X-chromosome analysis
Carmen Navarro
2021-07-06
Last updated: 2021-07-07
Checks: 7 0
Knit directory: hesc-epigenomics/
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Summary
Corresponding figures for chromosome X analysis.
Helper functions
Extra functions used to cleanup the relevant code. Click code to see the source.
#' Summarize stats per chromosome on a scaled bigWig file
#'
#' @param bwfile BigWig file to summarize
#' @param chromosomes Array of chromosome names to include.
#'
#' @return A data frame with stats per chromosome: mean, chr size, #reads
#' (estimated as (score * chr size) / fraglen), %reads.
scaled_reads_per_chromosome <- function(bwfile, chromosomes, fraglen = 150) {
granges <- unlist(summary(BigWigFile(bwfile)))
df <- data.frame(granges[seqnames(granges) %in% chromosomes, ])
rownames(df) <- df$seqnames
# Calculate scaled number of reads as mean x chromosome length / read length
df$nreads <- (df$score * df$width) / fraglen
# Perc of total
df$perc <- (df$nreads / sum(df$nreads)) * 100
# Perc size
df$size <- df$width / sum(df$width)
df$group <- basename(bwfile)
df[chromosomes, ]
}
chromosomes <- paste0("chr", c(1:22, "X"))
# Fix some parameters on treemap function to remove some clutter from nb.
chr_treeplot <- partial(
treemap,
index = "seqnames",
vSize = "nreads",
vColor = "score",
type = "value",
mapping = c(0, 3),
range = c(0, 3),
fontsize.labels = 16,
fontsize.legend = 16,
fontsize.title = 20
)
ridges_chromosome_plot <- function(values, column, color, main_seqs = chromosomes, scale = 1.7) {
value_name <- column
df <- values[values$seqnames %in% main_seqs, c("seqnames", value_name)]
colnames(df) <- c("seqnames", "value")
df$value <- as.numeric(df$value)
df$seqnames <- factor(df$seqnames, levels = rev(main_seqs))
df_summary <- df %>% group_by(seqnames) %>%
summarise(value=median(value, na.rm = T))
x_nudge <- quantile(df$value, 0.02, na.rm = T)
ggplot(df, aes(x = value, y = seqnames, fill = seqnames)) +
geom_density_ridges(
rel_min_height = 0.001,
scale = 1.7,
calc_ecdf = TRUE,
quantile_lines = TRUE, quantiles = 2,
) +
theme_default(base_size = 12) +
labs(y = "", x = "log2FC") +
scale_fill_manual(values = c(color, rep("#bbbbbb", 22))) + theme(legend.position = "none") +
geom_vline(xintercept = 0, linetype = "dashed", size = 0.2) +
geom_text(data=df_summary,
aes(label=sprintf("%1.2f", value)),
position=position_nudge(y=0.35, x = x_nudge), colour="black", size=3)
}
get_long_format_heatmap_data <- function(df, mark) {
columns <- grep("mean_cov", colnames(df), value = T)
main_seqs <- paste0("chr", c(1:22, "X"))
df <- df[df$seqnames %in% main_seqs, c("seqnames", columns)]
summary_mat <- df %>% group_by(seqnames) %>% summarise_at(columns, mean, na.rm = TRUE)
to_plot <- summary_mat %>%
select("seqnames", contains(mark) & contains("mean_cov") & !contains("rep"))
# Reorder chromosomes and conditions
conditions <- c("Ni", "Ni_EZH2i", "Pr", "Pr_EZH2i")
to_plot$seqnames <- gsub("chr", "", to_plot$seqnames)
to_plot$seqnames <- factor(to_plot$seqnames, levels = c(1:22, "X"))
colnames(to_plot) <- c("seqnames", conditions)
to_plot_melt <- pivot_longer(to_plot, !seqnames)
to_plot_melt$name <- factor(to_plot_melt$name, levels = rev(conditions))
to_plot_melt
}Read the main tables:
genes <- read.table("./data/meta/Kumar_2020_master_gene_table_rnaseq_shrunk.tsv",
header = T, sep = "\t",
colClasses = c(rep("character", 5), rep("numeric", 86)))
bins <- read.table("./data/meta/Kumar_2020_master_bins_5kb_table_raw.tsv",
header = T, sep = "\t",
colClasses = c(rep("character", 4), rep("numeric", 112)))Heatmaps
to_plot_melt <- get_long_format_heatmap_data(bins, "H3K4m3")
ggplot(to_plot_melt, aes(fill = value, x = seqnames, y = name)) +
geom_tile(color = "white", size = 1) +
coord_fixed() +
theme_minimal(base_size = 12) +
labs(x = "", y = "", title = "H3K4m3 mean of 5kb bins per chromosome") +
scale_fill_gradient(low = "white", high = "#b64c28", limits = c(0, 4.5))
Download plot data: download plot data
to_plot_melt <- get_long_format_heatmap_data(bins, "H3K27m3")
ggplot(to_plot_melt, aes(fill = value, x = seqnames, y = name)) +
geom_tile(color = "white", size = 1) +
coord_fixed() +
theme_minimal(base_size = 12) +
labs(x = "", y = "", title = "H3K27m3 mean of 5kb bins per chromosome") +
scale_fill_gradient(low = "white", high = gl_mark_colors$H3K27m3, limits = c(0, 3))
Download plot data: download plot data
to_plot_melt <- get_long_format_heatmap_data(bins, "H2Aub")
ggplot(to_plot_melt, aes(fill = value, x = seqnames, y = name)) +
geom_tile(color = "white", size = 1) +
coord_fixed() +
theme_minimal(base_size = 12) +
labs(x = "", y = "", title = "H2AUb mean of 5kb bins per chromosome") +
scale_fill_gradient(low = "white", high = gl_mark_colors$H2Aub, limits = c(0, 2))
Download plot data: download plot data
columns <- grep("mean_cov", colnames(bins), value = T)
main_seqs <- paste0("chr", c(1:22, "X"))
df <- bins[bins$seqnames %in% main_seqs, c("seqnames", columns)]
summary_mat <- df %>% group_by(seqnames) %>% summarise_at(columns, mean, na.rm = TRUE)
to_plot <- summary_mat %>% select("seqnames", contains("mean_cov") & !contains("IN") & !contains("rep"))
# Reorder chromosomes
to_plot$seqnames <- gsub("chr", "", to_plot$seqnames)
to_plot$seqnames <- factor(to_plot$seqnames, levels = c(1:22, "X"))
to_plot_melt <- pivot_longer(to_plot, !seqnames)
to_plot_melt$name <- gsub("_mean_cov", "", to_plot_melt$name)
to_plot_melt$ip <- str_split_fixed(to_plot_melt$name, "_", 2)[, 1]
to_plot_melt$condition <- str_split_fixed(to_plot_melt$name, "_", 2)[, 2]
ggplot(to_plot_melt, aes(color = ip, x = condition, y = value, label = seqnames)) +
geom_boxplot(color = "gray", alpha = 0.9) +
geom_jitter(position = "dodge") +
geom_jitter(data = to_plot_melt %>% filter(seqnames == "X"), color = "black", size = 3.5, position = "dodge") +
geom_label_repel(data = to_plot_melt %>% filter(
seqnames == "X" & ip == "H3K27m3" & condition == "Ni"), color = "black", box.padding = 1.5) +
theme_default(base_size=12) +
facet_wrap(. ~ ip, nrow = 1) +
geom_hline(yintercept = 1, linetype = "dashed", alpha = 0.8) +
scale_color_manual(
values = c("H2Aub" = gl_mark_colors$H2Aub,
"H3K27m3" = gl_mark_colors$H3K27m3,
"H3K4m3" = gl_mark_colors$H3K4m3)) +
labs(y="FPGC", title =
"Mean 5kb bin RPGC per chromosome and histone mark",
subtitle = "Chromosome X in black")
Treemaps
H3K27me3
H3K27me3 is highly abundant on X chromosome on naïve cells.
If we take a look at coverage per chromosome for both Naïve and Primed cells:
bw <- file.path(params$bwdir, "H3K27m3_H9_Ni_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Naive_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H3K27m3 - Naïve"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Values can be downloaded here: download plot data.
In this and subsequent plots, each rectangle’s size is proportional to the number of read mapped to its corresponding chromosome. Color intensity represents mean coverage per chromosome, and rectangles are ordered according to size. Top-left is the highest value.
As opposed to primed, where values are very even:
bw <- file.path(params$bwdir, "H3K27m3_H9_Pr_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Primed_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H3K27m3 - Primed"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Values can be downloaded here: download plot data.
EZH2i-treated cells, in comparison, have H3K27m3 globally removed:
bw <- file.path(params$bwdir, "H3K27m3_H9_Ni-EZH2i_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Naive_Untreated"]]),
fontcolor.labels = "#555555",
border.col = "#999999",
title = "H3K27m3 - Naïve-EZH2i"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Values can be downloaded here: download plot data.
bw <- file.path(params$bwdir, "H3K27m3_H9_Pr-EZH2i_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Primed_Untreated"]]),
fontcolor.labels = "#555555",
border.col = "#999999",
title = "H3K27m3 - Primed-EZH2i"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Values can be downloaded here: download plot data.
If we look at the rest of the histone marks:
H3K4me3
H3K4me3 does not show this X-chromosome specificity.
bw <- file.path(params$bwdir, "H3K4m3_H9_Ni_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Naive_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H3K4m3 - Naïve"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Underlying values can be downloaded here: download plot data.
bw <- file.path(params$bwdir, "H3K4m3_H9_Pr_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Primed_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H3K4m3 - Primed"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Underlying values can be downloaded here: download plot data.
EZH2i-treated cells, in comparison, have H3K27m3 globally removed:
bw <- file.path(params$bwdir, "H3K4m3_H9_Ni-EZH2i_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Naive_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H3K4m3 - Naïve-EZH2i"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Underlying values can be downloaded here: download plot data.
bw <- file.path(params$bwdir, "H3K4m3_H9_Pr-EZH2i_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Primed_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H3K4m3 - Primed-EZH2i"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
H2AUb
bw <- file.path(params$bwdir, "H2Aub_H9_Ni_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Naive_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H2Aub - Naïve"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Underlying values can be downloaded here: download plot data.
bw <- file.path(params$bwdir, "H2Aub_H9_Pr_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Primed_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H2Aub - Primed"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Underlying values can be downloaded here: download plot data.
EZH2i-treated cells, in comparison, have H3K27m3 globally removed:
bw <- file.path(params$bwdir, "H2Aub_H9_Ni-EZH2i_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Naive_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H2Aub - Naïve-EZH2i"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Values can be downloaded here: download plot data.
bw <- file.path(params$bwdir, "H2Aub_H9_Pr-EZH2i_pooled.hg38.scaled.bw")
values <- scaled_reads_per_chromosome(bw, chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Primed_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H2Aub - Primed-EZH2i"
)
| Version | Author | Date |
|---|---|---|
| ded0029 | C. Navarro | 2021-07-06 |
Values can be downloaded here: download plot data.
Ridgeplots
These figures are made using the package ggridges: https://wilkelab.org/ggridges/
Primed vs Naive
RNA-seq data
ridges_chromosome_plot(genes, "RNASeq_DS_Pr_vs_Ni_log2FoldChange", "#F08080") +
labs(title = "RNA Seq log2FC distribution Primed vs Naïve DESeq2") +
coord_cartesian(xlim=c(-7, 7))
Values used in this plot.
H3K27m3
ridges_chromosome_plot(genes, "H3K27m3_DS_Pr_vs_Ni_log2FoldChange", "#3e5aa8") +
labs(title = "H3K27m3 log2FC distribution Primed vs Naïve DESeq2") + coord_cartesian(xlim=c(-7, 7))
Values used in this plot.
EZH2i vs Naive
RNA-seq data
ridges_chromosome_plot(genes, "RNASeq_DS_EZH2i_vs_Ni_log2FoldChange", "#F08080") +
labs(title = "RNA Seq log2FC distribution EZH2i vs Naïve DESeq2") +
coord_cartesian(xlim=c(-5, 5))
Values used in this plot.
MA plots
# Too many points make huge svgs
library(ggrastr)
interest_genes <- c("XIST", "VGLL1", "HUWE1", "ATRX", "THOC2", "IDS", "MPP1", "RBM3")
ggplot(genes, aes(x=RNASeq_DS_Pr_vs_Ni_baseMean, y=RNASeq_DS_Pr_vs_Ni_log2FoldChange)) +
rasterise(geom_point(size = 0.5, alpha = 0.8, color = "gray"), dpi = 300) +
rasterise(geom_point(data = genes %>% filter(seqnames == "chrX"), color = "black", size = 1.5), dpi = 300) +
theme_default(base_size = 12) +
labs(x = "Base mean",
y = "Log2 FC",
title = paste("MA plot - Primed vs Naive"),
subtitle = "Highlighted in black chrX genes") +
geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.8) +
geom_label_repel(data = genes %>% filter(name %in% interest_genes), aes(label = name), box.padding = 0.5) +
scale_x_log10(breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x)))
ggplot(genes, aes(x=RNASeq_DS_EZH2i_vs_Ni_baseMean, y=RNASeq_DS_EZH2i_vs_Ni_log2FoldChange)) +
rasterise(geom_point(size = 0.5, alpha = 0.8, color = "gray"), dpi = 300) +
rasterise(geom_point(data = genes %>% filter(seqnames == "chrX"), color = "black", size = 1.6), dpi = 300) +
theme_default(base_size = 12) +
labs(x = "Base mean",
y = "Log2 FC",
title = paste("MA plot - EZH2i vs Naive"),
subtitle = "Highlighted in black chrX genes") +
geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.8) +
geom_label_repel(data = genes %>% filter(name %in% interest_genes), aes(label = name), box.padding = 0.5) +
scale_x_log10(breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x)))
Karyoplots
These figures are made using the package karyoploteR: https://academic.oup.com/bioinformatics/article/33/19/3088/3857734
H3K27m3
bwfiles <- list(
k4 = list.files(params$bwdir, pattern = "H3K4m3.*pooled.hg38.scaled.*", full.names = T),
k27 = list.files(params$bwdir, pattern = "H3K27m3.*pooled.hg38.scaled.*", full.names = T),
ub = list.files(params$bwdir, pattern = "H2Aub.*pooled.hg38.scaled.*", full.names = T),
input = list.files(params$bwdir, pattern = "IN.*pooled.hg38.*", full.names = T))
kp <-
plotKaryotype(
genome = "hg38",
plot.type = 1,
main = "H3K27m3 Naïve vs Primed",
chromosomes = c("chr7", "chrX")
)
kpPlotDensity(
kp,
rtracklayer::import(bwfiles$k27[[1]]),
data.panel = 1,
col = "#092ba8",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
kpPlotDensity(
kp,
rtracklayer::import(bwfiles$k27[[3]]),
data.panel = 2,
col =
"#5d9ddd",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
H3K4m3
kp <-
plotKaryotype(
genome = "hg38",
plot.type = 1,
main = "H3K4m3 Naïve vs Primed",
chromosomes = c("chr7", "chrX")
)
kpPlotDensity(
kp,
rtracklayer::import(bwfiles$k4[[1]]),
data.panel = 1,
col = "#e76e3b",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
kpPlotDensity(
kp,
rtracklayer::import(bwfiles$k4[[3]]),
data.panel = 2,
col =
"#ffab45",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
H2Aub
kp <-
plotKaryotype(
genome = "hg38",
plot.type = 1,
main = "H2Aub Naïve vs Primed",
chromosomes = c("chr7", "chrX")
)
kpPlotDensity(
kp,
rtracklayer::import(bwfiles$ub[[1]]),
data.panel = 1,
col = "#400c84",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
kpPlotDensity(
kp,
rtracklayer::import(bwfiles$ub[[3]]),
data.panel = 2,
col =
"#a07af0",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=sv_SE.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=sv_SE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=sv_SE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=sv_SE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ggrastr_0.2.3 scales_1.1.1 ggrepel_0.9.1
[4] cowplot_1.1.1 karyoploteR_1.18.0 regioneR_1.24.0
[7] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[10] readr_1.4.0 tidyr_1.1.3 tibble_3.1.2
[13] ggplot2_3.3.5 tidyverse_1.3.1 ggridges_0.5.3
[16] purrr_0.3.4 treemap_2.4-2 rtracklayer_1.52.0
[19] GenomicRanges_1.44.0 GenomeInfoDb_1.28.1 IRanges_2.26.0
[22] S4Vectors_0.30.0 BiocGenerics_0.38.0 wigglescout_0.13.1
[25] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.1 tidyselect_1.1.1
[3] RSQLite_2.2.7 AnnotationDbi_1.54.1
[5] htmlwidgets_1.5.3 grid_4.1.0
[7] BiocParallel_1.26.0 munsell_0.5.0
[9] codetools_0.2-18 future_1.21.0
[11] withr_2.4.2 colorspace_2.0-2
[13] Biobase_2.52.0 filelock_1.0.2
[15] highr_0.9 knitr_1.33
[17] rstudioapi_0.13 listenv_0.8.0
[19] MatrixGenerics_1.4.0 labeling_0.4.2
[21] git2r_0.28.0 GenomeInfoDbData_1.2.6
[23] bit64_4.0.5 farver_2.1.0
[25] rprojroot_2.0.2 parallelly_1.26.1
[27] vctrs_0.3.8 generics_0.1.0
[29] xfun_0.24 biovizBase_1.40.0
[31] BiocFileCache_2.0.0 R6_2.5.0
[33] ggbeeswarm_0.6.0 AnnotationFilter_1.16.0
[35] bitops_1.0-7 cachem_1.0.5
[37] DelayedArray_0.18.0 assertthat_0.2.1
[39] promises_1.2.0.1 BiocIO_1.2.0
[41] nnet_7.3-16 beeswarm_0.4.0
[43] gtable_0.3.0 Cairo_1.5-12.2
[45] globals_0.14.0 ensembldb_2.16.2
[47] rlang_0.4.11 splines_4.1.0
[49] lazyeval_0.2.2 dichromat_2.0-0
[51] broom_0.7.8 checkmate_2.0.0
[53] yaml_2.2.1 reshape2_1.4.4
[55] modelr_0.1.8 GenomicFeatures_1.44.0
[57] backports_1.2.1 httpuv_1.6.1
[59] Hmisc_4.5-0 tools_4.1.0
[61] gridBase_0.4-7 ellipsis_0.3.2
[63] jquerylib_0.1.4 RColorBrewer_1.1-2
[65] Rcpp_1.0.6 plyr_1.8.6
[67] base64enc_0.1-3 progress_1.2.2
[69] zlibbioc_1.38.0 RCurl_1.98-1.3
[71] prettyunits_1.1.1 rpart_4.1-15
[73] openssl_1.4.4 SummarizedExperiment_1.22.0
[75] haven_2.4.1 cluster_2.1.2
[77] fs_1.5.0 furrr_0.2.3
[79] magrittr_2.0.1 data.table_1.14.0
[81] reprex_2.0.0 whisker_0.4
[83] ProtGenerics_1.24.0 matrixStats_0.59.0
[85] hms_1.1.0 mime_0.11
[87] evaluate_0.14 xtable_1.8-4
[89] XML_3.99-0.6 jpeg_0.1-8.1
[91] readxl_1.3.1 gridExtra_2.3
[93] compiler_4.1.0 biomaRt_2.48.2
[95] crayon_1.4.1 htmltools_0.5.1.1
[97] later_1.2.0 Formula_1.2-4
[99] lubridate_1.7.10 DBI_1.1.1
[101] dbplyr_2.1.1 rappdirs_0.3.3
[103] Matrix_1.3-4 cli_3.0.0
[105] igraph_1.2.6 pkgconfig_2.0.3
[107] GenomicAlignments_1.28.0 foreign_0.8-81
[109] xml2_1.3.2 vipor_0.4.5
[111] bslib_0.2.5.1 XVector_0.32.0
[113] rvest_1.0.0 bezier_1.1.2
[115] VariantAnnotation_1.38.0 digest_0.6.27
[117] Biostrings_2.60.1 rmarkdown_2.9
[119] cellranger_1.1.0 htmlTable_2.2.1
[121] restfulr_0.0.13 curl_4.3.2
[123] shiny_1.6.0 Rsamtools_2.8.0
[125] rjson_0.2.20 lifecycle_1.0.0
[127] jsonlite_1.7.2 askpass_1.1
[129] BSgenome_1.60.0 fansi_0.5.0
[131] pillar_1.6.1 lattice_0.20-44
[133] KEGGREST_1.32.0 fastmap_1.1.0
[135] httr_1.4.2 survival_3.2-11
[137] glue_1.4.2 bamsignals_1.24.0
[139] png_0.1-7 bit_4.0.4
[141] stringi_1.6.2 sass_0.4.0
[143] blob_1.2.1 latticeExtra_0.6-29
[145] memoise_2.0.0