Figure 1. Genome-wide and chromosome X histone marks analysis.
Last updated: 2022-02-04
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Knit directory: hesc-epigenomics/
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Summary
Supplementary code for panel 1 figures.
Helper functions
#' Calculate INRC from a mapped read counts table, and append such values
#' to it.
#'
#' @param counts Counts table. Corresponding file is provided as part of the
#' included metadata.
#' @param selector Counts column used. Final_mapped represents the final number
#' of reads after deduplication and blacklisting.
#' @return A table including INRC and INRC norm to naive reference
calculate_inrc <- function(counts, selector = "final_mapped") {
counts$condition <- paste(counts$celltype, counts$treatment, sep="_")
inputs <- counts[counts$ip == "Input", c("library", selector)]
colnames(inputs) <- c("library", "input_reads")
non_inputs <- counts[counts$ip != "Input",]
counts <- merge(non_inputs, inputs, by.x="input", by.y="library")
counts$inrc <- counts[, selector] / counts[, "input_reads"]
references <- counts[grepl("_Ni_pooled", counts$library), c("ip", "inrc")]
colnames(references) <- c("ip", "ref_inrc")
counts <- merge(counts, references, by="ip")
counts$norm_to_naive <- counts$inrc / counts$ref_inrc
id_vars <- c("ip", "treatment", "celltype", "condition", "replicate", "norm_to_naive")
inrc <- counts[, c(id_vars)]
inrc$condition <- factor(
inrc$condition,
levels = c(
"Naive_Untreated",
"Primed_Untreated",
"Naive_EZH2i",
"Primed_EZH2i"
)
)
inrc
}
#' Barplot INRC pooled vs replicates per condition
#'
#' @param inrc Table with the INRC values
#' @param ip Which IP to plot
#' @param colors Corresponding colors
inrc_barplot <- function(inrc, ip, colors, font = 16) {
inrc <- inrc[inrc$ip == ip, ]
# So paired test takes right replicates
inrc <- inrc[order(inrc$condition, inrc$replicate), ]
max_v <- max(abs(inrc$norm_to_naive))
aesthetics <- aes(x = .data[["condition"]],
y = .data[["norm_to_naive"]],
color = .data[["condition"]])
my_comp <- list(c("Naive_Untreated", "Primed_Untreated"),
c("Naive_Untreated", "Naive_EZH2i"),
c("Primed_Untreated", "Primed_EZH2i"),
c("Naive_EZH2i", "Primed_EZH2i"))
stats_method <- "t.test"
ggplot(inrc[inrc$replicate != 'pooled',], aesthetics) +
geom_point() +
stat_compare_means(
method = stats_method,
paired = FALSE,
comparisons = my_comp,
label = "p.format"
) +
geom_bar(
data = inrc[inrc$replicate == 'pooled',],
stat = 'identity',
alpha = 0.6,
aes(fill = condition)
) +
scale_fill_manual(values = colors) +
scale_color_manual(values = colors) +
labs(
x = "",
y = 'INRC fraction vs Naïve',
title = paste(ip, "MINUTE-ChIP"),
caption = paste(stats_method, "signif. test, paired")
) +
theme_classic(base_size = font) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylim(0, 1.5)
}
#' 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
}
colors_list <- c("Naive_EZH2i"="#82c5c6",
"Naive_Untreated"="#278b8b",
"Primed_EZH2i"="#f49797",
"Primed_Untreated"="#f44b34")Data input
genes <- read.table("./data/meta/Kumar_2020_master_gene_table_rnaseq_shrunk_annotated.tsv",
header = T, sep = "\t",
colClasses = c(rep("character", 5), rep("factor", 3), rep("numeric", 86)))
bins <- read.table("./data/meta/Kumar_2020_master_bins_10kb_table_final_raw.tsv",
header = T, sep = "\t",
colClasses = c(rep("character", 5), rep("numeric", 112)))
counts_file <- file.path(params$datadir, "meta", "Kumar_2020_stats_summary.csv")
counts <- read.table(counts_file, sep="\t", header = T, na.strings = "NA", stringsAsFactors = F)Global quantification
Read counts
H2AUb
inrc <- calculate_inrc(counts)
inrc_barplot(inrc, "H2Aub", colors_list)
You can download data values here: download plot data.
H3K27m3
inrc_barplot(inrc, "H3K27m3", colors_list)
You can download data values here: download plot data.
H3K4m3
inrc_barplot(inrc, "H3K4m3", colors_list)
You can download data values here: download plot data.
ChromHMM
Here global average per ChromHMM categories are shown.
H3K27m3
labels <- gsub("_pooled.hg38.scaled.bw", "", basename(bwfiles$k27))
labels <- gsub("_H9", "", labels)
chromhmm <- params$chromhmm
plot_bw_loci_summary_heatmap(bwfiles$k27, chromhmm, labels = labels, remove_top=0.001)
You can download data values here: download plot data.
H3K4m3
labels <- gsub("_pooled.hg38.scaled.bw", "", basename(bwfiles$k4))
labels <- gsub("_H9", "", labels)
plot_bw_loci_summary_heatmap(bwfiles$k4, chromhmm, labels = labels, remove_top=0.001)
You can download data values here: download plot data.
H2AUb
labels <- gsub("_pooled.hg38.scaled.bw", "", basename(bwfiles$ub))
labels <- gsub("_H9", "", labels)
plot_bw_loci_summary_heatmap(bwfiles$ub, chromhmm, labels = labels, remove_top=0.001)
You can download data values here: download plot data.
Genome-wide 10kb bins
H3K27m3 Naïve vs Primed
points_color <- "#112233"
points_shape <- "."
raster_dpi <- 300
ggplot(bins, aes(x=log2(H3K27m3_Ni_mean_cov), y=log2(H3K27m3_Pr_mean_cov))) +
rasterise(geom_point(size = 1, alpha = 0.2, color = points_color, shape = points_shape), dpi = raster_dpi) +
geom_density2d(binwidth = 0.1) +
geom_abline(slope = 1, linetype = "dashed") +
theme_default() +
labs(x = "Log2 H3K27m3 Naïve - FPGC",
y = "Log2 H3K27m3 Primed - FPGC", title = "H3K27m3", subtitle = "10kb bins") +
coord_cartesian(xlim = c(-8, 8), ylim = c(-8, 8))
H2AUb Naïve vs Primed
ggplot(bins, aes(x=log2(H2Aub_Ni_mean_cov), y=log2(H2Aub_Pr_mean_cov))) +
rasterise(geom_point(size = 1, alpha = 0.2, color = "#2f1547", shape = points_shape), dpi = raster_dpi) +
geom_abline(slope = 1, linetype = "dashed") +
geom_density2d(binwidth = 0.1) +
theme_default() +
labs(x = "Log2 H2Aub Naïve - FPGC",
y = "Log2 H2Aub Primed - FPGC", title = "H2Aub", subtitle = "10kb bins") +
coord_cartesian(xlim = c(-8, 8), ylim = c(-8, 8))
H3K4m3 Naïve vs Primed
ggplot(bins, aes(x=log2(H3K4m3_Ni_mean_cov), y=log2(H3K4m3_Pr_mean_cov))) +
rasterise(geom_point(size = 1, alpha = 0.2, color = "#614925", shape = points_shape), dpi = raster_dpi) +
geom_abline(slope = 1, linetype = "dashed") +
geom_density2d(binwidth = 0.1) +
theme_default() +
labs(x = "Log2 H3K4m3 Naïve - FPGC",
y = "Log2 H3K4m3 Primed - FPGC", title = "H3K4m3", subtitle = "10kb bins") +
coord_cartesian(xlim = c(-8, 8), ylim = c(-8, 8))
Chromosome X
10kb bin per chromosome plot
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 10kb bin RPGC per chromosome and histone mark",
subtitle = "Chromosome X in black")
Whole chromosome-view of histone marks autosome vs chromosome X
These figures are made using the package karyoploteR: https://academic.oup.com/bioinformatics/article/33/19/3088/3857734
H3K27m3
kp <-
plotKaryotype(
genome = "hg38",
plot.type = 1,
main = "H3K27m3 Naïve vs Primed",
chromosomes = c("chr7", "chrX")
)
d1 <- kpPlotDensity(
kp,
rtracklayer::import(bwfiles$k27[[1]]),
data.panel = 1,
col = "#092ba8",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
d2 <- kpPlotDensity(
kp,
rtracklayer::import(bwfiles$k27[[3]]),
data.panel = 2,
col =
"#5d9ddd",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
# Store these values on a table
df1 <- cbind(data.frame(d1$latest.plot$computed.values$windows, d1$latest.plot$computed.values$density))
df2 <- cbind(data.frame(d2$latest.plot$computed.values$windows, d2$latest.plot$computed.values$density))
df_karyo <- left_join(df1, df2)
colnames(df_karyo) <- c("seqnames", "start", "end", "width", "strand", "H3K27m3_Pr", "H3K27m3_Ni")
final_df <- df_karyoH3K4m3
kp <-
plotKaryotype(
genome = "hg38",
plot.type = 1,
main = "H3K4m3 Naïve vs Primed",
chromosomes = c("chr7", "chrX")
)
d1 <- kpPlotDensity(
kp,
rtracklayer::import(bwfiles$k4[[3]]),
data.panel = 2,
col =
"#ffab45",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
d2 <- kpPlotDensity(
kp,
rtracklayer::import(bwfiles$k4[[1]]),
data.panel = 1,
col = "#e76e3b",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
# Store these values on a table
df1 <- cbind(data.frame(d1$latest.plot$computed.values$windows, d1$latest.plot$computed.values$density))
df2 <- cbind(data.frame(d2$latest.plot$computed.values$windows, d2$latest.plot$computed.values$density))
df_karyo <- left_join(df1, df2)
colnames(df_karyo) <- c("seqnames", "start", "end", "width", "strand", "H3K4m3_Pr", "H3K4m3_Ni")
final_df <- left_join(final_df, df_karyo)H2Aub
kp <-
plotKaryotype(
genome = "hg38",
plot.type = 1,
main = "H2Aub Naïve vs Primed",
chromosomes = c("chr7", "chrX")
)
d1 <- kpPlotDensity(
kp,
rtracklayer::import(bwfiles$ub[[1]]),
data.panel = 1,
col = "#400c84",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
d2 <- kpPlotDensity(
kp,
rtracklayer::import(bwfiles$ub[[3]]),
data.panel = 2,
col =
"#a07af0",
chromosomes = c("chr7", "chrX"),
window.size = 500000
)
# Store these values on a table
df1 <- cbind(data.frame(d1$latest.plot$computed.values$windows, d1$latest.plot$computed.values$density))
df2 <- cbind(data.frame(d2$latest.plot$computed.values$windows, d2$latest.plot$computed.values$density))
df_karyo <- left_join(df1, df2)
colnames(df_karyo) <- c("seqnames", "start", "end", "width", "strand", "H2Aub_Pr", "H2Aub_Ni")
final_df <- left_join(final_df, df_karyo)H3K27m3 naïve per chromosome treemap
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:
values <- scaled_reads_per_chromosome(bwfiles$k27[[1]], chromosomes = chromosomes)
chr_treeplot(
values,
palette = c("#ffffff", gl_condition_colors[["Naive_Untreated"]]),
fontcolor.labels = "#555555",
border.col = c("white"),
title = "H3K27m3 - Naïve"
)
Values can be downloaded here: download plot data.
In this and subsequent plots, each rectangle’s size is proportional to the number of reads 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.
Per chromosome distribution of logFC values
These figures are made using the package ggridges: https://wilkelab.org/ggridges/
Primed vs Naive RNA-seq
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.
Primed vs Naive H3K27m3
ridges_chromosome_plot(genes, "H3K27m3_DS_Pr_vs_Ni_log2FoldChange", "#3e5aa8") +
labs(title = "H3K27m3 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 EZH2i vs Naïve DESeq2") +
coord_cartesian(xlim=c(-5, 5))
Values used in this plot.
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] svglite_2.0.0 scales_1.1.1 ggrepel_0.9.1
[4] rtracklayer_1.54.0 cowplot_1.1.1 karyoploteR_1.20.0
[7] regioneR_1.26.0 GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
[10] IRanges_2.28.0 S4Vectors_0.32.2 BiocGenerics_0.40.0
[13] ggridges_0.5.3 treemap_2.4-3 knitr_1.36
[16] ggrastr_0.2.3 ggpubr_0.4.0 wigglescout_0.13.5
[19] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[22] purrr_0.3.4 readr_2.1.0 tidyr_1.1.4
[25] tibble_3.1.6 tidyverse_1.3.1 ggplot2_3.3.5
[28] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.2 tidyselect_1.1.1
[3] RSQLite_2.2.8 AnnotationDbi_1.56.2
[5] htmlwidgets_1.5.4 grid_4.1.2
[7] BiocParallel_1.28.0 munsell_0.5.0
[9] codetools_0.2-18 future_1.23.0
[11] withr_2.4.2 colorspace_2.0-2
[13] Biobase_2.54.0 filelock_1.0.2
[15] highr_0.9 rstudioapi_0.13
[17] ggsignif_0.6.3 listenv_0.8.0
[19] labeling_0.4.2 MatrixGenerics_1.6.0
[21] git2r_0.28.0 GenomeInfoDbData_1.2.7
[23] farver_2.1.0 bit64_4.0.5
[25] rprojroot_2.0.2 parallelly_1.28.1
[27] vctrs_0.3.8 generics_0.1.1
[29] xfun_0.28 biovizBase_1.42.0
[31] BiocFileCache_2.2.0 R6_2.5.1
[33] ggbeeswarm_0.6.0 isoband_0.2.5
[35] AnnotationFilter_1.18.0 bitops_1.0-7
[37] cachem_1.0.6 DelayedArray_0.20.0
[39] assertthat_0.2.1 promises_1.2.0.1
[41] BiocIO_1.4.0 nnet_7.3-16
[43] beeswarm_0.4.0 gtable_0.3.0
[45] Cairo_1.5-12.2 globals_0.14.0
[47] ensembldb_2.18.2 rlang_0.4.12
[49] systemfonts_1.0.3 splines_4.1.2
[51] rstatix_0.7.0 lazyeval_0.2.2
[53] dichromat_2.0-0 broom_0.7.10
[55] checkmate_2.0.0 yaml_2.2.1
[57] reshape2_1.4.4 abind_1.4-5
[59] modelr_0.1.8 GenomicFeatures_1.46.1
[61] backports_1.3.0 httpuv_1.6.3
[63] Hmisc_4.6-0 tools_4.1.2
[65] gridBase_0.4-7 ellipsis_0.3.2
[67] jquerylib_0.1.4 RColorBrewer_1.1-2
[69] Rcpp_1.0.7 plyr_1.8.6
[71] base64enc_0.1-3 progress_1.2.2
[73] zlibbioc_1.40.0 RCurl_1.98-1.5
[75] prettyunits_1.1.1 openssl_1.4.5
[77] rpart_4.1-15 SummarizedExperiment_1.24.0
[79] haven_2.4.3 cluster_2.1.2
[81] fs_1.5.0 furrr_0.2.3
[83] magrittr_2.0.1 data.table_1.14.2
[85] reprex_2.0.1 whisker_0.4
[87] ProtGenerics_1.26.0 matrixStats_0.61.0
[89] hms_1.1.1 mime_0.12
[91] evaluate_0.14 xtable_1.8-4
[93] XML_3.99-0.8 jpeg_0.1-9
[95] readxl_1.3.1 gridExtra_2.3
[97] compiler_4.1.2 biomaRt_2.50.0
[99] crayon_1.4.2 htmltools_0.5.2
[101] later_1.3.0 tzdb_0.2.0
[103] Formula_1.2-4 lubridate_1.8.0
[105] DBI_1.1.1 dbplyr_2.1.1
[107] MASS_7.3-54 rappdirs_0.3.3
[109] Matrix_1.4-0 car_3.0-12
[111] cli_3.1.0 parallel_4.1.2
[113] igraph_1.2.8 pkgconfig_2.0.3
[115] GenomicAlignments_1.30.0 foreign_0.8-81
[117] xml2_1.3.2 vipor_0.4.5
[119] bslib_0.3.1 XVector_0.34.0
[121] rvest_1.0.2 bezier_1.1.2
[123] VariantAnnotation_1.40.0 digest_0.6.28
[125] Biostrings_2.62.0 rmarkdown_2.11
[127] cellranger_1.1.0 htmlTable_2.3.0
[129] restfulr_0.0.13 curl_4.3.2
[131] shiny_1.7.1 Rsamtools_2.10.0
[133] rjson_0.2.20 lifecycle_1.0.1
[135] jsonlite_1.7.2 carData_3.0-4
[137] askpass_1.1 BSgenome_1.62.0
[139] fansi_0.5.0 pillar_1.6.4
[141] lattice_0.20-45 KEGGREST_1.34.0
[143] fastmap_1.1.0 httr_1.4.2
[145] survival_3.2-13 glue_1.5.1
[147] bamsignals_1.26.0 png_0.1-7
[149] bit_4.0.4 stringi_1.7.6
[151] sass_0.4.0 blob_1.2.2
[153] latticeExtra_0.6-29 memoise_2.0.0