Load libraries and set directories
library(data.table)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
-## ✔ dplyr 1.1.1 ✔ readr 2.1.4
+## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
+## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
-## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
+## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::between() masks data.table::between()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks data.table::first()
@@ -499,7 +499,9 @@ 2024
analysis_dir <- file.path(root_dir, "analyses", "braf-fusions")
results_dir <- file.path(analysis_dir, "results")
input_dir <- file.path(analysis_dir, "input")
-plot_dir <- file.path(analysis_dir, "plots")
+plot_dir <- file.path(analysis_dir, "plots")
+
+source(file.path(analysis_dir, "util", "heatmap_function.R"))Set file paths
fusion_file <- file.path(results_dir, "braf-fusions-exon-annotation.tsv")
@@ -513,7 +515,7 @@ 2024
dplyr::filter(plot_group == "Low-grade glioma" & (grepl("-BRAF|wildtype|classified", molecular_subtype) | is.na(molecular_subtype))) %>%
dplyr::mutate(predicted_ancestry = factor(predicted_ancestry))## Rows: 1485 Columns: 41
-## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (22): Kids_First_Biospecimen_ID, predicted_ancestry, Kids_First_Particip...
## dbl (18): EAS_prob, AFR_prob, AMR_prob, SAS_prob, EUR_prob, PC1, PC2, PC3, P...
@@ -523,7 +525,7 @@ 2024
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.hist <- read_tsv(hist_file)## Rows: 47633 Columns: 58
-## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (41): Kids_First_Participant_ID, Kids_First_Biospecimen_ID, sample_id, a...
## dbl (17): cell_line_passage, OS_days, EFS_days, age_at_diagnosis_days, age_a...
@@ -540,11 +542,12 @@ 2024
# The below samples have two different common breakpoints and one is low conf in arriba, so we will remove that one
mutate(keep = case_when(Sample %in% c("BS_SDZY6RZ3", "BS_063ERW0R") & LeftBreakpoint_position == 138867975 ~ "no",
TRUE ~ keep))## Warning: One or more parsing issues, call `problems()` on your data frame for details, e.g.:
+## Warning: One or more parsing issues, call `problems()` on your data frame for details,
+## e.g.:
## dat <- vroom(...)
## problems(dat)
## Rows: 260 Columns: 31
-## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (18): Sample, FusionName, DomainRetainedGene1A, DomainRetainedGene1B, G...
## dbl (6): caller.count, Chromosome, LeftBreakpoint_position, RightBreakpoin...
@@ -565,7 +568,7 @@ 2024
keep = "yes") %>%
left_join(hist[,c("Kids_First_Participant_ID", "Kids_First_Biospecimen_ID")])
## Rows: 533 Columns: 22
-## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (14): Sample, FusionName, Fusion_Type, Gene1A, Gene1B, 5_prime_transcrip...
## dbl (2): JunctionReadCount, SpanningFragCount
@@ -627,10 +630,26 @@ 2024
)) %>%
write_tsv(file.path(results_dir, "braf-fusion-breakpoints-by-patient.tsv"))
Merge breakpoint data to braf hist
-braf_hist <- braf_hist %>%
+# add high conf methylation subtypes to the braf hist
+scores <- hist %>%
+ filter(dkfz_v12_methylation_subclass_score >= 0.8,
+ # grab only PA subtypes
+ grepl("PA_", dkfz_v12_methylation_subclass)) %>%
+ select("match_id", "dkfz_v12_methylation_subclass") %>%
+ group_by(match_id) %>%
+ summarise(dkfz_v12_methylation_subclass = str_c(unique(dkfz_v12_methylation_subclass), collapse = ";")) %>%
+ unique()
+
+braf_hist <- braf_hist %>%
+ dplyr::rename(Kids_First_Biospecimen_ID_normal = Kids_First_Biospecimen_ID,
+ Kids_First_Biospecimen_ID = Kids_First_Biospecimen_ID_tumor) %>%
left_join(fusions_all) %>%
- dplyr::filter(!is.na(breakpoint_type))
-## Joining with `by = join_by(Kids_First_Participant_ID)`
+ dplyr::filter(!is.na(breakpoint_type)) %>%
+ left_join(hist[,c("Kids_First_Biospecimen_ID", "match_id")]) %>%
+ left_join(scores)
+## Joining with `by = join_by(Kids_First_Participant_ID)`
+## Joining with `by = join_by(Kids_First_Biospecimen_ID)`
+## Joining with `by = join_by(match_id)`
How many patients do we have breakpoint data for?
nrow(braf_hist)
## [1] 172
@@ -683,57 +702,21 @@ Common breakpoint summary
TRUE ~ predicted_ancestry
))
-enr <- matrix(0, length(unique(braf_hist$AS_ancestry)),
- length(unique(braf_hist$breakpoint_group[!is.na(braf_hist$breakpoint_group)])),
- dimnames = list(c("AFR", "AMR", "AS", "EUR"),
- c(unique(braf_hist$breakpoint_group[!is.na(braf_hist$breakpoint_group)]))))
-pval <- enr
-
-# loop through cancer groups to calculate enrichment
-for (i in 1:nrow(enr)){
- no_ancestry <- sum(grepl(rownames(enr)[i], braf_hist$AS_ancestry))
- for (j in 1:ncol(enr)){
- no_group <- sum(braf_hist$breakpoint_group == colnames(enr)[j] & !is.na(braf_hist$breakpoint_group))
- no_anc_group <- sum(braf_hist$AS_ancestry == rownames(enr)[i] & braf_hist$breakpoint_group == colnames(enr)[j])
- enr[i,j] <- (no_anc_group/no_group)/(no_ancestry/nrow(braf_hist))
- pval[i,j] <- phyper(no_anc_group, no_ancestry, nrow(braf_hist) - no_ancestry, no_group, lower.tail = F)
- }
-}
-
-enr <- t(enr[,order(colnames(enr))])
-pval <- t(pval[,order(colnames(pval))])
-
-sig_mat <- ifelse(pval < 0.05 & enr > 1, "*", "")
-
-fill_mat <- matrix(glue::glue("{round(enr, 1)}{sig_mat}"),
- nrow(enr), ncol(enr))
-
-count_mat <- table(braf_hist$breakpoint_group, braf_hist$AS_ancestry)
-ct_enr_mat <- matrix(glue::glue("{count_mat}\n({fill_mat})"),
- nrow(count_mat), ncol(count_mat))
-
-col_fun = colorRamp2(c(0, 6), c("white", "orangered"))
+group_ht <- plot_enr(braf_hist,
+ "breakpoint_group", "AS_ancestry",
+ var2_names = c("AFR", "AMR", "AS", "EUR"),
+ var1_names = c("15:9", "16:9", "16:11", "18:10", "rare"))
# plot enrichment results
pdf(file.path(plot_dir, "breakpoint_group_ancestry_ct_enr_heatmap.pdf"),
- height = 3, width = 6)
+ height = 3, width = 4)
-ht <- Heatmap(enr,
- name = "Odds ratio",
- cluster_rows = F,
- cluster_columns = F,
- rect_gp = gpar(col = "black", lwd = 2),
- col = col_fun,
- cell_fun = function(j, i, x, y, width, height, fill) {
- grid.text(sprintf("%s", ct_enr_mat[i, j]), x, y, gp = gpar(fontsize = 12))
- })
-
-draw(ht)
+draw(group_ht)
invisible(dev.off())
-draw(ht)
Print rare/novel fusion breakpoint count by predicted ancestry
table(braf_hist$breakpoint_exons_rare, braf_hist$predicted_ancestry)##
@@ -745,6 +728,86 @@ Common breakpoint summary
## 15:11 0 0 0 4 0
## 16:10 0 0 0 1 0
## 19:9 0 1 0 0 0Create heatmap of rare breakpoints by ancestry
+pdf(file.path(plot_dir, "rare_breakpoints_by_ancestry.pdf"),
+ height = 3, width = 4)
+
+rare_ct <- table(braf_hist$breakpoint_exons_rare[!is.na(braf_hist$breakpoint_exons_rare)],
+ braf_hist$predicted_ancestry[!is.na(braf_hist$breakpoint_exons_rare)])
+
+col_fun = colorRamp2(c(0, ceiling(max(rare_ct))), c("white", "orangered"))
+
+rare_ht <- Heatmap(rare_ct,
+ name = "Count",
+ cluster_rows = F,
+ cluster_columns = F,
+ rect_gp = gpar(col = "black", lwd = 2),
+ col = col_fun,
+ cell_fun = function(j, i, x, y, width, height, fill) {
+ grid.text(sprintf("%s", rare_ct[i, j]), x, y, gp = gpar(fontsize = 12))
+ })
+
+rare_ht
+
+invisible(dev.off())
+
+rare_ht
Print ancestry by methyl subtype
+table(braf_hist$dkfz_v12_methylation_subclass, braf_hist$predicted_ancestry)##
+## AFR AMR EAS EUR SAS
+## PA_CORT 0 0 0 1 0
+## PA_INF 9 8 0 43 1
+## PA_INF_FGFR 0 0 0 1 0
+## PA_MID 1 2 1 8 1Print breakpoint by methyl subtype
+table(braf_hist$breakpoint_type, braf_hist$dkfz_v12_methylation_subclass)##
+## PA_CORT PA_INF PA_INF_FGFR PA_MID
+## common 1 61 1 10
+## rare 0 0 0 3fisher.test(table(braf_hist$breakpoint_type, braf_hist$dkfz_v12_methylation_subclass), simulate.p.value = T)##
+## Fisher's Exact Test for Count Data with simulated p-value (based on
+## 2000 replicates)
+##
+## data: table(braf_hist$breakpoint_type, braf_hist$dkfz_v12_methylation_subclass)
+## p-value = 0.008996
+## alternative hypothesis: two.sidedPrint breakpoint by methyl subtype
+table(braf_hist$breakpoint_group, braf_hist$dkfz_v12_methylation_subclass)##
+## PA_CORT PA_INF PA_INF_FGFR PA_MID
+## 15:9 1 6 0 7
+## 16:11 0 6 0 0
+## 16:9 0 45 1 3
+## 18:10 0 4 0 0
+## rare 0 0 0 3fisher.test(table(braf_hist$breakpoint_group, braf_hist$dkfz_v12_methylation_subclass), simulate.p.value = T)##
+## Fisher's Exact Test for Count Data with simulated p-value (based on
+## 2000 replicates)
+##
+## data: table(braf_hist$breakpoint_group, braf_hist$dkfz_v12_methylation_subclass)
+## p-value = 0.0004998
+## alternative hypothesis: two.sidedPlot methyl subtype by breakpoint group
+methyl_ht <- plot_enr(braf_hist[!is.na(braf_hist$dkfz_v12_methylation_subclass),],
+ "breakpoint_group", "dkfz_v12_methylation_subclass",
+ var1_names = c("15:9", "16:9", "16:11", "18:10", "rare"),
+ var2_names = sort(unique(braf_hist$dkfz_v12_methylation_subclass)))
+
+# plot enrichment results
+pdf(file.path(plot_dir, "breakpoint_group_methyl_subtype_ct_enr_heatmap.pdf"),
+ height = 3, width = 4)
+
+draw(methyl_ht)
+
+invisible(dev.off())
+
+draw(methyl_ht)
Assess distribution of resection level in patients by breakpoint type (common vs. rare)
round(table(braf_hist$breakpoint_type, braf_hist$extent_of_tumor_resection)/as.vector(table(braf_hist$breakpoint_type)), 2)Plot extent of tumor resection by breakpoint group
+braf_hist <- braf_hist %>%
+ dplyr::mutate(resection = str_replace(extent_of_tumor_resection, " resection", ""))
+
+resection_ht <- plot_enr(braf_hist[!grepl("Not Reported", braf_hist$resection),],
+ "breakpoint_group", "resection",
+ var1_names = c("15:9", "16:9", "16:11", "18:10", "rare"),
+ var2_names = c("Gross/Near total", "Partial", "Biopsy only"))
+
+# plot enrichment results
+pdf(file.path(plot_dir, "breakpoint_group_resection_ct_enr_heatmap.pdf"),
+ height = 3, width = 4)
+
+draw(resection_ht)
+
+invisible(dev.off())
+
+draw(resection_ht)
Assess distribution of tumor CNS region in patients by breakpoint type and group
round(table(braf_hist$breakpoint_type, braf_hist$CNS_region)/as.vector(table(braf_hist$breakpoint_type)), 3) * 100fisher.test(table(braf_hist$breakpoint_type, braf_hist$CNS_region))fisher.test(table(braf_hist$breakpoint_type, braf_hist$CNS_region), simulate.p.value = T)##
-## Fisher's Exact Test for Count Data
+## Fisher's Exact Test for Count Data with simulated p-value (based on
+## 2000 replicates)
##
## data: table(braf_hist$breakpoint_type, braf_hist$CNS_region)
-## p-value = 1.868e-05
+## p-value = 0.0004998
## alternative hypothesis: two.sidedround(table(braf_hist$breakpoint_group, braf_hist$CNS_region)/as.vector(table(braf_hist$breakpoint_group)), 3) * 100##
@@ -818,6 +901,22 @@ Common breakpoint summary
## data: table(braf_hist$breakpoint_group, braf_hist$CNS_region)
## p-value = 0.0004998
## alternative hypothesis: two.sidedPlot CNS region by breakpoint group
+region_ht <- plot_enr(braf_hist[braf_hist$CNS_region != "Mixed",],
+ "breakpoint_group", "CNS_region",
+ var1_names = c("15:9", "16:9", "16:11", "18:10", "rare"),
+ var2_names = sort(unique(braf_hist$CNS_region[braf_hist$CNS_region != "Mixed"])))
+
+# plot enrichment results
+pdf(file.path(plot_dir, "breakpoint_group_region_ct_enr_heatmap.pdf"),
+ height = 3, width = 6.5)
+
+draw(region_ht)
+
+invisible(dev.off())
+
+draw(region_ht)
Confirm that CNS region and degree of tumor resection are significantly associated:
round(table(braf_hist$CNS_region, braf_hist$extent_of_tumor_resection)/as.vector(table(braf_hist$CNS_region)), 3) * 100Save braf-specific hist file with breakpoint annotation
-write_tsv(braf_hist,
- file.path(results_dir, "lgg-braf-fusion-breakpoint-annotation.tsv"))braf_hist %>%
+ dplyr::select(-resection) %>%
+ write_tsv(file.path(results_dir, "lgg-braf-fusion-breakpoint-annotation.tsv"))Print session info
sessionInfo()## R version 4.2.3 (2023-03-15)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 22.04.3 LTS
+## R version 4.2.2 (2022-10-31)
+## Platform: x86_64-apple-darwin17.0 (64-bit)
+## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
-## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
-## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
+## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## 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=en_US.UTF-8
-## [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] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
@@ -876,33 +971,33 @@ Common breakpoint summary
##
## other attached packages:
## [1] circlize_0.4.15 ComplexHeatmap_2.14.0 lubridate_1.9.2
-## [4] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.1
+## [4] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
## [7] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
## [10] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
## [13] data.table_1.14.8
##
## loaded via a namespace (and not attached):
-## [1] Rcpp_1.0.10 png_0.1-8 rprojroot_2.0.3
-## [4] digest_0.6.31 foreach_1.5.2 utf8_1.2.3
-## [7] R6_2.5.1 stats4_4.2.3 evaluate_0.20
-## [10] highr_0.10 pillar_1.9.0 GlobalOptions_0.1.2
-## [13] rlang_1.1.0 jquerylib_0.1.4 magick_2.7.4
-## [16] S4Vectors_0.36.2 GetoptLong_1.0.5 rmarkdown_2.21
-## [19] bit_4.0.5 munsell_0.5.0 compiler_4.2.3
-## [22] xfun_0.38 pkgconfig_2.0.3 BiocGenerics_0.44.0
-## [25] shape_1.4.6 htmltools_0.5.5 tidyselect_1.2.0
-## [28] IRanges_2.32.0 codetools_0.2-19 matrixStats_0.63.0
-## [31] fansi_1.0.4 crayon_1.5.2 tzdb_0.3.0
-## [34] withr_2.5.0 jsonlite_1.8.4 gtable_0.3.3
-## [37] lifecycle_1.0.3 magrittr_2.0.3 scales_1.2.1
-## [40] cli_3.6.1 stringi_1.7.12 vroom_1.6.1
-## [43] cachem_1.0.7 doParallel_1.0.17 bslib_0.4.2
-## [46] generics_0.1.3 vctrs_0.6.2 rjson_0.2.21
-## [49] RColorBrewer_1.1-3 iterators_1.0.14 tools_4.2.3
-## [52] bit64_4.0.5 glue_1.6.2 hms_1.1.3
-## [55] parallel_4.2.3 fastmap_1.1.1 yaml_2.3.7
-## [58] timechange_0.2.0 clue_0.3-64 colorspace_2.1-0
-## [61] cluster_2.1.4 knitr_1.42 sass_0.4.5
+## [1] png_0.1-8 rprojroot_2.0.3 digest_0.6.31
+## [4] foreach_1.5.2 utf8_1.2.3 R6_2.5.1
+## [7] stats4_4.2.2 evaluate_0.20 highr_0.10
+## [10] pillar_1.9.0 GlobalOptions_0.1.2 rlang_1.1.0
+## [13] rstudioapi_0.14 jquerylib_0.1.4 S4Vectors_0.36.2
+## [16] GetoptLong_1.0.5 rmarkdown_2.21 bit_4.0.5
+## [19] munsell_0.5.0 compiler_4.2.2 xfun_0.38
+## [22] pkgconfig_2.0.3 BiocGenerics_0.44.0 shape_1.4.6
+## [25] htmltools_0.5.5 tidyselect_1.2.0 IRanges_2.32.0
+## [28] codetools_0.2-19 matrixStats_0.63.0 fansi_1.0.4
+## [31] crayon_1.5.2 tzdb_0.3.0 withr_2.5.0
+## [34] jsonlite_1.8.4 gtable_0.3.3 lifecycle_1.0.3
+## [37] magrittr_2.0.3 scales_1.2.1 cli_3.6.1
+## [40] stringi_1.7.12 vroom_1.6.1 cachem_1.0.7
+## [43] doParallel_1.0.17 bslib_0.4.2 generics_0.1.3
+## [46] vctrs_0.6.1 rjson_0.2.21 RColorBrewer_1.1-3
+## [49] iterators_1.0.14 tools_4.2.2 bit64_4.0.5
+## [52] glue_1.6.2 hms_1.1.3 parallel_4.2.2
+## [55] fastmap_1.1.1 yaml_2.3.7 timechange_0.2.0
+## [58] clue_0.3-64 colorspace_2.1-1 cluster_2.1.4
+## [61] knitr_1.42 sass_0.4.5library(tidyverse)── Attaching core tidyverse packages ─────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
+
+── Attaching core tidyverse packages ──────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.1 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ lubridate 1.9.2 ✔ tibble 3.2.1
✔ purrr 1.0.1 ✔ tidyr 1.3.0
-
-── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
+
+── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
@@ -2966,9 +2966,9 @@ Import the metadata and append updated EFS data from base
ancestry <- readr::read_tsv(ancestry_file)
-
+
Rows: 1485 Columns: 35
-── Column specification ───────────────────────────────────────────────────────────────────────────────────────────
+── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (19): Kids_First_Biospecimen_ID, predicted_ancestry, Kids_First_Particip...
dbl (15): EAS_prob, AFR_prob, AMR_prob, SAS_prob, EUR_prob, PC1, PC2, PC3, P...
diff --git a/analyses/survival/02-run-survival.Rmd b/analyses/survival/02-run-survival.Rmd
index 4803e056..153c4c2b 100644
--- a/analyses/survival/02-run-survival.Rmd
+++ b/analyses/survival/02-run-survival.Rmd
@@ -22,12 +22,15 @@ Read in set up script.
library(survival)
library(ggpubr)
library(tidyverse)
+library(ComplexHeatmap)
+library(circlize)
# Set directories
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
analysis_dir <- file.path(root_dir, "analyses", "survival")
# Source this script, which contains a wrapper function that can conduct the survival analyses, from OpenPBTA
source(file.path(analysis_dir, "util", "survival_models.R"))
+source(file.path(analysis_dir, "util", "heatmap_function.R"))
# Magrittr pipe
`%>%` <- dplyr::`%>%`
```
@@ -66,7 +69,8 @@ metadata <- metadata %>%
levels = c("AFR", "AMR", "EAS", "EUR", "SAS")
)) %>%
dplyr::mutate(extent_of_tumor_resection = case_when(
- extent_of_tumor_resection %in% c('Not Reported', "Unavailable", "Not Applicable") ~ "Unavailable/Unreported",
+ extent_of_tumor_resection %in% c('Not Reported', "Unavailable") ~ NA_character_,
+ extent_of_tumor_resection == "Not Applicable" ~ "No resection",
TRUE ~ extent_of_tumor_resection
))
@@ -99,6 +103,7 @@ Define plot groups for which survival analyses should be run
# Define groups for survival analyses
groups <- c("Atypical Teratoid Rhabdoid Tumor", "Craniopharyngioma",
"DIPG or DMG|Other high-grade glioma", "Ependymoma",
+ "Germ cell tumor",
"Mixed neuronal-glial tumor", "Low-grade glioma",
"Medulloblastoma", "Meningioma",
"Mesenchymal tumor", "Neurofibroma plexiform",
@@ -106,7 +111,7 @@ groups <- c("Atypical Teratoid Rhabdoid Tumor", "Craniopharyngioma",
)
# create names vector for output files
-plot_names <- c("ATRT", "CRANIO", "HGG", "EPN", "GNG", "LGG", "MB", "MNG", "MES", "NFP", "OLIGO", "SWN")
+plot_names <- c("ATRT", "CRANIO", "HGG", "EPN", "GERM", "GNG", "LGG", "MB", "MNG", "MES", "NFP", "OLIGO", "SWN")
names(plot_names) <- groups
```
@@ -149,7 +154,8 @@ for (var in variable){
# Subset histology
group_hist <- metadata %>%
- filter(grepl(group, plot_group))
+ filter(grepl(group, plot_group)) %>%
+ dplyr::mutate(mol_sub_group = factor(mol_sub_group))
if (group == "DIPG or DMG|Other high-grade glioma"){
group_hist <- group_hist %>%
@@ -184,6 +190,15 @@ for (var in variable){
"MB, Group 4", "MB, SHH")))
}
+ if (group == "Mesenchymal tumor") {
+ group_hist <- group_hist %>%
+ dplyr::mutate(cancer_group = fct_relevel(cancer_group,
+ c("Sarcoma", "Chordoma", "Chondromyxoid fibroma",
+ "Ewing sarcoma", "Hemangioblastoma",
+ "Inflammatory Myofibroblastic Tumor",
+ "Mesenchymal tumor")))
+ }
+
if (group == "Mixed neuronal-glial tumor") {
group_hist <- group_hist %>%
dplyr::mutate(mol_sub_group = fct_relevel(mol_sub_group,
@@ -196,22 +211,19 @@ for (var in variable){
# create heatmap of subtype by predicted ancestry
if (length(table(group_hist$mol_sub_group)) >= 2 & var == "predicted_ancestry") {
- subtype_plot <- group_hist %>%
- dplyr::filter(!is.na(mol_sub_group)) %>%
- dplyr::mutate(mol_sub_group = factor(mol_sub_group)) %>%
- dplyr::count(predicted_ancestry, mol_sub_group, name = "count", .drop = FALSE) %>%
- ggplot(aes(x = predicted_ancestry, y = mol_sub_group, fill = count)) +
- geom_tile(color = "black",
- lwd = 1,
- linetype = 1, show.legend = FALSE) +
- scale_fill_gradient(low="white", high="orangered") +
- geom_text(aes(label = count), color = "black", size = 5) +
- labs(x = NULL, y = NULL) +
- coord_fixed() +
- theme_minimal()
+
+ pdf(subtype_plot_pdf,
+ height = length(unique(group_hist$predicted_ancestry[!is.na(group_hist$mol_sub_group) & !grepl("classified", group_hist$mol_sub_group)]))/1.5,
+ width = 6)
- ggsave(subtype_plot_pdf, subtype_plot,
- width = 4, height = 3)
+ group_ht <- plot_enr(group_hist[!is.na(group_hist$mol_sub_group) & !grepl("classified", group_hist$mol_sub_group),], "mol_sub_group", "predicted_ancestry",
+ var1_names = levels(group_hist$mol_sub_group)[!grepl("classified", levels(group_hist$mol_sub_group))],
+ var2_names = sort(unique(group_hist$predicted_ancestry[!is.na(group_hist$mol_sub_group) & !grepl("classified", group_hist$mol_sub_group)])))
+
+ draw(group_ht)
+
+ invisible(dev.off())
+
}
# Run kaplain-meier survival models
@@ -251,9 +263,13 @@ for (var in variable){
group %in% c("Low-grade glioma"),
glue::glue("extent_of_tumor_resection+mol_sub_group+age_at_diagnosis_years+{var}"),
ifelse(
- group %in% c("DIPG or DMG|Other high-grade glioma", "Ependymoma", "Medulloblastoma", "Mixed neuronal-glial tumor", "Atypical Teratoid Rhabdoid Tumor"), glue::glue("mol_sub_group+age_at_diagnosis_years+{var}"),
- glue::glue("age_at_diagnosis_years+{var}")
- )),
+ group %in% c("DIPG or DMG|Other high-grade glioma", "Ependymoma", "Medulloblastoma", "Mixed neuronal-glial tumor", "Atypical Teratoid Rhabdoid Tumor"),
+ glue::glue("mol_sub_group+age_at_diagnosis_years+{var}"),
+ ifelse(
+ group %in% c("Germ cell tumor", "Mesenchymal tumor"),
+ glue::glue("cancer_group+age_at_diagnosis_years+{var}"),
+ glue::glue("age_at_diagnosis_years+{var}")
+ ))),
coxph_os_model_file,
"multivariate",
years_col = "OS_years", status_col = "OS_status"
@@ -264,9 +280,13 @@ for (var in variable){
group %in% c("Low-grade glioma"),
glue::glue("extent_of_tumor_resection+mol_sub_group+age_at_diagnosis_years+{var}"),
ifelse(
- group %in% c("DIPG or DMG|Other high-grade glioma", "Ependymoma", "Medulloblastoma", "Mixed neuronal-glial tumor", "Atypical Teratoid Rhabdoid Tumor"), glue::glue("mol_sub_group+age_at_diagnosis_years+{var}"),
- glue::glue("age_at_diagnosis_years+{var}")
- )),
+ group %in% c("DIPG or DMG|Other high-grade glioma", "Ependymoma", "Medulloblastoma", "Mixed neuronal-glial tumor", "Atypical Teratoid Rhabdoid Tumor"),
+ glue::glue("mol_sub_group+age_at_diagnosis_years+{var}"),
+ ifelse(
+ group %in% c("Germ cell tumor", "Mesenchymal tumor"),
+ glue::glue("cancer_group+age_at_diagnosis_years+{var}"),
+ glue::glue("age_at_diagnosis_years+{var}")
+ ))),
coxph_efs_model_file,
"multivariate",
years_col = "EFS_years", status_col = "EFS_status"
diff --git a/analyses/survival/02-run-survival.nb.html b/analyses/survival/02-run-survival.nb.html
index be82cc82..718685df 100644
--- a/analyses/survival/02-run-survival.nb.html
+++ b/analyses/survival/02-run-survival.nb.html
@@ -2912,19 +2912,61 @@ Packages and functions
library(tidyverse)
-
-── Attaching core tidyverse packages ─────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
+
+── Attaching core tidyverse packages ────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.1 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ lubridate 1.9.2 ✔ tibble 3.2.1
✔ purrr 1.0.1 ✔ tidyr 1.3.0
-
-── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
+
+── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
+
+library(ComplexHeatmap)
+
+
+Loading required package: grid
+========================================
+ComplexHeatmap version 2.14.0
+Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
+Github page: https://github.com/jokergoo/ComplexHeatmap
+Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
+
+If you use it in published research, please cite either one:
+- Gu, Z. Complex Heatmap Visualization. iMeta 2022.
+- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
+ genomic data. Bioinformatics 2016.
+
+
+The new InteractiveComplexHeatmap package can directly export static
+complex heatmaps into an interactive Shiny app with zero effort. Have a try!
+
+This message can be suppressed by:
+ suppressPackageStartupMessages(library(ComplexHeatmap))
+========================================
+
+
+library(circlize)
+
+
+========================================
+circlize version 0.4.15
+CRAN page: https://cran.r-project.org/package=circlize
+Github page: https://github.com/jokergoo/circlize
+Documentation: https://jokergoo.github.io/circlize_book/book/
+
+If you use it in published research, please cite:
+Gu, Z. circlize implements and enhances circular visualization
+ in R. Bioinformatics 2014.
+
+This message can be suppressed by:
+ suppressPackageStartupMessages(library(circlize))
+========================================
+
# Set directories
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
@@ -2940,8 +2982,9 @@ Packages and functions
myeloma
-
-# Magrittr pipe
+
+source(file.path(analysis_dir, "util", "heatmap_function.R"))
+# Magrittr pipe
`%>%` <- dplyr::`%>%`
@@ -2989,9 +3032,9 @@ Import the metadata
metadata <- readr::read_tsv(ancestry_file)
-
+
Rows: 1485 Columns: 41
-── Column specification ───────────────────────────────────────────────────────────────────────────────────────────
+── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (22): Kids_First_Biospecimen_ID, predicted_ancestry, Kids_First_Particip...
dbl (18): EAS_prob, AFR_prob, AMR_prob, SAS_prob, EUR_prob, PC1, PC2, PC3, P...
@@ -3000,13 +3043,14 @@ Import the metadata
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
-
+
metadata <- metadata %>%
dplyr::mutate(predicted_ancestry = factor(predicted_ancestry,
levels = c("AFR", "AMR", "EAS", "EUR", "SAS")
)) %>%
dplyr::mutate(extent_of_tumor_resection = case_when(
- extent_of_tumor_resection %in% c('Not Reported', "Unavailable", "Not Applicable") ~ "Unavailable/Unreported",
+ extent_of_tumor_resection %in% c('Not Reported', "Unavailable") ~ NA_character_,
+ extent_of_tumor_resection == "Not Applicable" ~ "No resection",
TRUE ~ extent_of_tumor_resection
))
@@ -3040,10 +3084,11 @@ Import the metadata
Define plot groups for which survival analyses should be run
-
+
# Define groups for survival analyses
groups <- c("Atypical Teratoid Rhabdoid Tumor", "Craniopharyngioma",
"DIPG or DMG|Other high-grade glioma", "Ependymoma",
+ "Germ cell tumor",
"Mixed neuronal-glial tumor", "Low-grade glioma",
"Medulloblastoma", "Meningioma",
"Mesenchymal tumor", "Neurofibroma plexiform",
@@ -3051,7 +3096,7 @@ Import the metadata
)
# create names vector for output files
-plot_names <- c("ATRT", "CRANIO", "HGG", "EPN", "GNG", "LGG", "MB", "MNG", "MES", "NFP", "OLIGO", "SWN")
+plot_names <- c("ATRT", "CRANIO", "HGG", "EPN", "GERM", "GNG", "LGG", "MB", "MNG", "MES", "NFP", "OLIGO", "SWN")
names(plot_names) <- groups
@@ -3060,7 +3105,7 @@ Import the metadata
Proportional Hazard models for OS and EFS
-
+
for (var in variable){
for (group in groups){
@@ -3098,7 +3143,8 @@ Import the metadata
# Subset histology
group_hist <- metadata %>%
- filter(grepl(group, plot_group))
+ filter(grepl(group, plot_group)) %>%
+ dplyr::mutate(mol_sub_group = factor(mol_sub_group))
if (group == "DIPG or DMG|Other high-grade glioma"){
group_hist <- group_hist %>%
@@ -3133,6 +3179,15 @@ Import the metadata
"MB, Group 4", "MB, SHH")))
}
+ if (group == "Mesenchymal tumor") {
+ group_hist <- group_hist %>%
+ dplyr::mutate(cancer_group = fct_relevel(cancer_group,
+ c("Sarcoma", "Chordoma", "Chondromyxoid fibroma",
+ "Ewing sarcoma", "Hemangioblastoma",
+ "Inflammatory Myofibroblastic Tumor",
+ "Mesenchymal tumor")))
+ }
+
if (group == "Mixed neuronal-glial tumor") {
group_hist <- group_hist %>%
dplyr::mutate(mol_sub_group = fct_relevel(mol_sub_group,
@@ -3145,22 +3200,19 @@ Import the metadata
# create heatmap of subtype by predicted ancestry
if (length(table(group_hist$mol_sub_group)) >= 2 & var == "predicted_ancestry") {
- subtype_plot <- group_hist %>%
- dplyr::filter(!is.na(mol_sub_group)) %>%
- dplyr::mutate(mol_sub_group = factor(mol_sub_group)) %>%
- dplyr::count(predicted_ancestry, mol_sub_group, name = "count", .drop = FALSE) %>%
- ggplot(aes(x = predicted_ancestry, y = mol_sub_group, fill = count)) +
- geom_tile(color = "black",
- lwd = 1,
- linetype = 1, show.legend = FALSE) +
- scale_fill_gradient(low="white", high="orangered") +
- geom_text(aes(label = count), color = "black", size = 5) +
- labs(x = NULL, y = NULL) +
- coord_fixed() +
- theme_minimal()
+
+ pdf(subtype_plot_pdf,
+ height = length(unique(group_hist$predicted_ancestry[!is.na(group_hist$mol_sub_group) & !grepl("classified", group_hist$mol_sub_group)]))/1.5,
+ width = 6)
+
+ group_ht <- plot_enr(group_hist[!is.na(group_hist$mol_sub_group) & !grepl("classified", group_hist$mol_sub_group),], "mol_sub_group", "predicted_ancestry",
+ var1_names = levels(group_hist$mol_sub_group)[!grepl("classified", levels(group_hist$mol_sub_group))],
+ var2_names = sort(unique(group_hist$predicted_ancestry[!is.na(group_hist$mol_sub_group) & !grepl("classified", group_hist$mol_sub_group)])))
- ggsave(subtype_plot_pdf, subtype_plot,
- width = 4, height = 3)
+ draw(group_ht)
+
+ invisible(dev.off())
+
}
# Run kaplain-meier survival models
@@ -3200,9 +3252,13 @@ Import the metadata
group %in% c("Low-grade glioma"),
glue::glue("extent_of_tumor_resection+mol_sub_group+age_at_diagnosis_years+{var}"),
ifelse(
- group %in% c("DIPG or DMG|Other high-grade glioma", "Ependymoma", "Medulloblastoma", "Mixed neuronal-glial tumor", "Atypical Teratoid Rhabdoid Tumor"), glue::glue("mol_sub_group+age_at_diagnosis_years+{var}"),
- glue::glue("age_at_diagnosis_years+{var}")
- )),
+ group %in% c("DIPG or DMG|Other high-grade glioma", "Ependymoma", "Medulloblastoma", "Mixed neuronal-glial tumor", "Atypical Teratoid Rhabdoid Tumor"),
+ glue::glue("mol_sub_group+age_at_diagnosis_years+{var}"),
+ ifelse(
+ group %in% c("Germ cell tumor", "Mesenchymal tumor"),
+ glue::glue("cancer_group+age_at_diagnosis_years+{var}"),
+ glue::glue("age_at_diagnosis_years+{var}")
+ ))),
coxph_os_model_file,
"multivariate",
years_col = "OS_years", status_col = "OS_status"
@@ -3213,9 +3269,13 @@ Import the metadata
group %in% c("Low-grade glioma"),
glue::glue("extent_of_tumor_resection+mol_sub_group+age_at_diagnosis_years+{var}"),
ifelse(
- group %in% c("DIPG or DMG|Other high-grade glioma", "Ependymoma", "Medulloblastoma", "Mixed neuronal-glial tumor", "Atypical Teratoid Rhabdoid Tumor"), glue::glue("mol_sub_group+age_at_diagnosis_years+{var}"),
- glue::glue("age_at_diagnosis_years+{var}")
- )),
+ group %in% c("DIPG or DMG|Other high-grade glioma", "Ependymoma", "Medulloblastoma", "Mixed neuronal-glial tumor", "Atypical Teratoid Rhabdoid Tumor"),
+ glue::glue("mol_sub_group+age_at_diagnosis_years+{var}"),
+ ifelse(
+ group %in% c("Germ cell tumor", "Mesenchymal tumor"),
+ glue::glue("cancer_group+age_at_diagnosis_years+{var}"),
+ glue::glue("age_at_diagnosis_years+{var}")
+ ))),
coxph_efs_model_file,
"multivariate",
years_col = "EFS_years", status_col = "EFS_status"
@@ -3243,13 +3303,13 @@ Import the metadata
Testing model: survival::Surv(EFS_days, EFS_status) ~ predicted_ancestry with kap.meier
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 6 ; coefficient may be infinite.
+Loglik converged before variable 7 ; coefficient may be infinite.
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 6 ; coefficient may be infinite.
+Loglik converged before variable 7 ; coefficient may be infinite.
Testing model: survival::Surv(OS_days, OS_status) ~ predicted_ancestry with kap.meier
@@ -3281,6 +3341,14 @@ Import the metadata
Testing model: survival::Surv(OS_days, OS_status) ~ predicted_ancestry with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ predicted_ancestry with kap.meier
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Ran out of iterations and did not converge
+
+
+Testing model: survival::Surv(OS_days, OS_status) ~ predicted_ancestry with kap.meier
+Testing model: survival::Surv(EFS_days, EFS_status) ~ predicted_ancestry with kap.meier
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2,7,8,10 ; coefficient may be infinite.
@@ -3293,9 +3361,9 @@ Import the metadata
Testing model: survival::Surv(OS_days, OS_status) ~ predicted_ancestry with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ predicted_ancestry with kap.meier
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 12 ; coefficient may be infinite.
+Loglik converged before variable 11 ; coefficient may be infinite.
Warning: There was 1 warning in `dplyr::mutate()`.
@@ -3329,16 +3397,19 @@ Import the metadata
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Loglik converged before variable 2 ; coefficient may be infinite.
+
Testing model: survival::Surv(OS_days, OS_status) ~ predicted_ancestry with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ predicted_ancestry with kap.meier
-
-Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Ran out of iterations and did not converge
-
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Ran out of iterations and did not converge
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 2 ; coefficient may be infinite.
@@ -3361,9 +3432,9 @@ Import the metadata
Testing model: survival::Surv(EFS_days, EFS_status) ~ race with kap.meier
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 4 ; coefficient may be infinite.
+Loglik converged before variable 5 ; coefficient may be infinite.
Testing model: survival::Surv(OS_days, OS_status) ~ race with kap.meier
@@ -3395,6 +3466,14 @@ Import the metadata
Testing model: survival::Surv(OS_days, OS_status) ~ race with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ race with kap.meier
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Ran out of iterations and did not converge
+
+
+Testing model: survival::Surv(OS_days, OS_status) ~ race with kap.meier
+Testing model: survival::Surv(EFS_days, EFS_status) ~ race with kap.meier
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2,8,9 ; coefficient may be infinite.
@@ -3403,9 +3482,9 @@ Import the metadata
Testing model: survival::Surv(OS_days, OS_status) ~ race with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ race with kap.meier
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 10 ; coefficient may be infinite.
+Loglik converged before variable 9 ; coefficient may be infinite.
Warning: There was 1 warning in `dplyr::mutate()`.
@@ -3435,9 +3514,12 @@ Import the metadata
Testing model: survival::Surv(OS_days, OS_status) ~ race with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ race with kap.meier
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Ran out of iterations and did not converge
+Ran out of iterations and did not converge
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Loglik converged before variable 2 ; coefficient may be infinite.
Testing model: survival::Surv(OS_days, OS_status) ~ race with kap.meier
@@ -3497,6 +3579,14 @@ Import the metadata
Testing model: survival::Surv(OS_days, OS_status) ~ ethnicity with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ ethnicity with kap.meier
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Ran out of iterations and did not converge
+
+
+Testing model: survival::Surv(OS_days, OS_status) ~ ethnicity with kap.meier
+Testing model: survival::Surv(EFS_days, EFS_status) ~ ethnicity with kap.meier
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2,7 ; coefficient may be infinite.
@@ -3529,11 +3619,17 @@ Import the metadata
Testing model: survival::Surv(OS_days, OS_status) ~ ethnicity with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ ethnicity with kap.meier
-
-Testing model: survival::Surv(OS_days, OS_status) ~ ethnicity with kap.meier
-
-
-Testing model: survival::Surv(EFS_days, EFS_status) ~ ethnicity with kap.meier
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Loglik converged before variable 2,4,5,6 ; coefficient may be infinite.
+
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Loglik converged before variable 2 ; coefficient may be infinite.
+
+
+Testing model: survival::Surv(OS_days, OS_status) ~ ethnicity with kap.meier
+Testing model: survival::Surv(EFS_days, EFS_status) ~ ethnicity with kap.meier
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
@@ -3577,17 +3673,26 @@ Import the metadata
Testing model: survival::Surv(EFS_days, EFS_status) ~ EUR_status with kap.meier
-
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Loglik converged before variable 1,3,4,5,6 ; coefficient may be infinite.
+
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 1,3,4,5,6 ; coefficient may be infinite.
-
-Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1 ; coefficient may be infinite.
Testing model: survival::Surv(OS_days, OS_status) ~ EUR_status with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ EUR_status with kap.meier
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Ran out of iterations and did not converge
+
+
+Testing model: survival::Surv(OS_days, OS_status) ~ EUR_status with kap.meier
+Testing model: survival::Surv(EFS_days, EFS_status) ~ EUR_status with kap.meier
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2,7 ; coefficient may be infinite.
@@ -3620,11 +3725,17 @@ Import the metadata
Testing model: survival::Surv(OS_days, OS_status) ~ EUR_status with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ EUR_status with kap.meier
-
-Testing model: survival::Surv(OS_days, OS_status) ~ EUR_status with kap.meier
-
-
-Testing model: survival::Surv(EFS_days, EFS_status) ~ EUR_status with kap.meier
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Loglik converged before variable 2,4,5,6 ; coefficient may be infinite.
+
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Loglik converged before variable 2 ; coefficient may be infinite.
+
+
+Testing model: survival::Surv(OS_days, OS_status) ~ EUR_status with kap.meier
+Testing model: survival::Surv(EFS_days, EFS_status) ~ EUR_status with kap.meier
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
@@ -3827,17 +3938,13 @@ Import the metadata
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
-
-Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 3 ; coefficient may be infinite.
-
Testing model: survival::Surv(OS_days, OS_status) ~ predicted_ancestry with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ predicted_ancestry with kap.meier
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 7 ; coefficient may be infinite.
+Loglik converged before variable 6 ; coefficient may be infinite.
Testing model: survival::Surv(OS_days, OS_status) ~ predicted_ancestry with kap.meier
@@ -3969,17 +4076,13 @@ Import the metadata
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
-
-Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 3 ; coefficient may be infinite.
-
Testing model: survival::Surv(OS_days, OS_status) ~ race with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ race with kap.meier
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 5 ; coefficient may be infinite.
+Loglik converged before variable 4 ; coefficient may be infinite.
Testing model: survival::Surv(OS_days, OS_status) ~ race with kap.meier
@@ -4100,10 +4203,6 @@ Import the metadata
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
-
-Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 3 ; coefficient may be infinite.
-
Testing model: survival::Surv(OS_days, OS_status) ~ ethnicity with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ ethnicity with kap.meier
@@ -4214,10 +4313,6 @@ Import the metadata
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
-
-Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 3 ; coefficient may be infinite.
-
Testing model: survival::Surv(OS_days, OS_status) ~ EUR_status with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ EUR_status with kap.meier
@@ -4254,12 +4349,11 @@ Import the metadata
Testing model: survival::Surv(EFS_days, EFS_status) ~ EUR_status with kap.meier
-
-Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
-Loglik converged before variable 2 ; coefficient may be infinite.
-
-
+
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
+Loglik converged before variable 2 ; coefficient may be infinite.
+
+Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 2 ; coefficient may be infinite.
@@ -4270,7 +4364,7 @@ Import the metadata
sessionInfo()
-
+
R version 4.2.3 (2023-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
@@ -4288,37 +4382,49 @@ Import the metadata
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
-[1] stats graphics grDevices utils datasets methods base
+[1] grid stats graphics grDevices utils datasets methods
+[8] base
other attached packages:
- [1] survminer_0.4.9 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
- [5] dplyr_1.1.1 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
- [9] tibble_3.2.1 tidyverse_2.0.0 ggpubr_0.6.0 ggplot2_3.4.2
-[13] survival_3.5-5
+ [1] survminer_0.4.9 circlize_0.4.15 ComplexHeatmap_2.14.0
+ [4] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
+ [7] dplyr_1.1.1 purrr_1.0.1 readr_2.1.4
+[10] tidyr_1.3.0 tibble_3.2.1 tidyverse_2.0.0
+[13] ggpubr_0.6.0 ggplot2_3.4.2 survival_3.5-5
loaded via a namespace (and not attached):
- [1] lattice_0.20-45 zoo_1.8-12 rprojroot_2.0.3 digest_0.6.31
- [5] utf8_1.2.3 R6_2.5.1 backports_1.4.1 evaluate_0.20
- [9] pillar_1.9.0 rlang_1.1.0 data.table_1.14.8 car_3.1-2
-[13] jquerylib_0.1.4 Matrix_1.5-3 rmarkdown_2.21 labeling_0.4.2
-[17] textshaping_0.3.6 splines_4.2.3 bit_4.0.5 munsell_0.5.0
-[21] broom_1.0.4 compiler_4.2.3 xfun_0.38 systemfonts_1.0.4
-[25] pkgconfig_2.0.3 htmltools_0.5.5 tidyselect_1.2.0 gridExtra_2.3
-[29] km.ci_0.5-6 fansi_1.0.4 crayon_1.5.2 tzdb_0.3.0
-[33] withr_2.5.0 grid_4.2.3 jsonlite_1.8.4 xtable_1.8-4
-[37] gtable_0.3.3 lifecycle_1.0.3 magrittr_2.0.3 KMsurv_0.1-5
-[41] scales_1.2.1 cli_3.6.1 stringi_1.7.12 vroom_1.6.1
-[45] cachem_1.0.7 carData_3.0-5 farver_2.1.1 ggsignif_0.6.4
-[49] bslib_0.4.2 ragg_1.2.5 survMisc_0.5.6 generics_0.1.3
-[53] vctrs_0.6.2 tools_4.2.3 bit64_4.0.5 glue_1.6.2
-[57] hms_1.1.3 parallel_4.2.3 abind_1.4-5 fastmap_1.1.1
-[61] yaml_2.3.7 timechange_0.2.0 colorspace_2.1-0 rstatix_0.7.2
-[65] knitr_1.42 sass_0.4.5
+ [1] matrixStats_0.63.0 bit64_4.0.5 doParallel_1.0.17
+ [4] RColorBrewer_1.1-3 rprojroot_2.0.3 tools_4.2.3
+ [7] backports_1.4.1 bslib_0.4.2 utf8_1.2.3
+[10] R6_2.5.1 BiocGenerics_0.44.0 colorspace_2.1-0
+[13] GetoptLong_1.0.5 withr_2.5.0 tidyselect_1.2.0
+[16] gridExtra_2.3 bit_4.0.5 compiler_4.2.3
+[19] cli_3.6.1 sass_0.4.5 scales_1.2.1
+[22] survMisc_0.5.6 digest_0.6.31 rmarkdown_2.21
+[25] pkgconfig_2.0.3 htmltools_0.5.5 fastmap_1.1.1
+[28] rlang_1.1.0 GlobalOptions_0.1.2 shape_1.4.6
+[31] jquerylib_0.1.4 generics_0.1.3 zoo_1.8-12
+[34] jsonlite_1.8.4 vroom_1.6.1 car_3.1-2
+[37] magrittr_2.0.3 Matrix_1.5-3 Rcpp_1.0.10
+[40] munsell_0.5.0 S4Vectors_0.36.2 fansi_1.0.4
+[43] abind_1.4-5 lifecycle_1.0.3 stringi_1.7.12
+[46] yaml_2.3.7 carData_3.0-5 parallel_4.2.3
+[49] crayon_1.5.2 lattice_0.20-45 splines_4.2.3
+[52] hms_1.1.3 magick_2.7.4 knitr_1.42
+[55] pillar_1.9.0 rjson_0.2.21 ggsignif_0.6.4
+[58] codetools_0.2-19 stats4_4.2.3 glue_1.6.2
+[61] evaluate_0.20 data.table_1.14.8 png_0.1-8
+[64] vctrs_0.6.2 tzdb_0.3.0 foreach_1.5.2
+[67] gtable_0.3.3 clue_0.3-64 km.ci_0.5-6
+[70] cachem_1.0.7 xfun_0.38 xtable_1.8-4
+[73] broom_1.0.4 rstatix_0.7.2 iterators_1.0.14
+[76] IRanges_2.32.0 KMsurv_0.1-5 cluster_2.1.4
+[79] timechange_0.2.0
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diff --git a/analyses/survival/plots/CPT/forest_CPT_EFS_subtype_EUR_status.pdf b/analyses/survival/plots/CPT/forest_CPT_EFS_subtype_EUR_status.pdf
index dc1c909c..61f13056 100644
Binary files a/analyses/survival/plots/CPT/forest_CPT_EFS_subtype_EUR_status.pdf and b/analyses/survival/plots/CPT/forest_CPT_EFS_subtype_EUR_status.pdf differ
diff --git a/analyses/survival/plots/CPT/forest_CPT_EFS_subtype_ethnicity.pdf b/analyses/survival/plots/CPT/forest_CPT_EFS_subtype_ethnicity.pdf
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index 2d01aaff..557d6a93 100644
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diff --git a/analyses/survival/results/mb/logrank_mb_SHH_OS_race.RDS b/analyses/survival/results/mb/logrank_mb_SHH_OS_race.RDS
index 67f82d39..cf01c4d2 100644
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diff --git a/analyses/survival/results/median-survival-by-ancestry-cancer-group.tsv b/analyses/survival/results/median-survival-by-ancestry-cancer-group.tsv
index 81b632f3..ff368f7a 100644
--- a/analyses/survival/results/median-survival-by-ancestry-cancer-group.tsv
+++ b/analyses/survival/results/median-survival-by-ancestry-cancer-group.tsv
@@ -9,7 +9,7 @@ EPN, ST ZFTA 2.4 (2) 6.1 (4.2) 6.8 (5.8) 5.1 (2.5) -- (--) 9.3 0.05 1 (2.2) 0.7
Ependymoma 2.6 (3.2) 4 (3.6) 5.2 (4.2) 4.7 (3.3) 1 (--) 4.6 0.1 2.3 (2.3) 1.2 (1.7) 1.4 (5.1) 2.5 (2.7) 1 (--) 3.6 0.47
GNG/GNT, BRAF V600E 3.8 (0.8) 1.9 (5) 5.1 (--) 3.2 (3.1) 2.4 (--) 0 1 3.1 (1) 1.9 (0.5) 5.1 (--) 2.8 (1.9) 1.8 (--) 3.7 0.45
GNG/GNT, Other alteration 3.1 (3.4) 2.8 (1.7) -- (--) 3.8 (3.7) 6.7 (4.6) 0.5 0.92 3.1 (3.4) 2.8 (1.7) -- (--) 2.8 (3) 2.4 (1.8) 5.3 0.15
-Germ cell tumor 3.8 (0.2) 3.6 (1.3) 5.2 (3.6) 3.9 (2.4) -- (--) 0 1 0.3 (0.3) 0.8 (0.5) 0.4 (1.1) 2.1 (1.9) -- (--) 11.8 0.01
+Germ cell tumor 3.8 (0.2) 3.6 (1.3) 5.2 (3.6) 3.9 (2.4) -- (--) 0 1 0.3 (0.3) 0.8 (0.5) 0.4 (1.1) 2.1 (1.9) -- (--) 12.4 0.01
HGG, H3 WT 1.3 (1.1) 1.6 (2.1) 2.2 (3.5) 1.6 (5.3) 1.8 (1.4) 1.6 0.8 0.8 (1.3) 0.5 (2) 1.3 (2.5) 0.9 (3.7) 1.8 (1.5) 2.9 0.57
High-grade glioma 1.3 (0.7) 1 (1.5) 1.8 (2.3) 1.2 (3.7) 1.8 (43.5) 6.5 0.17 0.7 (0.8) 0.6 (1.4) 1.1 (1.8) 0.7 (2.8) 0.7 (1.1) 1 0.91
LGG, BRAF V600E 1.7 (1.8) 5.9 (1.3) 5.4 (5.4) 4.2 (3.9) 6.5 (--) 0 1 1.7 (1.8) 1.6 (1.1) 5 (5.9) 1.9 (2.4) 1.6 (--) 3.8 0.44
@@ -23,7 +23,7 @@ MB, SHH 6.9 (5.4) 1.7 (1.5) 3.1 (1.5) 3.5 (2.3) -- (--) 5.9 0.12 6.9 (5.3) 1 (1.
MB, WNT -- (--) 2 (--) 1.1 (--) 4.2 (4) 8.7 (--) 0.3 0.88 -- (--) 2 (--) 1.1 (--) 3.3 (4) 8.7 (--) 0.6 0.89
Medulloblastoma 4.8 (3.2) 2.7 (1.9) 4.4 (2.9) 3.6 (3.3) 8.2 (3.9) 1.1 0.89 4.4 (3.4) 2.3 (1.9) 3.3 (2.5) 2.9 (2.7) 8.2 (4.3) 1.3 0.87
Meningioma 4.9 (6.6) 3.7 (2.5) 6.3 (4.5) 3.7 (7.4) 4.7 (0.8) 1.7 0.79 3.9 (2.1) 3.4 (1.7) 1.4 (3.2) 1.5 (4.6) 4.7 (0.8) 4.3 0.37
-Mesenchymal tumor 0.4 (0.8) 2.9 (4.5) 4.1 (8) 3.9 (2.7) 2.3 (1.7) 12.4 0.01 0.4 (0.1) 1.5 (4.7) 3.4 (5) 3 (2.3) 2.3 (1.8) 5.6 0.23
+Mesenchymal tumor 0.4 (0.8) 2.9 (4.5) 4.1 (8) 3.9 (2.7) 2.3 (1.7) 14 0.01 0.4 (0.1) 1.5 (4.7) 3.4 (5) 3 (2.3) 2.3 (1.8) 5.5 0.24
Mixed neuronal-glial tumor 3.8 (2.5) 1.9 (3.3) 2.7 (3.4) 4 (4.4) 4.8 (4.5) 0.9 0.92 3.1 (2.5) 1.9 (2.3) 2.7 (3.4) 2.7 (2.7) 2 (1.6) 6.7 0.16
Neurofibroma plexiform 9.8 (7.5) 3.4 (3.2) -- (--) 4.9 (7.6) -- (--) 0 1 9.8 (3.7) 1.4 (3.7) -- (--) 3.3 (2.9) -- (--) 7.6 0.02
Oligodendroglioma -- (--) -- (--) -- (--) 4.5 (5.3) 6.9 (--) 0 1 -- (--) -- (--) -- (--) 2.7 (2.1) 1.2 (--) 0.3 0.56
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diff --git a/analyses/survival/results/swn/cox_swn_OS_additive_terms_subtype_race.RDS b/analyses/survival/results/swn/cox_swn_OS_additive_terms_subtype_race.RDS
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diff --git a/analyses/survival/util/heatmap_function.R b/analyses/survival/util/heatmap_function.R
new file mode 100644
index 00000000..02f54d04
--- /dev/null
+++ b/analyses/survival/util/heatmap_function.R
@@ -0,0 +1,63 @@
+# Functions for generating count and enrichment heatmaps
+#
+# Ryan Corbett
+#
+# 2024
+
+# Function to create frequency count and enrichment heatmap for two selected variables. Significance of enrichment is calculated using hypergeometric tests
+
+plot_enr <- function(df, var1, var2,
+ var1_names, var2_names,
+ padjust = FALSE) {
+
+ enr <- matrix(0, nrow(unique(df[,var1])),
+ nrow(unique(df[,var2])),
+ dimnames = list(var1_names,
+ var2_names))
+ pval <- enr
+ ct <- enr
+
+ # loop through cancer groups to calculate enrichment
+ for (i in 1:nrow(enr)){
+ no_var1 <- sum(unlist(df[,var1]) == rownames(enr)[i])
+ for (j in 1:ncol(enr)){
+ no_var2 <- sum(unlist(df[,var2]) == colnames(enr)[j] & !is.na(unlist(df[,var2])))
+ no_var1_var2 <- sum(unlist(df[,var1]) == rownames(enr)[i] & unlist(df[,var2]) == colnames(enr)[j])
+ ct[i,j] <- no_var1_var2
+ enr[i,j] <- (no_var1_var2/no_var2)/(no_var1/nrow(df))
+ pval[i,j] <- phyper(no_var1_var2, no_var1, nrow(df) - no_var1, no_var2, lower.tail = F)
+ }
+ }
+
+ if (padjust == TRUE) {
+
+ fdr <- t(apply(pval, 1, function(x) p.adjust(x, "fdr")))
+ sig_mat <- ifelse(fdr < 0.05 & enr > 1 & ct > 2, "*", "")
+
+ } else {
+
+ sig_mat <- ifelse(pval < 0.05 & enr > 1 & ct > 2, "*", "")
+
+ }
+
+ fill_mat <- matrix(glue::glue("{round(enr, 1)}{sig_mat}"),
+ nrow(enr), ncol(enr))
+
+ ct_enr_mat <- matrix(glue::glue("{ct}\n({fill_mat})"),
+ nrow(ct), ncol(ct))
+
+ col_fun = colorRamp2(c(0, ceiling(max(enr))), c("white", "orangered"))
+
+ region_ht <- Heatmap(enr,
+ name = "Odds ratio",
+ cluster_rows = F,
+ cluster_columns = F,
+ rect_gp = gpar(col = "black", lwd = 2),
+ col = col_fun,
+ cell_fun = function(j, i, x, y, width, height, fill) {
+ grid.text(sprintf("%s", ct_enr_mat[i, j]), x, y, gp = gpar(fontsize = 12))
+ })
+
+ return(region_ht)
+
+}