HGG-oncohistones project [source]
03A - Systematic analysis of HOX activation
Selin Jessa []
16 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: 03A# 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/03A## public/figures/03ASetting a random seed:
set.seed(100)2 Overview
This document systematically analyzes HOX expression/activation across tumor types and data types.
- In the section Assemble HOX coordinates, we describe how to get the coordinates of the HOX genes/transcripts/promoters
- In the section Load data, we load RNAseq and ChIPseq quantifications at the HOX genes, and generate the associated supplementary tables
- In the following two sections, we visualize bulk RNAseq/ChIPseq quantifications at the HOX loci
- In the section Chromatin state, we examine RNAseq, ChIPseq, and scATAC data together at the HOXD locus
- In the section 3D chromatin conformation, we examine HiC data as well as RNAseq/ChIPseq data at the HOXD locus
3 Libraries
# Load libraries here
library(here)
# For genome analysis / references
library(GenomicRanges)
library(BentoBox)
library(BSgenome.Hsapiens.UCSC.hg19)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(TxDb.Mmusculus.UCSC.mm9.knownGene)
library(org.Mm.eg.db)
library(rtracklayer)
# General purpose
library(tidyr)
library(dplyr)
library(ggrepel)
library(readr)
library(readxl)
library(glue)
library(tibble)
library(ggplot2)
library(purrr)
library(pheatmap)
library(cowplot)
# colour palettes for the HOX genes are defined in the style file
source(here("include/style.R"))
source(here("code/functions/BentoBox_helpers.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"))##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_character(),
## Exclude_entirely = col_logical(),
## Age = col_double(),
## Smartseq2 = col_logical(),
## Smartseq2_path = col_logical(),
## Smartseq2_ID2 = col_logical(),
## RING1B = col_double()
## )
## ℹ Use `spec()` for the full column specifications.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))##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_character(),
## Replicate = col_double()
## )
## ℹ Use `spec()` for the full column specifications.Load the RNAseq metadata:
info_samples <- data.table::fread(here("data/RNAseq/pipeline_l3/HGG-H3K27M_and_PFA.with_HOX/info.samples.tsv"), data.table = FALSE) %>%
mutate(Group = factor(Group, levels = names(palette_groups)))
info_groups <- data.table::fread(here("data/RNAseq/pipeline_l3/HGG-H3K27M_and_PFA.with_HOX/info.groups.tsv"), data.table = FALSE)
table(info_samples$Group) %>% discard(. == 0)##
## PFA-EZHIP-PF HGG-H3.1/2K27M-Pons HGG-H3.3K27M-Pons HGG-H3.3K27M-Thal.
## 13 9 11 65 Assemble HOX coordinates
This analysis involves 3 types of features:
- genes (for quantifying expression)
- transcripts (for quantifying tumor subgroup-specific expression of different HOX transcripts)
- promoters (for quantifying enrichment of H3K27ac and H3K27me3 histone marks)
In some cases, assembly of these features was done by othe scripts; where that's the case, I provide the path to those scripts from within the project repository/directory.
5.1 Transcripts
To get the coordinates for the HOX transcripts, I extracted all HOX exons from the Ensembl exon annotation used in the in-house bulk RNAseq pipeline. This is performed in the script code/scripts/make_HOX_gtf.R.
The GTF contains exons, so we compile the full HOX transcripts from the GTF file by taking, for each transcript, the longest interval spanning all exons associated with that transcipt:
# load Ensembl exon GTF subsetted to HOX genes
hox_exons <- rtracklayer::import(here("data/RNAseq/references/Ensembl.ensGene.exon.HOX.gtf"))
hox_exons_df <- as.data.frame(hox_exons)
# for each transcript, defined by Ensembl transcript ID,
# get the longest interval spanning all exons
hox_transcripts_df <- hox_exons_df %>%
# for each transcript...
group_by(ensembl_transcript) %>%
# get longest interval
mutate(start = min(start)) %>%
mutate(end = max(end)) %>%
# collapse by transcript
group_by(seqnames, start, end, ensembl_transcript, gene_symbol, strand) %>%
summarise() %>%
ungroup() %>%
# fix order of the transcripts based on HOX cluster (HOXA, HOXB, HOXC, HOXD)
mutate(gene_symbol = factor(gene_symbol, levels = c(hoxa, hoxb, hoxc, hoxd))) %>%
filter(!is.na(gene_symbol)) %>%
arrange(gene_symbol, seqnames, start) %>%
# make a unique ID for each transcript of ENSEMBL_TX_ID:GENE_SYMBOL
mutate(id = paste0(ensembl_transcript, ":", gene_symbol)) %>%
mutate(id = factor(id, levels = unique(.$id))) %>%
mutate(name = id) %>%
# get the length of the transcript
mutate(length = abs(start - end)) %>%
mutate(Hox_cluster = case_when(
grepl("HOXA", gene_symbol) ~ "HOXA",
grepl("HOXB", gene_symbol) ~ "HOXB",
grepl("HOXC", gene_symbol) ~ "HOXC",
grepl("HOXD", gene_symbol) ~ "HOXD"
))
rr_write_tsv(hox_transcripts_df,
glue("{out}/HOX_transcripts_coordinates.tsv"),
desc = "Genomic coordinates of all HOX transcripts (longest interval spanning all exons) used in the analysis.")
[output @ public/output/03A/HOX_transcripts_coordinates.tsv]
5.2 Genes
Next, to get the coordinates for the HOX genes, I extracted them from the Ensembl whole gene annotation used in the in-house bulk RNAseq pipeline. This is done in the following scripts:
code/scripts/ENSEMBL_00-gtf_to_bed.sh(convert whole-gene GTF to BED file)code/scripts/ENSEMBL_01-collapse_genes.R(for each gene, defined by ENSEMBL ID, get the coordinates of the gene i.e. the longest interval spanning all exons)code/scripts/ENSEMBL_02-keep_strand.sh(retrieve the strand information for each gene)
Here, we load the per-gene BED file produced by those scripts, and filter to HOX genes.
hox_genes_bed <- rtracklayer::import(here("data/ChIPseq/references/Ensembl.ensGene.hg19.collapsed.bed")) %>%
as.data.frame() %>%
separate(name, into = c("ENSG", "gene_symbol"), sep = ":") %>%
filter(gene_symbol %in% hox_transcripts_df$gene_symbol)
rtracklayer::export(hox_genes_bed, glue("{out}/HOX_genes_coordinates.bed"))
[output @ public/output/03A/HOX_genes_coordinates.bed]
5.3 Promoters
Finally, we'll define promoters around each HOX transcript as TSS +/- 2.5kbp, which will be used as input for quantifications of ChIPseq data for H3K27ac and H3K27me3. For this, I use the GenomicRanges::promoters() function to add flanking regions to the TSS.
# get promoters
hox_transcripts <- GRanges(hox_transcripts_df %>% dplyr::select(-length))
hox_transcripts_promoters <- GenomicRanges::promoters(hox_transcripts, upstream = 2500, downstream = 2500)
# sanity check
all(width(hox_transcripts_promoters) == 5000)## [1] TRUErtracklayer::export(hox_transcripts_promoters, glue("{out}/HOX_transcripts_promoters_5kb.bed"))
[output @ public/output/03A/HOX_transcripts_promoters_5kb.bed]
6 Load data
NOTE: RNAseq and ChIPseq quantifications were done outside this script. RNAseq quantification was done using the bulk RNAseq pipeline, using the HOX transcripts as features for custom feature counting. ChIPseq quantifications were performed by the Jabado lab.
Load the RNAseq quantifications per transcript. The quantification was performed with the in-house bulk RNAseq pipeline, configured here: /lustre03/project/6004736/pipeline/v0/levels1-2/sjessa/2021-04-21-HGG_HOX_transcript_counts, using the JSON file .HOX_plus_Ensembl.json in that directory
load(here("data/RNAseq/pipeline_l3/HGG-H3K27M_and_PFA.with_HOX/.counts.RData"))
# get an idea of the shape of the data (transcript x sample)
counts$norm$plus_HOX_tx.exon[1:5, 1:5]## EPT0508 EPT0509 EPT0562 EPT0578
## ENSG00000118473:SGIP1 114.01181 70.75683 272.8021 310.77025
## ENSG00000162426:SLC45A1 78.00808 99.89199 156.9547 77.69256
## ENSG00000157191:NECAP2 2361.24463 1846.61449 855.7766 1533.40582
## ENSG00000169504:CLIC4 28657.46896 13504.84255 8810.0145 19267.75526
## ENSG00000142920:ADC 99.01026 169.26143 151.3491 284.19121
## EPT0805
## ENSG00000118473:SGIP1 107.35236
## ENSG00000162426:SLC45A1 80.08827
## ENSG00000157191:NECAP2 1318.90042
## ENSG00000169504:CLIC4 10936.30869
## ENSG00000142920:ADC 294.79299# convert to long format, with columns Sample, Transcript, Expression
# using DESeq2-normalized expression data
counts_rna_long <- counts$norm$plus_HOX_tx.exon %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "id") %>%
filter(grepl(":HOX", id)) %>%
gather("Sample", "Norm_expression", 2:ncol(.)) %>%
separate(id, into = c("ensembl_transcript", "gene_symbol"), sep = ":") %>%
# join with sample metadata to get tumor group
left_join(info_samples, by = c("Sample" = "Nickname")) %>%
# join with HOX transcripts annotation to get HOX cluster
right_join(hox_transcripts_df)## Joining, by = c("ensembl_transcript", "gene_symbol")# calculate median expression per group
counts_rna_long_medians <- counts_rna_long %>%
group_by(Group, gene_symbol, id, Hox_cluster) %>%
summarize(Median = median(Norm_expression)) %>%
ungroup()## `summarise()` has grouped output by 'Group', 'gene_symbol', 'id'. You can override using the `.groups` argument.# make supplementary table
counts_rna_long %>%
dplyr::select(ensembl_transcript, gene_symbol, chr = seqnames, start, end, strand, length, Sample, Norm_expression, Hox_cluster) %>%
pivot_wider(names_from = Sample, values_from = Norm_expression) %>%
arrange(Hox_cluster, start) %>%
rr_write_tsv(glue("{out}/TABLE_HOX_expression_per_transcript.tsv"),
"Normalized (DESeq2) expression of each HOX transcript in each sample")## ...writing description of TABLE_HOX_expression_per_transcript.tsv to public/output/03A/TABLE_HOX_expression_per_transcript.descLoad the ChIPseq quantifications per transcript. ChIPseq data for histone marks H3K27ac and H3K27me3 was quantified at the HOX promoters in a per-transcript manner, using the TSS +/- 2.5kbp.
# the ChIPseq quantifications has two parts:
# 1. the counts (saved into the variable counts_chip)
# 2. the column annotations for the counts (saved into the variable meta_chipq)
counts_chip <- read_xlsx(here("data/ChIPseq/quantifications_@mhulswit/20210511_H3.3H3.1EZHIP_H3K27ac_H3K27me3_Ensembl.ensGene.exon.Hox.promoters_5kb_SJ.xlsx"), sheet = 1)
meta_chipq <- read_xlsx(here("data/ChIPseq/quantifications_@mhulswit/20210511_H3.3H3.1EZHIP_H3K27ac_H3K27me3_Ensembl.ensGene.exon.Hox.promoters_5kb_SJ.xlsx"), sheet = 2) %>%
mutate(Sample = gsub("_Count|_RPKM", "", Sample)) %>%
distinct(Sample, Group, Source) %>%
# make the group annotation consistent with the rest of the analysis/code
mutate(Group = case_when(
Group == "EPN-PFA_Posterior fossa" ~ "PFA-EZHIP-PF",
Group == "H3.1K27M_Pons" ~ "HGG-H3.1/2K27M-Pons",
Group == "H3.3K27M_Pons" ~ "HGG-H3.3K27M-Pons",
Group == "H3.3K27M_Thalamus" ~ "HGG-H3.3K27M-Thal.",
TRUE ~ "Drop")) %>%
# join with the project ChIPseq metadata table to get data paths
left_join(meta_chip %>%
mutate(bw = basename(Path_bw_internal)) %>%
dplyr::select(ID_paper, bw, Replicate),
by = c("Sample" = "bw"))
# convert to long format
counts_chip_long <- counts_chip %>%
dplyr::select(-chr, -strand, -name) %>%
dplyr::rename(id = ID) %>%
gather(Stat, Value, 4:ncol(.)) %>%
mutate(
Statistic = case_when(
grepl("Count", Stat) ~ "Count",
grepl("RPKM", Stat) ~ "RPKM" ),
Sample = gsub("_Count|_RPKM", "", Stat),
Mark = ifelse(grepl("K27me3", Sample), "H3K27me3", "H3K27ac")
) %>%
left_join(meta_chipq, by = "Sample") %>%
# exlcude tumor groups not included in the study
filter(Group != "Drop") %>%
right_join(hox_transcripts_df %>% dplyr::select(-start, -end, -length), by = "id")
counts_chip_long %>% distinct(Sample, Mark, Group) %>% count(Mark, Group)# calculate median promoter ChIP RPKM per group
counts_chip_long_medians <- counts_chip_long %>%
filter(Statistic == "RPKM") %>%
group_by(Mark, Group, id, Hox_cluster) %>%
summarize(Median_RPKM = median(Value)) %>%
ungroup()## `summarise()` has grouped output by 'Mark', 'Group', 'id'. You can override using the `.groups` argument.# make supplementary table
counts_chip_long %>%
filter(Statistic == "RPKM") %>%
mutate(Sample = paste0(ID_paper, "__", Replicate, "__", Mark)) %>%
mutate(length = end - start) %>%
dplyr::select(id, chr = seqnames, start, end, length, strand, Sample, Value, Hox_cluster) %>%
separate(id, into = c("ensembl_transcript", "gene_symbol"), sep = ":") %>%
pivot_wider(names_from = Sample, values_from = Value) %>%
arrange(Hox_cluster, start) %>%
rr_write_tsv(glue("{out}/TABLE_HOX_H3K27ac_H3K27me3_per_transcript.tsv"),
"Promoter (TSS +/- 5kbp) H3K27ac and H3K27me3 enrichment (RPKM) of each HOX transcript in each sample")## ...writing description of TABLE_HOX_H3K27ac_H3K27me3_per_transcript.tsv to public/output/03A/TABLE_HOX_H3K27ac_H3K27me3_per_transcript.desc7 Gene expression by bulk RNAseq
For a per-gene analysis, we'll select, for each gene, the most highly-expressed transcript, followed by the longest if there are ties.
# this variable will store the most highly expressed transcript for each HOX gene,
# which will be used for any downstream per-gene analyses
keep_transcripts <- counts_rna_long %>%
group_by(id, gene_symbol, length) %>%
summarize(mean_norm_expression = mean(Norm_expression)) %>%
ungroup() %>%
group_by(gene_symbol) %>%
# sort by descending expression
dplyr::arrange(desc(mean_norm_expression), length) %>%
# get the most highly expressed transcript
dplyr::slice(1) %>%
pull(id)## `summarise()` has grouped output by 'id', 'gene_symbol'. You can override using the `.groups` argument.# sanity check: we should have one selected transcript per gene, 39 in total (as many as there are HOX genes)
length(keep_transcripts)## [1] 39length(keep_transcripts) == length(unique(hox_transcripts_df$gene_symbol))## [1] TRUE# make a similar plot as above, but per gene
counts_rna_long %>%
filter(id %in% keep_transcripts) %>%
mutate(gene_symbol = factor(gene_symbol, levels = names(palette_hox))) %>%
dplyr::select(id, Norm_expression, gene_symbol, Group, Hox_cluster) %>%
rr_ggplot(plot_num = 1, aes(x = gene_symbol, y = Norm_expression)) +
geom_boxplot(aes(fill = gene_symbol), alpha = 0.5, width = 0.5, outlier.color = NA) +
geom_jitter(aes(color = gene_symbol), alpha = 0.5, width = 0.1, size = 0.8, stroke = 0.5) +
facet_grid(Group ~ Hox_cluster, scales = "free_x") +
scale_fill_manual(values = palette_hox) +
scale_color_manual(values = palette_hox) +
theme(strip.text.y = element_text(size = rel(0.8)),
strip.text.x = element_blank()) +
rotate_x() +
no_legend() ## ...writing source data of ggplot to public/figures/03A/hox_expression_boxplots_per_gene-1.source_data.tsv
[figure @ public/figures/03A/hox_expression_boxplots_per_gene...]
7.1 Highlight HOX genes in DGE genes
We highlight the HOX genes among the volcano plot of differentially-expressed genes between H3.3K27M thalamus and pons, to demonstrate that they are among the most significantly upregulated in pons tumors. The differential expression analysis was done with the in-house bulk RNAseq pipeline using the expression-tools module.
# load differentially-expressed genes from DESeq2 analysis from in-house pipeline
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 = ":") %>%
# note the HOX genes
mutate(HOX = ifelse(symbol %in% hox_genes_bed$gene_symbol, TRUE, FALSE)) %>%
# put HOX genes (points) on top
arrange(HOX) %>%
# filter to expressed genes
filter(baseMean > 100)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## ID = col_character(),
## baseMean = col_double(),
## log2FoldChange = col_double(),
## lfcSE = col_double(),
## stat = col_double(),
## pvalue = col_double(),
## padj = col_double()
## )# make volcano plot
dge_thalamus_vs_pons %>%
rr_ggplot(aes(x = log2FoldChange, y = -log10(padj)), plot_num = 1) +
geom_point(alpha = 0.5, aes(colour = HOX, size = HOX)) +
scale_colour_manual(values = c("TRUE" = "darkorchid4", "FALSE" = "gray70")) +
scale_size_manual(values = c("TRUE" = 2, "FALSE" = 1)) +
geom_text_repel(data = dge_thalamus_vs_pons %>%
filter(HOX & baseMean > 100 & abs(log2FoldChange) > 2),
inherit.aes = TRUE, aes(label = symbol), colour = "darkorchid4") +
ggtitle("H3.3K27M thalamus vs. H3.3K27M pons")## ...writing source data of ggplot to public/figures/03A/dge_volcano_hox-1.source_data.tsv
[figure @ public/figures/03A/dge_volcano_hox...]
8 Heatmap quantification
To complement the RNAseq, we'll also examine the quantification of HOX expression, again per gene and per transcript, in both ChIPseq data for H3K27ac and H3K27me3.
To put all data types in the same range, for each data type, we scale the values for all HOX genes across samples to [0, 1]. This will mean that any values for the same data type are comparable across HOX clusters/genes and across samples. However, values between data types are not comparable.
For the per-gene analysis, we first filter to the most highly expressed transcript per gene, and then do the scaling. For the per-transcript analysis, we scale on the per-transcript values.
# per gene
rna_per_gene_scaled <- counts_rna_long_medians %>%
# filter to most highly expressed transcript first
filter(id %in% keep_transcripts) %>%
mutate(Median_scaled = scales::rescale(Median))
k27ac_per_gene_scaled <- counts_chip_long_medians %>%
filter(Mark == "H3K27ac") %>%
filter(id %in% keep_transcripts) %>%
mutate(Median_scaled = scales::rescale(Median_RPKM))
k27me3_per_gene_scaled <- counts_chip_long_medians %>%
filter(Mark == "H3K27me3") %>%
filter(id %in% keep_transcripts) %>%
mutate(Median_scaled = scales::rescale(Median_RPKM))
# per tx
rna_per_tx_scaled <- counts_rna_long_medians %>%
mutate(Median_scaled = scales::rescale(Median))
k27ac_per_tx_scaled <- counts_chip_long_medians %>%
filter(Mark == "H3K27ac") %>%
mutate(Median_scaled = scales::rescale(Median_RPKM))
k27me3_per_tx_scaled <- counts_chip_long_medians %>%
filter(Mark == "H3K27me3") %>%
mutate(Median_scaled = scales::rescale(Median_RPKM))Function for generating the heatmaps across data types:
# for each unique group, add a suffix for each data type
group_order <- paste0(
rep(names(palette_groups)[names(palette_groups) %in% unique(rna_per_gene_scaled$Group)], each = 3),
c(":RNA", ":H3K27ac", ":H3K27me3")
)
# define order of genes and transcripts
gene_order <- hox_transcripts_df %>% filter(id %in% keep_transcripts) %>% pull(id) %>%
as.character()
tx_order <- levels(hox_transcripts_df$id)
#' Plot heatmap of HOX activation based on RNA/H3K27ac/H3K27me3
#'
#' @param hox_cluster Character, one of "HOXA", "HOXB", "HOXC", "HOXD"
#' @param feature_order Character, vector of genes/transcripts in the order they
#' should be plot along the columns of the heatmap
#' @param type Character, one of "per_gene" or "per_transcript", indicating whether
#' to generate heatmaps by genes or by transcripts
#'
#' @examples
#' plot_hox_heatmap("HOXA", gene_order, "per_gene")
plot_hox_heatmap <- function(hox_cluster, feature_order, type) {
# extract the data for the genes in this cluster,
# for either the per_gene quantification or per_transcript quantification
if (type == "per_gene") {
d1 <- rna_per_gene_scaled %>% filter(Hox_cluster == hox_cluster) %>%
dplyr::select(id, Group, RNA = Median_scaled)
d2 <- k27ac_per_gene_scaled %>% filter(Hox_cluster == hox_cluster) %>%
dplyr::select(id, Group, H3K27ac = Median_scaled)
d3 <- k27me3_per_gene_scaled %>% filter(Hox_cluster == hox_cluster) %>%
dplyr::select(id, Group, H3K27me3 = Median_scaled)
} else if (type == "per_tx") {
d1 <- rna_per_tx_scaled %>% filter(Hox_cluster == hox_cluster) %>%
dplyr::select(id, Group, RNA = Median_scaled)
d2 <- k27ac_per_tx_scaled %>% filter(Hox_cluster == hox_cluster) %>%
dplyr::select(id, Group, H3K27ac = Median_scaled)
d3 <- k27me3_per_tx_scaled %>% filter(Hox_cluster == hox_cluster) %>%
dplyr::select(id, Group, H3K27me3 = Median_scaled)
}
# merge the RNA and ChIPseq data
d4 <- d1 %>% left_join(d2) %>% left_join(d3) %>%
ungroup() %>%
gather(stat, value, 3:5) %>%
mutate(stat = paste0(Group, ":", stat)) %>%
dplyr::select(-Group) %>%
spread(stat, value) %>%
tibble::column_to_rownames(var = "id")
# plot in the order provided
input <- d4 %>%
t() %>%
.[group_order, feature_order[feature_order %in% colnames(.)] ]
# function to generate the heatmap -- these parameters will be kept constant
hm_fun <- purrr::partial(pheatmap,
input,
scale = "none",
cluster_rows = FALSE,
cluster_cols = FALSE,
border_color = "black",
color = purples,
cellwidth = 50, cellheight = 25,
breaks = seq(0, 1, length.out = 101))
# make both file types
hm_fun(filename = glue("{figout}/heatmap_{hox_cluster}_{type}.pdf"))
hm_fun(filename = glue("{figout}/heatmap_{hox_cluster}_{type}.png"))
# show the PNG in the HTML
knitr::include_graphics(glue("{figout}/heatmap_{hox_cluster}_{type}.png"))
}Now make a heatmap for each cluster per gene. The rows of the heatmaps are re-ordered using Illustrator for the final figure.
plot_hox_heatmap("HOXA", gene_order, "per_gene")
plot_hox_heatmap("HOXB", gene_order, "per_gene")
plot_hox_heatmap("HOXC", gene_order, "per_gene")
plot_hox_heatmap("HOXD", gene_order, "per_gene")
9 Chromatin state
Next, we show that the tumors exhibit bipartite functional domains at the HOX clusters based on chromatin state data. We'll assemble representative data tracks for each tumor group and for normal cells at the HOXD cluster.
For this section, I heavily use helper functions defined in code/functions/BentoBox_helpers.R. To configure the plots, I define the samples to include in configuration TSV files at data/BentoBox_config/hoxd_*.config.tsv
First, define the region and the BentoBox genomic plotting parameters:
# define region
hoxd <- GRanges("chr2", 176927000:177075000)
params_hoxd <- bb_params(chrom = as.character(seqnames(hoxd)),
chromstart = start(hoxd),
chromend = end(hoxd),
assembly = "hg19")
# set up gene track & genomic ranges object
hoxd_genes <- hox_transcripts_df %>% filter(Hox_cluster == "HOXD") %>%
pull(gene_symbol) %>%
unique() %>%
as.character()
hoxd_genes_gr <- hox_genes_bed %>% filter(gene_symbol %in% hoxd_genes) %>%
dplyr::rename(name = gene_symbol) %>%
mutate(name = factor(name, levels = hoxd_genes)) %>%
arrange(name) %>%
GRanges()
hoxd_genes_gr## GRanges object with 9 ranges and 3 metadata columns:
## seqnames ranges strand | ENSG name score
## <Rle> <IRanges> <Rle> | <character> <factor> <numeric>
## [1] chr2 177053307-177055688 + | ENSG00000128645 HOXD1 0
## [2] chr2 177001340-177037830 + | ENSG00000128652 HOXD3 0
## [3] chr2 177015950-177017954 + | ENSG00000170166 HOXD4 0
## [4] chr2 176994422-176997423 + | ENSG00000175879 HOXD8 0
## [5] chr2 176987088-176989853 + | ENSG00000128709 HOXD9 0
## [6] chr2 176973518-176984670 + | ENSG00000128710 HOXD10 0
## [7] chr2 176968944-176974722 + | ENSG00000128713 HOXD11 0
## [8] chr2 176964458-176966408 + | ENSG00000170178 HOXD12 0
## [9] chr2 176957619-176960666 + | ENSG00000128714 HOXD13 0
## -------
## seqinfo: 68 sequences from an unspecified genome; no seqlengths9.1 H3.3K27M pons
# define tracks (function defined in code/functions/BentoBox_helpers.R)
hoxd_config <- prep_track_input(here("data/BentoBox_config/hoxd_H33.config.tsv"))
# make figure
x <- 0.5
y_positions <- seq(0.5, by = 0.5, length.out = nrow(hoxd_config))
bb_pageCreate(width = 4, height = 5, default.units = "inches")
# add bw tracks
pwalk(list(hoxd_config$bw, hoxd_config$ID_paper, hoxd_config$Data, y_positions),
# helper function defined in code/functions/BentoBox_helpers.R
~ bb_placeSignalAndLabel(data = ..1,
annotation = ..2,
color = palette_tracks[..3],
y = ..4, x = x,
params = params_hoxd,
ymax = 1,
fontsize = 4))
bb_plotGenomeLabel(params = params_hoxd, scale = "Kb", x = 0.5, y = "0.03b", length = 3)
# plot ticks for the genes in colour
# bb_plotBed(params = params_hoxd, data = hoxd_genes_gr, fill = palette_hox[hoxd_genes], colorby = colorby("name"),
# width = 3, height = 0.6, y = 3.5, x = 0.5,
# baseline.lwd = 2, boxHeight = unit(3, "mm"), collapse = FALSE)
bb_plotGenes(params = params_hoxd, x = 0.5, y = "0.1b", height = 1, width = 3,
strandcolors = c("navy", "black"), fontcolors = c("navy", "black"),
stroke = 0.05,
just = c("left", "top"), default.units = "inches")
bb_pageGuideHide()
[figure @ public/figures/03A/hoxd_tracks_H3.3...]
9.2 H3.1K27M pons
# define tracks
hoxd_config <- prep_track_input(here("data/BentoBox_config/hoxd_H31.config.tsv"))
# make figure
x <- 0.5
y_positions <- seq(0.5, by = 0.5, length.out = nrow(hoxd_config))
bb_pageCreate(width = 4, height = 5.5, default.units = "inches")
# add bw tracks
pwalk(list(hoxd_config$bw, hoxd_config$ID_paper, hoxd_config$Data, y_positions),
~ bb_placeSignalAndLabel(data = ..1,
annotation = ..2,
color = palette_tracks[..3],
y = ..4, x = x,
params = params_hoxd,
ymax = 1,
fontsize = 4))
bb_plotGenomeLabel(params = params_hoxd, scale = "Kb", x = 0.5, y = 3.53, length = 3)
bb_plotGenes(params = params_hoxd, x = 0.5, y = "0.1b", height = 1, width = 3,
strandcolors = c("navy", "black"), fontcolors = c("navy", "black"),
stroke = 0.05,
just = c("left", "top"), default.units = "inches")
bb_pageGuideHide()
[figure @ public/figures/03A/hoxd_tracks_H3.1...]
9.3 PFA
# define tracks
hoxd_config <- prep_track_input(here("data/BentoBox_config/hoxd_PFA.config.tsv"))
# make figure
x <- 0.5
y_positions <- seq(0.5, by = 0.5, length.out = nrow(hoxd_config))
bb_pageCreate(width = 4, height = 4, default.units = "inches")
# add bw tracks
pwalk(list(hoxd_config$bw, hoxd_config$ID_paper, hoxd_config$Data, y_positions),
~ bb_placeSignalAndLabel(data = ..1,
annotation = ..2,
color = palette_tracks[..3],
y = ..4, x = x,
params = params_hoxd,
ymax = 1,
fontsize = 4))
bb_plotGenomeLabel(params = params_hoxd, scale = "Kb", x = 0.5, y = "0.03b", length = 3)
bb_plotGenes(params = params_hoxd, x = 0.5, y = "0.1b", height = 1, width = 3,
strandcolors = c("navy", "black"), fontcolors = c("navy", "black"),
stroke = 0.05,
just = c("left", "top"), default.units = "inches")
bb_pageGuideHide()
[figure @ public/figures/03A/hoxd_tracks_PFA...]
9.4 Motor neurons & ESCs
This is data from Narendra et al, Science, 2015, downloaded from GEO ID GSE60232.
# set region in mm9
hoxd_mm9 <- GRanges("chr2", 74480000:74610000)
params_hoxd_mm9 <- bb_params(chrom = as.character(seqnames(hoxd_mm9)),
chromstart = start(hoxd_mm9),
chromend = end(hoxd_mm9),
assembly = "mm9")
# build the config data frame row-wise
hoxd_config <- tribble(
~Data, ~ID, ~Ymax, ~bw,
"H3K27me3", "H3K27me3 (ESC)", 8, "GSM1468396_H3K27me3.ESC.WT.bw",
"Pol2", "Pol2 (MN)", 20, "GSM1468406_Pol2.MN.WT.bw",
"H3K4me3", "H3K4me3 (MN)", 150, "GSM1468401_H3K4me3.MN.WT.bw",
"H3K27me3", "H3K27me3 (MN)", 120, "GSM1468398_H3K27me3.MN.WT.bw",
"EZH2", "EZH2 (MN)", 150, "GSM1468404_EZH2.MN.WT.bw",
"H2AK119Ub", "H2AK119Ub (MN)", 20, "GSM1468408_H2AK119Ub.MN.WT.bw",
"CTCF", "CTCF (MN)", 30, "GSM1468394_CTCF.MN.WT.bw"
) %>%
mutate(bw = here(file.path("data/ChIPseq/bigwig/Narendra_et_al_2015/", bw)))
# make figure
x <- 0.5
y_positions <- seq(0.5, by = 0.5, length.out = nrow(hoxd_config))
bb_pageCreate(width = 4, height = 6, default.units = "inches")
# add bw tracks
pwalk(list(hoxd_config$bw, hoxd_config$ID, hoxd_config$Data, y_positions, hoxd_config$Ymax),
~ bb_placeSignalAndLabel(data = ..1,
annotation = ..2,
color = palette_tracks[..3],
y = ..4, x = x,
range = c(0, ..5),
params = params_hoxd_mm9,
fontsize = 4))
bb_plotGenomeLabel(params = params_hoxd_mm9, scale = "Kb", x = 0.5, y = "0.03b", length = 3)
bb_plotGenes(params = params_hoxd_mm9, x = 0.5, y = "0.1b", height = 1, width = 3,
strandcolors = c("navy", "black"), fontcolors = c("navy", "black"),
stroke = 0.05,
just = c("left", "top"), default.units = "inches")
bb_pageGuideHide()
[figure @ public/figures/03A/hoxd_tracks_MN_Narendra...]
10 3D chromatin conformation
Finally, we interrogate 3D chromatin conformation at the HOX locus using HiC data, and plot this together with other genomic data.
First, we'll assemble the plotting parameters:
# define gene palette and region
palette_hoxd <- palette_hox[grepl("HOXD", names(palette_hox))]
hoxd_genes_gr <- GRanges(hox_genes_bed[grepl("HOXD", hox_genes_bed$gene_symbol),])
region <- GRanges("chr2", 176725000:177320000)
params_hoxd_hic <- bb_params(chrom = "chr2",
chromstart = start(region),
chromend = end(region),
assembly = "hg19",
zrange = c(-4, 4),
palette = colorRampPalette(colors = c("blue", "white", "red")))
# get x/y-positions; heatmaps can be placed 2 inches apart starting at y = 0.5
y_positions <- 0.5
x_positions <- c(2, 5.5, 9)
x_starts <- c(0.5, 4, 7.5)Next, assemble the inputs:
# define input data ------------------------------------------------------------
hic_files <- file.path(here("data/HiC/hic/"), c("tumor/E823_hg19.hic",
"cell/HSJ-031_hg19.hic",
"cell/BT245_hg19.hic"))
# load normalized HiC counts for this region, for all samples
hic_counts <- map(hic_files, function(hic) {
out <- bb_readHic(file = hic,
chrom = "chr2",
chromstart = start(region),
chromend = end(region),
assembly = "hg19",
norm = "KR",
matrix = "oe",
resolution = 10000)
# get log2(O/E)
out$counts <- log2(out$counts)
return(out)
})## Read in hic file with KR normalization at 10000 BP resolution.
## Read in hic file with KR normalization at 10000 BP resolution.
## Read in hic file with KR normalization at 10000 BP resolution.# HiC
inputs <- list(data = hic_counts,
annotation = c("E823 (PFA)", "HSJ-031 \n(H3.3 pons)", "BT245 \n(H3.3 thal)"),
x = x_positions)
# add ChIP bw tracks
inputs_ctcf <- list("ctcf" = c(here("data/ChIPseq/bigwig/cell_parental/CTCF/DIPGXIII-1__CTCF.bw"),
here("data/ChIPseq/bigwig/cell_parental/CTCF/BT245-1__CTCF.bw")),
"annotation" = c("DIPGXIII", "BT245"),
x = x_starts[2:3])
inputs_suz12 <- list("suz12" = c(here("data/ChIPseq/bigwig/cell_parental/SUZ12/DIPGXIII-1__SUZ12.bw"),
here("data/ChIPseq/bigwig/cell_parental/SUZ12/BT245-1__SUZ12.bw")),
"annotation" = c("DIPGXIII", "BT245"),
x = x_starts[2:3])
# helper function to get the internal pipleine path for a sample without exposing ID
get_rna_path <- function(id, material = "Tumor") {
RNAseq_path <- meta %>% filter(ID_paper == id & Material == material) %>% pull(RNAseq_path)
file.path("/lustre06/project/6004736/pipeline/v0/levels1-2", RNAseq_path,
"star", paste0(basename(RNAseq_path), ".sorted.bw"))
}
inputs_rna <- list("rna" = c(get_rna_path("P-5426_S-6887"),
get_rna_path("P-3407_S-3447"),
get_rna_path("P-4111_S-4496")),
"annotation" = c("P-5426_S-6887", "P-3407_S-3447", "P-4111_S-4496"),
x = x_starts)
inputs_k27ac <- list("k27ac" = c(here("data/ChIPseq/bigwig/tumor/H3K27ac/P-5430_S-6891-1__H3K27ac.bw"),
here("data/ChIPseq/bigwig/cell_parental/H3K27ac/DIPGXIII-1__H3K27ac.bw"),
here("data/ChIPseq/bigwig/cell_parental/H3K27ac/BT245-1__H3K27ac.bw")),
"annotation" = c("P-5430_S-6891", "DIPGXIII", "BT245"),
x = x_starts)
inputs_k27me3 <- list("k27me3" = c(here("data/ChIPseq/bigwig/tumor/H3K27me3/P-5430_S-6891-1__H3K27me3.bw"),
here("data/ChIPseq/bigwig/cell_parental/H3K27me3/DIPGXIII-1__H3K27me3.bw"),
here("data/ChIPseq/bigwig/cell_parental/H3K27me3/BT245-1__H3K27me3.bw")),
"annotation" = c("P-5430_S-6891", "DIPGXIII", "BT245"),
x = x_starts)Assemble the plot:
# make figure ------------------------------------------------------------------
# create BB page
bb_pageCreate(width = 11.5, height = 6, default.units = "inches")
# place the heatmap & annotations for each sample, 1 x 3
pwalk(inputs,
# helper function defined in code/functions/BentoBox_helpers.R
~ bb_placeHeatmapAndLegend(counts = ..1,
annotation = ..2,
y = 0.5, x = ..3,
params = params_hoxd_hic))
# add ChIP using helper function defined in code/functions/BentoBox_helpers.R
pwalk(inputs_rna, ~ bb_placeSignalAndLabel(..1, y = 2.05, x = ..3, annotation = ..2,
params = params_hoxd_hic, ymax = 1, color = "black"))
pwalk(inputs_k27ac, ~ bb_placeSignalAndLabel(..1, y = 2.55, x = ..3, annotation = ..2,
params = params_hoxd_hic, ymax = 1, color = "blue"))
pwalk(inputs_k27me3, ~ bb_placeSignalAndLabel(..1, y = 3.05, x = ..3, annotation = ..2,
params = params_hoxd_hic, ymax = 1, color = "red"))
pwalk(inputs_suz12, ~ bb_placeSignalAndLabel(..1, y = 3.55, x = ..3, annotation = ..2,
params = params_hoxd_hic, ymax = 1, color = "red"))
pwalk(inputs_ctcf, ~ bb_placeSignalAndLabel(..1, y = 4.05, x = ..3, annotation = ..2,
params = params_hoxd_hic, ymax = 1, color = "black"))
# annotate data types
bb_plotText("RNA", x = 10.55, y = 2.25, just = c("left", "top"), fontcolor = "black")
bb_plotText("H3K27ac", x = 10.55, y = 2.75, just = c("left", "top"), fontcolor = "blue")
bb_plotText("H3K27me3", x = 10.55, y = 3.25, just = c("left", "top"), fontcolor = "red")
bb_plotText("SUZ12", x = 10.55, y = 3.75, just = c("left", "top"), fontcolor = "red")
bb_plotText("CTCF", x = 10.55, y = 4.25, just = c("left", "top"), fontcolor = "black")
# annotate x-axis genome label
walk(x_starts, ~ bb_plotGenomeLabel(params = params_hoxd_hic,
scale = "Kb", x = .x, y = 4.55, length = 3))
# plot ticks for the genes in colour
walk(x_starts, ~ bb_plotBed(params = params_hoxd_hic, data = hoxd_genes_gr,
fill = palette_hoxd, colorby = colorby("gene_symbol"),
width = 3, height = 0.6, y = 4.75, x = .x,
baseline.lwd = 2, boxHeight = unit(3, "mm"), collapse = FALSE))
# plot the gene names in colour
x_starts_ticks <- seq(1.6, by = 0.1, length.out = 9)
x_starts_ticks <- list(x_starts_ticks, x_starts_ticks + 3.5, x_starts_ticks + 7)
walk(x_starts_ticks, ~ bb_plotText(label = rev(names(palette_hoxd)),
fontcolor = rev(unname(palette_hoxd)),
rot = 90, fontsize = 6,
x = .x, y = 5.5,
just = c("right"), default.units = "inches"))
# done!
bb_pageGuideHide()
[figure @ public/figures/03A/hoxd_hic...]
11 Reproducibility
This document was last rendered on:
## 2022-06-16 14:57:36The 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 2022-06-16
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## ! package * version date lib
## P AnnotationDbi * 1.48.0 2019-10-29 [?]
## P askpass 1.1 2019-01-13 [?]
## P assertthat 0.2.1 2019-03-21 [?]
## P BentoBox * 0.1.0 2022-06-14 [?]
## P Biobase * 2.46.0 2019-10-29 [?]
## P BiocFileCache 1.10.2 2019-11-08 [?]
## P BiocGenerics * 0.32.0 2019-10-29 [?]
## P BiocManager 1.30.15 2021-05-11 [?]
## P BiocParallel 1.20.1 2019-12-21 [?]
## P biomaRt 2.42.1 2020-03-26 [?]
## P Biostrings * 2.54.0 2019-10-29 [?]
## P bit 4.0.4 2020-08-04 [?]
## P bit64 4.0.5 2020-08-30 [?]
## P bitops 1.0-7 2021-04-24 [?]
## P blob 1.2.1 2020-01-20 [?]
## P BSgenome * 1.54.0 2019-10-29 [?]
## P BSgenome.Hsapiens.UCSC.hg19 * 1.4.0 2021-12-06 [?]
## P bslib 0.2.5 2021-05-12 [?]
## P callr 3.7.0 2021-04-20 [?]
## P cellranger 1.1.0 2016-07-27 [?]
## P cli 2.5.0 2021-04-26 [?]
## P colorspace 2.0-1 2021-05-04 [?]
## P cowplot * 1.1.1 2020-12-30 [?]
## P crayon 1.4.1 2021-02-08 [?]
## P curl 4.3.1 2021-04-30 [?]
## P data.table * 1.14.0 2021-02-21 [?]
## P DBI 1.1.1 2021-01-15 [?]
## P dbplyr 2.1.1 2021-04-06 [?]
## P DelayedArray 0.12.3 2020-04-09 [?]
## P desc 1.2.0 2018-05-01 [?]
## P devtools 2.3.0 2020-04-10 [?]
## P digest 0.6.27 2020-10-24 [?]
## P dplyr * 1.0.6 2021-05-05 [?]
## P ellipsis 0.3.2 2021-04-29 [?]
## P evaluate 0.14 2019-05-28 [?]
## P fansi 0.4.2 2021-01-15 [?]
## P fs 1.5.0 2020-07-31 [?]
## P generics 0.1.0 2020-10-31 [?]
## P GenomeInfoDb * 1.22.1 2020-03-27 [?]
## P GenomeInfoDbData 1.2.2 2021-12-06 [?]
## P GenomicAlignments 1.22.1 2019-11-12 [?]
## P GenomicFeatures * 1.38.2 2020-02-15 [?]
## P GenomicRanges * 1.38.0 2019-10-29 [?]
## P ggplot2 * 3.3.3 2020-12-30 [?]
## P ggplotify * 0.0.7 2021-05-11 [?]
## P ggrepel * 0.9.1 2021-01-15 [?]
## P git2r 0.27.1 2020-05-03 [?]
## P glue * 1.4.2 2020-08-27 [?]
## P gmp * 0.6-2 2021-01-07 [?]
## P gridExtra 2.3 2017-09-09 [?]
## P gridGraphics 0.5-1 2020-12-13 [?]
## P gtable 0.3.0 2019-03-25 [?]
## P here * 0.1 2017-05-28 [?]
## P hms 1.0.0 2021-01-13 [?]
## P htmltools 0.5.1.1 2021-01-22 [?]
## P httr 1.4.2 2020-07-20 [?]
## P IRanges * 2.20.2 2020-01-13 [?]
## P jquerylib 0.1.4 2021-04-26 [?]
## P jsonlite 1.7.2 2020-12-09 [?]
## P knitr 1.33 2021-04-24 [?]
## P lattice 0.20-44 2021-05-02 [?]
## P lifecycle 1.0.0 2021-02-15 [?]
## P magrittr * 2.0.1 2020-11-17 [?]
## P Matrix 1.2-18 2019-11-27 [?]
## P matrixStats 0.58.0 2021-01-29 [?]
## P memoise 1.1.0 2017-04-21 [?]
## P munsell 0.5.0 2018-06-12 [?]
## P openssl 1.4.4 2021-04-30 [?]
## P org.Hs.eg.db * 3.10.0 2021-12-06 [?]
## P org.Mm.eg.db * 3.10.0 2021-12-06 [?]
## P pheatmap * 1.0.12 2019-01-04 [?]
## P pillar 1.6.0 2021-04-13 [?]
## P pkgbuild 1.0.8 2020-05-07 [?]
## P pkgconfig 2.0.3 2019-09-22 [?]
## P pkgload 1.0.2 2018-10-29 [?]
## P prettyunits 1.1.1 2020-01-24 [?]
## P processx 3.5.2 2021-04-30 [?]
## P progress 1.2.2 2019-05-16 [?]
## P ps 1.6.0 2021-02-28 [?]
## P purrr * 0.3.4 2020-04-17 [?]
## P R6 2.5.0 2020-10-28 [?]
## P rappdirs 0.3.3 2021-01-31 [?]
## P RColorBrewer * 1.1-2 2014-12-07 [?]
## P Rcpp 1.0.6 2021-01-15 [?]
## P RCurl 1.98-1.3 2021-03-16 [?]
## P readr * 1.4.0 2020-10-05 [?]
## P readxl * 1.3.1 2019-03-13 [?]
## P remotes 2.1.1 2020-02-15 [?]
## renv 0.14.0 2021-07-21 [1]
## P rlang 0.4.11 2021-04-30 [?]
## P rmarkdown 2.8 2021-05-07 [?]
## P Rmpfr * 0.8-4 2021-04-11 [?]
## P rprojroot 2.0.2 2020-11-15 [?]
## P Rsamtools 2.2.3 2020-02-23 [?]
## P RSQLite 2.2.1 2020-09-30 [?]
## P rstudioapi 0.13 2020-11-12 [?]
## P rtracklayer * 1.46.0 2019-10-29 [?]
## P rvcheck 0.1.8 2020-03-01 [?]
## P S4Vectors * 0.24.4 2020-04-09 [?]
## P sass 0.4.0 2021-05-12 [?]
## P scales 1.1.1 2020-05-11 [?]
## P sessioninfo 1.1.1 2018-11-05 [?]
## P stringi 1.6.1 2021-05-10 [?]
## P stringr 1.4.0 2019-02-10 [?]
## P SummarizedExperiment 1.16.1 2019-12-19 [?]
## P testrmd 0.0.1.9000 2021-12-06 [?]
## P testthat 2.3.2 2020-03-02 [?]
## P tibble * 3.1.1 2021-04-18 [?]
## P tidyr * 1.1.3 2021-03-03 [?]
## P tidyselect 1.1.1 2021-04-30 [?]
## P TxDb.Hsapiens.UCSC.hg19.knownGene * 3.2.2 2021-12-06 [?]
## P TxDb.Mmusculus.UCSC.mm9.knownGene * 3.2.2 2021-12-06 [?]
## P usethis 1.6.1 2020-04-29 [?]
## P utf8 1.2.1 2021-03-12 [?]
## P vctrs 0.3.8 2021-04-29 [?]
## P viridis * 0.5.1 2018-03-29 [?]
## P viridisLite * 0.4.0 2021-04-13 [?]
## P withr 2.4.2 2021-04-18 [?]
## P xfun 0.22 2021-03-11 [?]
## P XML 3.99-0.3 2020-01-20 [?]
## P XVector * 0.26.0 2019-10-29 [?]
## P yaml 2.2.1 2020-02-01 [?]
## P zlibbioc 1.32.0 2019-10-29 [?]
## source
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Github (PhanstielLab/BentoBox@72701d1)
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 3.6.1)
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## CRAN (R 3.6.1)
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## Github (rmflight/testrmd@0735c20)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## Bioconductor
## CRAN (R 3.6.1)
## Bioconductor
##
## [1] /lustre06/project/6004736/sjessa/from_narval/HGG-oncohistones/public/renv/library/R-3.6/x86_64-pc-linux-gnu
## [2] /tmp/Rtmp2jElRW/renv-system-library
##
## P ── Loaded and on-disk path mismatch.The resources requested when this document was last rendered:
## #SBATCH --time=00:20:00
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A project of the Kleinman Lab at McGill University, using the rr reproducible research template.