HGG-oncohistones project [source]
00 - Generate oncoprints
Selin Jessa []
29 June, 2022
1 Configuration
Configuration of project directory & analysis outputs:
Show full config
source(here("rr_helpers.R"))
# Set up outputs
message("Document index: ", doc_id)## Document index: 00# Specify where to save outputs
out <- here("output", doc_id); dir.create(out, recursive = TRUE)
figout <- here("figures", doc_id); dir.create(figout, recursive = TRUE)
cache <- paste0(readLines(here("include/project_root.txt")), basename(here()), "/", doc_id, "/")Outputs and figures will be saved at these paths, relative to project root:
## public/output/00## public/figures/00Setting a random seed:
set.seed(100)2 Overview
This document plots summaries of the samples included in this analysis as oncoprints as shown in Figure 1.
3 Libraries
# Load libraries here
library(here)
library(tidyr)
library(dplyr)
library(readr)
library(glue)
library(tibble)
library(ggplot2)
library(cowplot)
source(here("include/style.R")) # contains palettes & plotting utils
ggplot2::theme_set(theme_min())4 Oncoprint
We'll generate an oncoprint for unique patient tumor samples, and patient-derived cell lines.
4.1 Tumors
oncoprint_input_tumors <- read_tsv(here("data/metadata/oncoprint_input_tumors.tsv")) %>%
mutate(Molecular = factor(Molecular, levels = names(palette_molecular)),
Location = factor(Location, levels = names(palette_location))) %>%
arrange(desc(Type), Molecular, Location, GrowthFactorReceptor,
desc(scRNAseq), desc(scATACseq), desc(RNAseq), desc(H3K27ac), desc(H3K27me3), desc(H3K27me2)) %>%
mutate(ID_patient = factor(ID_patient, levels = .$ID_patient))
p0 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = Material), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Tumor" = "azure2", "Cell line" = "gray80", "Both" = "gray50"),
guide = guide_legend(ncol = 2)) +
theme_min() + # the theme_row() theme is defined in include/style.R
theme_row()
p1 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = Molecular), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = palette_molecular, guide = guide_legend(ncol = 2)) +
theme_min() +
theme_row()
p2 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = GrowthFactorReceptor), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = palette_gfr, na.value = "gray90", guide = guide_legend(ncol = 2)) +
theme_min() +
theme_row()
p3 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = Location), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = palette_location, na.value = "gray90", guide = guide_legend(ncol = 2)) +
theme_min() +
theme_row()
p4 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = Age), colour = "white", width = 0.9, height = 0.9) +
scale_fill_gradientn(colours = brewer.pal(9, "YlGnBu"), na.value = "gray90") +
theme_min() +
theme_row()
p5 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = scRNAseq), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#2a9135", "Y" = "#6fc978", "-" = "gray90")) +
theme_min() +
theme_row()
p6 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = scATACseq), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#0ad1b7", "-" = "gray90")) +
theme_min() +
theme_row()
p7 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = RNAseq), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#2064f7", "Y" = "#6c88c4", "-" = "gray90")) +
theme_min() +
theme_row()
p8 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = H3K27ac), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#f72076", "Y" = "#c95583", "-" = "gray90")) +
theme_min() +
theme_row()
p9 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = H3K27me3), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#650ac7", "Y" = "#9363c7", "-" = "gray90")) +
theme_min() +
theme_row()
p10 <- oncoprint_input_tumors %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = H3K27me2), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#e04902", "Y" = "#d18460", "-" = "gray90")) +
theme(panel.border = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks = element_blank()) +
rotate_x()
cowplot::plot_grid(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10,
align = "v", axis = "r", ncol = 1,
rel_heights = c(rep(0.08, 10), 0.2))
4.2 Cell lines
oncoprint_input_cl <- read_tsv(here("data/metadata/oncoprint_input_cl.tsv")) %>%
mutate(Molecular = factor(Molecular, levels = names(palette_molecular)),
Location = factor(Location, levels = names(palette_location))) %>%
arrange(desc(Type), Molecular, Location, GrowthFactorReceptor,
desc(scRNAseq), desc(scATACseq), desc(RNAseq), desc(H3K27ac), desc(H3K27me3), desc(H3K27me2)) %>%
mutate(ID_patient = factor(ID_patient, levels = .$ID_patient))
p0 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = Material), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Tumor" = "azure2", "Cell line" = "gray80", "Both" = "gray50"),
guide = guide_legend(ncol = 2)) +
theme_min() +
theme_row()
p1 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = Molecular), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = palette_molecular, guide = guide_legend(ncol = 2)) +
theme_min() +
theme_row()
p2 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = GrowthFactorReceptor), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = palette_gfr, na.value = "gray90", guide = guide_legend(ncol = 2)) +
theme_min() +
theme_row()
p3 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = Location), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = palette_location, na.value = "gray90", guide = guide_legend(ncol = 2)) +
theme_min() +
theme_row()
p4 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = Age), colour = "white", width = 0.9, height = 0.9) +
scale_fill_gradientn(colours = brewer.pal(9, "YlGnBu"), na.value = "gray90", limits = c(0, 20)) +
theme_min() +
theme_row()
p5 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = scRNAseq), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#2a9135", "Y" = "#6fc978", "-" = "gray90")) +
theme_min() +
theme_row()
p6 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = scATACseq), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#0ad1b7", "-" = "gray90")) +
theme_min() +
theme_row()
p7 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = RNAseq), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#2064f7", "Y" = "#6c88c4", "-" = "gray90")) +
theme_min() +
theme_row()
p8 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = H3K27ac), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#f72076", "Y" = "#c95583", "-" = "gray90")) +
theme_min() +
theme_row()
p9 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = H3K27me3), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#650ac7", "Y" = "#9363c7", "-" = "gray90")) +
theme_min() +
theme_row()
p10 <- oncoprint_input_cl %>%
ggplot(aes(x = ID_patient, y = 1)) +
geom_tile(aes(fill = H3K27me2), colour = "white", width = 0.9, height = 0.9) +
scale_fill_manual(values = c("Y (new)" = "#e04902", "Y" = "#d18460", "-" = "gray90")) +
theme(panel.border = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks = element_blank()) +
rotate_x()
cowplot::plot_grid(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10,
align = "v", axis = "r", ncol = 1,
rel_heights = c(rep(0.08, 10), 0.2))
5 Reproducibility
This document was last rendered on:
## 2022-06-29 09:45:56The git repository and last commit:
## Local: master /lustre06/project/6004736/sjessa/from_narval/HGG-oncohistones/public
## Remote: master @ origin (git@github.com:fungenomics/HGG-oncohistones.git)
## Head: [056f679] 2022-06-14: Add R-4 renv lockfileThe random seed was set with set.seed(100)
The R session info:
## Error in get(genname, envir = envir) : object 'testthat_print' not found## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.1 (2019-07-05)
## os Rocky Linux 8.5 (Green Obsidian)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_CA.UTF-8
## ctype en_CA.UTF-8
## tz EST5EDT
## date 2022-06-29
##
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## [2] /tmp/RtmpZIxQ4e/renv-system-library
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A project of the Kleinman Lab at McGill University, using the rr reproducible research template.