NB-FOXR2 project [source]
03 - Prep tumor bulk RNAseq data
Selin Jessa [selin.jessa@mail.mcgill.ca] and Bhavyaa Chandarana [bhavyaa.chandarana@mail.mcgill.ca]
01 November, 2024
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: 03# Specify where to save outputs
out <- here("output", doc_id); dir.create(out, recursive = TRUE)
figout <- here("figures", doc_id, "/")
cache <- paste0("/project/kleinman/bhavyaa.chandarana/cache/NB-FOXR2/public/", doc_id, "/")The root directory of this project is:
## /project/kleinman/bhavyaa.chandarana/from_hydra/2023-05-NB-FOXR2/publicOutputs and figures will be saved at these paths, relative to project root:
## public/output/03## public/figures/03//Setting a random seed:
set.seed(100)2 Overview
This document performs a few preparatory steps in the analysis of bulk RNAseq data, including checking QC, expression levels of FOXR2 and other genes, and distribution of samples in the PCA space.
For an external dataset of extra-cranial neuroblastomas, we load the raw counts, normalize the counts using DESeq2, and plot the samples in PCA space.
3 Libraries
library(here)
library(magrittr)
library(tidyr)
library(dplyr)
library(readr)
library(readxl)
library(glue)
library(purrr)
library(ggplot2)
library(DESeq2)
library(scales)
library(cowplot)
source(here("include/style.R"))
source(here("code/functions/RNAseq.R"))
ggplot2::theme_set(theme_min())4 Load metadata
meta <- read_tsv(here("output/00/metadata_patients_NGS.tsv"))## Rows: 176 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (20): Sample, ID_Patient, ID_Sample, ID, FOXR2_positive, Group, Source, ...
##
## 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.meta_bulk <- meta %>% filter(RNAseq == "Y")5 In-house pediatric brain tumors
info_samples <- read_tsv(here("data/RNAseq/pipeline_l3/2023-05-test_pbt/info.samples.tsv"))## Rows: 121 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): ID, Nickname, Group
##
## 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.table(info_samples$Group)##
## DIPG-H3K27M DIPG-H3K27M-FOXR2 EP-PFA ETMR
## 19 6 13 12
## HGG-H3.3G34R/V HGG-IDH MB-SHH MB-WNT
## 18 10 8 10
## NB-FOXR2
## 25# load counts for FOXR2
counts_foxr2 <- extract_pipeline_counts(path = here("data/RNAseq/pipeline_l3/2023-05-test_pbt/counts/Ensembl.ensGene.exon.norm.tsv.gz"),
goi = "FOXR2") %>%
left_join(info_samples, by = c("sample" = "Nickname")) %>%
mutate(Group = factor(Group, levels = names(palette_groups)))## Joining with `by = join_by(gene_symbol)`5.1 Counts
counts_RNA <- read.table(here("data/RNAseq/pipeline_l3/2023-05-test_pbt/counts/Ensembl.ensGene.exon.raw.tsv.gz"),
header = T, sep = "\t", check.names = FALSE) %>%
tibble::rownames_to_column(var = "ID") %>%
separate(ID, into = c("ENSID", "symbol"), sep = ":") %>%
arrange(ENSID) %>%
.[, c("ENSID", "symbol", info_samples$Nickname)] %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/03/TABLE_bulk_counts.desc5.2 QC
Alignment stats:
align_stats <- read_tsv(here("data/RNAseq/pipeline_l3/2023-05-test_pbt/alignment.statistics.tsv"))## Rows: 121 Columns: 24
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (12): name, group, cleanReadRetentionPercentage, mappedPercentage, whole...
## dbl (12): rawReadCount, cleanReadCount, unmappedReadCount, mappedReadCount, ...
##
## 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.align_stats <- align_stats %>%
left_join(info_samples, by = c("name" = "Nickname")) %>%
separate(mitochondrialPercentage, sep = "%", into = "mitochondrialPercentage") %>%
separate(ribosomalPercentage, sep = "%", into = "ribosomalPercentage") %>%
mutate(mitochondrialPercentage = as.numeric(mitochondrialPercentage),
ribosomalPercentage = as.numeric(ribosomalPercentage))
align_stats %>%
select(name, group, cleanReadCount, mappedReadCount,
mitochondrialPercentage, ribosomalPercentage) %>%
gather(stat, value, 3:6) %>%
mutate(group = factor(group, levels = names(palette_groups))) %>%
arrange(desc(group)) %>%
mutate(name = factor(name, levels = unique(.$name))) %>%
ggplot(aes(x = name, y = value)) +
geom_bar(aes(fill = group), stat = "identity") +
scale_fill_manual(values = palette_groups) +
facet_wrap(~ stat, scales = "free_y", ncol = 1) +
rotate_x()
5.2.1 TABLE: Human bulk QC
Export a supplementary table for human bulk RNA-seq QC.
align_stats %>%
select(-ID, -group) %>%
dplyr::rename(ID = name) %>%
dplyr::relocate(Group, .after = 1) %>%
rr_write_tsv(glue("{out}/TABLE_bulk_stats.tsv"),
"QC stats for bulk RNAseq data")## ...writing description of TABLE_bulk_stats.tsv to public/output/03/TABLE_bulk_stats.desc5.3 PCA
# load the counts saved by the pipeline
load(here("data/RNAseq/pipeline_l3/2023-05-test_pbt/.counts.RData"))
# get highly-variable genes
var_idx <- apply(counts$norm$Ensembl.ensGene.exon, 1, var) %>%
order(decreasing = TRUE) %>%
.[1:1000]
pbt_counts_vst_hvg <- counts$vst$Ensembl.ensGene.exon[var_idx, ]
pca <- prcomp(t(pbt_counts_vst_hvg))
variance <- round(pca$sdev^2 / sum(pca$sdev^2), 3) * 100
pca_df <- data.frame(Dim1 = pca$x[, 1],
Dim2 = pca$x[, 2],
Sample = colnames(pbt_counts_vst_hvg))
p1 <- pca_df %>%
left_join(meta_bulk, by = c("Sample" = "ID")) %>%
ggplot(aes(x = Dim1, y = Dim2)) +
geom_point(aes(colour = Group), alpha = 0.7, size = 2) +
scale_color_manual(values = palette_groups) +
xlab(paste0("PC1 (", variance[1], "%)")) +
ylab(paste0("PC2 (", variance[2], "%)")) +
color_legend_ncol(2) +
ggtitle("PCA")
p2 <- pca_df %>%
left_join(meta_bulk, by = c("Sample" = "ID")) %>%
ggplot(aes(x = Dim1, y = Dim2)) +
geom_point(aes(colour = Source), alpha = 0.7, size = 2) +
xlab(paste0("PC1 (", variance[1], "%)")) +
ylab(paste0("PC2 (", variance[2], "%)")) +
color_legend_ncol(2) +
ggtitle("PCA")Coloured by QC stats:
pca_with_align_stats <- pca_df %>%
left_join(info_samples, by = c("Sample" = "Nickname")) %>%
left_join(align_stats, by = c("Sample" = "name", "Group" = "group"))
pca_stat_plots <- list()
stats <- c("cleanReadCount",
"mappedReadCount",
"exonReadCount",
"intronReadCount",
"mitochondrialPercentage",
"ribosomalPercentage")
for (i in seq_along(stats)) {
df <- pca_with_align_stats
df$stat <- df[, stats[i]]
gg <- df %>%
arrange(stat) %>%
ggplot(aes(x = Dim1, y = Dim2)) +
geom_point(aes(colour = stat), alpha = 0.7, size = 2) +
scale_colour_viridis(labels = comma) +
xlab(paste0("PC1 (", variance[1], "%)")) +
ylab(paste0("PC2 (", variance[2], "%)")) +
ggtitle(stats[i])
pca_stat_plots[[i]] <- gg
}
plot_grid(plotlist = c(list(p1, p2), pca_stat_plots), align = "hv", axis = "rltb", ncol = 3)
5.4 FOXR2 expression
p1 <- counts_foxr2 %>%
ggplot(aes(x = Group, y = gene_expression)) +
geom_boxplot(aes(fill = Group), outlier.shape = NA) +
geom_jitter(size = 2, alpha = 0.8) +
scale_fill_manual(values = palette_groups) +
ylab("Normalized expression") + xlab("Group") +
no_legend() + rotate_x() +
ggtitle("FOXR2 expression")
p2 <- counts_foxr2 %>%
filter(Group == "NB-FOXR2") %>%
ggplot(aes(x = sample, y = gene_expression)) +
geom_bar(stat = "identity") +
coord_flip() +
ggtitle("FOXR2 expression per sample")
plot_grid(p1, p2, nrow = 1, align = "h", axis = "tb")
Check FOXR2 expression across samples:
counts_foxr2 %>%
arrange(Group) %>%
mutate(sample = factor(sample, levels = unique(.$sample))) %>%
ggplot(aes(x = sample, y = gene_expression)) +
geom_bar(stat = "identity", aes(fill = Group)) +
scale_fill_manual(values = palette_groups) +
ggtitle("FOXR2 expression per sample") +
rotate_x()
5.5 MDM4 expression
The ubiquitous loss of chr1 in NB-FOXR2 may lead to overexpression of MDM2/MDM4, which phenocopies p53 loss in the tumor context (Girish et al. Science 2023).
Here, we investigate this possibility by comparing expression of MDM4 across the bulk intracranial tumor cohort.
# load counts for MDM genes
counts_MDM <- extract_pipeline_counts(path = here("data/RNAseq/pipeline_l3/2023-05-test_pbt/counts/Ensembl.ensGene.exon.norm.tsv.gz"),
goi = c("MDM4")) %>%
left_join(info_samples, by = c("sample" = "Nickname")) %>%
mutate(Group = factor(Group, levels = names(palette_groups)))## Joining with `by = join_by(gene_symbol)`palette_highlight_nbfoxr2 <- rep("grey70", length(palette_groups))
names(palette_highlight_nbfoxr2) <- names(palette_groups)
palette_highlight_nbfoxr2[which(names(palette_highlight_nbfoxr2) == "NB-FOXR2")] <- "red"
counts_MDM %>%
filter(gene_symbol == "MDM4") %>%
ggplot(aes(x = Group, y = gene_expression, fill = Group)) +
geom_violin(scale = "width") +
scale_fill_manual(values = palette_highlight_nbfoxr2) +
ylab("Normalized expression") + xlab("Group") +
no_legend() + rotate_x() +
ggtitle("MDM4 expression") +
stat_summary(fun.y=median, geom="crossbar", size=1, color="black", aes(width = 0.3))
6 Extra-cranial neuroblastoma
6.1 Loading and data wrangling
Load processed bulk RNAseq counts from Gartlgruber et al, Nature Cancer, 2020.
The data was downloaded via their shiny app (https://nbseb087.dkfz.de/project_NB_SE_viz/).
gartlgruber_meta <- readRDS(here("data/RNAseq/external_data/Gartlgruber_NatCancer_2021/annotation_tumor_RNAseq.rds"))
length(unique(gartlgruber_meta$ProjectID))## [1] 579# load annotation of ChIPseq data to look for overlapping samples
gartlgruber_meta_chip <- readRDS(here("data/ChIPseq/external_data/Gartlgruber_NatCancer_2021/annotation_tumor_ChIPseq.rds"))
gartlgruber_meta <- gartlgruber_meta %>%
left_join(gartlgruber_meta_chip, by = c("ProjectID", "MYCN", "Stage",
"Age", "Risk", "Relapse"))
gartlgruber_counts <- readRDS(here("data/RNAseq/external_data/Gartlgruber_NatCancer_2021/tumor_RNAseq_Counts_Matrix.rds"))
dim(gartlgruber_counts)## [1] 57820 579gartlgruber_counts[1:5, 1:5]## NSP001-PT01 NSP002-PT01 NSP003-RM01 NSP005-RT01
## ENSG00000223972.4|DDX11L1 4 0 0 3
## ENSG00000227232.4|WASH7P 431 1348 1242 1117
## ENSG00000243485.2|MIR1302-11 0 0 0 0
## ENSG00000237613.2|FAM138A 0 0 0 0
## ENSG00000268020.2|OR4G4P 0 0 0 0
## NSP006-RM01
## ENSG00000223972.4|DDX11L1 1
## ENSG00000227232.4|WASH7P 1391
## ENSG00000243485.2|MIR1302-11 0
## ENSG00000237613.2|FAM138A 0
## ENSG00000268020.2|OR4G4P 0Normalize and reformat:
# normalize using DESeq2
dds <- DESeqDataSetFromMatrix(countData = gartlgruber_counts,
colData = gartlgruber_meta,
design = ~ 1)
dds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 6774 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingecnb_counts_norm <- counts(dds, normalized = TRUE)
vsd <- vst(dds)
ecnb_counts_vst <- assay(vsd)Convert to tidy format with metadata:
ecnb_counts_tidy <- ecnb_counts_norm %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "id") %>%
separate(id, into = c("ens_id.x", "gene_symbol"), sep = "\\|") %>%
separate(ens_id.x, into = c("ens_id", "drop"), sep = "\\.") %>%
select(-drop) %>%
gather(sample, gene_expression, 3:ncol(.)) %>%
left_join(gartlgruber_meta, by = c("sample" = "ProjectID"))
save(ecnb_counts_tidy, ecnb_counts_norm, ecnb_counts_vst, file = glue("{out}/Gartlgruber_et_al_counts.Rda"))7 Reproducibility
This document was last rendered on:
## 2024-11-01 11:50:05The git repository and last commit:
## Local: main /project/kleinman/bhavyaa.chandarana/from_hydra/2023-05-NB-FOXR2/public
## Remote: main @ origin (https://github.com/fungenomics/NB-FOXR2.git)
## Head: [0e89693] 2024-09-12: Initial commitThe random seed was set with set.seed(100)
The R session info:
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.2 (2021-11-01)
## os Rocky Linux 8.10 (Green Obsidian)
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## P xfun 0.29 2021-12-14 [?] CRAN (R 4.1.2)
## P XML 3.99-0.8 2021-09-17 [?] CRAN (R 4.1.2)
## P xtable 1.8-4 2019-04-21 [?] CRAN (R 4.1.2)
## P XVector 0.34.0 2021-10-26 [?] Bioconductor
## P yaml 2.2.1 2020-02-01 [?] CRAN (R 4.1.2)
## P zlibbioc 1.40.0 2021-10-26 [?] Bioconductor
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## [1] /project/kleinman/bhavyaa.chandarana/from_hydra/2023-05-NB-FOXR2/public/renv/library/R-4.1/x86_64-pc-linux-gnu
## [2] /home/kleinman/bhavyaa.chandarana/.cache/R/renv/sandbox/R-4.1/x86_64-pc-linux-gnu/145cef2c
##
## P ── Loaded and on-disk path mismatch.
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A project of the Kleinman Lab at McGill University, using the rr reproducible research template.