of callers and plot the VAF density.
+Get list of callers per mutation and the median vaf for each.
+Graph the median VAF for each combination of callers.
+
+```{r}
+# Make this plot
+callers_per_mutation %>%
+ ggplot2::ggplot(ggplot2::aes(x = caller_combo, y = median_vaf)) +
+ ggplot2::geom_violin() +
+ ggplot2::theme_classic() +
+ ggupset::scale_x_mergelist(sep = "-") +
+ ggupset::axis_combmatrix(sep = "-") +
+ ggplot2::xlab("") +
+ ggplot2::ylab("Median VAF Across Callers")
+```
+
+```{r}
+# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-upset_median_vaf_plot.png"))
+```
+
+## Base Changes
+
+Summarize base change information per caller.
+
+```{r}
+# Summarize by the number of times each base change shows up in each category.
+perc_change_df <- all_caller_df %>%
+ # Make change variable
+ dplyr::mutate(base_change = paste0(Allele, ">", Reference_Allele)) %>%
+ dplyr::mutate(
+ # From the base_change variable, summarize insertions, deletions, and
+ # changes that are more than one base into their own groups.
+ change = dplyr::case_when(
+ grepl("^-", base_change) ~ "ins",
+ grepl("-$", base_change) ~ "del",
+ nchar(base_change) > 3 ~ "long_change",
+ TRUE ~ base_change
+ )
+ ) %>%
+ # Whittle down to necessary columns
+ dplyr::select(change, dplyr::starts_with("VAF_")) %>%
+ # Make this long form
+ tidyr::gather(key = "caller", value = "vaf", -change) %>%
+ # Drop the prefix
+ dplyr::mutate(caller = gsub("VAF_", "", caller)) %>%
+ # Get rid of NA rows
+ dplyr::filter(!is.na(vaf)) %>%
+ # Summarize the number of mutations per caller
+ dplyr::count(caller, change, name = "count") %>%
+ dplyr::add_count(caller, wt = count) %>%
+ # Calculate the percent of each
+ dplyr::mutate(percent = count / n) %>%
+ # Drop nonsensical change
+ dplyr::filter(!grepl("N>*|*>N|C>C|T>T|A>A|G>G", change),
+ !is.na(change)) %>%
+ dplyr::mutate(
+ change = as.factor(change),
+ # Change factor level order so ins and del are at the end
+ change = forcats::fct_relevel(change, "ins", "del", "long_change", after = Inf)
+ )
+```
+
+Make a barplot illustrating the percent of the mutations for each caller that
+that are each type of change.
+
+```{r}
+perc_change_df %>%
+ ggplot2::ggplot(ggplot2::aes(x = change, y = percent)) +
+ ggplot2::theme_classic() +
+ ggplot2::geom_bar(
+ position = "dodge", stat = "identity",
+ ggplot2::aes(fill = caller)
+ ) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
+ ggplot2::xlab("") +
+ ggplot2::ylab("Percent of callers' mutations") +
+ colorblindr::scale_fill_OkabeIto()
+```
+
+```{r}
+# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-perc_base_change_plot.png"))
+```
+
+## VAF distributions
+
+Make a violin plot of each caller.
+
+```{r}
+long_vaf_df %>%
+ ggplot2::ggplot(ggplot2::aes(x = caller, y = vaf, color = caller)) +
+ ggplot2::geom_violin() +
+ ggplot2::theme_classic() +
+ colorblindr::scale_color_OkabeIto()
+```
+
+```{r}
+# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-vaf_violin_plot.png"))
+```
+
+## VAF correlations
+
+Make the plot with ggpairs.
+
+```{r}
+GGally::ggpairs(vaf_df %>% dplyr::select(-VAF_consensus, -Variant_Classification),
+ mapping = ggplot2::aes(alpha = 0.05)) +
+ ggplot2::theme_classic()
+```
+
+```{r}
+# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-vaf_correlations_plot.png"))
+```
+
+## Mutation Region barplot
+
+Summarize `Variant_Classification` information per caller.
+
+```{r}
+# Summarize by the number of times each base change shows up in each category.
+perc_var_df <- long_vaf_df %>%
+ # Summarize the number of mutations per caller
+ dplyr::count(caller, Variant_Classification, name = "count") %>%
+ dplyr::add_count(caller, wt = count) %>%
+ # Calculate the percent of each
+ dplyr::mutate(percent = count / n)
+```
+
+Make a barplot illustrating the percent of the mutations for each caller that
+that are each type of change.
+
+```{r}
+perc_var_df %>%
+ ggplot2::ggplot(ggplot2::aes(x = caller, y = percent)) +
+ ggplot2::theme_classic() +
+ ggplot2::geom_bar(
+ position = "stack", stat = "identity",
+ ggplot2::aes(fill = Variant_Classification)
+ ) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
+ ggplot2::xlab("") +
+ ggplot2::ylab("Percent of callers' mutations")
+```
+
+```{r}
+# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-variant_classification_plot.png"))
+```
+
+## Session Info
+
+```{r}
+sessionInfo()
+```
diff --git a/analyses/snv-callers/compare_snv_callers_plots-tcga.nb.html b/analyses/snv-callers/compare_snv_callers_plots-tcga.nb.html
new file mode 100644
index 0000000000..a791baffae
--- /dev/null
+++ b/analyses/snv-callers/compare_snv_callers_plots-tcga.nb.html
@@ -0,0 +1,3553 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+TCGA Plot Caller Comparisons
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
Purpose: After creating a TCGA consensus file with 02-merge_callers.R, this notebook plots these data in a series of plots to compare the consensus calls to the individual callers.
+
+
Summary of Findings:
+
Lancet does not appear to be doing well with the WXS data. See the further analyses on this in the lancet-wxs-tests folder in this analysis folder.
+
+
Individual Caller’s Reports
+
+
+
+
Outline of analyses completed:
+
+
+
Usage
+
To run this from the command line, use:
+
Rscript -e "rmarkdown::render('analyses/snv-callers/compare_snv_callers_plots-tcga.Rmd',
+ clean = TRUE)"
+
This assumes you are in the top directory of the repository.
+
+
+
+
Setup
+
+
Packages and functions
+
Read in set up script.
+
+
+
+
if (!("ggupset" %in% installed.packages())) {
+ install.packages("ggupset", repos = "http://cran.us.r-project.org")
+}
+if (!("UpSetR" %in% installed.packages())) {
+ install.packages("UpSetR", repos = "http://cran.us.r-project.org")
+}
+if (!("GGally" %in% installed.packages())) {
+ install.packages("GGally", repos = "http://cran.us.r-project.org")
+}
+# Magrittr pipe
+`%>%` <- dplyr::`%>%`
+
+
+
+
Set up directories.
+
+
+
+
scratch_dir <- file.path("..", "..", "scratch")
+results_dir <- file.path("results", "tcga-comparison")
+plots_dir <- file.path("plots", "tcga-comparison")
+
+if (!dir.exists(results_dir)) {
+ dir.create(results_dir)
+}
+if (!dir.exists(plots_dir)) {
+ dir.create(plots_dir)
+}
+
+
+
+
+
+
+
upset_plot <- function(callers_per_mutation_df) {
+callers_per_mutation_df %>%
+ dplyr::group_by(caller_combo) %>%
+ dplyr::tally() %>%
+ ggplot2::ggplot(ggplot2::aes(x = reorder(caller_combo, -n), y = n)) +
+ ggplot2::geom_bar(
+ position = "dodge", stat = "identity") +
+ ggplot2::theme_classic() +
+ ggupset::scale_x_mergelist(sep = "-") +
+ ggupset::axis_combmatrix(sep = "-") +
+ ggplot2::xlab("") +
+ ggplot2::ylab("")
+}
+
+
+
+
+
+
+
Connect to TCGA database
+
Connect to SQLite database.
+
+
+
+
# Start up connection
+con <- DBI::dbConnect(RSQLite::SQLite(),
+ file.path(scratch_dir, "tcga_snv_db.sqlite"))
+
+
+
+
Note what columns we will join by.
+
+
+
+
join_cols = c("Chromosome",
+ "Start_Position",
+ "Reference_Allele",
+ "Allele",
+ "Tumor_Sample_Barcode",
+ "Variant_Classification")
+
+
+
+
Set up tables from the database but only call the columns we need.
+
+
+
+
strelka <- dplyr::tbl(con, "strelka") %>%
+ dplyr::select(join_cols, "VAF") %>%
+ as.data.frame()
+
+lancet <- dplyr::tbl(con, "lancet") %>%
+ dplyr::select(join_cols, "VAF") %>%
+ as.data.frame()
+
+mutect <- dplyr::tbl(con, "mutect") %>%
+ dplyr::select(join_cols, "VAF") %>%
+ as.data.frame()
+
+
+
+
Full join to get the summary of mutations called by each caller.
+
+
+
+
# Bring out the data.frame
+all_caller_df <- strelka %>%
+ dplyr::full_join(mutect, by = join_cols,
+ suffix = c("_strelka", "_mutect")) %>%
+ dplyr::full_join(lancet,
+ by = join_cols) %>%
+ dplyr::rename(VAF_lancet = VAF)
+
+# Take a peek at what this looks like
+head(all_caller_df)
+
+
+
+
+
+
+
+
+
+
+
+
# Set up as data.frame with each column a caller's VAF
+vaf_df <- all_caller_df %>%
+ dplyr::select("VAF_lancet", "VAF_strelka", "VAF_mutect",
+ "Variant_Classification") %>%
+ dplyr::mutate(VAF_consensus = VAF_strelka)
+
+# Determine which mutations are not part of the consensus
+consensus_index <- vaf_df %>%
+ dplyr::select(-c(VAF_consensus, Variant_Classification)) %>%
+ rowSums(is.na(.)) > 0
+
+# Make non consensus VAF into an NA
+vaf_df$VAF_consensus[consensus_index] <- NA
+
+# Make this long form for plotting
+long_vaf_df <- vaf_df %>%
+ # This index needs to be a function so tapply step in the next chunk works more seamlessly
+ dplyr::mutate(index = factor(1:nrow(.))) %>%
+ tidyr::gather(key = "caller", value = "vaf", -index, -Variant_Classification) %>%
+ dplyr::mutate(caller = gsub("VAF_", "", caller)) %>%
+ dplyr::filter(!is.na(vaf))
+
+
+
+
Get median vaf for each mutation with each caller combination.
+
+
+
+
# Take out the consensus VAF for this combination
+long_caller_df <- long_vaf_df %>%
+ dplyr::filter(caller != "consensus")
+
+# Determine the combination of callers for each mutation
+callers_per_mutation <- tapply(
+ long_caller_df$caller,
+ long_caller_df$index,
+ paste0,
+ collapse = "-"
+ ) %>%
+ as.data.frame() %>%
+ tibble::rownames_to_column("index")
+
+# Determine Median VAF for each mutation
+vaf_med <- tapply(
+ long_caller_df$vaf,
+ long_caller_df$index,
+ median
+ ) %>%
+ as.data.frame() %>%
+ tibble::rownames_to_column("index")
+
+# Join the median VAF and the callers that call that mutation into one data.frame
+callers_per_mutation <- callers_per_mutation %>%
+ dplyr::inner_join(vaf_med, by = "index") %>%
+ dplyr::left_join(long_caller_df %>%
+ dplyr::select(Variant_Classification, index),
+ by = "index") %>%
+ # Make column names more sensible
+ dplyr::rename(caller_combo = "..x", median_vaf = "..y")
+
+
+
Column `index` joining character vector and factor, coercing into character vector
+
+
+
+
+
+
Upset graph
+
Make the upset plot.
+
+
+
+
upsettr_plot <- callers_per_mutation %>%
+ upset_plot()
+
+# Print this out here
+upsettr_plot
+
+
+
+
+
+
+
Save this plot to a png
+
+
+
+
# We can save the plot like a normal ggplot
+png(file = file.path(plots_dir, "tcga-upset_plot.png"))
+upsettr_plot
+dev.off()
+
+
+
null device
+ 1
+
+
+
+
Transcripts only upset plot.
+
+
+
+
callers_per_mutation %>%
+ dplyr::filter(!(Variant_Classification %in% c("5'Flank", "3'Flank", "IGR", "Intron"))) %>%
+ upset_plot()
+
+
+
+
+
+
+
Non transcript upset plot.
+
+
+
+
callers_per_mutation %>%
+ dplyr::filter(Variant_Classification %in% c("5'Flank", "3'Flank", "IGR", "Intron")) %>%
+ upset_plot()
+
+
+
+
+
+
+
+
+
VAF for combination sets of callers
+
Graph mutations by combinations of callers and plot the VAF density. Get list of callers per mutation and the median vaf for each. Graph the median VAF for each combination of callers.
+
+
+
+
# Make this plot
+callers_per_mutation %>%
+ ggplot2::ggplot(ggplot2::aes(x = caller_combo, y = median_vaf)) +
+ ggplot2::geom_violin() +
+ ggplot2::theme_classic() +
+ ggupset::scale_x_mergelist(sep = "-") +
+ ggupset::axis_combmatrix(sep = "-") +
+ ggplot2::xlab("") +
+ ggplot2::ylab("Median VAF Across Callers")
+
+
+
+
+
+
+
+
+
+
# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-upset_median_vaf_plot.png"))
+
+
+
Saving 7 x 7 in image
+
+
+
+
+
+
Base Changes
+
Summarize base change information per caller.
+
+
+
+
# Summarize by the number of times each base change shows up in each category.
+perc_change_df <- all_caller_df %>%
+ # Make change variable
+ dplyr::mutate(base_change = paste0(Allele, ">", Reference_Allele)) %>%
+ dplyr::mutate(
+ # From the base_change variable, summarize insertions, deletions, and
+ # changes that are more than one base into their own groups.
+ change = dplyr::case_when(
+ grepl("^-", base_change) ~ "ins",
+ grepl("-$", base_change) ~ "del",
+ nchar(base_change) > 3 ~ "long_change",
+ TRUE ~ base_change
+ )
+ ) %>%
+ # Whittle down to necessary columns
+ dplyr::select(change, dplyr::starts_with("VAF_")) %>%
+ # Make this long form
+ tidyr::gather(key = "caller", value = "vaf", -change) %>%
+ # Drop the prefix
+ dplyr::mutate(caller = gsub("VAF_", "", caller)) %>%
+ # Get rid of NA rows
+ dplyr::filter(!is.na(vaf)) %>%
+ # Summarize the number of mutations per caller
+ dplyr::count(caller, change, name = "count") %>%
+ dplyr::add_count(caller, wt = count) %>%
+ # Calculate the percent of each
+ dplyr::mutate(percent = count / n) %>%
+ # Drop nonsensical change
+ dplyr::filter(!grepl("N>*|*>N|C>C|T>T|A>A|G>G", change),
+ !is.na(change)) %>%
+ dplyr::mutate(
+ change = as.factor(change),
+ # Change factor level order so ins and del are at the end
+ change = forcats::fct_relevel(change, "ins", "del", "long_change", after = Inf)
+ )
+
+
+
+
Make a barplot illustrating the percent of the mutations for each caller that that are each type of change.
+
+
+
+
perc_change_df %>%
+ ggplot2::ggplot(ggplot2::aes(x = change, y = percent)) +
+ ggplot2::theme_classic() +
+ ggplot2::geom_bar(
+ position = "dodge", stat = "identity",
+ ggplot2::aes(fill = caller)
+ ) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
+ ggplot2::xlab("") +
+ ggplot2::ylab("Percent of callers' mutations") +
+ colorblindr::scale_fill_OkabeIto()
+
+
+
+
+
+
+
+
+
+
# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-perc_base_change_plot.png"))
+
+
+
Saving 7 x 7 in image
+
+
+
+
+
+
VAF distributions
+
Make a violin plot of each caller.
+
+
+
+
long_vaf_df %>%
+ ggplot2::ggplot(ggplot2::aes(x = caller, y = vaf, color = caller)) +
+ ggplot2::geom_violin() +
+ ggplot2::theme_classic() +
+ colorblindr::scale_color_OkabeIto()
+
+
+
+
+
+
+
+
+
+
# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-vaf_violin_plot.png"))
+
+
+
Saving 7 x 7 in image
+
+
+
+
+
+
VAF correlations
+
Make the plot with ggpairs.
+
+
+
+
GGally::ggpairs(vaf_df %>% dplyr::select(-VAF_consensus, -Variant_Classification),
+ mapping = ggplot2::aes(alpha = 0.05)) +
+ ggplot2::theme_classic()
+
+
+
+
+
+
+
+
+
+
# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-vaf_correlations_plot.png"))
+
+
+
Saving 7 x 7 in image
+
+
+
+
+
+
Mutation Region barplot
+
Summarize Variant_Classification information per caller.
+
+
+
+
# Summarize by the number of times each base change shows up in each category.
+perc_var_df <- long_vaf_df %>%
+ # Summarize the number of mutations per caller
+ dplyr::count(caller, Variant_Classification, name = "count") %>%
+ dplyr::add_count(caller, wt = count) %>%
+ # Calculate the percent of each
+ dplyr::mutate(percent = count / n)
+
+
+
+
Make a barplot illustrating the percent of the mutations for each caller that that are each type of change.
+
+
+
+
perc_var_df %>%
+ ggplot2::ggplot(ggplot2::aes(x = caller, y = percent)) +
+ ggplot2::theme_classic() +
+ ggplot2::geom_bar(
+ position = "stack", stat = "identity",
+ ggplot2::aes(fill = Variant_Classification)
+ ) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
+ ggplot2::xlab("") +
+ ggplot2::ylab("Percent of callers' mutations")
+
+
+
+
+
+
+
+
+
+
# Save this plot
+ggplot2::ggsave(file.path(plots_dir, "tcga-variant_classification_plot.png"))
+
+
+
Saving 7 x 7 in image
+
+
+
+
+
+
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
+ [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
+
+attached base packages:
+[1] stats4 parallel stats graphics grDevices utils datasets methods base
+
+other attached packages:
+ [1] ShatterSeek_0.4 S4Vectors_0.24.1 graph_1.62.0 foreach_1.4.4 BiocGenerics_0.32.0
+ [6] plyr_1.8.4 usethis_1.5.1 devtools_2.0.2 broom_0.5.2 forcats_0.4.0
+[11] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2 readr_1.3.1 tidyr_0.8.3
+[16] tibble_2.1.3 ggplot2_3.2.0 tidyverse_1.2.1
+
+loaded via a namespace (and not attached):
+ [1] colorspace_1.4-1 rprojroot_1.3-2 htmlTable_1.13.1
+ [4] futile.logger_1.4.3 XVector_0.26.0 GenomicRanges_1.38.0
+ [7] base64enc_0.1-3 fs_1.3.1 dichromat_2.0-0
+ [10] rstudioapi_0.10 remotes_2.1.0 bit64_0.9-7
+ [13] lubridate_1.7.4 xml2_1.2.0 codetools_0.2-16
+ [16] splines_3.6.0 knitr_1.23 pkgload_1.0.2
+ [19] Formula_1.2-3 jsonlite_1.6 colorblindr_0.1.0
+ [22] Rsamtools_2.2.1 dbplyr_1.4.2 cluster_2.1.0
+ [25] compiler_3.6.0 httr_1.4.0 backports_1.1.4
+ [28] assertthat_0.2.1 Matrix_1.2-17 lazyeval_0.2.2
+ [31] cli_1.1.0 formatR_1.7 acepack_1.4.1
+ [34] htmltools_0.3.6 prettyunits_1.0.2 tools_3.6.0
+ [37] gtable_0.3.0 glue_1.3.1 GenomeInfoDbData_1.2.2
+ [40] reshape2_1.4.3 Rcpp_1.0.1 Biobase_2.46.0
+ [43] cellranger_1.1.0 Biostrings_2.54.0 nlme_3.1-140
+ [46] iterators_1.0.10 xfun_0.8 ps_1.3.0
+ [49] testthat_2.1.1 rvest_0.3.4 zlibbioc_1.32.0
+ [52] MASS_7.3-51.4 scales_1.0.0 hms_0.4.2
+ [55] ggupset_0.1.0.9000 SummarizedExperiment_1.16.1 lambda.r_1.2.3
+ [58] RColorBrewer_1.1-2 yaml_2.2.0 memoise_1.1.0
+ [61] gridExtra_2.3 rpart_4.1-15 reshape_0.8.8
+ [64] latticeExtra_0.6-28 stringi_1.4.3 RSQLite_2.1.1
+ [67] desc_1.2.0 checkmate_1.9.4 BiocParallel_1.20.1
+ [70] pkgbuild_1.0.3 GenomeInfoDb_1.22.0 matrixStats_0.55.0
+ [73] rlang_0.4.0 pkgconfig_2.0.2 bitops_1.0-6
+ [76] evaluate_0.14 lattice_0.20-38 labeling_0.3
+ [79] GenomicAlignments_1.22.1 htmlwidgets_1.3 cowplot_0.9.4
+ [82] bit_1.1-14 processx_3.4.0 tidyselect_0.2.5
+ [85] GGally_1.4.0 magrittr_1.5 R6_2.4.0
+ [88] IRanges_2.20.1 generics_0.0.2 Hmisc_4.2-0
+ [91] DelayedArray_0.12.2 DBI_1.0.0 pillar_1.4.2
+ [94] haven_2.1.1 foreign_0.8-71 withr_2.1.2
+ [97] survival_2.44-1.1 RCurl_1.95-4.12 nnet_7.3-12
+[100] modelr_0.1.4 crayon_1.3.4 futile.options_1.0.1
+[103] rmarkdown_1.13 grid_3.6.0 readxl_1.3.1
+[106] data.table_1.12.2 blob_1.1.1 callr_3.3.0
+[109] digest_0.6.20 VennDiagram_1.6.20 munsell_0.5.0
+[112] sessioninfo_1.1.1
+
+
+
+
+
---
title: "TCGA Plot Caller Comparisons"
output: 
  html_notebook:
    toc: TRUE
    toc_float: TRUE
author: C. Savonen for ALSF CCDL
date: 2020
---

Purpose: After creating a TCGA consensus file with `02-merge_callers.R`, this notebook 
plots these data in a series of plots to compare the consensus calls to the individual 
callers. 

### Summary of Findings:
Lancet does not appear to be doing well with the WXS data. 
See the further analyses on this in the `lancet-wxs-tests` folder in this analysis folder. 

#### Individual Caller's Reports 

### Outline of analyses completed:

- [Upset graph](#upset-graph)  
- [VAF of combinations of callers](#vaf-for-combination-sets-of-callers)
- [Base changes by caller](#base-changes)
- [VAF distributions by caller](#vaf-distributions)
- [VAF correlations between callers](#vaf-correlations)

#### Usage

To run this from the command line, use:
```
Rscript -e "rmarkdown::render('analyses/snv-callers/compare_snv_callers_plots-tcga.Rmd', 
                              clean = TRUE)"
```

_This assumes you are in the top directory of the repository._

## Setup

#### Packages and functions

Read in set up script.

```{r}
if (!("ggupset" %in% installed.packages())) {
  install.packages("ggupset", repos = "http://cran.us.r-project.org")
}
if (!("UpSetR" %in% installed.packages())) {
  install.packages("UpSetR", repos = "http://cran.us.r-project.org")
}
if (!("GGally" %in% installed.packages())) {
  install.packages("GGally", repos = "http://cran.us.r-project.org")
}
# Magrittr pipe
`%>%` <- dplyr::`%>%`
```

Set up directories. 

```{r}
scratch_dir <- file.path("..", "..", "scratch")
results_dir <- file.path("results", "tcga-comparison")
plots_dir <- file.path("plots", "tcga-comparison")

if (!dir.exists(results_dir)) {
  dir.create(results_dir)
}
if (!dir.exists(plots_dir)) {
  dir.create(plots_dir)
}
```

```{r}
upset_plot <- function(callers_per_mutation_df) {
callers_per_mutation_df %>% 
  dplyr::group_by(caller_combo) %>%
  dplyr::tally() %>% 
  ggplot2::ggplot(ggplot2::aes(x = reorder(caller_combo, -n), y = n)) +
  ggplot2::geom_bar(
        position = "dodge", stat = "identity") +
  ggplot2::theme_classic() +
  ggupset::scale_x_mergelist(sep = "-") +
  ggupset::axis_combmatrix(sep = "-") +
  ggplot2::xlab("") +
  ggplot2::ylab("")
}
```

## Connect to TCGA database

Connect to SQLite database.

```{r}
# Start up connection
con <- DBI::dbConnect(RSQLite::SQLite(), 
                      file.path(scratch_dir, "tcga_snv_db.sqlite"))
```

Note what columns we will join by.

```{r}
join_cols = c("Chromosome",
              "Start_Position",
              "Reference_Allele",
              "Allele",
              "Tumor_Sample_Barcode", 
              "Variant_Classification")
```

Set up tables from the database but only call the columns we need.

```{r}
strelka <- dplyr::tbl(con, "strelka") %>% 
  dplyr::select(join_cols, "VAF") %>% 
  as.data.frame()

lancet <- dplyr::tbl(con, "lancet") %>% 
  dplyr::select(join_cols, "VAF") %>% 
  as.data.frame()

mutect <- dplyr::tbl(con, "mutect") %>% 
  dplyr::select(join_cols, "VAF") %>% 
  as.data.frame()
```

Full join to get the summary of mutations called by each caller. 

```{r}
# Bring out the data.frame
all_caller_df <- strelka %>%
  dplyr::full_join(mutect, by = join_cols, 
                   suffix = c("_strelka", "_mutect")) %>%
  dplyr::full_join(lancet, 
                   by = join_cols) %>% 
  dplyr::rename(VAF_lancet = VAF)

# Take a peek at what this looks like
head(all_caller_df)
```

```{r}
# Set up as data.frame with each column a caller's VAF
vaf_df <- all_caller_df %>% 
  dplyr::select("VAF_lancet", "VAF_strelka", "VAF_mutect",
                "Variant_Classification") %>%
  dplyr::mutate(VAF_consensus = VAF_strelka)     
 
# Determine which mutations are not part of the consensus
consensus_index <- vaf_df %>%
  dplyr::select(-c(VAF_consensus, Variant_Classification)) %>%
  rowSums(is.na(.)) > 0

# Make non consensus VAF into an NA
vaf_df$VAF_consensus[consensus_index] <- NA

# Make this long form for plotting
long_vaf_df <- vaf_df %>% 
  # This index needs to be a function so tapply step in the next chunk works more seamlessly
  dplyr::mutate(index = factor(1:nrow(.))) %>% 
  tidyr::gather(key = "caller", value = "vaf", -index, -Variant_Classification) %>% 
  dplyr::mutate(caller = gsub("VAF_", "", caller)) %>% 
  dplyr::filter(!is.na(vaf))
```

Get median vaf for each mutation with each caller combination. 

```{r}
# Take out the consensus VAF for this combination
long_caller_df <- long_vaf_df %>% 
  dplyr::filter(caller != "consensus")

# Determine the combination of callers for each mutation
callers_per_mutation <- tapply(
    long_caller_df$caller,
    long_caller_df$index,
    paste0,
    collapse = "-"
  ) %>% 
  as.data.frame() %>%
  tibble::rownames_to_column("index")

# Determine Median VAF for each mutation 
vaf_med <- tapply(
    long_caller_df$vaf,
    long_caller_df$index,
    median
  ) %>% 
  as.data.frame() %>%
  tibble::rownames_to_column("index")

# Join the median VAF and the callers that call that mutation into one data.frame
callers_per_mutation <- callers_per_mutation %>%
  dplyr::inner_join(vaf_med, by = "index") %>%
  dplyr::left_join(long_caller_df %>% 
                      dplyr::select(Variant_Classification, index), 
                   by = "index") %>%
  # Make column names more sensible
  dplyr::rename(caller_combo = "..x", median_vaf = "..y") 
```

## Upset graph

Make the upset plot. 

```{r}
upsettr_plot <- callers_per_mutation %>%
  upset_plot()

# Print this out here
upsettr_plot
```

Save this plot to a png

```{r}
# We can save the plot like a normal ggplot
png(file = file.path(plots_dir, "tcga-upset_plot.png"))
upsettr_plot
dev.off()
```

Transcripts only upset plot. 

```{r}
callers_per_mutation %>% 
  dplyr::filter(!(Variant_Classification %in% c("5'Flank", "3'Flank", "IGR", "Intron"))) %>%
  upset_plot()
```

Non transcript upset plot. 

```{r}
callers_per_mutation %>% 
  dplyr::filter(Variant_Classification %in% c("5'Flank", "3'Flank", "IGR", "Intron")) %>%
  upset_plot()
```

## VAF for combination sets of callers

Graph mutations by combinations of callers and plot the VAF density.
Get list of callers per mutation and the median vaf for each. 
Graph the median VAF for each combination of callers.

```{r}
# Make this plot
callers_per_mutation %>%
  ggplot2::ggplot(ggplot2::aes(x = caller_combo, y = median_vaf)) +
  ggplot2::geom_violin() +
  ggplot2::theme_classic() +
  ggupset::scale_x_mergelist(sep = "-") +
  ggupset::axis_combmatrix(sep = "-") +
  ggplot2::xlab("") +
  ggplot2::ylab("Median VAF Across Callers")
```

```{r}
# Save this plot
ggplot2::ggsave(file.path(plots_dir, "tcga-upset_median_vaf_plot.png"))
```

## Base Changes 

Summarize base change information per caller. 

```{r}
# Summarize by the number of times each base change shows up in each category.
perc_change_df <- all_caller_df %>% 
  # Make change variable
  dplyr::mutate(base_change = paste0(Allele, ">", Reference_Allele)) %>% 
  dplyr::mutate(
    # From the base_change variable, summarize insertions, deletions, and
    # changes that are more than one base into their own groups.
    change = dplyr::case_when(
      grepl("^-", base_change) ~ "ins",
      grepl("-$", base_change) ~ "del",
      nchar(base_change) > 3 ~ "long_change",
      TRUE ~ base_change
    )
  ) %>%
  # Whittle down to necessary columns
  dplyr::select(change, dplyr::starts_with("VAF_")) %>% 
  # Make this long form
  tidyr::gather(key = "caller", value = "vaf", -change) %>% 
  # Drop the prefix
  dplyr::mutate(caller = gsub("VAF_", "", caller)) %>% 
  # Get rid of NA rows
  dplyr::filter(!is.na(vaf)) %>%
  # Summarize the number of mutations per caller
  dplyr::count(caller, change, name = "count") %>%
  dplyr::add_count(caller, wt = count) %>%
  # Calculate the percent of each 
  dplyr::mutate(percent = count / n) %>%
  # Drop nonsensical change
  dplyr::filter(!grepl("N>*|*>N|C>C|T>T|A>A|G>G", change), 
                !is.na(change)) %>%
  dplyr::mutate(
    change = as.factor(change),
    # Change factor level order so ins and del are at the end
    change = forcats::fct_relevel(change, "ins", "del", "long_change", after = Inf)
  ) 
```

Make a barplot illustrating the percent of the mutations for each caller that 
that are each type of change. 

```{r}
perc_change_df %>%
  ggplot2::ggplot(ggplot2::aes(x = change, y = percent)) +
  ggplot2::theme_classic() +
  ggplot2::geom_bar(
    position = "dodge", stat = "identity",
    ggplot2::aes(fill = caller)
  ) +
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
  ggplot2::xlab("") +
  ggplot2::ylab("Percent of callers' mutations") +
  colorblindr::scale_fill_OkabeIto()
```

```{r}
# Save this plot
ggplot2::ggsave(file.path(plots_dir, "tcga-perc_base_change_plot.png"))
```

## VAF distributions

Make a violin plot of each caller. 

```{r}
long_vaf_df %>%
  ggplot2::ggplot(ggplot2::aes(x = caller, y = vaf, color = caller)) +
  ggplot2::geom_violin() +
  ggplot2::theme_classic() +
  colorblindr::scale_color_OkabeIto()
```

```{r}
# Save this plot
ggplot2::ggsave(file.path(plots_dir, "tcga-vaf_violin_plot.png"))
```

## VAF correlations

Make the plot with ggpairs. 

```{r}
GGally::ggpairs(vaf_df %>% dplyr::select(-VAF_consensus, -Variant_Classification), 
                mapping = ggplot2::aes(alpha = 0.05)) +
  ggplot2::theme_classic()
``` 

```{r}
# Save this plot
ggplot2::ggsave(file.path(plots_dir, "tcga-vaf_correlations_plot.png"))
```

## Mutation Region barplot

Summarize `Variant_Classification` information per caller. 

```{r}
# Summarize by the number of times each base change shows up in each category.
perc_var_df <- long_vaf_df %>% 
  # Summarize the number of mutations per caller
  dplyr::count(caller, Variant_Classification, name = "count") %>%
  dplyr::add_count(caller, wt = count) %>%
  # Calculate the percent of each 
  dplyr::mutate(percent = count / n)
```

Make a barplot illustrating the percent of the mutations for each caller that 
that are each type of change. 

```{r}
perc_var_df %>%
  ggplot2::ggplot(ggplot2::aes(x = caller, y = percent)) +
  ggplot2::theme_classic() +
  ggplot2::geom_bar(
    position = "stack", stat = "identity",
    ggplot2::aes(fill = Variant_Classification)
  ) +
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
  ggplot2::xlab("") +
  ggplot2::ylab("Percent of callers' mutations")
```

```{r}
# Save this plot
ggplot2::ggsave(file.path(plots_dir, "tcga-variant_classification_plot.png"))
```

## Session Info

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

+
+
+
+
+
+
Setup
Packages and functions
-
-if (!("TCGAbiolinks" %in% installed.packages())) {
- install.packages("TCGAbiolinks")
-}
-
-# magrittr pipe
+
+# magrittr pipe
`%>%` <- dplyr::`%>%`
# Load in these functions so we can use `maf_to_granges`
@@ -2972,7 +2968,7 @@ Packages and functions
Declare names of input and output directories.
-
+
data_dir <- file.path("..", "..", "data")
scratch_dir <- file.path("..", "..", "scratch")
results_dir <- "results"
@@ -2983,7 +2979,7 @@ Packages and functions
Create output directories for this analysis.
-
+
if (!dir.exists(results_dir)) {
dir.create(results_dir)
}
@@ -2994,241 +2990,154 @@ Packages and functions
Custom function for plotting the TMB.
+Function for making a combined CDF plot for TMB. This plotting function was adapted from the breaks_cdf_plot function in the chromosomal-instability
-
-sina_plot <- function(data, colour = NULL, x_axis = "short_histology", title) {
- # Given a data.frame with metadata and `tmb` information, make a sina plot from it
- #
- # Args:
- #
- # data : a data.frame with `tmb` as columns
- # x_axis : A name of a column in data that you would like to make the x_axis from
- # Given as a character string.
- # colour: the color you would like the points to be
- # title: The title for the plot. To be passed to `ggtitle`
- #
- # Returns:
- # A sina plot by histology group
- data %>%
- # eval parse bit will treat the variable as the text it has stored
- ggplot2::ggplot(ggplot2::aes(x = reorder(eval(parse(text = x_axis)), tmb, mean),
- y = log10(tmb))) +
- ggforce::geom_sina(color = colour, alpha = 0.3, maxwidth = 0.9) +
- ggplot2::stat_summary(
- fun.y = mean, fun.ymin = mean, fun.ymax = mean,
- geom = "crossbar",
- width = 0.95, size = 0.15
+
+tmb_cdf_plot <- function(tmb_df, plot_title, colour) {
+ tmb_df %>%
+ as.data.frame() %>%
+ dplyr::mutate(short_histology = tools::toTitleCase(short_histology)) %>%
+ # Only plot histologies groups with more than `min_samples` number of samples
+ dplyr::group_by(short_histology, add = TRUE) %>%
+ # Only keep groups with this amount of samples
+ dplyr::filter(dplyr::n() > 5) %>%
+ # Calculate histology group mean
+ dplyr::mutate(
+ hist_median = median(tmb),
+ hist_rank = rank(tmb, ties.method = "first") / dplyr::n(),
+ sample_size = paste0("n = ", dplyr::n())
+ ) %>%
+ dplyr::ungroup() %>%
+ dplyr::mutate(short_histology = reorder(short_histology, hist_median)) %>%
+ # Now we will plot these as cummulative distribution plots
+ ggplot2::ggplot(ggplot2::aes(
+ x = hist_rank,
+ y = tmb
+ )) +
+ ggplot2::geom_point(color = colour) +
+ # Add summary line for mean
+ ggplot2::geom_segment(
+ x = 0, xend = 1, color = "grey",
+ ggplot2::aes(y = hist_median, yend = hist_median)
) +
+ # Separate by histology
+ ggplot2::facet_wrap(~ short_histology + sample_size, nrow = 1, strip.position = "bottom") +
ggplot2::theme_classic() +
- ggplot2::ylab("log10 (Number of Mutations per Mb)") +
ggplot2::xlab("") +
- ggplot2::ylim(c(-3, 3)) +
+ ggplot2::ylab("Coding mutations per Mb") +
+ # Transform to log10 make non-log y-axis labels
+ ggplot2::scale_y_continuous(trans = "log1p", breaks = c(0, 1, 3, 10, 30, 100, 10000, 30000),
+ limits = c(0, 30000)) +
+ ggplot2::scale_x_continuous(limits = c(-0.2, 1.2), breaks = c()) +
+ # Making it pretty
+ ggplot2::theme(legend.position = "none") +
ggplot2::theme(
- axis.text.x = ggplot2::element_text(angle = 60, hjust = 1),
- legend.position = "none"
+ axis.text.x = ggplot2::element_blank(),
+ axis.ticks.x = ggplot2::element_blank(),
+ strip.placement = "outside",
+ strip.text = ggplot2::element_text(size = 10, angle = 90, hjust = 1),
+ strip.background = ggplot2::element_rect(fill = NA, color = NA)
) +
- ggplot2::ggtitle(title) +
- ggplot2::theme(
- panel.grid.major.x = ggplot2::element_line(colour = c("gray93", "white"), size = 9)
-)
+ ggplot2::ggtitle(plot_title)
}
-
-
Set up consensus SNV PBTA data
-
Download consensus SNV files.
-
-
-
-
# Declare file path for consensus file
-consensus_file <- file.path(data_dir,
- "pbta-snv-consensus-mutation-tmb-coding.tsv")
-
-
-
+
+
Set up consensus data
Read in the consensus TMB file.
-
-
-
-
Set up the TCGA data.
-
We will only use the brain related TCGA projects, so we are setting up a file using the MC3 data but subset that to only the brain-related TCGA projects: LGG GBM, and PCPG. The MC3 data can be obtained here.
-
-
-
# Designate the name of the brain related TCGA file
-mc3_brain_maf_file <- file.path(scratch_dir, "tcga_mc3_brain_related.RDS")
-
-if (file.exists(mc3_brain_maf_file)) {
- # Read in the file if it already exists
- mc3_maf_brain <- readr::read_rds(mc3_brain_maf_file)
-
-} else { # Only run this if it hasn't been set up yet
- # Designate the file path to the original MC3 file
- mc3_maf_file <- file.path(scratch_dir, "mc3.v0.2.8.PUBLIC.maf.gz")
-
- # Download the original MC3 maf file
- if (!file.exists(mc3_maf_file)) {
- download.file("https://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc",
- destfile = mc3_maf_file)
- }
-
- # Read in data and set up sample ID from the Tumor Sample Barcode info
- mc3_maf <- data.table::fread(mc3_maf_file) %>%
- # The patient ID information is the first 12 character of the sample ID so we
- # can link this information to the clinical data if we make a new variable from it.
- dplyr::mutate(sample_id = substr(Tumor_Sample_Barcode, 0, 12))
-
- # Get the TCGA metadata for these brain related projects.
- tcga_datasets <- c("LGG", "GBM", "PCPG")
-
- # Download the clinical info for these projects
- clinical <- lapply(tcga_datasets, function(dataset) {
- TCGAbiolinks::GDCquery_clinic(project = paste0("TCGA-", dataset), type = "clinical") %>%
- dplyr::select(primary_diagnosis, bcr_patient_barcode)
- })
-
- # Bind the clinical data together
- clinical <- dplyr::bind_rows(clinical)
-
- # Do a inner join so we append the diagnosis info but only keep samples that are in the
- # brain-related clinical information
- mc3_maf_brain <- mc3_maf %>%
- dplyr::inner_join(clinical, by = c("sample_id" = "bcr_patient_barcode"))
-
- # Save this to RDS
- readr::write_rds(mc3_maf_brain, mc3_brain_maf_file)
-}
+
+
# TODO: update if the this tmb consensus file gets updated in a future data release
+tmb_pbta <- data.table::fread(file.path("..",
+ "snv-callers",
+ "results",
+ "consensus",
+ "pbta-snv-mutation-tmb-coding.tsv")) %>%
+ # This variable is weird when binding but we don't need it for the plot so we'll just remove it.
+ dplyr::select(-genome_size) %>%
+ dplyr::filter(experimental_strategy != "Panel")
-
Download the Target BED region file for these data. Although not shown, all mutations are within these ranges so no filtering was needed.
-
-
wgs_bed_file <- file.path(scratch_dir, "gencode.v19.basic.exome.bed")
-
-if (!file.exists(wgs_bed_file)) {
- download.file("https://api.gdc.cancer.gov/data/7f0d3ab9-8bef-4e3b-928a-6090caae885b",
- destfile = wgs_bed_file)
-}
-
-# Set up BED region files for TMB calculations
-wgs_bed <- readr::read_tsv(wgs_bed_file, col_names = FALSE, col_types = list(readr::col_character(),
- readr::col_double(),
- readr::col_double()))
-
-# Calculate size of genome surveyed
-# The second and third column of the bed file have the Start and End coordinates of the genome window
-wgs_genome_size <- sum(wgs_bed[, 3] - wgs_bed[, 2])
+
+
# TODO: update if this tmb consensus file gets updated in a future data release
+tmb_tcga <- data.table::fread(file.path("..",
+ "snv-callers",
+ "results",
+ "consensus",
+ "tcga-snv-mutation-tmb-coding.tsv")) %>%
+ dplyr::select(-genome_size)
-
-
Calculate TCGA tumor mutation burden
+
+
Plot the TMB as a CDF plot
+
Plot each dataset as its own CDF plot.
-
-
# Calculate the TMB for TCGA samples
-tmb_tcga <- mc3_maf_brain %>%
- dplyr::group_by(Tumor_Sample_Barcode, primary_diagnosis) %>%
-
- # Count number of mutations for that sample
- dplyr::summarize(mutation_count = dplyr::n()) %>%
-
- # Calculate TMB
- dplyr::mutate(tmb = mutation_count / (wgs_genome_size / 1000000))
-
-
-
-
Set up TCGA TMB data with its histology.
-
-
-
-
# Get the patient barcode from the Tumor Sample Barcode category
-tmb_tcga <- tmb_tcga %>%
- # Rename so it matches PBTA data
- dplyr::rename(short_histology = primary_diagnosis) %>%
-
- # If there is no information for this, for TCGA, we will get rid of these rows
- dplyr::filter(!is.na(short_histology)) %>%
- readr::write_tsv(file.path(results_dir, "brain_related_tcga_tmb.tsv"))
+
+
pbta_plot <- tmb_cdf_plot(tmb_pbta, plot_title = "PBTA", colour = "#3BC8A2") +
+ ggplot2::theme(
+ strip.text.x = ggplot2::element_text(size = 12)
+ )
-
-
-
Plot the TMB as a sina plot
-
Plot the TCGA data.
-
-
# Make TCGA plot and get rid of y axis
-tcga_plot <- sina_plot(tmb_tcga, colour = "#3BC8A2", title = "TCGA (Adult)") +
- ggplot2::theme(
+
+tcga_plot <- tmb_cdf_plot(tmb_tcga, plot_title = "TCGA (Adult)", colour = "#630882") +
+ ggplot2::theme(
axis.title.y = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
- axis.text.x = ggplot2::element_text(size = 7),
- panel.grid.major.x = ggplot2::element_line(colour = c("gray93", "white"), size = 9)
- )
+ strip.text.x = ggplot2::element_text(size = 9)
+ )
-
Plot the PBTA data.
+
Combine both TMB data.frames into one plot
-
-
# Make the plot for PBTA
-pbta_plot <- sina_plot(tmb_pbta, colour = "#630882", title = "PBTA")
-
-
-
-
Use cowplot to put them together.
-
-
-
+
# Put the plots together
tmb_plot <- cowplot::plot_grid(pbta_plot, tcga_plot,
align = "v",
axis = "left",
rel_widths = c(2.5, 1),
label_size = 12
-)
+ )
-
-
Warning: Removed 18 rows containing non-finite values (stat_sina).
-
-
-
Warning: Removed 18 rows containing non-finite values (stat_summary).
-
Save this final plot to a png file.
-
+
# Save the plot to a png
-cowplot::save_plot(file.path(plots_dir, "tmb_tcga_and_pbta_plot.png"),
+cowplot::save_plot(file.path(plots_dir, "tmb-cdf-pbta-tcga.png"),
plot = tmb_plot, base_width = 35, base_height = 20, unit = "cm")
-
Print from png so rendering is smoother 
+
Print from png so rendering is smoother 
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)
@@ -3237,37 +3146,64 @@ Session Info
BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.19.so
locale:
- [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
+ [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
attached base packages:
-[1] stats graphics grDevices utils datasets methods base
+[1] stats4 parallel stats graphics grDevices utils datasets methods base
+
+other attached packages:
+ [1] ShatterSeek_0.4 S4Vectors_0.24.1 graph_1.62.0 foreach_1.4.4 BiocGenerics_0.32.0
+ [6] plyr_1.8.4 usethis_1.5.1 devtools_2.0.2 broom_0.5.2 forcats_0.4.0
+[11] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2 readr_1.3.1 tidyr_0.8.3
+[16] tibble_2.1.3 ggplot2_3.2.0 tidyverse_1.2.1
loaded via a namespace (and not attached):
- [1] Rcpp_1.0.1 pillar_1.4.2 compiler_3.6.0
- [4] base64enc_0.1-3 tools_3.6.0 digest_0.6.20
- [7] bit_1.1-14 jsonlite_1.6 evaluate_0.14
-[10] tibble_2.1.3 gtable_0.3.0 pkgconfig_2.0.2
-[13] rlang_0.4.0 yaml_2.2.0 xfun_0.8
-[16] dplyr_0.8.3 stringr_1.4.0 knitr_1.23
-[19] hms_0.4.2 cowplot_0.9.4 bit64_0.9-7
-[22] grid_3.6.0 tidyselect_0.2.5 glue_1.3.1
-[25] data.table_1.12.2 R6_2.4.0 rmarkdown_1.13
-[28] polyclip_1.10-0 farver_1.1.0 readr_1.3.1
-[31] purrr_0.3.2 ggplot2_3.2.0 tweenr_1.0.1
-[34] magrittr_1.5 scales_1.0.0 htmltools_0.3.6
-[37] MASS_7.3-51.4 assertthat_0.2.1 ggforce_0.2.2
-[40] colorspace_1.4-1 labeling_0.3 stringi_1.4.3
-[43] lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4
+ [1] colorspace_1.4-1 rprojroot_1.3-2 htmlTable_1.13.1
+ [4] futile.logger_1.4.3 XVector_0.26.0 GenomicRanges_1.38.0
+ [7] base64enc_0.1-3 fs_1.3.1 dichromat_2.0-0
+ [10] rstudioapi_0.10 remotes_2.1.0 bit64_0.9-7
+ [13] lubridate_1.7.4 xml2_1.2.0 codetools_0.2-16
+ [16] splines_3.6.0 knitr_1.23 pkgload_1.0.2
+ [19] Formula_1.2-3 jsonlite_1.6 colorblindr_0.1.0
+ [22] Rsamtools_2.2.1 dbplyr_1.4.2 cluster_2.1.0
+ [25] compiler_3.6.0 httr_1.4.0 backports_1.1.4
+ [28] assertthat_0.2.1 Matrix_1.2-17 lazyeval_0.2.2
+ [31] cli_1.1.0 formatR_1.7 acepack_1.4.1
+ [34] htmltools_0.3.6 prettyunits_1.0.2 tools_3.6.0
+ [37] gtable_0.3.0 glue_1.3.1 GenomeInfoDbData_1.2.2
+ [40] reshape2_1.4.3 Rcpp_1.0.1 Biobase_2.46.0
+ [43] cellranger_1.1.0 Biostrings_2.54.0 nlme_3.1-140
+ [46] iterators_1.0.10 xfun_0.8 ps_1.3.0
+ [49] testthat_2.1.1 rvest_0.3.4 zlibbioc_1.32.0
+ [52] MASS_7.3-51.4 scales_1.0.0 hms_0.4.2
+ [55] ggupset_0.1.0.9000 SummarizedExperiment_1.16.1 lambda.r_1.2.3
+ [58] RColorBrewer_1.1-2 yaml_2.2.0 memoise_1.1.0
+ [61] gridExtra_2.3 rpart_4.1-15 reshape_0.8.8
+ [64] latticeExtra_0.6-28 stringi_1.4.3 RSQLite_2.1.1
+ [67] desc_1.2.0 checkmate_1.9.4 BiocParallel_1.20.1
+ [70] pkgbuild_1.0.3 GenomeInfoDb_1.22.0 matrixStats_0.55.0
+ [73] rlang_0.4.0 pkgconfig_2.0.2 bitops_1.0-6
+ [76] evaluate_0.14 lattice_0.20-38 labeling_0.3
+ [79] GenomicAlignments_1.22.1 htmlwidgets_1.3 cowplot_0.9.4
+ [82] bit_1.1-14 processx_3.4.0 tidyselect_0.2.5
+ [85] GGally_1.4.0 magrittr_1.5 R6_2.4.0
+ [88] IRanges_2.20.1 generics_0.0.2 Hmisc_4.2-0
+ [91] DelayedArray_0.12.2 DBI_1.0.0 pillar_1.4.2
+ [94] haven_2.1.1 foreign_0.8-71 withr_2.1.2
+ [97] survival_2.44-1.1 RCurl_1.95-4.12 nnet_7.3-12
+[100] modelr_0.1.4 crayon_1.3.4 futile.options_1.0.1
+[103] rmarkdown_1.13 grid_3.6.0 readxl_1.3.1
+[106] data.table_1.12.2 blob_1.1.1 callr_3.3.0
+[109] digest_0.6.20 VennDiagram_1.6.20 munsell_0.5.0
+[112] sessioninfo_1.1.1
-
---
title: "Tumor Mutation Burden Comparison with TCGA data"
output: 
  html_notebook:
    toc: TRUE
    toc_float: TRUE
author: C. Savonen for ALSF CCDL
date: 2019
---

#### Purpose

Plot the consensus tumor mutation burden statistics for PBTA in comparison to TCGA brain-related data. 

#### Usage

To run this from the command line, use:
```
Rscript -e "rmarkdown::render('analyses/tmb-compare-tcga/compare-tmb.Rmd', 
                              clean = TRUE)"
```

_This assumes you are in the top directory of the repository._

## Setup

#### Packages and functions

```{r}
if (!("TCGAbiolinks" %in% installed.packages())) {
  install.packages("TCGAbiolinks")
}

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

# Load in these functions so we can use `maf_to_granges`
source(file.path("..", "snv-callers", "util", "tmb_functions.R"))
```

Declare names of input and output directories.

```{r}
data_dir <- file.path("..", "..", "data")
scratch_dir <- file.path("..", "..", "scratch")
results_dir <- "results"
plots_dir <- "plots"
```

Create output directories for this analysis. 

```{r}
if (!dir.exists(results_dir)) {
  dir.create(results_dir)
}
if (!dir.exists(plots_dir)) {
  dir.create(plots_dir)
}
```

Custom function for plotting the TMB. 

```{r}
sina_plot <- function(data, colour = NULL, x_axis = "short_histology", title) {
  # Given a data.frame with metadata and `tmb` information, make a sina plot from it
  #
  # Args:
  #
  # data : a data.frame with `tmb` as columns
  # x_axis : A name of a column in data that you would like to make the x_axis from
  #           Given as a character string. 
  # colour: the color you would like the points to be
  # title: The title for the plot. To be passed to `ggtitle`
  #
  # Returns:
  #   A sina plot by histology group
  data %>%
    # eval parse bit will treat the variable as the text it has stored
    ggplot2::ggplot(ggplot2::aes(x = reorder(eval(parse(text = x_axis)), tmb, mean), 
                                 y = log10(tmb))) +
    ggforce::geom_sina(color = colour, alpha = 0.3, maxwidth = 0.9) +
    ggplot2::stat_summary(
      fun.y = mean, fun.ymin = mean, fun.ymax = mean,
      geom = "crossbar",
      width = 0.95, size = 0.15
    ) +
    ggplot2::theme_classic() +
    ggplot2::ylab("log10 (Number of Mutations per Mb)") +
    ggplot2::xlab("") +
    ggplot2::ylim(c(-3, 3)) +
    ggplot2::theme(
      axis.text.x = ggplot2::element_text(angle = 60, hjust = 1),
      legend.position = "none"
    ) +
    ggplot2::ggtitle(title) + 
    ggplot2::theme(
    panel.grid.major.x = ggplot2::element_line(colour = c("gray93", "white"), size = 9)
)
}
```

## Set up consensus SNV PBTA data

Download consensus SNV files. 

```{r}
# Declare file path for consensus file
consensus_file <- file.path(data_dir, 
                            "pbta-snv-consensus-mutation-tmb-coding.tsv")
```

Read in the consensus TMB file. 

```{r include=FALSE}
# Read in the file
tmb_pbta <- data.table::fread(consensus_file)
```

## Set up the TCGA data. 

We will only use the brain related TCGA projects, so we are setting up a file using the MC3 data but subset 
that to only the brain-related TCGA projects: LGG GBM, and PCPG. 
The MC3 data can be obtained [here](https://gdc.cancer.gov/about-data/publications/mc3-2017). 

```{r}
# Designate the name of the brain related TCGA file
mc3_brain_maf_file <- file.path(scratch_dir, "tcga_mc3_brain_related.RDS")

if (file.exists(mc3_brain_maf_file)) {
  # Read in the file if it already exists
  mc3_maf_brain <- readr::read_rds(mc3_brain_maf_file)
    
} else { # Only run this if it hasn't been set up yet
  # Designate the file path to the original MC3 file
  mc3_maf_file <- file.path(scratch_dir, "mc3.v0.2.8.PUBLIC.maf.gz")
  
  # Download the original MC3 maf file 
  if (!file.exists(mc3_maf_file)) {
    download.file("https://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc", 
                  destfile = mc3_maf_file)
  }

  # Read in data and set up sample ID from the Tumor Sample Barcode info
  mc3_maf <- data.table::fread(mc3_maf_file) %>% 
    # The patient ID information is the first 12 character of the sample ID so we
    # can link this information to the clinical data if we make a new variable from it. 
    dplyr::mutate(sample_id = substr(Tumor_Sample_Barcode, 0, 12))

  # Get the TCGA metadata for these brain related projects.
  tcga_datasets <- c("LGG", "GBM", "PCPG")

  # Download the clinical info for these projects  
  clinical <- lapply(tcga_datasets, function(dataset) {
    TCGAbiolinks::GDCquery_clinic(project = paste0("TCGA-", dataset), type = "clinical") %>%
      dplyr::select(primary_diagnosis, bcr_patient_barcode)
  })
  
  # Bind the clinical data together
  clinical <- dplyr::bind_rows(clinical)
  
  # Do a inner join so we append the diagnosis info but only keep samples that are in the 
  # brain-related clinical information
  mc3_maf_brain <- mc3_maf %>% 
    dplyr::inner_join(clinical, by = c("sample_id" = "bcr_patient_barcode"))
  
  # Save this to RDS
  readr::write_rds(mc3_maf_brain, mc3_brain_maf_file)
}
```

Download the Target BED region file for these data. 
Although not shown, all mutations are within these ranges so no filtering was needed. 

```{r}
wgs_bed_file <- file.path(scratch_dir, "gencode.v19.basic.exome.bed")

if (!file.exists(wgs_bed_file)) {
  download.file("https://api.gdc.cancer.gov/data/7f0d3ab9-8bef-4e3b-928a-6090caae885b", 
              destfile = wgs_bed_file)
}

# Set up BED region files for TMB calculations
wgs_bed <- readr::read_tsv(wgs_bed_file, col_names = FALSE, col_types = list(readr::col_character(), 
                                                                             readr::col_double(), 
                                                                             readr::col_double()))

# Calculate size of genome surveyed
# The second and third column of the bed file have the Start and End coordinates of the genome window
wgs_genome_size <- sum(wgs_bed[, 3] - wgs_bed[, 2])
```

## Calculate TCGA tumor mutation burden

```{r}
# Calculate the TMB for TCGA samples
tmb_tcga <- mc3_maf_brain %>%
  dplyr::group_by(Tumor_Sample_Barcode, primary_diagnosis) %>%

  # Count number of mutations for that sample
  dplyr::summarize(mutation_count = dplyr::n()) %>%

  # Calculate TMB
  dplyr::mutate(tmb = mutation_count / (wgs_genome_size / 1000000))
```

Set up TCGA TMB data with its histology. 

```{r}
# Get the patient barcode from the Tumor Sample Barcode category
tmb_tcga <- tmb_tcga %>%
  # Rename so it matches PBTA data
  dplyr::rename(short_histology = primary_diagnosis) %>%

  # If there is no information for this, for TCGA, we will get rid of these rows
  dplyr::filter(!is.na(short_histology)) %>%
  readr::write_tsv(file.path(results_dir, "brain_related_tcga_tmb.tsv"))
```

## Plot the TMB as a sina plot

Plot the TCGA data. 

```{r}
# Make TCGA plot and get rid of y axis
tcga_plot <- sina_plot(tmb_tcga, colour = "#3BC8A2", title = "TCGA (Adult)") +
  ggplot2::theme(
    axis.title.y = ggplot2::element_blank(),
    axis.text.y = ggplot2::element_blank(),
    axis.ticks.y = ggplot2::element_blank(),
    axis.text.x = ggplot2::element_text(size = 7),
    panel.grid.major.x = ggplot2::element_line(colour = c("gray93", "white"), size = 9)
  )
```

Plot the PBTA data. 

```{r}
# Make the plot for PBTA
pbta_plot <- sina_plot(tmb_pbta, colour = "#630882", title = "PBTA")
```

Use cowplot to put them together. 

```{r}
# Put the plots together
tmb_plot <- cowplot::plot_grid(pbta_plot, tcga_plot,
  align = "v",
  axis = "left",
  rel_widths = c(2.5, 1),
  label_size = 12
)
```

Save this final plot to a png file.

```{r}
# Save the plot to a png
cowplot::save_plot(file.path(plots_dir, "tmb_tcga_and_pbta_plot.png"), 
                   plot = tmb_plot, base_width = 35, base_height = 20, unit = "cm")
```

Print from png so rendering is smoother
![TMB Plot](./plots/tmb_tcga_and_pbta_plot.png)

## Session Info

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

+
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diff --git a/analyses/tmb-compare-tcga/plots/tmb-cdf-pbta-tcga.png b/analyses/tmb-compare-tcga/plots/tmb-cdf-pbta-tcga.png
new file mode 100644
index 0000000000..7362b1df64
Binary files /dev/null and b/analyses/tmb-compare-tcga/plots/tmb-cdf-pbta-tcga.png differ
diff --git a/analyses/tmb-compare-tcga/plots/tmb_tcga_and_pbta_plot.png b/analyses/tmb-compare-tcga/plots/tmb_tcga_and_pbta_plot.png
deleted file mode 100644
index 3639b5c96a..0000000000
Binary files a/analyses/tmb-compare-tcga/plots/tmb_tcga_and_pbta_plot.png and /dev/null differ
diff --git a/analyses/tmb-compare-tcga/results/brain_related_tcga_tmb.tsv b/analyses/tmb-compare-tcga/results/brain_related_tcga_tmb.tsv
deleted file mode 100644
index bcdad8e5a8..0000000000
--- a/analyses/tmb-compare-tcga/results/brain_related_tcga_tmb.tsv
+++ /dev/null
@@ -1,1110 +0,0 @@
-Tumor_Sample_Barcode short_histology mutation_count tmb
-TCGA-02-0003-01A-01D-1490-08 Glioblastoma 61 0.3183354973710863
-TCGA-02-0033-01A-01D-1490-08 Glioblastoma 41 0.21396320315105802
-TCGA-02-0047-01A-01D-1490-08 Glioblastoma 87 0.4540194798571231
-TCGA-02-0055-01A-01D-1490-08 Glioblastoma 77 0.4018333327471089
-TCGA-02-2466-01A-01W-0837-08 Glioblastoma 142 0.741043288962201
-TCGA-02-2470-01A-01D-1494-08 Glioblastoma 76 0.39661471803610754
-TCGA-02-2483-01A-01D-1494-08 Glioblastoma 70 0.36530302977009904
-TCGA-02-2485-01A-01D-1494-08 Glioblastoma 79 0.4122705621691118
-TCGA-02-2486-01A-01D-1494-08 Glioblastoma 74 0.38617748861410467
-TCGA-06-0119-01A-08D-1490-08 Glioblastoma 87 0.4540194798571231
-TCGA-06-0122-01A-01D-1490-08 Glioblastoma 105 0.5479545446551486
-TCGA-06-0124-01A-01D-1490-08 Glioblastoma 78 0.40705194745811035
-TCGA-06-0125-01A-01D-1490-08 Glioblastoma 84 0.43836363572411885
-TCGA-06-0125-02A-11D-2280-08 Glioblastoma 106 0.55317315936615
-TCGA-06-0126-01A-01D-1490-08 Glioblastoma 79 0.4122705621691118
-TCGA-06-0128-01A-01D-1490-08 Glioblastoma 76 0.39661471803610754
-TCGA-06-0129-01A-01D-1490-08 Glioblastoma 49 0.2557121208390693
-TCGA-06-0130-01A-01D-1490-08 Glioblastoma 39 0.20352597372905518
-TCGA-06-0132-01A-02D-1491-08 Glioblastoma 31 0.16177705604104387
-TCGA-06-0137-01A-01D-1490-08 Glioblastoma 108 0.5636103887881528
-TCGA-06-0139-01A-01D-1490-08 Glioblastoma 4 0.02087445884400566
-TCGA-06-0140-01A-01D-1490-08 Glioblastoma 64 0.33399134150409054
-TCGA-06-0141-01A-01D-1490-08 Glioblastoma 23 0.12002813835303254
-TCGA-06-0142-01A-01D-1490-08 Glioblastoma 35 0.18265151488504952
-TCGA-06-0145-01A-01D-1490-08 Glioblastoma 127 0.6627640682971797
-TCGA-06-0151-01A-01D-1491-08 Glioblastoma 24 0.12524675306403396
-TCGA-06-0152-01A-02D-1492-08 Glioblastoma 80 0.41748917688011317
-TCGA-06-0152-02A-01D-2280-08 Glioblastoma 90 0.46967532399012735
-TCGA-06-0154-01A-03D-1491-08 Glioblastoma 72 0.37574025919210186
-TCGA-06-0155-01B-01D-1492-08 Glioblastoma 94 0.490549782834133
-TCGA-06-0157-01A-01D-1491-08 Glioblastoma 75 0.3913961033251061
-TCGA-06-0158-01A-01D-1491-08 Glioblastoma 73 0.3809588739031033
-TCGA-06-0165-01A-01D-1491-08 Glioblastoma 2 0.01043722942200283
-TCGA-06-0166-01A-01D-1491-08 Glioblastoma 49 0.2557121208390693
-TCGA-06-0167-01A-01D-1491-08 Glioblastoma 4 0.02087445884400566
-TCGA-06-0168-01A-01D-1491-08 Glioblastoma 55 0.2870238091050778
-TCGA-06-0169-01A-01D-1490-08 Glioblastoma 69 0.3600844150590976
-TCGA-06-0171-01A-02D-1491-08 Glioblastoma 72 0.37574025919210186
-TCGA-06-0171-02A-11D-2280-08 Glioblastoma 47 0.2452748914170665
-TCGA-06-0173-01A-01D-1491-08 Glioblastoma 98 0.5114242416781386
-TCGA-06-0174-01A-01D-1491-08 Glioblastoma 92 0.48011255341213016
-TCGA-06-0184-01A-01D-1491-08 Glioblastoma 72 0.37574025919210186
-TCGA-06-0185-01A-01D-1491-08 Glioblastoma 82 0.42792640630211604
-TCGA-06-0187-01A-01D-1491-08 Glioblastoma 52 0.27136796497207355
-TCGA-06-0188-01A-01D-1491-08 Glioblastoma 73 0.3809588739031033
-TCGA-06-0189-01A-01D-1491-08 Glioblastoma 30 0.15655844133004243
-TCGA-06-0190-01A-01D-1491-08 Glioblastoma 83 0.4331450210131174
-TCGA-06-0190-02A-01D-2280-08 Glioblastoma 142 0.741043288962201
-TCGA-06-0192-01B-01D-1492-08 Glioblastoma 91 0.4748939387011287
-TCGA-06-0195-01B-01D-1491-08 Glioblastoma 94 0.490549782834133
-TCGA-06-0209-01A-01D-1491-08 Glioblastoma 103 0.5375173152331457
-TCGA-06-0210-01B-01D-1491-08 Glioblastoma 78 0.40705194745811035
-TCGA-06-0210-02A-01D-2280-08 Glioblastoma 88 0.4592380945681245
-TCGA-06-0211-01B-01D-1491-08 Glioblastoma 86 0.44880086514612166
-TCGA-06-0211-02A-02D-2280-08 Glioblastoma 73 0.3809588739031033
-TCGA-06-0213-01A-01D-1491-08 Glioblastoma 79 0.4122705621691118
-TCGA-06-0214-01A-02D-1491-08 Glioblastoma 103 0.5375173152331457
-TCGA-06-0216-01B-01D-1492-08 Glioblastoma 76 0.39661471803610754
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-TCGA-28-1746-01A-01W-0643-08 Glioblastoma 107 0.5583917740771513
-TCGA-28-1747-01C-01D-1494-08 Glioblastoma 60 0.31311688266008486
-TCGA-28-1749-01A-01W-0643-08 Glioblastoma 67 0.3496471856370948
-TCGA-28-1750-01A-01W-0643-08 Glioblastoma 128 0.6679826830081811
-TCGA-28-1751-01A-02W-0643-08 Glioblastoma 156 0.8141038949162207
-TCGA-28-1752-01A-01W-0643-08 Glioblastoma 118 0.615796535898167
-TCGA-28-1753-01A-01D-1494-08 Glioblastoma 70 0.36530302977009904
-TCGA-28-1755-01A-01W-0643-08 Glioblastoma 117 0.6105779211871656
-TCGA-28-1757-01A-02W-0643-08 Glioblastoma 287 1.497742422057406
-TCGA-28-1760-01A-01W-0643-08 Glioblastoma 460 2.400562767060651
-TCGA-28-2499-01A-01D-1494-08 Glioblastoma 37 0.19308874430705233
-TCGA-28-2502-01B-01D-1494-08 Glioblastoma 67 0.3496471856370948
-TCGA-28-2506-01A-02W-0837-08 Glioblastoma 447 2.3327207758176325
-TCGA-28-2509-01A-01D-1494-08 Glioblastoma 82 0.42792640630211604
-TCGA-28-2513-01A-01D-1494-08 Glioblastoma 99 0.5166428563891401
-TCGA-28-2514-01A-02D-1494-08 Glioblastoma 72 0.37574025919210186
-TCGA-28-5204-01A-01D-1486-08 Glioblastoma 60 0.31311688266008486
-TCGA-28-5207-01A-01D-1486-08 Glioblastoma 65 0.339209956215092
-TCGA-28-5208-01A-01D-1486-08 Glioblastoma 78 0.40705194745811035
-TCGA-28-5209-01A-01D-1486-08 Glioblastoma 112 0.5844848476321585
-TCGA-28-5211-01C-11D-1845-08 Glioblastoma 579 3.021577917669819
-TCGA-28-5213-01A-01D-1486-08 Glioblastoma 69 0.3600844150590976
-TCGA-28-5214-01A-01D-1486-08 Glioblastoma 62 0.32355411208208773
-TCGA-28-5215-01A-01D-1486-08 Glioblastoma 49 0.2557121208390693
-TCGA-28-5216-01A-01D-1486-08 Glioblastoma 67 0.3496471856370948
-TCGA-28-5218-01A-01D-1486-08 Glioblastoma 27 0.1409025971970382
-TCGA-28-5219-01A-01D-1486-08 Glioblastoma 67 0.3496471856370948
-TCGA-28-5220-01A-01D-1486-08 Glioblastoma 47 0.2452748914170665
-TCGA-28-6450-01A-11D-1696-08 Glioblastoma 74 0.38617748861410467
-TCGA-32-1970-01A-01D-1494-08 Glioblastoma 98 0.5114242416781386
-TCGA-32-1976-01A-01W-0837-08 Glioblastoma 90 0.46967532399012735
-TCGA-32-1977-01A-01D-1353-08 Glioblastoma 110 0.5740476182101556
-TCGA-32-1979-01A-01D-1696-08 Glioblastoma 99 0.5166428563891401
-TCGA-32-1980-01A-01D-1696-08 Glioblastoma 21 0.10959090893102971
-TCGA-32-1982-01A-01D-1494-08 Glioblastoma 101 0.5270800858111429
-TCGA-32-1986-01A-01D-1494-08 Glioblastoma 62 0.32355411208208773
-TCGA-32-1991-01A-01D-1353-08 Glioblastoma 75 0.3913961033251061
-TCGA-32-2491-01A-01D-1353-08 Glioblastoma 116 0.6053593064761641
-TCGA-32-2494-01A-01D-1353-08 Glioblastoma 87 0.4540194798571231
-TCGA-32-2495-01A-01D-1353-08 Glioblastoma 93 0.4853311681231316
-TCGA-32-2615-01A-01D-1495-08 Glioblastoma 50 0.26093073555007074
-TCGA-32-2616-01A-01D-1495-08 Glioblastoma 1539 8.031448040231178
-TCGA-32-2632-01A-01D-1495-08 Glioblastoma 130 0.678419912430184
-TCGA-32-2634-01A-01D-1495-08 Glioblastoma 60 0.31311688266008486
-TCGA-32-2638-01A-01D-1495-08 Glioblastoma 81 0.4227077915911146
-TCGA-32-4208-01A-01D-1353-08 Glioblastoma 53 0.276586579683075
-TCGA-32-4209-01A-01D-1353-08 Glioblastoma 44 0.22961904728406224
-TCGA-32-4210-01A-01D-1353-08 Glioblastoma 105 0.5479545446551486
-TCGA-32-4211-01A-01D-1353-08 Glioblastoma 92 0.48011255341213016
-TCGA-32-4213-01A-01D-1353-08 Glioblastoma 86 0.44880086514612166
-TCGA-32-4719-01A-01D-1353-08 Glioblastoma 67 0.3496471856370948
-TCGA-32-5222-01A-01D-1486-08 Glioblastoma 95 0.4957683975451344
-TCGA-41-2571-01A-01D-1495-08 Glioblastoma 70 0.36530302977009904
-TCGA-41-2572-01A-01D-1353-08 Glioblastoma 82 0.42792640630211604
-TCGA-41-2573-01A-01D-1495-08 Glioblastoma 62 0.32355411208208773
-TCGA-41-2575-01A-01D-1495-08 Glioblastoma 85 0.4435822504351203
-TCGA-41-3392-01A-01D-1495-08 Glioblastoma 90 0.46967532399012735
-TCGA-41-3393-01A-01D-1353-08 Glioblastoma 89 0.4644567092791259
-TCGA-41-3915-01A-01D-1353-08 Glioblastoma 58 0.30267965323808205
-TCGA-41-4097-01A-01D-1353-08 Glioblastoma 76 0.39661471803610754
-TCGA-41-5651-01A-01D-1696-08 Glioblastoma 121 0.6314523800311712
-TCGA-41-6646-01A-11D-1845-08 Glioblastoma 71 0.3705216444811005
-TCGA-4W-AA9R-01A-11D-A391-08 Glioblastoma 102 0.5322987005221443
-TCGA-4W-AA9S-01A-11D-A391-08 Glioblastoma 71 0.3705216444811005
-TCGA-4W-AA9T-01A-11D-A391-08 Glioblastoma 66 0.34442857092609336
-TCGA-74-6573-01A-12D-1845-08 Glioblastoma 56 0.29224242381607923
-TCGA-74-6575-01A-11D-1845-08 Glioblastoma 132 0.6888571418521867
-TCGA-74-6577-01A-11D-1845-08 Glioblastoma 125 0.6523268388751768
-TCGA-74-6578-01A-11D-1845-08 Glioblastoma 84 0.43836363572411885
-TCGA-74-6581-01A-11D-1845-08 Glioblastoma 59 0.3078982679490835
-TCGA-74-6584-01A-11D-1845-08 Glioblastoma 45 0.23483766199506367
-TCGA-76-4925-01A-01D-1486-08 Glioblastoma 85 0.4435822504351203
-TCGA-76-4926-01B-01D-1486-08 Glioblastoma 80 0.41748917688011317
-TCGA-76-4927-01A-01D-1486-08 Glioblastoma 73 0.3809588739031033
-TCGA-76-4928-01B-01D-1486-08 Glioblastoma 98 0.5114242416781386
-TCGA-76-4929-01A-01D-1486-08 Glioblastoma 90 0.46967532399012735
-TCGA-76-4931-01A-01D-1486-08 Glioblastoma 65 0.339209956215092
-TCGA-76-4932-01A-01D-1486-08 Glioblastoma 77 0.4018333327471089
-TCGA-76-4934-01A-01D-1486-08 Glioblastoma 69 0.3600844150590976
-TCGA-76-4935-01A-01D-1486-08 Glioblastoma 69 0.3600844150590976
-TCGA-76-6191-01A-12D-1696-08 Glioblastoma 73 0.3809588739031033
-TCGA-76-6192-01A-11D-1696-08 Glioblastoma 66 0.34442857092609336
-TCGA-76-6193-01A-11D-1696-08 Glioblastoma 59 0.3078982679490835
-TCGA-76-6280-01A-21D-1845-08 Glioblastoma 73 0.3809588739031033
-TCGA-76-6282-01A-11D-1696-08 Glioblastoma 61 0.3183354973710863
-TCGA-76-6283-01A-11D-1845-08 Glioblastoma 132 0.6888571418521867
-TCGA-76-6285-01A-11D-1696-08 Glioblastoma 76 0.39661471803610754
-TCGA-76-6286-01A-11D-1845-08 Glioblastoma 91 0.4748939387011287
-TCGA-76-6656-01A-11D-1845-08 Glioblastoma 120 0.6262337653201697
-TCGA-76-6657-01A-11D-1845-08 Glioblastoma 77 0.4018333327471089
-TCGA-76-6660-01A-11D-1845-08 Glioblastoma 110 0.5740476182101556
-TCGA-76-6661-01B-11D-1845-08 Glioblastoma 70 0.36530302977009904
-TCGA-76-6662-01A-11D-1845-08 Glioblastoma 52 0.27136796497207355
-TCGA-76-6663-01A-11D-1845-08 Glioblastoma 75 0.3913961033251061
-TCGA-76-6664-01A-11D-1845-08 Glioblastoma 70 0.36530302977009904
-TCGA-81-5910-01A-11D-1696-08 Glioblastoma 62 0.32355411208208773
-TCGA-81-5911-01A-12D-1845-08 Glioblastoma 37 0.19308874430705233
-TCGA-87-5896-01A-01D-1696-08 Glioblastoma 72 0.37574025919210186
-TCGA-CS-4938-01B-11D-1893-08 Astrocytoma, NOS 23 0.12002813835303254
-TCGA-CS-4941-01A-01D-1468-08 Astrocytoma, anaplastic 60 0.31311688266008486
-TCGA-CS-4942-01A-01D-1468-08 Astrocytoma, anaplastic 38 0.19830735901805377
-TCGA-CS-4943-01A-01D-1468-08 Astrocytoma, anaplastic 39 0.20352597372905518
-TCGA-CS-4944-01A-01D-1468-08 Astrocytoma, NOS 24 0.12524675306403396
-TCGA-CS-5390-01A-02D-1468-08 Oligodendroglioma, NOS 57 0.29746103852708067
-TCGA-CS-5393-01A-01D-1468-08 Astrocytoma, anaplastic 36 0.18787012959605093
-TCGA-CS-5394-01A-01D-1468-08 Astrocytoma, anaplastic 31 0.16177705604104387
-TCGA-CS-5395-01A-01D-1468-08 Mixed glioma 57 0.29746103852708067
-TCGA-CS-5396-01A-02D-1468-08 Oligodendroglioma, anaplastic 40 0.20874458844005658
-TCGA-CS-5397-01A-01D-1893-08 Astrocytoma, anaplastic 58 0.30267965323808205
-TCGA-CS-6186-01A-12D-2024-08 Mixed glioma 73 0.3809588739031033
-TCGA-CS-6188-01A-11D-1893-08 Astrocytoma, anaplastic 58 0.30267965323808205
-TCGA-CS-6290-01A-11D-1705-08 Astrocytoma, anaplastic 26 0.13568398248603677
-TCGA-CS-6665-01A-11D-1893-08 Astrocytoma, anaplastic 93 0.4853311681231316
-TCGA-CS-6666-01A-11D-1893-08 Astrocytoma, anaplastic 54 0.2818051943940764
-TCGA-CS-6667-01A-12D-2024-08 Astrocytoma, NOS 33 0.17221428546304668
-TCGA-CS-6668-01A-11D-1893-08 Oligodendroglioma, NOS 34 0.1774329001740481
-TCGA-CS-6669-01A-11D-1893-08 Oligodendroglioma, NOS 2 0.01043722942200283
-TCGA-CS-6670-01A-11D-1893-08 Oligodendroglioma, anaplastic 48 0.2504935061280679
-TCGA-DB-5270-01A-02D-1468-08 Mixed glioma 33 0.17221428546304668
-TCGA-DB-5273-01A-01D-1468-08 Astrocytoma, anaplastic 19 0.09915367950902688
-TCGA-DB-5274-01A-01D-1468-08 Mixed glioma 69 0.3600844150590976
-TCGA-DB-5275-01A-01D-1468-08 Mixed glioma 48 0.2504935061280679
-TCGA-DB-5276-01A-01D-1468-08 Mixed glioma 22 0.11480952364203112
-TCGA-DB-5277-01A-01D-1468-08 Astrocytoma, anaplastic 55 0.2870238091050778
-TCGA-DB-5278-01A-01D-1468-08 Oligodendroglioma, NOS 8 0.04174891768801132
-TCGA-DB-5279-01A-01D-1468-08 Oligodendroglioma, NOS 65 0.339209956215092
-TCGA-DB-5280-01A-01D-1468-08 Mixed glioma 31 0.16177705604104387
-TCGA-DB-5281-01A-01D-1468-08 Mixed glioma 69 0.3600844150590976
-TCGA-DB-A4X9-01A-11D-A26M-08 Mixed glioma 33 0.17221428546304668
-TCGA-DB-A4XA-01A-11D-A26M-08 Mixed glioma 20 0.10437229422002829
-TCGA-DB-A4XB-01A-11D-A26M-08 Astrocytoma, anaplastic 39 0.20352597372905518
-TCGA-DB-A4XC-01A-11D-A26M-08 Mixed glioma 22 0.11480952364203112
-TCGA-DB-A4XD-01A-11D-A27K-08 Astrocytoma, anaplastic 40 0.20874458844005658
-TCGA-DB-A4XE-01A-11D-A27K-08 Mixed glioma 33 0.17221428546304668
-TCGA-DB-A4XF-01A-11D-A27K-08 Astrocytoma, anaplastic 44 0.22961904728406224
-TCGA-DB-A4XG-01A-11D-A27K-08 Oligodendroglioma, anaplastic 30 0.15655844133004243
-TCGA-DB-A4XH-01A-11D-A27K-08 Mixed glioma 59 0.3078982679490835
-TCGA-DB-A64L-01A-11D-A29Q-08 Oligodendroglioma, NOS 98 0.5114242416781386
-TCGA-DB-A64O-01A-11D-A29Q-08 Mixed glioma 32 0.16699567075204527
-TCGA-DB-A64P-01A-11D-A29Q-08 Oligodendroglioma, anaplastic 33 0.17221428546304668
-TCGA-DB-A64Q-01A-11D-A29Q-08 Mixed glioma 26 0.13568398248603677
-TCGA-DB-A64R-01A-11D-A29Q-08 Oligodendroglioma, NOS 17 0.08871645008702406
-TCGA-DB-A64S-01A-11D-A29Q-08 Mixed glioma 15 0.07827922066502122
-TCGA-DB-A64U-01A-11D-A29Q-08 Mixed glioma 18 0.09393506479802546
-TCGA-DB-A64V-01A-11D-A29Q-08 Oligodendroglioma, NOS 41 0.21396320315105802
-TCGA-DB-A64W-01A-11D-A29Q-08 Mixed glioma 56 0.29224242381607923
-TCGA-DB-A64X-01A-11D-A29Q-08 Astrocytoma, anaplastic 99 0.5166428563891401
-TCGA-DB-A75K-01A-11D-A32B-08 Mixed glioma 50 0.26093073555007074
-TCGA-DB-A75L-01A-11D-A32B-08 Astrocytoma, anaplastic 49 0.2557121208390693
-TCGA-DB-A75M-01A-11D-A32B-08 Astrocytoma, NOS 30 0.15655844133004243
-TCGA-DB-A75O-01A-11D-A32B-08 Astrocytoma, anaplastic 19 0.09915367950902688
-TCGA-DB-A75P-01A-11D-A32B-08 Astrocytoma, NOS 6 0.03131168826600849
-TCGA-DH-5140-01A-01D-1468-08 Mixed glioma 38 0.19830735901805377
-TCGA-DH-5141-01A-01D-1468-08 Oligodendroglioma, anaplastic 33 0.17221428546304668
-TCGA-DH-5142-01A-01D-1468-08 Astrocytoma, anaplastic 49 0.2557121208390693
-TCGA-DH-5143-01A-01D-1468-08 Mixed glioma 37 0.19308874430705233
-TCGA-DH-5144-01A-01D-1468-08 Oligodendroglioma, anaplastic 55 0.2870238091050778
-TCGA-DH-A669-01A-12D-A31L-08 Oligodendroglioma, anaplastic 69 0.3600844150590976
-TCGA-DH-A669-02A-11D-A31L-08 Oligodendroglioma, anaplastic 75 0.3913961033251061
-TCGA-DH-A66B-01A-11D-A29Q-08 Astrocytoma, anaplastic 61 0.3183354973710863
-TCGA-DH-A66D-01A-11D-A31L-08 Astrocytoma, anaplastic 52 0.27136796497207355
-TCGA-DH-A66F-01A-11D-A29Q-08 Oligodendroglioma, NOS 23 0.12002813835303254
-TCGA-DH-A66G-01A-21D-A31L-08 Oligodendroglioma, anaplastic 45 0.23483766199506367
-TCGA-DH-A7UR-01A-11D-A33T-08 Oligodendroglioma, anaplastic 87 0.4540194798571231
-TCGA-DH-A7US-01A-11D-A33T-08 Oligodendroglioma, NOS 43 0.22440043257306083
-TCGA-DH-A7UT-01A-12D-A34A-08 Astrocytoma, anaplastic 26 0.13568398248603677
-TCGA-DH-A7UU-01A-12D-A34A-08 Astrocytoma, anaplastic 62 0.32355411208208773
-TCGA-DH-A7UV-01A-12D-A34A-08 Astrocytoma, anaplastic 44 0.22961904728406224
-TCGA-DU-5847-01A-11D-1705-08 Astrocytoma, anaplastic 51 0.26614935026107217
-TCGA-DU-5849-01A-11D-1705-08 Oligodendroglioma, NOS 47 0.2452748914170665
-TCGA-DU-5851-01A-13D-1893-08 Mixed glioma 33 0.17221428546304668
-TCGA-DU-5852-01A-11D-1705-08 Mixed glioma 96 0.5009870122561358
-TCGA-DU-5853-01A-11D-1893-08 Mixed glioma 23 0.12002813835303254
-TCGA-DU-5854-01A-11D-1705-08 Astrocytoma, anaplastic 58 0.30267965323808205
-TCGA-DU-5855-01A-11D-1705-08 Mixed glioma 74 0.38617748861410467
-TCGA-DU-5870-01A-11D-1705-08 Oligodendroglioma, NOS 20 0.10437229422002829
-TCGA-DU-5870-02A-12D-A36O-08 Oligodendroglioma, NOS 53 0.276586579683075
-TCGA-DU-5871-01A-12D-1705-08 Mixed glioma 39 0.20352597372905518
-TCGA-DU-5872-01A-11D-1705-08 Mixed glioma 35 0.18265151488504952
-TCGA-DU-5872-02A-21D-A36O-08 Mixed glioma 9 0.04696753239901273
-TCGA-DU-5874-01A-11D-1705-08 Oligodendroglioma, NOS 60 0.31311688266008486
-TCGA-DU-6392-01A-11D-1705-08 Astrocytoma, anaplastic 15906 83.0072855931885
-TCGA-DU-6393-01A-11D-1705-08 Oligodendroglioma, anaplastic 40 0.20874458844005658
-TCGA-DU-6394-01A-11D-1705-08 Oligodendroglioma, anaplastic 39 0.20352597372905518
-TCGA-DU-6395-01A-12D-1705-08 Mixed glioma 35 0.18265151488504952
-TCGA-DU-6396-01A-11D-1705-08 Mixed glioma 54 0.2818051943940764
-TCGA-DU-6397-01A-11D-1705-08 Oligodendroglioma, anaplastic 41 0.21396320315105802
-TCGA-DU-6397-02A-12D-A36O-08 Oligodendroglioma, anaplastic 73 0.3809588739031033
-TCGA-DU-6399-01A-12D-1705-08 Oligodendroglioma, NOS 74 0.38617748861410467
-TCGA-DU-6400-01A-12D-1705-08 Oligodendroglioma, NOS 57 0.29746103852708067
-TCGA-DU-6401-01A-11D-1705-08 Oligodendroglioma, NOS 32 0.16699567075204527
-TCGA-DU-6402-01A-11D-1705-08 Astrocytoma, anaplastic 63 0.3287727267930891
-TCGA-DU-6403-01A-11D-1705-08 Mixed glioma 78 0.40705194745811035
-TCGA-DU-6404-01A-11D-1705-08 Oligodendroglioma, anaplastic 16 0.08349783537602264
-TCGA-DU-6404-02B-11D-A36O-08 Oligodendroglioma, anaplastic 115 0.6001406917651627
-TCGA-DU-6405-01A-11D-1705-08 Astrocytoma, anaplastic 65 0.339209956215092
-TCGA-DU-6406-01A-11D-1705-08 Mixed glioma 5 0.026093073555007073
-TCGA-DU-6407-01A-13D-1705-08 Oligodendroglioma, NOS 34 0.1774329001740481
-TCGA-DU-6407-02B-11D-A36O-08 Oligodendroglioma, NOS 2635 13.751049763488728
-TCGA-DU-6408-01A-11D-1705-08 Oligodendroglioma, anaplastic 33 0.17221428546304668
-TCGA-DU-6410-01A-11D-1893-08 Oligodendroglioma, anaplastic 54 0.2818051943940764
-TCGA-DU-6542-01A-11D-1893-08 Mixed glioma 32 0.16699567075204527
-TCGA-DU-7006-01A-11D-2024-08 Astrocytoma, anaplastic 79 0.4122705621691118
-TCGA-DU-7007-01A-11D-2024-08 Astrocytoma, NOS 51 0.26614935026107217
-TCGA-DU-7008-01A-11D-2024-08 Oligodendroglioma, NOS 45 0.23483766199506367
-TCGA-DU-7009-01A-11D-2024-08 Oligodendroglioma, NOS 28 0.14612121190803962
-TCGA-DU-7010-01A-11D-2024-08 Astrocytoma, anaplastic 118 0.615796535898167
-TCGA-DU-7011-01A-11D-2024-08 Mixed glioma 22 0.11480952364203112
-TCGA-DU-7012-01A-11D-2024-08 Astrocytoma, anaplastic 78 0.40705194745811035
-TCGA-DU-7013-01A-11D-2024-08 Astrocytoma, anaplastic 53 0.276586579683075
-TCGA-DU-7015-01A-11D-2024-08 Oligodendroglioma, NOS 37 0.19308874430705233
-TCGA-DU-7018-01A-11D-2024-08 Oligodendroglioma, anaplastic 49 0.2557121208390693
-TCGA-DU-7019-01A-11D-2024-08 Mixed glioma 48 0.2504935061280679
-TCGA-DU-7290-01A-11D-2024-08 Astrocytoma, anaplastic 42 0.21918181786205942
-TCGA-DU-7292-01A-11D-2024-08 Astrocytoma, anaplastic 63 0.3287727267930891
-TCGA-DU-7294-01A-11D-2024-08 Oligodendroglioma, NOS 625 3.261634194375884
-TCGA-DU-7298-01A-11D-2024-08 Astrocytoma, anaplastic 409 2.1344134167995787
-TCGA-DU-7299-01A-21D-2024-08 Astrocytoma, anaplastic 40 0.20874458844005658
-TCGA-DU-7300-01A-21D-2086-08 Oligodendroglioma, anaplastic 66 0.34442857092609336
-TCGA-DU-7301-01A-11D-2086-08 Oligodendroglioma, NOS 36 0.18787012959605093
-TCGA-DU-7302-01A-11D-2086-08 Oligodendroglioma, anaplastic 56 0.29224242381607923
-TCGA-DU-7304-01A-12D-2086-08 Mixed glioma 39 0.20352597372905518
-TCGA-DU-7304-02A-12D-A36O-08 Mixed glioma 107 0.5583917740771513
-TCGA-DU-7306-01A-11D-2086-08 Mixed glioma 80 0.41748917688011317
-TCGA-DU-7309-01A-11D-2086-08 Oligodendroglioma, anaplastic 50 0.26093073555007074
-TCGA-DU-8158-01A-11D-2253-08 Astrocytoma, anaplastic 50 0.26093073555007074
-TCGA-DU-8161-01A-11D-2253-08 Mixed glioma 58 0.30267965323808205
-TCGA-DU-8162-01A-21D-2253-08 Mixed glioma 35 0.18265151488504952
-TCGA-DU-8163-01A-11D-2253-08 Mixed glioma 24 0.12524675306403396
-TCGA-DU-8164-01A-11D-2253-08 Oligodendroglioma, NOS 44 0.22961904728406224
-TCGA-DU-8165-01A-11D-2253-08 Oligodendroglioma, NOS 92 0.48011255341213016
-TCGA-DU-8166-01A-11D-2253-08 Mixed glioma 38 0.19830735901805377
-TCGA-DU-8167-01A-11D-2253-08 Mixed glioma 84 0.43836363572411885
-TCGA-DU-8168-01A-11D-2253-08 Oligodendroglioma, anaplastic 84 0.43836363572411885
-TCGA-DU-A5TP-01A-11D-A289-08 Astrocytoma, anaplastic 37 0.19308874430705233
-TCGA-DU-A5TR-01A-11D-A289-08 Mixed glioma 49 0.2557121208390693
-TCGA-DU-A5TS-01A-11D-A289-08 Oligodendroglioma, NOS 51 0.26614935026107217
-TCGA-DU-A5TT-01A-11D-A289-08 Oligodendroglioma, anaplastic 71 0.3705216444811005
-TCGA-DU-A5TU-01A-11D-A289-08 Astrocytoma, NOS 59 0.3078982679490835
-TCGA-DU-A5TW-01A-11D-A289-08 Astrocytoma, anaplastic 65 0.339209956215092
-TCGA-DU-A5TY-01A-11D-A289-08 Astrocytoma, anaplastic 70 0.36530302977009904
-TCGA-DU-A6S2-01A-21D-A32B-08 Oligodendroglioma, NOS 20 0.10437229422002829
-TCGA-DU-A6S3-01A-12D-A32B-08 Oligodendroglioma, NOS 41 0.21396320315105802
-TCGA-DU-A6S6-01A-21D-A32B-08 Mixed glioma 19 0.09915367950902688
-TCGA-DU-A6S7-01A-21D-A32B-08 Astrocytoma, anaplastic 40 0.20874458844005658
-TCGA-DU-A6S8-01A-12D-A32B-08 Oligodendroglioma, anaplastic 68 0.3548658003480962
-TCGA-DU-A76K-01A-11D-A33T-08 Oligodendroglioma, NOS 71 0.3705216444811005
-TCGA-DU-A76L-01A-11D-A32B-08 Oligodendroglioma, anaplastic 51 0.26614935026107217
-TCGA-DU-A76O-01A-11D-A32B-08 Astrocytoma, NOS 35 0.18265151488504952
-TCGA-DU-A76R-01A-11D-A32B-08 Oligodendroglioma, anaplastic 53 0.276586579683075
-TCGA-DU-A7T6-01A-11D-A33T-08 Oligodendroglioma, anaplastic 143 0.7462619036732023
-TCGA-DU-A7T8-01A-21D-A34J-08 Mixed glioma 22 0.11480952364203112
-TCGA-DU-A7TA-01A-11D-A33T-08 Oligodendroglioma, NOS 47 0.2452748914170665
-TCGA-DU-A7TB-01A-11D-A33T-08 Oligodendroglioma, NOS 43 0.22440043257306083
-TCGA-DU-A7TC-01A-21D-A34J-08 Astrocytoma, NOS 37 0.19308874430705233
-TCGA-DU-A7TD-01A-12D-A34A-08 Mixed glioma 55 0.2870238091050778
-TCGA-DU-A7TG-01A-21D-A34J-08 Oligodendroglioma, NOS 14 0.07306060595401981
-TCGA-DU-A7TJ-01A-11D-A34J-08 Astrocytoma, anaplastic 59 0.3078982679490835
-TCGA-E1-5302-01A-01D-1468-08 Astrocytoma, anaplastic 46 0.24005627670606508
-TCGA-E1-5303-01A-01D-1468-08 Astrocytoma, anaplastic 37 0.19308874430705233
-TCGA-E1-5304-01A-01D-1468-08 Astrocytoma, anaplastic 39 0.20352597372905518
-TCGA-E1-5305-01A-01D-1893-08 Astrocytoma, anaplastic 36 0.18787012959605093
-TCGA-E1-5307-01A-01D-1893-08 Astrocytoma, anaplastic 89 0.4644567092791259
-TCGA-E1-5311-01A-01D-1468-08 Oligodendroglioma, anaplastic 23 0.12002813835303254
-TCGA-E1-5318-01A-01D-1468-08 Oligodendroglioma, NOS 50 0.26093073555007074
-TCGA-E1-5319-01A-01D-1893-08 Oligodendroglioma, NOS 51 0.26614935026107217
-TCGA-E1-5322-01A-01D-1468-08 Mixed glioma 37 0.19308874430705233
-TCGA-E1-A7YD-01A-11D-A34A-08 Astrocytoma, anaplastic 78 0.40705194745811035
-TCGA-E1-A7YE-01A-11D-A34A-08 Astrocytoma, anaplastic 90 0.46967532399012735
-TCGA-E1-A7YH-01A-11D-A34A-08 Astrocytoma, anaplastic 36 0.18787012959605093
-TCGA-E1-A7YI-01A-11D-A34A-08 Astrocytoma, anaplastic 54 0.2818051943940764
-TCGA-E1-A7YJ-01A-11D-A34A-08 Astrocytoma, anaplastic 59 0.3078982679490835
-TCGA-E1-A7YK-01A-11D-A34A-08 Astrocytoma, anaplastic 73 0.3809588739031033
-TCGA-E1-A7YL-01A-11D-A34A-08 Astrocytoma, anaplastic 70 0.36530302977009904
-TCGA-E1-A7YM-01A-11D-A34A-08 Astrocytoma, anaplastic 53 0.276586579683075
-TCGA-E1-A7YN-01A-11D-A34A-08 Astrocytoma, anaplastic 82 0.42792640630211604
-TCGA-E1-A7YO-01A-11D-A34A-08 Oligodendroglioma, anaplastic 35 0.18265151488504952
-TCGA-E1-A7YQ-01A-11D-A34J-08 Oligodendroglioma, anaplastic 49 0.2557121208390693
-TCGA-E1-A7YS-01A-11D-A34A-08 Oligodendroglioma, anaplastic 75 0.3913961033251061
-TCGA-E1-A7YU-01A-11D-A34J-08 Mixed glioma 28 0.14612121190803962
-TCGA-E1-A7YV-01A-11D-A34J-08 Mixed glioma 62 0.32355411208208773
-TCGA-E1-A7YW-01A-11D-A34J-08 Mixed glioma 27 0.1409025971970382
-TCGA-E1-A7YY-01A-11D-A34J-08 Oligodendroglioma, NOS 10 0.052186147110014146
-TCGA-E1-A7Z2-01A-21D-A34J-08 Oligodendroglioma, NOS 46 0.24005627670606508
-TCGA-E1-A7Z3-01A-11D-A34J-08 Astrocytoma, NOS 39 0.20352597372905518
-TCGA-E1-A7Z4-01A-11D-A34J-08 Astrocytoma, NOS 26 0.13568398248603677
-TCGA-E1-A7Z6-01A-11D-A34J-08 Astrocytoma, NOS 30 0.15655844133004243
-TCGA-EZ-7264-01A-11D-2024-08 Oligodendroglioma, NOS 39 0.20352597372905518
-TCGA-F6-A8O3-01A-11D-A36O-08 Oligodendroglioma, NOS 30 0.15655844133004243
-TCGA-F6-A8O4-01A-11D-A36O-08 Astrocytoma, NOS 40 0.20874458844005658
-TCGA-FG-5962-01B-11D-1893-08 Oligodendroglioma, anaplastic 48 0.2504935061280679
-TCGA-FG-5963-01A-11D-1705-08 Astrocytoma, anaplastic 29 0.15133982661904102
-TCGA-FG-5963-02A-12D-A29Q-08 Astrocytoma, anaplastic 36 0.18787012959605093
-TCGA-FG-5964-01A-11D-1705-08 Oligodendroglioma, NOS 50 0.26093073555007074
-TCGA-FG-5965-01B-11D-1893-08 Mixed glioma 57 0.29746103852708067
-TCGA-FG-5965-02B-11D-A29Q-08 Mixed glioma 85 0.4435822504351203
-TCGA-FG-6688-01A-11D-1893-08 Astrocytoma, anaplastic 85 0.4435822504351203
-TCGA-FG-6689-01A-11D-1893-08 Astrocytoma, NOS 30 0.15655844133004243
-TCGA-FG-6690-01A-11D-1893-08 Oligodendroglioma, NOS 44 0.22961904728406224
-TCGA-FG-6691-01A-11D-1893-08 Astrocytoma, NOS 27 0.1409025971970382
-TCGA-FG-6692-01A-11D-1893-08 Oligodendroglioma, anaplastic 103 0.5375173152331457
-TCGA-FG-7634-01A-11D-2086-08 Oligodendroglioma, NOS 24 0.12524675306403396
-TCGA-FG-7636-01A-11D-2086-08 Astrocytoma, anaplastic 56 0.29224242381607923
-TCGA-FG-7637-01A-11D-2086-08 Mixed glioma 42 0.21918181786205942
-TCGA-FG-7638-01B-11D-2086-08 Oligodendroglioma, anaplastic 24 0.12524675306403396
-TCGA-FG-7641-01B-11D-2253-08 Oligodendroglioma, NOS 35 0.18265151488504952
-TCGA-FG-7643-01A-11D-2086-08 Mixed glioma 71 0.3705216444811005
-TCGA-FG-8181-01A-11D-2253-08 Mixed glioma 2 0.01043722942200283
-TCGA-FG-8182-01A-11D-2253-08 Oligodendroglioma, NOS 40 0.20874458844005658
-TCGA-FG-8185-01A-11D-2253-08 Astrocytoma, anaplastic 51 0.26614935026107217
-TCGA-FG-8186-01A-11D-2253-08 Mixed glioma 42 0.21918181786205942
-TCGA-FG-8187-01A-11D-2253-08 Mixed glioma 23 0.12002813835303254
-TCGA-FG-8188-01A-11D-2253-08 Mixed glioma 42 0.21918181786205942
-TCGA-FG-8189-01B-11D-A289-08 Oligodendroglioma, NOS 11 0.05740476182101556
-TCGA-FG-8191-01A-11D-2253-08 Oligodendroglioma, anaplastic 33 0.17221428546304668
-TCGA-FG-A4MT-01A-11D-A26M-08 Oligodendroglioma, NOS 21 0.10959090893102971
-TCGA-FG-A4MT-02A-11D-A29Q-08 Oligodendroglioma, NOS 22 0.11480952364203112
-TCGA-FG-A4MU-01B-11D-A289-08 Mixed glioma 78 0.40705194745811035
-TCGA-FG-A4MW-01A-11D-A26M-08 Mixed glioma 101 0.5270800858111429
-TCGA-FG-A4MX-01A-11D-A26M-08 Astrocytoma, NOS 29 0.15133982661904102
-TCGA-FG-A4MY-01A-11D-A26M-08 Mixed glioma 36 0.18787012959605093
-TCGA-FG-A60J-01A-11D-A289-08 Mixed glioma 48 0.2504935061280679
-TCGA-FG-A60K-01A-11D-A29Q-08 Mixed glioma 28 0.14612121190803962
-TCGA-FG-A60L-01A-12D-A31L-08 Astrocytoma, NOS 32 0.16699567075204527
-TCGA-FG-A6IZ-01A-11D-A31L-08 Oligodendroglioma, NOS 82 0.42792640630211604
-TCGA-FG-A6J1-01A-11D-A31L-08 Oligodendroglioma, NOS 39 0.20352597372905518
-TCGA-FG-A6J3-01A-11D-A31L-08 Astrocytoma, anaplastic 99 0.5166428563891401
-TCGA-FG-A70Y-01A-12D-A34J-08 Oligodendroglioma, NOS 24 0.12524675306403396
-TCGA-FG-A70Z-01A-12D-A33T-08 Mixed glioma 81 0.4227077915911146
-TCGA-FG-A710-01A-12D-A33T-08 Oligodendroglioma, NOS 52 0.27136796497207355
-TCGA-FG-A711-01A-21D-A33T-08 Oligodendroglioma, NOS 56 0.29224242381607923
-TCGA-FG-A713-01A-11D-A32B-08 Mixed glioma 40 0.20874458844005658
-TCGA-FG-A87N-01A-11D-A36O-08 Astrocytoma, anaplastic 39 0.20352597372905518
-TCGA-FG-A87Q-01A-11D-A36O-08 Astrocytoma, anaplastic 82 0.42792640630211604
-TCGA-FN-7833-01A-11D-2086-08 Mixed glioma 26 0.13568398248603677
-TCGA-HT-7467-01A-11D-2024-08 Oligodendroglioma, NOS 39 0.20352597372905518
-TCGA-HT-7468-01A-11D-2024-08 Oligodendroglioma, anaplastic 18 0.09393506479802546
-TCGA-HT-7469-01A-11D-2253-08 Oligodendroglioma, anaplastic 51 0.26614935026107217
-TCGA-HT-7470-01A-12D-2086-08 Oligodendroglioma, anaplastic 52 0.27136796497207355
-TCGA-HT-7471-01A-11D-2253-08 Oligodendroglioma, anaplastic 26 0.13568398248603677
-TCGA-HT-7472-01A-11D-2024-08 Oligodendroglioma, NOS 30 0.15655844133004243
-TCGA-HT-7473-01A-11D-2024-08 Mixed glioma 26 0.13568398248603677
-TCGA-HT-7474-01A-11D-2024-08 Mixed glioma 35 0.18265151488504952
-TCGA-HT-7475-01A-11D-2024-08 Mixed glioma 70 0.36530302977009904
-TCGA-HT-7476-01A-11D-2024-08 Astrocytoma, NOS 51 0.26614935026107217
-TCGA-HT-7477-01B-11D-A289-08 Astrocytoma, anaplastic 60 0.31311688266008486
-TCGA-HT-7478-01A-11D-2024-08 Astrocytoma, NOS 34 0.1774329001740481
-TCGA-HT-7479-01A-11D-2024-08 Astrocytoma, anaplastic 39 0.20352597372905518
-TCGA-HT-7480-01A-11D-2086-08 Oligodendroglioma, NOS 39 0.20352597372905518
-TCGA-HT-7481-01A-11D-2024-08 Oligodendroglioma, NOS 35 0.18265151488504952
-TCGA-HT-7482-01A-11D-2024-08 Mixed glioma 24 0.12524675306403396
-TCGA-HT-7483-01A-11D-2024-08 Mixed glioma 22 0.11480952364203112
-TCGA-HT-7485-01A-11D-2024-08 Astrocytoma, NOS 19 0.09915367950902688
-TCGA-HT-7601-01A-11D-2086-08 Astrocytoma, anaplastic 32 0.16699567075204527
-TCGA-HT-7602-01A-21D-2086-08 Oligodendroglioma, NOS 14 0.07306060595401981
-TCGA-HT-7603-01A-21D-2086-08 Oligodendroglioma, NOS 38 0.19830735901805377
-TCGA-HT-7604-01A-11D-2086-08 Astrocytoma, NOS 56 0.29224242381607923
-TCGA-HT-7605-01A-11D-2086-08 Oligodendroglioma, NOS 39 0.20352597372905518
-TCGA-HT-7606-01A-11D-2086-08 Astrocytoma, NOS 41 0.21396320315105802
-TCGA-HT-7607-01A-11D-2086-08 Astrocytoma, NOS 39 0.20352597372905518
-TCGA-HT-7608-01A-11D-2086-08 Mixed glioma 31 0.16177705604104387
-TCGA-HT-7609-01A-11D-2086-08 Mixed glioma 36 0.18787012959605093
-TCGA-HT-7610-01A-21D-2086-08 Mixed glioma 22 0.11480952364203112
-TCGA-HT-7611-01A-11D-2395-08 Mixed glioma 41 0.21396320315105802
-TCGA-HT-7616-01A-11D-2253-08 Oligodendroglioma, anaplastic 60 0.31311688266008486
-TCGA-HT-7620-01A-11D-2253-08 Oligodendroglioma, anaplastic 23 0.12002813835303254
-TCGA-HT-7676-01A-11D-2395-08 Oligodendroglioma, NOS 21 0.10959090893102971
-TCGA-HT-7677-01A-11D-2253-08 Oligodendroglioma, anaplastic 54 0.2818051943940764
-TCGA-HT-7680-01A-11D-2253-08 Astrocytoma, NOS 4 0.02087445884400566
-TCGA-HT-7681-01A-11D-2395-08 Mixed glioma 19 0.09915367950902688
-TCGA-HT-7684-01A-11D-2253-08 Mixed glioma 48 0.2504935061280679
-TCGA-HT-7686-01A-11D-2253-08 Astrocytoma, anaplastic 27 0.1409025971970382
-TCGA-HT-7687-01A-11D-2253-08 Oligodendroglioma, anaplastic 61 0.3183354973710863
-TCGA-HT-7688-01A-11D-2253-08 Oligodendroglioma, anaplastic 113 0.5897034623431598
-TCGA-HT-7689-01A-11D-2253-08 Oligodendroglioma, NOS 68 0.3548658003480962
-TCGA-HT-7690-01A-11D-2253-08 Mixed glioma 27 0.1409025971970382
-TCGA-HT-7691-01A-11D-2253-08 Astrocytoma, NOS 9 0.04696753239901273
-TCGA-HT-7692-01A-12D-2253-08 Mixed glioma 34 0.1774329001740481
-TCGA-HT-7693-01A-11D-2253-08 Oligodendroglioma, NOS 51 0.26614935026107217
-TCGA-HT-7694-01A-11D-2253-08 Oligodendroglioma, anaplastic 47 0.2452748914170665
-TCGA-HT-7695-01A-11D-2253-08 Oligodendroglioma, NOS 31 0.16177705604104387
-TCGA-HT-7854-01A-11D-2253-08 Astrocytoma, NOS 33 0.17221428546304668
-TCGA-HT-7855-01A-11D-2395-08 Astrocytoma, anaplastic 53 0.276586579683075
-TCGA-HT-7856-01A-11D-2395-08 Oligodendroglioma, anaplastic 18 0.09393506479802546
-TCGA-HT-7857-01A-11D-2395-08 Astrocytoma, anaplastic 25 0.13046536777503537
-TCGA-HT-7858-01A-11D-2395-08 Astrocytoma, NOS 27 0.1409025971970382
-TCGA-HT-7860-01A-11D-2395-08 Astrocytoma, anaplastic 105 0.5479545446551486
-TCGA-HT-7873-01B-11D-2395-08 Mixed glioma 37 0.19308874430705233
-TCGA-HT-7874-01A-11D-2395-08 Oligodendroglioma, anaplastic 30 0.15655844133004243
-TCGA-HT-7875-01A-11D-2395-08 Oligodendroglioma, NOS 48 0.2504935061280679
-TCGA-HT-7877-01A-11D-2395-08 Oligodendroglioma, NOS 20 0.10437229422002829
-TCGA-HT-7879-01A-11D-2395-08 Mixed glioma 23 0.12002813835303254
-TCGA-HT-7880-01A-11D-2395-08 Mixed glioma 11 0.05740476182101556
-TCGA-HT-7881-01A-11D-2395-08 Oligodendroglioma, NOS 23 0.12002813835303254
-TCGA-HT-7882-01A-11D-2395-08 Oligodendroglioma, anaplastic 66 0.34442857092609336
-TCGA-HT-7884-01B-11D-2395-08 Astrocytoma, NOS 51 0.26614935026107217
-TCGA-HT-7902-01A-12D-2395-08 Mixed glioma 34 0.1774329001740481
-TCGA-HT-8010-01A-11D-2395-08 Oligodendroglioma, NOS 17 0.08871645008702406
-TCGA-HT-8011-01A-11D-2395-08 Astrocytoma, anaplastic 58 0.30267965323808205
-TCGA-HT-8012-01A-11D-2395-08 Oligodendroglioma, NOS 33 0.17221428546304668
-TCGA-HT-8013-01A-11D-2395-08 Mixed glioma 33 0.17221428546304668
-TCGA-HT-8015-01B-11D-A289-08 Astrocytoma, NOS 3 0.015655844133004245
-TCGA-HT-8018-01A-11D-2395-08 Mixed glioma 26 0.13568398248603677
-TCGA-HT-8104-01A-11D-2395-08 Astrocytoma, anaplastic 85 0.4435822504351203
-TCGA-HT-8105-01A-11D-2395-08 Oligodendroglioma, anaplastic 63 0.3287727267930891
-TCGA-HT-8106-01A-11D-2395-08 Astrocytoma, anaplastic 44 0.22961904728406224
-TCGA-HT-8107-01A-13D-2395-08 Oligodendroglioma, NOS 3 0.015655844133004245
-TCGA-HT-8108-01A-11D-2395-08 Oligodendroglioma, NOS 28 0.14612121190803962
-TCGA-HT-8109-01A-11D-2395-08 Oligodendroglioma, anaplastic 51 0.26614935026107217
-TCGA-HT-8110-01A-11D-2395-08 Astrocytoma, anaplastic 49 0.2557121208390693
-TCGA-HT-8111-01A-11D-2395-08 Mixed glioma 19 0.09915367950902688
-TCGA-HT-8113-01A-11D-2395-08 Oligodendroglioma, NOS 30 0.15655844133004243
-TCGA-HT-8114-01A-11D-2395-08 Mixed glioma 24 0.12524675306403396
-TCGA-HT-8558-01A-21D-2395-08 Oligodendroglioma, NOS 2 0.01043722942200283
-TCGA-HT-8563-01A-11D-2395-08 Astrocytoma, anaplastic 40 0.20874458844005658
-TCGA-HT-8564-01A-11D-2395-08 Astrocytoma, anaplastic 732 3.8200259684530358
-TCGA-HT-A4DS-01A-11D-A26M-08 Astrocytoma, anaplastic 46 0.24005627670606508
-TCGA-HT-A4DV-01A-11D-A26M-08 Oligodendroglioma, anaplastic 23 0.12002813835303254
-TCGA-HT-A5R5-01A-11D-A289-08 Oligodendroglioma, NOS 38 0.19830735901805377
-TCGA-HT-A5R7-01A-11D-A289-08 Astrocytoma, anaplastic 41 0.21396320315105802
-TCGA-HT-A5R9-01A-11D-A289-08 Oligodendroglioma, anaplastic 55 0.2870238091050778
-TCGA-HT-A5RA-01A-11D-A289-08 Astrocytoma, anaplastic 69 0.3600844150590976
-TCGA-HT-A5RB-01A-11D-A289-08 Astrocytoma, NOS 28 0.14612121190803962
-TCGA-HT-A5RC-01A-11D-A289-08 Astrocytoma, anaplastic 71 0.3705216444811005
-TCGA-HT-A614-01A-11D-A29Q-08 Mixed glioma 50 0.26093073555007074
-TCGA-HT-A615-01A-11D-A29Q-08 Oligodendroglioma, NOS 40 0.20874458844005658
-TCGA-HT-A616-01A-11D-A29Q-08 Astrocytoma, NOS 34 0.1774329001740481
-TCGA-HT-A617-01A-11D-A29Q-08 Oligodendroglioma, NOS 30 0.15655844133004243
-TCGA-HT-A618-01A-11D-A29Q-08 Astrocytoma, anaplastic 35 0.18265151488504952
-TCGA-HT-A619-01A-11D-A29Q-08 Oligodendroglioma, anaplastic 69 0.3600844150590976
-TCGA-HT-A61A-01A-11D-A29Q-08 Oligodendroglioma, NOS 8 0.04174891768801132
-TCGA-HT-A61B-01A-11D-A29Q-08 Astrocytoma, anaplastic 48 0.2504935061280679
-TCGA-HT-A61C-01A-11D-A29Q-08 Oligodendroglioma, anaplastic 50 0.26093073555007074
-TCGA-HT-A74H-01A-11D-A32B-08 Astrocytoma, anaplastic 66 0.34442857092609336
-TCGA-HT-A74J-01A-12D-A32B-08 Mixed glioma 30 0.15655844133004243
-TCGA-HT-A74K-01A-11D-A32B-08 Oligodendroglioma, anaplastic 40 0.20874458844005658
-TCGA-HT-A74L-01A-11D-A32B-08 Mixed glioma 18 0.09393506479802546
-TCGA-HT-A74O-01A-11D-A32B-08 Astrocytoma, anaplastic 28 0.14612121190803962
-TCGA-HW-7486-01A-11D-2024-08 Oligodendroglioma, NOS 20 0.10437229422002829
-TCGA-HW-7487-01A-11D-2024-08 Oligodendroglioma, NOS 24 0.12524675306403396
-TCGA-HW-7489-01A-11D-2024-08 Mixed glioma 30 0.15655844133004243
-TCGA-HW-7490-01A-11D-2024-08 Astrocytoma, NOS 144 0.7514805183842037
-TCGA-HW-7491-01A-11D-2024-08 Oligodendroglioma, NOS 25 0.13046536777503537
-TCGA-HW-7495-01A-11D-2024-08 Oligodendroglioma, NOS 24 0.12524675306403396
-TCGA-HW-8319-01A-11D-2395-08 Astrocytoma, anaplastic 40 0.20874458844005658
-TCGA-HW-8320-01A-11D-2395-08 Astrocytoma, anaplastic 40 0.20874458844005658
-TCGA-HW-8321-01A-11D-2395-08 Astrocytoma, NOS 45 0.23483766199506367
-TCGA-HW-8322-01A-11D-2395-08 Oligodendroglioma, NOS 31 0.16177705604104387
-TCGA-HW-A5KJ-01A-12D-A27K-08 Oligodendroglioma, anaplastic 53 0.276586579683075
-TCGA-HW-A5KK-01A-11D-A27K-08 Astrocytoma, anaplastic 51 0.26614935026107217
-TCGA-HW-A5KL-01A-11D-A27K-08 Astrocytoma, NOS 24 0.12524675306403396
-TCGA-HW-A5KM-01A-11D-A27K-08 Astrocytoma, NOS 26 0.13568398248603677
-TCGA-IK-7675-01A-11D-2086-08 Oligodendroglioma, NOS 50 0.26093073555007074
-TCGA-IK-8125-01A-11D-2253-08 Mixed glioma 68 0.3548658003480962
-TCGA-KT-A74X-01A-11D-A32B-08 Mixed glioma 16 0.08349783537602264
-TCGA-KT-A7W1-01A-11D-A34A-08 Astrocytoma, anaplastic 60 0.31311688266008486
-TCGA-OX-A56R-01A-11D-A33T-08 Glioblastoma 53 0.276586579683075
-TCGA-P5-A5ET-01A-11D-A27K-08 Oligodendroglioma, NOS 32 0.16699567075204527
-TCGA-P5-A5EU-01A-11D-A27K-08 Astrocytoma, anaplastic 35 0.18265151488504952
-TCGA-P5-A5EV-01A-11D-A27K-08 Astrocytoma, NOS 120 0.6262337653201697
-TCGA-P5-A5EW-01A-11D-A27K-08 Astrocytoma, NOS 26 0.13568398248603677
-TCGA-P5-A5EX-01A-12D-A289-08 Oligodendroglioma, anaplastic 31 0.16177705604104387
-TCGA-P5-A5EY-01A-11D-A27K-08 Astrocytoma, NOS 2 0.01043722942200283
-TCGA-P5-A5EZ-01A-11D-A27K-08 Astrocytoma, anaplastic 53 0.276586579683075
-TCGA-P5-A5F0-01A-11D-A289-08 Oligodendroglioma, NOS 39 0.20352597372905518
-TCGA-P5-A5F1-01A-11D-A289-08 Astrocytoma, NOS 30 0.15655844133004243
-TCGA-P5-A5F2-01A-11D-A289-08 Astrocytoma, NOS 37 0.19308874430705233
-TCGA-P5-A5F4-01A-11D-A289-08 Oligodendroglioma, anaplastic 43 0.22440043257306083
-TCGA-P5-A5F6-01A-11D-A289-08 Oligodendroglioma, NOS 4 0.02087445884400566
-TCGA-P5-A72U-01A-31D-A32B-08 Oligodendroglioma, anaplastic 73 0.3809588739031033
-TCGA-P5-A72W-01A-11D-A32B-08 Astrocytoma, anaplastic 44 0.22961904728406224
-TCGA-P5-A72X-01A-11D-A32B-08 Astrocytoma, anaplastic 13 0.06784199124301839
-TCGA-P5-A72Z-01A-11D-A32B-08 Oligodendroglioma, anaplastic 38 0.19830735901805377
-TCGA-P5-A730-01A-11D-A32B-08 Mixed glioma 25 0.13046536777503537
-TCGA-P5-A731-01A-11D-A32B-08 Mixed glioma 30 0.15655844133004243
-TCGA-P5-A733-01A-11D-A32B-08 Astrocytoma, NOS 51 0.26614935026107217
-TCGA-P5-A735-01A-11D-A32B-08 Astrocytoma, NOS 30 0.15655844133004243
-TCGA-P5-A736-01A-11D-A32B-08 Astrocytoma, anaplastic 35 0.18265151488504952
-TCGA-P5-A737-01A-11D-A32B-08 Mixed glioma 31 0.16177705604104387
-TCGA-P5-A77W-01A-11D-A32B-08 Mixed glioma 39 0.20352597372905518
-TCGA-P5-A77X-01A-11D-A32B-08 Mixed glioma 42 0.21918181786205942
-TCGA-P5-A780-01A-12D-A32B-08 Astrocytoma, anaplastic 69 0.3600844150590976
-TCGA-P5-A781-01A-11D-A32B-08 Astrocytoma, anaplastic 23 0.12002813835303254
-TCGA-P7-A5NX-01A-11D-A35D-08 Pheochromocytoma, malignant 20 0.10437229422002829
-TCGA-P7-A5NY-01A-12D-A35D-08 Pheochromocytoma, malignant 6 0.03131168826600849
-TCGA-P7-A5NY-05A-11D-A35D-08 Pheochromocytoma, malignant 5 0.026093073555007073
-TCGA-P8-A5KC-01A-11D-A35D-08 Pheochromocytoma, NOS 32 0.16699567075204527
-TCGA-P8-A5KD-01A-11D-A35D-08 Pheochromocytoma, NOS 22 0.11480952364203112
-TCGA-P8-A6RX-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 22 0.11480952364203112
-TCGA-P8-A6RY-01A-12D-A35D-08 Pheochromocytoma, malignant 9 0.04696753239901273
-TCGA-PR-A5PF-01A-11D-A35D-08 Pheochromocytoma, malignant 26 0.13568398248603677
-TCGA-PR-A5PG-01A-11D-A35D-08 Pheochromocytoma, malignant 22 0.11480952364203112
-TCGA-PR-A5PH-01A-11D-A35D-08 Pheochromocytoma, malignant 13 0.06784199124301839
-TCGA-QH-A65R-01A-21D-A31L-08 Oligodendroglioma, anaplastic 41 0.21396320315105802
-TCGA-QH-A65S-01A-11D-A29Q-08 Mixed glioma 24 0.12524675306403396
-TCGA-QH-A65V-01A-11D-A29Q-08 Oligodendroglioma, NOS 31 0.16177705604104387
-TCGA-QH-A65X-01A-11D-A32B-08 Mixed glioma 33 0.17221428546304668
-TCGA-QH-A65Z-01A-11D-A29Q-08 Oligodendroglioma, NOS 39 0.20352597372905518
-TCGA-QH-A6CS-01A-11D-A31L-08 Astrocytoma, anaplastic 27 0.1409025971970382
-TCGA-QH-A6CU-01A-11D-A31L-08 Oligodendroglioma, anaplastic 16 0.08349783537602264
-TCGA-QH-A6CV-01A-11D-A31L-08 Mixed glioma 78 0.40705194745811035
-TCGA-QH-A6CW-01A-11D-A32B-08 Mixed glioma 54 0.2818051943940764
-TCGA-QH-A6CX-01A-11D-A32B-08 Astrocytoma, NOS 48 0.2504935061280679
-TCGA-QH-A6CY-01A-11D-A32B-08 Mixed glioma 22 0.11480952364203112
-TCGA-QH-A6CZ-01A-11D-A32B-08 Mixed glioma 46 0.24005627670606508
-TCGA-QH-A6X3-01A-21D-A32B-08 Mixed glioma 29 0.15133982661904102
-TCGA-QH-A6X4-01A-51D-A32B-08 Mixed glioma 34 0.1774329001740481
-TCGA-QH-A6X5-01A-12D-A32B-08 Mixed glioma 34 0.1774329001740481
-TCGA-QH-A6X8-01A-12D-A32B-08 Oligodendroglioma, anaplastic 39 0.20352597372905518
-TCGA-QH-A6X9-01A-12D-A32B-08 Oligodendroglioma, NOS 66 0.34442857092609336
-TCGA-QH-A6XA-01A-12D-A32B-08 Mixed glioma 15 0.07827922066502122
-TCGA-QH-A6XC-01A-12D-A32B-08 Astrocytoma, anaplastic 65 0.339209956215092
-TCGA-QH-A86X-01A-11D-A36O-08 Oligodendroglioma, NOS 23 0.12002813835303254
-TCGA-QH-A870-01A-11D-A36O-08 Mixed glioma 27 0.1409025971970382
-TCGA-QR-A6GO-01A-11D-A35D-08 Pheochromocytoma, malignant 13 0.06784199124301839
-TCGA-QR-A6GR-01A-11D-A35D-08 Pheochromocytoma, NOS 17 0.08871645008702406
-TCGA-QR-A6GS-01A-11D-A35D-08 Pheochromocytoma, NOS 14 0.07306060595401981
-TCGA-QR-A6GT-01A-11D-A35D-08 Pheochromocytoma, NOS 36 0.18787012959605093
-TCGA-QR-A6GU-01A-11D-A35D-08 Pheochromocytoma, NOS 22 0.11480952364203112
-TCGA-QR-A6GW-01A-11D-A35D-08 Pheochromocytoma, NOS 21 0.10959090893102971
-TCGA-QR-A6GX-01A-11D-A35D-08 Pheochromocytoma, NOS 15 0.07827922066502122
-TCGA-QR-A6GY-01A-11D-A35D-08 Pheochromocytoma, NOS 23 0.12002813835303254
-TCGA-QR-A6GZ-01A-11D-A35D-08 Pheochromocytoma, NOS 7 0.036530302977009904
-TCGA-QR-A6GZ-05A-11D-A35D-08 Pheochromocytoma, NOS 7 0.036530302977009904
-TCGA-QR-A6H0-01A-11D-A35D-08 Paraganglioma, NOS 8 0.04174891768801132
-TCGA-QR-A6H1-01A-11D-A35D-08 Pheochromocytoma, NOS 19 0.09915367950902688
-TCGA-QR-A6H2-01A-11D-A35D-08 Pheochromocytoma, NOS 12 0.06262337653201698
-TCGA-QR-A6H3-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 7 0.036530302977009904
-TCGA-QR-A6H4-01A-11D-A35D-08 Pheochromocytoma, NOS 19 0.09915367950902688
-TCGA-QR-A6H5-01A-11D-A35D-08 Pheochromocytoma, NOS 8 0.04174891768801132
-TCGA-QR-A6H6-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 10 0.052186147110014146
-TCGA-QR-A6ZZ-01A-11D-A35D-08 Pheochromocytoma, NOS 15 0.07827922066502122
-TCGA-QR-A700-01A-11D-A35D-08 Pheochromocytoma, NOS 46 0.24005627670606508
-TCGA-QR-A702-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 12 0.06262337653201698
-TCGA-QR-A703-01A-11D-A35D-08 Pheochromocytoma, NOS 5 0.026093073555007073
-TCGA-QR-A705-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 14 0.07306060595401981
-TCGA-QR-A706-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 11 0.05740476182101556
-TCGA-QR-A707-01A-11D-A35D-08 Pheochromocytoma, NOS 13 0.06784199124301839
-TCGA-QR-A708-01A-11D-A35D-08 Pheochromocytoma, NOS 22 0.11480952364203112
-TCGA-QR-A70A-01A-11D-A35D-08 Pheochromocytoma, NOS 13 0.06784199124301839
-TCGA-QR-A70C-01A-21D-A35D-08 Pheochromocytoma, NOS 15 0.07827922066502122
-TCGA-QR-A70D-01A-11D-A35D-08 Paraganglioma, NOS 12 0.06262337653201698
-TCGA-QR-A70E-01A-11D-A35D-08 Pheochromocytoma, NOS 13 0.06784199124301839
-TCGA-QR-A70G-01B-11D-A35D-08 Pheochromocytoma, NOS 20 0.10437229422002829
-TCGA-QR-A70H-01A-12D-A35D-08 Pheochromocytoma, NOS 14 0.07306060595401981
-TCGA-QR-A70I-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 6 0.03131168826600849
-TCGA-QR-A70J-01A-11D-A35D-08 Pheochromocytoma, NOS 19 0.09915367950902688
-TCGA-QR-A70K-01A-12D-A35D-08 Pheochromocytoma, NOS 33 0.17221428546304668
-TCGA-QR-A70M-01A-11D-A35D-08 Pheochromocytoma, NOS 11 0.05740476182101556
-TCGA-QR-A70N-01A-12D-A35D-08 Pheochromocytoma, NOS 18 0.09393506479802546
-TCGA-QR-A70O-01A-11D-A35D-08 Pheochromocytoma, NOS 18 0.09393506479802546
-TCGA-QR-A70P-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 34 0.1774329001740481
-TCGA-QR-A70Q-01A-13D-A35D-08 Extra-adrenal paraganglioma, NOS 11 0.05740476182101556
-TCGA-QR-A70R-01A-11D-A35D-08 Pheochromocytoma, NOS 14 0.07306060595401981
-TCGA-QR-A70T-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 17 0.08871645008702406
-TCGA-QR-A70U-01A-11D-A35D-08 Pheochromocytoma, NOS 22 0.11480952364203112
-TCGA-QR-A70V-01A-11D-A35D-08 Pheochromocytoma, NOS 17 0.08871645008702406
-TCGA-QR-A70W-01A-12D-A35D-08 Pheochromocytoma, NOS 6 0.03131168826600849
-TCGA-QR-A70X-01A-11D-A35D-08 Pheochromocytoma, NOS 10 0.052186147110014146
-TCGA-QR-A7IN-01A-11D-A35D-08 Pheochromocytoma, NOS 14 0.07306060595401981
-TCGA-QR-A7IP-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 14 0.07306060595401981
-TCGA-QT-A5XJ-01A-11D-A35D-08 Pheochromocytoma, malignant 34 0.1774329001740481
-TCGA-QT-A5XK-01A-11D-A35D-08 Pheochromocytoma, malignant 12 0.06262337653201698
-TCGA-QT-A5XL-01A-11D-A35D-08 Pheochromocytoma, malignant 8 0.04174891768801132
-TCGA-QT-A5XM-01A-11D-A35D-08 Pheochromocytoma, malignant 11 0.05740476182101556
-TCGA-QT-A5XN-01A-11D-A35D-08 Pheochromocytoma, malignant 17 0.08871645008702406
-TCGA-QT-A5XO-01A-11D-A35D-08 Pheochromocytoma, malignant 28 0.14612121190803962
-TCGA-QT-A5XP-01A-11D-A35D-08 Pheochromocytoma, malignant 22 0.11480952364203112
-TCGA-QT-A69Q-01A-11D-A35D-08 Pheochromocytoma, malignant 13 0.06784199124301839
-TCGA-QT-A7U0-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 7 0.036530302977009904
-TCGA-R8-A6MK-01A-11D-A32B-08 Oligodendroglioma, NOS 37 0.19308874430705233
-TCGA-R8-A6ML-01A-11D-A32B-08 Oligodendroglioma, anaplastic 23 0.12002813835303254
-TCGA-R8-A6MO-01A-11D-A33T-08 Oligodendroglioma, NOS 43 0.22440043257306083
-TCGA-R8-A73M-01A-11D-A32B-08 Oligodendroglioma, NOS 37 0.19308874430705233
-TCGA-RM-A68T-01A-11D-A35D-08 Extra-adrenal paraganglioma, malignant 13 0.06784199124301839
-TCGA-RM-A68W-01A-11D-A35D-08 Extra-adrenal paraganglioma, malignant 9 0.04696753239901273
-TCGA-RR-A6KA-01A-21D-A33T-08 Glioblastoma 69 0.3600844150590976
-TCGA-RR-A6KB-01A-12D-A33T-08 Glioblastoma 61 0.3183354973710863
-TCGA-RR-A6KC-01A-31D-A33T-08 Glioblastoma 64 0.33399134150409054
-TCGA-RT-A6Y9-01A-12D-A35D-08 Pheochromocytoma, NOS 6 0.03131168826600849
-TCGA-RT-A6YA-01A-12D-A35D-08 Pheochromocytoma, NOS 24 0.12524675306403396
-TCGA-RT-A6YC-01A-12D-A35D-08 Pheochromocytoma, NOS 5 0.026093073555007073
-TCGA-RW-A67V-01A-11D-A35D-08 Pheochromocytoma, NOS 15 0.07827922066502122
-TCGA-RW-A67W-01A-11D-A35D-08 Pheochromocytoma, NOS 11 0.05740476182101556
-TCGA-RW-A67X-01A-11D-A35D-08 Pheochromocytoma, NOS 15 0.07827922066502122
-TCGA-RW-A67Y-01A-11D-A35D-08 Pheochromocytoma, NOS 16 0.08349783537602264
-TCGA-RW-A680-01A-11D-A35D-08 Paraganglioma, NOS 35 0.18265151488504952
-TCGA-RW-A681-01A-11D-A35D-08 Pheochromocytoma, NOS 27 0.1409025971970382
-TCGA-RW-A684-01A-12D-A35D-08 Pheochromocytoma, NOS 20 0.10437229422002829
-TCGA-RW-A685-01A-11D-A35D-08 Pheochromocytoma, NOS 13 0.06784199124301839
-TCGA-RW-A686-01A-11D-A35D-08 Pheochromocytoma, malignant 12 0.06262337653201698
-TCGA-RW-A686-06A-11D-A35D-08 Pheochromocytoma, malignant 15 0.07827922066502122
-TCGA-RW-A688-01A-11D-A35D-08 Pheochromocytoma, NOS 17 0.08871645008702406
-TCGA-RW-A689-01A-11D-A35D-08 Pheochromocytoma, NOS 16 0.08349783537602264
-TCGA-RW-A68A-01A-11D-A35D-08 Pheochromocytoma, NOS 9 0.04696753239901273
-TCGA-RW-A68B-01A-11D-A35D-08 Pheochromocytoma, NOS 7 0.036530302977009904
-TCGA-RW-A68C-01A-11D-A35D-08 Pheochromocytoma, NOS 13 0.06784199124301839
-TCGA-RW-A68D-01A-11D-A35D-08 Pheochromocytoma, NOS 15 0.07827922066502122
-TCGA-RW-A68F-01A-11D-A35D-08 Pheochromocytoma, NOS 17 0.08871645008702406
-TCGA-RW-A68G-01A-11D-A35D-08 Pheochromocytoma, NOS 4 0.02087445884400566
-TCGA-RW-A7CZ-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 18 0.09393506479802546
-TCGA-RW-A7D0-01A-11D-A35D-08 Pheochromocytoma, NOS 12 0.06262337653201698
-TCGA-RW-A8AZ-01A-11D-A35D-08 Pheochromocytoma, malignant 32 0.16699567075204527
-TCGA-RX-A8JQ-01A-11D-A35D-08 Extra-adrenal paraganglioma, NOS 12 0.06262337653201698
-TCGA-RY-A83X-01A-11D-A36O-08 Oligodendroglioma, NOS 27 0.1409025971970382
-TCGA-RY-A83Y-01A-11D-A36O-08 Oligodendroglioma, NOS 42 0.21918181786205942
-TCGA-RY-A83Z-01A-11D-A36O-08 Astrocytoma, anaplastic 66 0.34442857092609336
-TCGA-RY-A840-01A-11D-A36O-08 Oligodendroglioma, anaplastic 23 0.12002813835303254
-TCGA-RY-A843-01A-11D-A36O-08 Astrocytoma, anaplastic 27 0.1409025971970382
-TCGA-RY-A845-01A-11D-A36O-08 Mixed glioma 33 0.17221428546304668
-TCGA-RY-A847-01A-11D-A36O-08 Oligodendroglioma, NOS 22 0.11480952364203112
-TCGA-S7-A7WL-01A-11D-A35I-08 Pheochromocytoma, malignant 18 0.09393506479802546
-TCGA-S7-A7WM-01A-12D-A35I-08 Pheochromocytoma, malignant 20 0.10437229422002829
-TCGA-S7-A7WN-01A-12D-A35I-08 Pheochromocytoma, malignant 8 0.04174891768801132
-TCGA-S7-A7WO-01A-11D-A35I-08 Pheochromocytoma, NOS 5 0.026093073555007073
-TCGA-S7-A7WP-01A-11D-A35I-08 Pheochromocytoma, NOS 4 0.02087445884400566
-TCGA-S7-A7WQ-01A-12D-A35I-08 Pheochromocytoma, malignant 14 0.07306060595401981
-TCGA-S7-A7WR-01A-11D-A35I-08 Pheochromocytoma, NOS 4 0.02087445884400566
-TCGA-S7-A7WT-01A-12D-A35I-08 Pheochromocytoma, malignant 23 0.12002813835303254
-TCGA-S7-A7WU-01A-11D-A35I-08 Pheochromocytoma, malignant 5 0.026093073555007073
-TCGA-S7-A7WV-01A-11D-A35I-08 Pheochromocytoma, malignant 6 0.03131168826600849
-TCGA-S7-A7WW-01A-11D-A35I-08 Pheochromocytoma, NOS 11 0.05740476182101556
-TCGA-S7-A7WX-01A-11D-A35I-08 Pheochromocytoma, malignant 9 0.04696753239901273
-TCGA-S7-A7X0-01A-12D-A35I-08 Extra-adrenal paraganglioma, NOS 21 0.10959090893102971
-TCGA-S7-A7X1-01A-11D-A35I-08 Pheochromocytoma, NOS 8 0.04174891768801132
-TCGA-S7-A7X2-01A-12D-A35I-08 Pheochromocytoma, NOS 19 0.09915367950902688
-TCGA-S9-A6TS-01A-12D-A33T-08 Astrocytoma, anaplastic 88 0.4592380945681245
-TCGA-S9-A6TU-01A-12D-A32B-08 Astrocytoma, NOS 27 0.1409025971970382
-TCGA-S9-A6TV-01A-12D-A34J-08 Mixed glioma 39 0.20352597372905518
-TCGA-S9-A6TW-01A-12D-A32B-08 Oligodendroglioma, anaplastic 41 0.21396320315105802
-TCGA-S9-A6TX-01A-21D-A32B-08 Oligodendroglioma, anaplastic 46 0.24005627670606508
-TCGA-S9-A6TY-01A-12D-A32B-08 Oligodendroglioma, NOS 28 0.14612121190803962
-TCGA-S9-A6TZ-01A-21D-A32B-08 Astrocytoma, NOS 45 0.23483766199506367
-TCGA-S9-A6U0-01A-12D-A32B-08 Astrocytoma, anaplastic 47 0.2452748914170665
-TCGA-S9-A6U1-01A-21D-A33T-08 Astrocytoma, anaplastic 29 0.15133982661904102
-TCGA-S9-A6U2-01A-21D-A33T-08 Oligodendroglioma, NOS 32 0.16699567075204527
-TCGA-S9-A6U5-01A-12D-A33T-08 Astrocytoma, NOS 28 0.14612121190803962
-TCGA-S9-A6U6-01A-12D-A33T-08 Astrocytoma, anaplastic 45 0.23483766199506367
-TCGA-S9-A6U8-01A-21D-A33T-08 Astrocytoma, NOS 24 0.12524675306403396
-TCGA-S9-A6U9-01A-11D-A32B-08 Astrocytoma, anaplastic 30 0.15655844133004243
-TCGA-S9-A6UA-01A-12D-A33T-08 Astrocytoma, anaplastic 51 0.26614935026107217
-TCGA-S9-A6UB-01A-21D-A33T-08 Oligodendroglioma, NOS 36 0.18787012959605093
-TCGA-S9-A6WD-01A-12D-A33T-08 Oligodendroglioma, anaplastic 46 0.24005627670606508
-TCGA-S9-A6WE-01A-12D-A33T-08 Oligodendroglioma, NOS 16 0.08349783537602264
-TCGA-S9-A6WG-01A-11D-A33T-08 Astrocytoma, anaplastic 28 0.14612121190803962
-TCGA-S9-A6WH-01A-12D-A33T-08 Mixed glioma 103 0.5375173152331457
-TCGA-S9-A6WI-01A-21D-A33T-08 Mixed glioma 33 0.17221428546304668
-TCGA-S9-A6WL-01A-21D-A33T-08 Astrocytoma, anaplastic 73 0.3809588739031033
-TCGA-S9-A6WM-01A-12D-A33T-08 Astrocytoma, anaplastic 73 0.3809588739031033
-TCGA-S9-A6WN-01A-12D-A33T-08 Astrocytoma, anaplastic 44 0.22961904728406224
-TCGA-S9-A6WO-01A-21D-A34A-08 Astrocytoma, NOS 36 0.18787012959605093
-TCGA-S9-A6WP-01A-12D-A34A-08 Mixed glioma 46 0.24005627670606508
-TCGA-S9-A6WQ-01A-12D-A34A-08 Mixed glioma 49 0.2557121208390693
-TCGA-S9-A7IQ-01A-21D-A34A-08 Mixed glioma 20 0.10437229422002829
-TCGA-S9-A7IS-01A-11D-A34A-08 Astrocytoma, anaplastic 64 0.33399134150409054
-TCGA-S9-A7IX-01A-12D-A34A-08 Astrocytoma, anaplastic 38 0.19830735901805377
-TCGA-S9-A7IY-01A-11D-A34A-08 Mixed glioma 31 0.16177705604104387
-TCGA-S9-A7IZ-01A-11D-A34A-08 Astrocytoma, anaplastic 45 0.23483766199506367
-TCGA-S9-A7J0-01A-11D-A34A-08 Oligodendroglioma, anaplastic 52 0.27136796497207355
-TCGA-S9-A7J1-01A-21D-A34J-08 Oligodendroglioma, NOS 29 0.15133982661904102
-TCGA-S9-A7J2-01A-11D-A34A-08 Oligodendroglioma, anaplastic 13 0.06784199124301839
-TCGA-S9-A7J3-01A-21D-A34J-08 Oligodendroglioma, anaplastic 36 0.18787012959605093
-TCGA-S9-A7QW-01A-11D-A34A-08 Astrocytoma, anaplastic 48 0.2504935061280679
-TCGA-S9-A7QX-01A-11D-A34A-08 Astrocytoma, anaplastic 33 0.17221428546304668
-TCGA-S9-A7QY-01A-11D-A34A-08 Mixed glioma 57 0.29746103852708067
-TCGA-S9-A7QZ-01A-12D-A34J-08 Oligodendroglioma, NOS 71 0.3705216444811005
-TCGA-S9-A7R1-01A-12D-A34J-08 Oligodendroglioma, NOS 21 0.10959090893102971
-TCGA-S9-A7R2-01A-21D-A34J-08 Astrocytoma, anaplastic 57 0.29746103852708067
-TCGA-S9-A7R3-01A-11D-A34J-08 Astrocytoma, NOS 35 0.18265151488504952
-TCGA-S9-A7R4-01A-12D-A34J-08 Astrocytoma, anaplastic 34 0.1774329001740481
-TCGA-S9-A7R7-01A-11D-A34J-08 Astrocytoma, NOS 30 0.15655844133004243
-TCGA-S9-A7R8-01A-11D-A34J-08 Astrocytoma, anaplastic 43 0.22440043257306083
-TCGA-S9-A89V-01A-11D-A36O-08 Astrocytoma, anaplastic 56 0.29224242381607923
-TCGA-S9-A89Z-01A-11D-A36O-08 Astrocytoma, anaplastic 72 0.37574025919210186
-TCGA-SA-A6C2-01A-11D-A35I-08 Paraganglioma, NOS 30 0.15655844133004243
-TCGA-SP-A6QC-01A-11D-A35I-08 Pheochromocytoma, NOS 13 0.06784199124301839
-TCGA-SP-A6QD-01A-12D-A35I-08 Pheochromocytoma, NOS 7 0.036530302977009904
-TCGA-SP-A6QF-01A-12D-A35I-08 Pheochromocytoma, NOS 19 0.09915367950902688
-TCGA-SP-A6QG-01A-12D-A35I-08 Extra-adrenal paraganglioma, NOS 8 0.04174891768801132
-TCGA-SP-A6QH-01A-21D-A35I-08 Pheochromocytoma, NOS 13 0.06784199124301839
-TCGA-SP-A6QI-01A-12D-A35I-08 Pheochromocytoma, NOS 7 0.036530302977009904
-TCGA-SP-A6QJ-01A-11D-A35I-08 Pheochromocytoma, NOS 14 0.07306060595401981
-TCGA-SP-A6QK-01A-11D-A35I-08 Pheochromocytoma, NOS 12 0.06262337653201698
-TCGA-SQ-A6I4-01A-11D-A35I-08 Pheochromocytoma, NOS 14 0.07306060595401981
-TCGA-SQ-A6I6-01A-11D-A35I-08 Pheochromocytoma, malignant 19 0.09915367950902688
-TCGA-SR-A6MP-01A-11D-A35I-08 Pheochromocytoma, NOS 27 0.1409025971970382
-TCGA-SR-A6MQ-01A-11D-A35I-08 Pheochromocytoma, malignant 14 0.07306060595401981
-TCGA-SR-A6MR-01A-11D-A35I-08 Extra-adrenal paraganglioma, malignant 9 0.04696753239901273
-TCGA-SR-A6MS-01A-11D-A35I-08 Pheochromocytoma, NOS 9 0.04696753239901273
-TCGA-SR-A6MT-01A-11D-A35I-08 Pheochromocytoma, NOS 11 0.05740476182101556
-TCGA-SR-A6MU-01A-11D-A35I-08 Pheochromocytoma, malignant 14 0.07306060595401981
-TCGA-SR-A6MV-01A-11D-A35I-08 Paraganglioma, malignant 11 0.05740476182101556
-TCGA-SR-A6MX-01A-11D-A35I-08 Paraganglioma, malignant 31 0.16177705604104387
-TCGA-SR-A6MX-05A-11D-A35I-08 Paraganglioma, malignant 31 0.16177705604104387
-TCGA-SR-A6MX-06A-11D-A35I-08 Paraganglioma, malignant 28 0.14612121190803962
-TCGA-SR-A6MY-01A-11D-A35I-08 Pheochromocytoma, NOS 8 0.04174891768801132
-TCGA-SR-A6MZ-01A-11D-A35I-08 Pheochromocytoma, NOS 8 0.04174891768801132
-TCGA-SR-A6N0-01A-11D-A35I-08 Pheochromocytoma, NOS 10 0.052186147110014146
-TCGA-TM-A7C3-01A-11D-A32B-08 Astrocytoma, anaplastic 79 0.4122705621691118
-TCGA-TM-A7C4-01A-11D-A32B-08 Astrocytoma, NOS 48 0.2504935061280679
-TCGA-TM-A7C5-01A-11D-A32B-08 Mixed glioma 37 0.19308874430705233
-TCGA-TM-A7CA-01A-21D-A33T-08 Astrocytoma, NOS 47 0.2452748914170665
-TCGA-TM-A7CF-01A-11D-A32B-08 Astrocytoma, NOS 41 0.21396320315105802
-TCGA-TM-A7CF-02A-11D-A32B-08 Astrocytoma, NOS 25 0.13046536777503537
-TCGA-TM-A84B-01A-11D-A36O-08 Astrocytoma, anaplastic 48 0.2504935061280679
-TCGA-TM-A84C-01A-11D-A36O-08 Astrocytoma, NOS 21 0.10959090893102971
-TCGA-TM-A84F-01A-11D-A36O-08 Astrocytoma, anaplastic 21 0.10959090893102971
-TCGA-TM-A84G-01A-11D-A36O-08 Oligodendroglioma, anaplastic 41 0.21396320315105802
-TCGA-TM-A84H-01A-11D-A36O-08 Mixed glioma 53 0.276586579683075
-TCGA-TM-A84I-01A-11D-A36O-08 Astrocytoma, anaplastic 46 0.24005627670606508
-TCGA-TM-A84J-01A-11D-A36O-08 Oligodendroglioma, anaplastic 56 0.29224242381607923
-TCGA-TM-A84L-01A-11D-A36O-08 Mixed glioma 27 0.1409025971970382
-TCGA-TM-A84M-01A-11D-A36O-08 Oligodendroglioma, anaplastic 37 0.19308874430705233
-TCGA-TM-A84O-01A-11D-A36O-08 Oligodendroglioma, anaplastic 34 0.1774329001740481
-TCGA-TM-A84Q-01A-11D-A36O-08 Astrocytoma, NOS 39 0.20352597372905518
-TCGA-TM-A84R-01A-21D-A36O-08 Oligodendroglioma, NOS 14 0.07306060595401981
-TCGA-TM-A84S-01A-11D-A36O-08 Oligodendroglioma, anaplastic 25 0.13046536777503537
-TCGA-TM-A84T-01A-11D-A36O-08 Mixed glioma 26 0.13568398248603677
-TCGA-TQ-A7RF-01A-11D-A33T-08 Oligodendroglioma, anaplastic 34 0.1774329001740481
-TCGA-TQ-A7RG-01A-11D-A33T-08 Mixed glioma 37 0.19308874430705233
-TCGA-TQ-A7RH-01A-12D-A34A-08 Mixed glioma 54 0.2818051943940764
-TCGA-TQ-A7RI-01A-11D-A33T-08 Oligodendroglioma, NOS 39 0.20352597372905518
-TCGA-TQ-A7RJ-01A-11D-A33T-08 Mixed glioma 62 0.32355411208208773
-TCGA-TQ-A7RK-01A-11D-A33T-08 Oligodendroglioma, NOS 34 0.1774329001740481
-TCGA-TQ-A7RK-02A-11D-A36O-08 Oligodendroglioma, NOS 32 0.16699567075204527
-TCGA-TQ-A7RM-01A-11D-A33T-08 Mixed glioma 81 0.4227077915911146
-TCGA-TQ-A7RN-01A-11D-A33T-08 Oligodendroglioma, NOS 38 0.19830735901805377
-TCGA-TQ-A7RO-01A-11D-A33T-08 Mixed glioma 26 0.13568398248603677
-TCGA-TQ-A7RP-01A-21D-A34A-08 Mixed glioma 53 0.276586579683075
-TCGA-TQ-A7RQ-01A-11D-A33T-08 Oligodendroglioma, NOS 39 0.20352597372905518
-TCGA-TQ-A7RR-01A-21D-A34A-08 Mixed glioma 42 0.21918181786205942
-TCGA-TQ-A7RS-01A-12D-A33T-08 Oligodendroglioma, NOS 21 0.10959090893102971
-TCGA-TQ-A7RU-01A-21D-A34A-08 Oligodendroglioma, NOS 37 0.19308874430705233
-TCGA-TQ-A7RV-01A-21D-A34A-08 Astrocytoma, NOS 31 0.16177705604104387
-TCGA-TQ-A7RV-02A-11D-A36O-08 Astrocytoma, NOS 33 0.17221428546304668
-TCGA-TQ-A7RW-01A-11D-A33T-08 Mixed glioma 66 0.34442857092609336
-TCGA-TQ-A8XE-01A-11D-A36O-08 Oligodendroglioma, NOS 46 0.24005627670606508
-TCGA-TQ-A8XE-02A-11D-A36O-08 Oligodendroglioma, NOS 36 0.18787012959605093
-TCGA-TT-A6YJ-01A-11D-A35I-08 Pheochromocytoma, NOS 10 0.052186147110014146
-TCGA-TT-A6YK-01A-11D-A35I-08 Extra-adrenal paraganglioma, NOS 16 0.08349783537602264
-TCGA-TT-A6YN-01A-12D-A35I-08 Pheochromocytoma, malignant 6 0.03131168826600849
-TCGA-TT-A6YO-01A-11D-A35I-08 Pheochromocytoma, malignant 1 0.005218614711001415
-TCGA-TT-A6YP-01A-21D-A35I-08 Pheochromocytoma, NOS 11 0.05740476182101556
-TCGA-VM-A8C8-01A-11D-A36O-08 Oligodendroglioma, NOS 53 0.276586579683075
-TCGA-VM-A8C9-01A-11D-A36O-08 Astrocytoma, NOS 8 0.04174891768801132
-TCGA-VM-A8CA-01A-11D-A36O-08 Oligodendroglioma, NOS 34 0.1774329001740481
-TCGA-VM-A8CB-01A-11D-A36O-08 Oligodendroglioma, anaplastic 33 0.17221428546304668
-TCGA-VM-A8CD-01A-11D-A36O-08 Astrocytoma, anaplastic 33 0.17221428546304668
-TCGA-VM-A8CE-01A-11D-A36O-08 Oligodendroglioma, NOS 22 0.11480952364203112
-TCGA-VM-A8CF-01A-11D-A36O-08 Astrocytoma, anaplastic 50 0.26093073555007074
-TCGA-VM-A8CH-01A-12D-A36O-08 Astrocytoma, NOS 17 0.08871645008702406
-TCGA-VV-A829-01A-21D-A36O-08 Mixed glioma 40 0.20874458844005658
-TCGA-VV-A86M-01A-11D-A36O-08 Astrocytoma, anaplastic 17 0.08871645008702406
-TCGA-VW-A7QS-01A-12D-A33T-08 Oligodendroglioma, anaplastic 52 0.27136796497207355
-TCGA-VW-A8FI-01A-11D-A36O-08 Astrocytoma, anaplastic 77 0.4018333327471089
-TCGA-W2-A7H5-01B-11D-A35I-08 Pheochromocytoma, NOS 7 0.036530302977009904
-TCGA-W2-A7H7-01A-11D-A35I-08 Pheochromocytoma, NOS 15 0.07827922066502122
-TCGA-W2-A7HA-01B-11D-A35I-08 Pheochromocytoma, NOS 29 0.15133982661904102
-TCGA-W2-A7HB-01A-11D-A35I-08 Pheochromocytoma, NOS 12 0.06262337653201698
-TCGA-W2-A7HC-01A-11D-A35I-08 Pheochromocytoma, NOS 22 0.11480952364203112
-TCGA-W2-A7HD-01A-11D-A35I-08 Pheochromocytoma, NOS 19 0.09915367950902688
-TCGA-W2-A7HE-01A-11D-A35I-08 Pheochromocytoma, NOS 6 0.03131168826600849
-TCGA-W2-A7HF-01A-11D-A35I-08 Pheochromocytoma, NOS 6 0.03131168826600849
-TCGA-W2-A7HH-01A-11D-A35I-08 Extra-adrenal paraganglioma, NOS 13 0.06784199124301839
-TCGA-W2-A7UY-01A-11D-A35I-08 Pheochromocytoma, NOS 26 0.13568398248603677
-TCGA-W9-A837-01A-11D-A36O-08 Oligodendroglioma, NOS 35 0.18265151488504952
-TCGA-WB-A80K-01A-11D-A35I-08 Pheochromocytoma, NOS 17 0.08871645008702406
-TCGA-WB-A80L-01A-11D-A35I-08 Pheochromocytoma, NOS 2 0.01043722942200283
-TCGA-WB-A80M-01A-11D-A35I-08 Pheochromocytoma, NOS 11 0.05740476182101556
-TCGA-WB-A80N-01A-11D-A35I-08 Pheochromocytoma, NOS 14 0.07306060595401981
-TCGA-WB-A80O-01A-11D-A35I-08 Pheochromocytoma, NOS 12 0.06262337653201698
-TCGA-WB-A80P-01A-11D-A35I-08 Extra-adrenal paraganglioma, malignant 6 0.03131168826600849
-TCGA-WB-A80Q-01A-11D-A35I-08 Pheochromocytoma, malignant 18 0.09393506479802546
-TCGA-WB-A80V-01A-12D-A35I-08 Pheochromocytoma, NOS 21 0.10959090893102971
-TCGA-WB-A80Y-01A-11D-A35I-08 Pheochromocytoma, NOS 9 0.04696753239901273
-TCGA-WB-A814-01A-11D-A35I-08 Extra-adrenal paraganglioma, malignant 9 0.04696753239901273
-TCGA-WB-A815-01A-11D-A35I-08 Pheochromocytoma, malignant 23 0.12002813835303254
-TCGA-WB-A816-01A-11D-A35I-08 Pheochromocytoma, NOS 12 0.06262337653201698
-TCGA-WB-A817-01A-11D-A35I-08 Pheochromocytoma, NOS 11 0.05740476182101556
-TCGA-WB-A818-01A-11D-A35I-08 Pheochromocytoma, NOS 10 0.052186147110014146
-TCGA-WB-A819-01A-11D-A35I-08 Pheochromocytoma, malignant 9 0.04696753239901273
-TCGA-WB-A81A-01A-11D-A35I-08 Pheochromocytoma, malignant 10 0.052186147110014146
-TCGA-WB-A81D-01A-11D-A35I-08 Pheochromocytoma, NOS 19 0.09915367950902688
-TCGA-WB-A81E-01A-11D-A35I-08 Extra-adrenal paraganglioma, malignant 21 0.10959090893102971
-TCGA-WB-A81F-01A-11D-A35I-08 Pheochromocytoma, NOS 6 0.03131168826600849
-TCGA-WB-A81G-01A-11D-A35I-08 Pheochromocytoma, NOS 14 0.07306060595401981
-TCGA-WB-A81H-01A-11D-A35I-08 Pheochromocytoma, NOS 17 0.08871645008702406
-TCGA-WB-A81I-01A-11D-A35I-08 Pheochromocytoma, NOS 14 0.07306060595401981
-TCGA-WB-A81J-01A-11D-A35I-08 Pheochromocytoma, NOS 11 0.05740476182101556
-TCGA-WB-A81K-01A-11D-A35I-08 Pheochromocytoma, NOS 11 0.05740476182101556
-TCGA-WB-A81M-01A-11D-A35I-08 Pheochromocytoma, NOS 15 0.07827922066502122
-TCGA-WB-A81N-01A-11D-A35I-08 Pheochromocytoma, NOS 8 0.04174891768801132
-TCGA-WB-A81P-01A-11D-A35I-08 Pheochromocytoma, NOS 25 0.13046536777503537
-TCGA-WB-A81Q-01A-11D-A35I-08 Pheochromocytoma, malignant 26 0.13568398248603677
-TCGA-WB-A81R-01A-11D-A35I-08 Pheochromocytoma, NOS 16 0.08349783537602264
-TCGA-WB-A81S-01A-11D-A35I-08 Pheochromocytoma, NOS 16 0.08349783537602264
-TCGA-WB-A81T-01A-11D-A35I-08 Pheochromocytoma, NOS 22 0.11480952364203112
-TCGA-WB-A81V-01A-11D-A35I-08 Pheochromocytoma, NOS 18 0.09393506479802546
-TCGA-WB-A81W-01A-11D-A35I-08 Pheochromocytoma, NOS 22 0.11480952364203112
-TCGA-WB-A820-01A-11D-A35I-08 Pheochromocytoma, malignant 17 0.08871645008702406
-TCGA-WB-A821-01A-11D-A35I-08 Pheochromocytoma, malignant 32 0.16699567075204527
-TCGA-WB-A822-01A-11D-A35I-08 Pheochromocytoma, malignant 8 0.04174891768801132
-TCGA-WH-A86K-01A-11D-A36O-08 Astrocytoma, NOS 55 0.2870238091050778
-TCGA-WY-A858-01A-11D-A36O-08 Astrocytoma, anaplastic 44 0.22961904728406224
-TCGA-WY-A859-01A-12D-A36O-08 Astrocytoma, NOS 37 0.19308874430705233
-TCGA-WY-A85A-01A-21D-A36O-08 Astrocytoma, NOS 19 0.09915367950902688
-TCGA-WY-A85B-01A-11D-A36O-08 Astrocytoma, NOS 19 0.09915367950902688
-TCGA-WY-A85C-01A-11D-A36O-08 Astrocytoma, NOS 38 0.19830735901805377
-TCGA-WY-A85D-01A-11D-A36O-08 Mixed glioma 48 0.2504935061280679
-TCGA-WY-A85E-01A-11D-A36O-08 Mixed glioma 84 0.43836363572411885
-TCGA-XG-A823-01A-11D-A35I-08 Pheochromocytoma, malignant 12 0.06262337653201698