HGG-oncohistones project [source]
02 - Bulk RNAseq/ChIPseq comparisons
Selin Jessa []
18 October, 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: 02# 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/02## public/figures/02Setting a random seed:
set.seed(100)2 Overview
Here we interrogate differences in bulk RNAseq as well as H3K27ac and H3K27me3 ChIPseq data between tumor types (H3.1 vs H3.3K27M pontine gliomas, and pontine vs thalamic H3.3K27M gliomas). This document generates the scatter plots integrating H3K27ac/H3K27me3/RNAseq, as shown in Figures 2 and 4.
3 Libraries
# Load libraries here
library(dplyr)
library(tidyr)
library(readr)
library(readxl)
library(glue)
library(tibble)
library(ggplot2)
library(ggrepel)
library(stringr)
library(purrr)
library(cowplot)
library(feather)
library(icytobox)
source(here("include/style.R")) # contains palettes & plotting utils
source(here("code/functions/RNAseq.R"))
ggplot2::theme_set(theme_min())4 Load metadata
Load the sample metadata for the project:
meta <- read_tsv(here("data/metadata/metadata_patient_samples_NGS.tsv"))meta_chip <- read_tsv(here("data/metadata/metadata_chip_all.tsv")) %>%
right_join(meta, by = c("BioID", "ID_paper", "Material")) %>%
mutate(Factor = gsub("-M", "", Factor)) %>%
filter(grepl("Cell-of-origin", Analyses))5 Differential expression (pons vs. thalamus, and H3.1 vs H3.3)
Load the differential expression analyses between H3.1 vs H3.3 and pons vs. thalamic tumors:
# H3.1 has log2FoldChange > 0
info_h31_vs_H33 <- read_tsv(here("data/RNAseq/pipeline_l3/DGE/HGG-H3.1.2K27M-Pons_vs_HGG-H3.3K27M-Pons/info.samples.tsv"))
dge_h31_vs_h33 <- read_tsv(here("data/RNAseq/pipeline_l3/DGE/HGG-H3.1.2K27M-Pons_vs_HGG-H3.3K27M-Pons/diff/Ensembl.ensGene.exon/HGG-H3.3K27M-PonsvsHGG-H3.1.2K27M-Pons.tsv")) %>%
separate(ID, into = c("ENSID", "symbol"), sep = ":")
# double check no. of samples in each category
table(info_h31_vs_H33$Group)##
## HGG-H3.1.2K27M-Pons HGG-H3.3K27M-Pons
## 9 11# Thalamus has log2FoldChange > 0
info_thalamus_vs_pons <- read_tsv(here("data/RNAseq/pipeline_l3/DGE/HGG-H3.3K27M-Thal._vs_HGG-H3.3K27M-Pons/info.samples.tsv"))
dge_thalamus_vs_pons <- read_tsv(here("data/RNAseq/pipeline_l3/DGE/HGG-H3.3K27M-Thal._vs_HGG-H3.3K27M-Pons/diff/Ensembl.ensGene.exon/HGG-H3.3K27M-PonsvsHGG-H3.3K27M-Thal..tsv")) %>%
separate(ID, into = c("ENSID", "symbol"), sep = ":")
# double check no. of samples in each category
table(info_thalamus_vs_pons$Group)##
## HGG-H3.3K27M-Pons HGG-H3.3K27M-Thal.
## 11 65.1 Prepare tables
We'll assemble 3 tables:
- raw counts
- DGE H3.1 vs H3.3
- DGE thalamus vs pons
# COUNTS -----------------------------------------------------------------------
# get sample order as Table 1
sample_order <- data.table::fread(here("data/metadata/bulk_info.samples.tsv"), data.table = FALSE) %>% .$Nickname
counts_RNA <- read.table(here("data/RNAseq/pipeline_l3/all/counts/Ensembl.ensGene.exon.raw.tsv.gz"),
header = T, sep = "\t", check.names = FALSE) %>%
tibble::rownames_to_column(var = "ID") %>%
separate(ID, into = c("ENSID", "symbol"), sep = ":") %>%
arrange(ENSID) %>%
.[, c("ENSID", "symbol", sample_order)] %T>%
rr_write_tsv(glue("{out}/TABLE_bulk_counts.tsv"),
"Raw counts for bulk RNAseq data")## ...writing description of TABLE_bulk_counts.tsv to public/output/02/TABLE_bulk_counts.desc# sanity checks
length(colnames(counts_RNA)) - 2## [1] 64dim(counts_RNA)## [1] 60234 66# DGE --------------------------------------------------------------------------
dge_h31_vs_h33 %>% arrange(ENSID) %>%
rr_write_tsv(glue("{out}/TABLE_dge_H3.1_vs_H3.3.tsv"),
"Differential gene expression analysis for H3.1K27M pons vs H3.3K27M pons (LFC > 0 indicates up in H3.1K27M)")## ...writing description of TABLE_dge_H3.1_vs_H3.3.tsv to public/output/02/TABLE_dge_H3.1_vs_H3.3.descdge_thalamus_vs_pons %>% arrange(ENSID) %>%
rr_write_tsv(glue("{out}/TABLE_dge_thal_vs_pons.tsv"),
"Differential gene expression analysis for H3.3K27M thalamic vs H3.3K27M pons (LFC > 0 indicates up in thalamic)")## ...writing description of TABLE_dge_thal_vs_pons.tsv to public/output/02/TABLE_dge_thal_vs_pons.desc# sanity checks
dim(dge_h31_vs_h33)## [1] 60234 8dim(dge_thalamus_vs_pons)## [1] 60234 86 ChIPseq
6.1 Load data
# slow
data_chip <- read_xlsx(here("data/ChIPseq/quantifications_@mhulswit/20210521_H3.3H3.3EZHIP_H3K27ac_H3K27me3_RefSeq_Promoters_SJ.xlsx"), skip = 1)Let's separate out the data into the summary stats (group medians and z-scores) and per-sample quantifications, and convert to tidy (long) format. Starting with the per-sample quantifications:
data_chip_tidy <- data_chip %>%
dplyr::select(1:100, matches("Median_"),
# only keep relevant Z-scores
Z_H3.3K27M_Thalamus_H3.3K27M_Pons_H3K27ac,
Z_H3.3K27M_Pons_H3.1K27M_Pons_H3K27ac,
Z_H3.3K27M_Thalamus_H3.3K27M_Pons_H3K27me3,
Z_H3.3K27M_Pons_H3.1K27M_Pons_H3K27me3) %>%
gather(dataset, value, 8:ncol(.)) %>%
mutate(Mark = case_when(
grepl("K27ac", dataset) ~ "H3K27ac",
grepl("K27me3", dataset) ~ "H3K27me3"
)) %>%
mutate(Sample = gsub("_Count|_RPKM", "", dataset)) %>%
left_join(meta_chip %>% mutate(bw = basename(Path_bw_internal)) %>% dplyr::select(ID_paper, bw, Replicate),
# column name in ChIP quantification matches bigwig (bw) filename
by = c("Sample" = "bw")) %>%
filter(!grepl("NormalPons", Sample)) %>%
# only keep the relevant Z
filter(!is.na(ID_paper) | grepl("^Median|^Z", Sample)) %>%
# tidy
mutate(Sample = case_when(
grepl("Median_PFA_PF", Sample) ~ gsub("PFA_PF", "EZHIP_PFA", Sample),
grepl("Median_H3.1K27M_PF", Sample) ~ gsub("PF", "PFA", Sample),
TRUE ~ Sample))
# sanity check: ensure that no samples present in this dataset are *NOT* included
# in the ChIPseq metadata file
base::setdiff(unique(data_chip_tidy$ID_paper), unique(meta_chip$ID_paper))## [1] NAsave(data_chip_tidy, file = glue("{out}/data_chip_tidy.Rda"))
# make table
data_chip_tidy %>%
mutate(ID_paper = case_when(
grepl("^Z|^Median", Sample) ~ Sample,
TRUE ~ paste0(ID_paper, "-", Replicate, "__", Mark)
)) %>%
dplyr::select(ID, gene_symbol = name, chr, start, end, strand, ID_paper, value) %>%
pivot_wider(names_from = ID_paper, values_from = value) %>%
arrange(chr, start) %>%
rr_write_tsv(glue("{out}/TABLE_promoter_H3K27ac_H3K27me3_per_sample.tsv"),
"Promoter H3K27ac and H3K27me3 enrichment (RPKM) in each sample, and summary stats")## ...writing description of TABLE_promoter_H3K27ac_H3K27me3_per_sample.tsv to public/output/02/TABLE_promoter_H3K27ac_H3K27me3_per_sample.descTidy summary stats:
data_chip_stats <- data_chip_tidy %>%
filter(grepl("Z_", Sample)) %>%
rename(statistic = Sample) %>%
select(-ID_paper, -Replicate, -Mark, -dataset) %>%
group_by(chr, strand, name, statistic) %>%
top_n(1, `Exon Length`) %>%
ungroup() %>%
pivot_wider(names_from = statistic, values_from = value)
save(data_chip_stats, file = glue("{out}/data_chip_stats.Rda"))Count samples:
data_chip_tidy %>%
left_join(meta %>% distinct(ID_paper, Group), by = "ID_paper") %>%
distinct(ID_paper, Group, Mark) %>%
group_by(Group, Mark) %>%
count() %>%
filter(Group %in% c("HGG-H3.1/2K27M-Pons",
"HGG-H3.3K27M-Pons",
"HGG-H3.3K27M-Thal."))6.2 Scatterplot for H3.1 vs H3.3 pons
Here, we'll combine the DGE data with each of different source of ChIP-seq analysis, following code used to do a similar analysis for H3.3G34 gliomas here: https://github.com/fungenomics/G34-gliomas/blob/master/bulk_transcriptome_epigenome/analysis/03-ChIPseq.Rmd#L151 (from Chen et al, 2020).
The input for the scatter plot is essentially a data frame where, for each gene, we have:
- A score for differential expression (e.g. log2FoldChange from DESeq2 differential expression analysis)
- A score for differential H3K27me3 (e.g. Z-score for promoter H3K27me3)
- A score for differential H3K27ac (e.g. Z-score for promoter H3K27ac)
dge_h31_vs_h33_and_chip <- data_chip_stats %>%
select(name, H3K27ac = Z_H3.3K27M_Pons_H3.1K27M_Pons_H3K27ac, H3K27me3 = Z_H3.3K27M_Pons_H3.1K27M_Pons_H3K27me3) %>%
distinct() %>%
mutate(H3K27ac = -as.numeric(H3K27ac),
H3K27me3 = -as.numeric(H3K27me3)) %>%
left_join(dge_h31_vs_h33, by = c("name" = "symbol")) %>%
filter(baseMean > 100) %>%
# remove mitochondrial and Y-RNA
filter(!grepl("Y_RNA|^MT_", name))
dim(dge_h31_vs_h33_and_chip)## [1] 14954 10# get limits of z-scores for x/y axes
xlim <- range(dge_h31_vs_h33_and_chip$H3K27ac)
ylim <- range(dge_h31_vs_h33_and_chip$H3K27me3)
# all genes
to_label <- dge_h31_vs_h33_and_chip %>%
filter((H3K27ac > 1 | H3K27ac < -0.5) |
(H3K27me3 > 2.5 | H3K27me3 < -3))
nrow(to_label)## [1] 241dge_h31_vs_h33_and_chip %>%
arrange(abs(log2FoldChange)) %>%
mutate(name = factor(name, levels = unique(.$name))) %>%
rr_ggplot(aes(x = H3K27ac, y = H3K27me3), plot_num = 1) +
geom_hline(yintercept = c(-0.5, 0.5), size = 0.5, colour = "gray90") +
geom_vline(xintercept = c(-0.5, 0.5), size = 0.5, colour = "gray90") +
geom_point(aes(colour = log2FoldChange, size = -log10(padj)), alpha = 0.8) +
scale_colour_gradient2(low = "navy", mid = "white", high = "red", midpoint = 0) +
geom_text_repel(aes(label = name), to_label,
inherit.aes = TRUE, size = 2, segment.color = "gray70") +
xlab("Z score H3K27ac (H3.1K27M pons / H3.3K27M pons)") +
ylab("Z score H3K27me3 (H3.1K27M pons / H3.3K27M pons)")## ...writing source data of ggplot to public/figures/02/scatterplot_h31_vs_h33-1.source_data.tsv
[figure @ public/figures/02/scatterplot_h31_vs_h33...]
6.3 Scatterplot for H3.3 pons vs H3.3 thalamus
This is a similar analysis to the one performed in the previous section.
dge_pons_vs_thal_and_chip <- data_chip_stats %>%
select(name,
H3K27ac = Z_H3.3K27M_Thalamus_H3.3K27M_Pons_H3K27ac,
H3K27me3 = Z_H3.3K27M_Thalamus_H3.3K27M_Pons_H3K27me3) %>%
distinct() %>%
mutate(H3K27ac = as.numeric(H3K27ac),
H3K27me3 = as.numeric(H3K27me3)) %>%
left_join(dge_thalamus_vs_pons, by = c("name" = "symbol")) %>%
filter(baseMean > 100) %>%
# remove mitochondrial and Y-RNA
filter(!grepl("Y_RNA|^MT_", name))
dim(dge_pons_vs_thal_and_chip)## [1] 14809 10# get limits of z-scores for x/y axes
xlim <- range(dge_pons_vs_thal_and_chip$H3K27ac)
ylim <- range(dge_pons_vs_thal_and_chip$H3K27me3)
to_label <- dge_pons_vs_thal_and_chip %>%
filter((H3K27ac > 0.5 | H3K27ac < -0.2) &
(H3K27me3 > 1 | H3K27me3 < -0.5) & abs(log2FoldChange) > 2) %>%
filter(-log10(padj) > 4 | log2FoldChange > 0)
nrow(to_label)## [1] 19dge_pons_vs_thal_and_chip %>%
arrange(abs(log2FoldChange)) %>%
mutate(name = factor(name, levels = unique(.$name))) %>%
rr_ggplot(aes(x = H3K27ac, y = H3K27me3), plot_num = 1) +
geom_hline(yintercept = c(-0.5, 0.5), size = 0.5, colour = "gray90") +
geom_vline(xintercept = c(-0.5, 0.5), size = 0.5, colour = "gray90") +
geom_point(aes(colour = log2FoldChange, size = -log10(padj)), alpha = 0.8) +
scale_colour_gradient2(low = "navy", mid = "white", high = "red", midpoint = 0) +
geom_text_repel(aes(label = name), to_label,
inherit.aes = TRUE, size = 2, segment.color = "gray70") +
xlab("Z score H3K27ac (H3.3K27M thalamus / H3.3K27M pons)") +
ylab("Z score H3K27me3 (H3.3K27M thalamus / H3.3K27M pons)")## ...writing source data of ggplot to public/figures/02/scatterplot_thal_vs_pons-1.source_data.tsv
[figure @ public/figures/02/scatterplot_thal_vs_pons...]
7 Reproducibility
This document was last rendered on:
## 2022-10-18 14:17:13The 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: [8082a4d] 2022-10-18: Merge branch 'master' of github.com:fungenomics/HGG-oncohistonesThe 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 8.6 (Green Obsidian)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_CA.UTF-8
## ctype en_CA.UTF-8
## tz EST5EDT
## date 2022-10-18
##
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## Github (fungenomics/icytobox@730e8b8)
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## [1] /lustre06/project/6004736/sjessa/from_narval/HGG-oncohistones/public/renv/library/R-3.6/x86_64-pc-linux-gnu
## [2] /tmp/Rtmp7TyGB4/renv-system-library
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## P ── Loaded and on-disk path mismatch.The resources requested when this document was last rendered:
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A project of the Kleinman Lab at McGill University, using the rr reproducible research template.