Plot genomic tracks at select loci
18 September, 2025
# **MODIFY THIS CHUNK**
library(here)
kundaje_dir <- trimws(readr::read_lines(here("code/AK_PROJ_DIR.txt")))
doc_id <- "02"
out <- here("output/03-chrombpnet/03-syntax/", doc_id); dir.create(out, recursive = TRUE)
figout <- here("figures/03-chrombpnet/03-syntax", doc_id, "/"); dir.create(figout, recursive = TRUE)
chrombpnet_dir <- here("output/03-chrombpnet")1 Overview
In this document we visualize tracks for chromatin accessibility and
ChromBPNet products. Plotting of genomic tracks is done with BPCells together
with some custom helper functions located in
code/utils/track_helpers*.R.
2 Set up
library(dplyr)
library(tidyr)
library(ggplot2)
library(readr)
library(scales)
library(glue)
library(purrr)
library(stringr)
library(ggrepel)
library(ggseqlogo)
library(BPCells)
library(patchwork)
library(ggrastr)
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg38)
script_path <- here("code/utils/")
source(file.path(script_path, "plotting_config.R"))
source(file.path(script_path, "hdma_palettes.R"))
source(file.path(script_path, "sj_scRNAseq_helpers.R"))
source(file.path(script_path, "track_helpers_BL.R"))
source(file.path(script_path, "track_helpers_SJ.R"))
source(file.path(script_path, "chrombpnet_utils.R"))
ggplot2::theme_set(theme_BOR())3 Load data
Cluster metadata
cluster_meta <- read_csv(here("output/05-misc/03/TableS2_cluster_meta_qc.csv")) %>%
mutate(Cluster_chrombpnet = Cluster_ChromBPNet)## Rows: 203 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): Cluster, organ, organ_code, compartment, L1_annot, L2_annot, L3_an...
## dbl (8): cluster_id, dend_order, ncell, median_numi, median_ngene, median_n...
##
## 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.organ_map <- cluster_meta %>% distinct(organ, organ_code)
cmap_cluster <- cluster_meta %>% dplyr::select(Cluster_chrombpnet, organ_color) %>% tibble::deframe()Set params:
finemo_param <- "counts_v0.23_a0.8_all"4 Load BPCells object
global_bp_obj <- readRDS(here("output/05-misc/01/global_bp_obj.rds"))
head(global_bp_obj$cell_metadata)## cb Cluster Cluster_chrombpnet organ
## 1 T318_b16_Adr_PCW21#CL131_E05+G07+B04 AG_0 Adrenal_c0 Adrenal
## 2 T318_b16_Adr_PCW21#CL132_E06+C05+G06 AG_0 Adrenal_c0 Adrenal
## 3 T318_b16_Adr_PCW21#CL131_E06+H08+A07 AG_0 Adrenal_c0 Adrenal
## 4 T318_b16_Adr_PCW21#CL131_E02+D10+G10 AG_0 Adrenal_c0 Adrenal
## 5 T318_b16_Adr_PCW21#CL131_E05+H06+A03 AG_0 Adrenal_c0 Adrenal
## 6 T318_b16_Adr_PCW21#CL131_E11+D02+G02 AG_0 Adrenal_c0 Adrenal
## organ_code nFrags L1_annot L2_annot L3_annot
## 1 AG 11499 AG_0_adrenal cortex 1 Adrenal adrenal cortex adrenal cortex
## 2 AG 4449 AG_0_adrenal cortex 1 Adrenal adrenal cortex adrenal cortex
## 3 AG 13058 AG_0_adrenal cortex 1 Adrenal adrenal cortex adrenal cortex
## 4 AG 11339 AG_0_adrenal cortex 1 Adrenal adrenal cortex adrenal cortex
## 5 AG 6374 AG_0_adrenal cortex 1 Adrenal adrenal cortex adrenal cortex
## 6 AG 11742 AG_0_adrenal cortex 1 Adrenal adrenal cortex adrenal cortex
## cluster_id compartment archive_L1_clusterID archive_L1_clusterName
## 1 0 epi 0 AG_epi
## 2 0 epi 0 AG_epi
## 3 0 epi 0 AG_epi
## 4 0 epi 0 AG_epi
## 5 0 epi 0 AG_epi
## 6 0 epi 0 AG_epi
## archive_L2_clusterID archive_L2_clusterName archive_L3_clusterName
## 1 AG_epi1 adrenal cortex adrenal cortex 1
## 2 AG_epi1 adrenal cortex adrenal cortex 1
## 3 AG_epi1 adrenal cortex adrenal cortex 1
## 4 AG_epi1 adrenal cortex adrenal cortex 1
## 5 AG_epi1 adrenal cortex adrenal cortex 1
## 6 AG_epi1 adrenal cortex adrenal cortex 1
## archive_L3_clusterID
## 1 AG_adrenal cortex 1
## 2 AG_adrenal cortex 1
## 3 AG_adrenal cortex 1
## 4 AG_adrenal cortex 1
## 5 AG_adrenal cortex 1
## 6 AG_adrenal cortex 15 Model performance in heart
region <- "chr6:43168502-43176649" # "chr6:43169979-43183223"
region_zoom <- "chr6:43171139-43171286"
cluster <- "Heart_c0"Once we’ve fixed the regions, we can load tracks once and subset them to avoid loading over and over.
# predicted signal
bw_pred <- rtracklayer::import.bw(glue("{chrombpnet_dir}/01-models/predictions/bias_corrected/{cluster}_avg_chrombpnet_nobias.bw"))
# nuc occupancy signal
nuc_bw <- rtracklayer::import.bedGraph(glue("{chrombpnet_dir}/02-compendium/nucleoatac/{cluster}/{cluster}.occ.bedgraph.gz"))
# contribution scores
bw_contrib <- rtracklayer::import.bw(glue("{chrombpnet_dir}/01-models/contribs/bias_Heart_c0_thresh0.4/{cluster}/average_shaps.counts.bw"))
# hits
hits <- rtracklayer::import.bed(
glue("{chrombpnet_dir}/02-compendium/hits_unified_motifs/reconciled_per_celltype_peaks/{cluster}/{finemo_param}/{cluster}__hits_unified.{finemo_param}.reconciled.bed.gz"),
extraCols = c("pattern_class" = "character"))
bw_pred_filt <- bw_pred[bw_pred %over% str_to_gr(region)]
nuc_bw_filt <- nuc_bw[nuc_bw %over% str_to_gr(region)]
contrib_filt <- bw_contrib[bw_contrib %over% str_to_gr(region)]
hits_filt <- hits[hits %over% str_to_gr(region)]
save(bw_pred_filt, nuc_bw_filt, contrib_filt, hits_filt, file = glue("{out}/heart_bw_inputs.Rda"))load(glue("{out}/heart_bw_inputs.Rda"))
# get the color palette
cmap_clusters <- cluster_meta %>%
filter(Cluster_chrombpnet %in% cluster) %>%
dplyr::select(Cluster_chrombpnet, organ_color) %>% tibble::deframe()
# subset the global object
sub_meta <- global_bp_obj$cell_metadata %>%
dplyr::filter(Cluster_chrombpnet %in% cluster)
# coverage track
track_cov <- trackplot_coverage2(
region = region,
fragments = global_bp_obj$frags %>% select_cells(sub_meta$cb),
groups = sub_meta$Cluster_chrombpnet,
cell_read_counts = sub_meta$nFrags,
colors = c("Heart_c0" = "black"),
bins = 500) +
ylab(NULL)## [1] 1track_cov <- BPCells:::set_trackplot_label(track_cov, "Obs (RPKM)")
# gene annotation
track_genes <- trackplot_gene(global_bp_obj$transcripts, region) +
ggplot2::guides(color = "none")
# scale bar for the top
scale_plot <- trackplot_scalebar(region)
# plot the predicted accessibility
track_preds_corrected <- trackplot_bw(
bw = bw_pred_filt,
region = region,
facet_label = "Pred (bias-corrected)",
track_label = NULL,
color = c("Heart" = "#BE1E2D"),
tile_width = 25)## @ preparing data...## @ plotting in region with width 8148## @ binning data with tile width 25## @ plotting...# continuous nuc signal
track_nuc <- trackplot_bw(
bw = nuc_bw_filt,
region = region,
facet_label = "Nucleosome signal",
tile_width = 1,
track_label = NULL,
color = c("Heart" = "navy"))## @ preparing data...## @ plotting in region with width 8148## @ plotting without binning.## @ plotting...# zoom in on pred. accessibility
track_preds_corrected_zoom <- trackplot_bw(
bw = bw_pred_filt,
region = region_zoom,
track_label = NULL,
facet_label = "Pred (bias-corrected) [zoom]",
color = c("Heart" = "#BE1E2D"),
tile_width = 1,
plot_as = "bar")## @ preparing data...## @ plotting in region with width 148## @ plotting without binning.## @ plotting...# zoomed-in contribs
track_contribs <- trackplot_contribs(contrib_filt,
region = region_zoom,
track_label = NULL,
facet_label = "Contributions",
genome = BSgenome.Hsapiens.UCSC.hg38,
rel_height = 1) + xlab(NULL)## @ plotting basepair-level contribs for width 148## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.track_hits <- trackplot_genome_annotation(loci = hits_filt,
region = region_zoom,
color_by = "score",
show_strand = TRUE,
colors = c("white", "red"),
label_size = 3,
label_by = "name",
track_label = "Instances")
plot_list <- map(list(track_cov, track_preds_corrected, track_nuc, track_genes),
~ .x + highlight_region(region_zoom, alpha = 0.4, color = "yellow"))
plot_list2 <- list(track_preds_corrected_zoom, track_contribs, track_hits)
trackplot_combine(c(list(scale_plot), plot_list, plot_list2),
title = paste0(cluster, ": ", str_to_pretty(region))) &
ggplot2::theme(legend.direction = "vertical")
6 Example of reducing motifs in muscle
region <- "chr11:17713291-17722392" # "chr6:43169979-43183223"
region_zoom1 <- "chr11:17713844-17713983"
region_zoom2 <- "chr11:17721432-17721577"
cluster <- "Muscle_c0"# predicted signal
bw_pred <- rtracklayer::import.bw(glue("{chrombpnet_dir}/01-models/predictions/bias_corrected/{cluster}_avg_chrombpnet_nobias.bw"))
# nuc occupancy signal
nuc_bw <- rtracklayer::import.bedGraph(glue("{chrombpnet_dir}/02-compendium/nucleoatac/{cluster}/{cluster}.occ.bedgraph.gz"))
# contribution scores
bw_contrib <- rtracklayer::import.bw(glue("{chrombpnet_dir}/01-models/contribs/bias_Heart_c0_thresh0.4/{cluster}/average_shaps.counts.bw"))
# hits
hits <- rtracklayer::import.bed(
glue("{chrombpnet_dir}/02-compendium/hits_unified_motifs/reconciled_per_celltype_peaks/{cluster}/{finemo_param}/{cluster}__hits_unified.{finemo_param}.reconciled.bed.gz"),
extraCols = c("pattern_class" = "character"))
bw_pred_filt <- bw_pred[bw_pred %over% str_to_gr(region)]
nuc_bw_filt <- nuc_bw[nuc_bw %over% str_to_gr(region)]
contrib_filt <- bw_contrib[bw_contrib %over% str_to_gr(region)]
hits_filt <- hits[hits %over% str_to_gr(region)]
save(bw_pred_filt, nuc_bw_filt, contrib_filt, hits_filt, file = glue("{out}/muscle_bw_inputs.Rda"))load(glue("{out}/muscle_bw_inputs.Rda"))
# get the color palette
cmap_clusters <- cluster_meta %>%
filter(Cluster_chrombpnet %in% cluster) %>%
dplyr::select(Cluster_chrombpnet, organ_color) %>% tibble::deframe()
# subset the global object
sub_meta <- global_bp_obj$cell_metadata %>%
dplyr::filter(Cluster_chrombpnet %in% cluster)
# coverage track
track_cov <- trackplot_coverage2(
region = region,
fragments = global_bp_obj$frags %>% select_cells(sub_meta$cb),
groups = sub_meta$Cluster_chrombpnet,
cell_read_counts = sub_meta$nFrags,
colors = c("Muscle_c0" = cmap_organ[["Muscle"]]),
bins = 500) +
ylab(NULL)## [1] 1track_cov <- BPCells:::set_trackplot_label(track_cov, "Obs (RPKM)")
# gene annotation
track_genes <- trackplot_gene(global_bp_obj$transcripts, region) +
ggplot2::guides(color = "none")
# scale bar for the top
scale_plot <- trackplot_scalebar(region)
track_preds_corrected <- trackplot_bw(
bw = bw_pred_filt,
region = region,
facet_label = "Pred (bias-corrected)",
track_label = NULL,
color = c("Muscle" = cmap_organ[["Muscle"]]),
tile_width = 25)## @ preparing data...## @ plotting in region with width 9102## @ binning data with tile width 25## @ plotting...track_nuc <- trackplot_bw(
bw = nuc_bw_filt,
region = region,
facet_label = "Nucleosome signal",
tile_width = 1,
track_label = NULL,
color = c("Muscle" = "navy"))## @ preparing data...## @ plotting in region with width 9102## @ plotting without binning.## @ plotting...track_preds_corrected_zoom1 <- trackplot_bw(
bw = bw_pred_filt,
region = region_zoom1,
track_label = NULL,
facet_label = "Pred (bias-corrected) [zoom]",
color = c("Muscle" = cmap_organ[["Muscle"]]),
tile_width = 1,
plot_as = "bar")## @ preparing data...## @ plotting in region with width 140## @ plotting without binning.## @ plotting...# zoomed-in contribs
track_contribs1 <- trackplot_contribs(contrib_filt,
region = region_zoom1,
track_label = NULL,
facet_label = "Contributions",
genome = BSgenome.Hsapiens.UCSC.hg38,
rel_height = 1) + xlab(NULL)## @ plotting basepair-level contribs for width 140## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.track_hits1 <- trackplot_genome_annotation(loci = hits_filt,
region = region_zoom1,
color_by = "pattern_class",
show_strand = TRUE,
colors = c("darkred", "darkgreen"),
label_size = 3,
label_by = "name",
track_label = "Instances")
# track_preds_corrected_zoom2 <- trackplot_bw(
# bw = bw_pred,
# region = region_zoom2,
# track_label = NULL,
# facet_label = "Pred (bias-corrected) [zoom]",
# color = c("Muscle" = cmap_organ[["Muscle"]]),
# tile_width = 1,
# plot_as = "bar")
#
# # zoomed-in contribs
# track_contribs2 <- trackplot_contribs(bw_contrib,
# region = region_zoom2,
# track_label = NULL,
# facet_label = "Contributions",
# genome = BSgenome.Hsapiens.UCSC.hg38,
# rel_height = 1) + xlab(NULL)
#
# track_hits2 <- trackplot_genome_annotation(loci = hits,
# region = region_zoom2,
# color_by = "pattern_class",
# show_strand = TRUE,
# colors = c("darkred", "darkgreen"),
# label_size = 3,
# label_by = "name",
# track_label = "Instances")
plot_list <- map(list(track_cov, track_preds_corrected, track_nuc, track_genes),
~ .x + highlight_region(region_zoom1, alpha = 0.4, color = "yellow"))
plot_list2 <- list(track_preds_corrected_zoom1, track_contribs1, track_hits1)
# plot_list3 <- list(track_preds_corrected_zoom2, track_contribs2, track_hits2)
trackplot_combine(c(list(scale_plot), plot_list, plot_list2),
title = paste0(cluster, ": ", str_to_pretty(region))) &
ggplot2::theme(legend.direction = "vertical")
7 Consistency of syntax in endothelial cells
Here we want to load the tracks for several endothelial clusters.
region <- "chr5:150128841-150131272"
region_gr <- str_to_gr(region)
region_zoom <- "chr5:150129885-150130189"
endo_clusters <- c("Muscle_c5", "Spleen_c2", "Thymus_c7", "Brain_c13",
"Liver_c10", "Stomach_c10")sub_meta <- global_bp_obj$cell_metadata %>%
dplyr::filter(Cluster_chrombpnet %in% endo_clusters)
# coverage track
track_cov <- trackplot_coverage2(
region = region,
fragments = global_bp_obj$frags %>% select_cells(sub_meta$cb),
groups = sub_meta$Cluster_chrombpnet,
cell_read_counts = sub_meta$nFrags,
colors = cmap_cluster[endo_clusters],
bins = 500) +
ylab(NULL) +
highlight_region(region_zoom, color = "yellow")## [1] 1tracks_pred_un <- list()
tracks_pred <- list()
tracks_contrib <- list()
# loop over clusters and plot the predicted accessibility and contribution scores
for (i in endo_clusters) {
organ <- str_split(i, pattern = "\\_")[[1]][1]
message(i)
# uncorrected signal
bw_pred_un <- rtracklayer::import.bw(glue("{chrombpnet_dir}/01-models/predictions/uncorrected/{i}_avg_chrombpnet_uncorrected.bw"))
bw_unc_filt <- bw_pred_un[bw_pred_un %over% region_gr]
rm(bw_pred_un)
# predicted signal
bw_pred <- rtracklayer::import.bw(glue("{chrombpnet_dir}/01-models/predictions/bias_corrected/{i}_avg_chrombpnet_nobias.bw"))
# subset the bigwig
bw_filt <- bw_pred[bw_pred %over% region_gr]
rm(bw_pred)
# contrib scores
bw_contrib <- rtracklayer::import.bw(glue("{chrombpnet_dir}/01-models/contribs/bias_Heart_c0_thresh0.4/{i}/average_shaps.counts.bw"))
bw_contrib_filt <- bw_contrib[bw_contrib %over% region_gr]
rm(bw_contrib)
tracks_pred_un[[i]] <- bw_unc_filt
tracks_pred[[i]] <- bw_filt
tracks_contrib[[i]] <- bw_contrib_filt
}
save(tracks_pred_un, tracks_pred, tracks_contrib, file = glue("{out}/tracks_endo.Rda"))Plot tracks:
load(glue("{out}/tracks_endo.Rda"))
# plot all the predictions in the wide region
track_preds_broad <- imap(tracks_pred, ~ trackplot_bw(
bw = .x,
region = region,
facet_label = .y,
track_label = "Pred (bias-corrected)",
color = cmap_cluster[.y],
tile_width = 5) + highlight_region(region_zoom, alpha = 0.4, color = "yellow"))## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...# plot all the predictions in the zoomed region
track_preds_zoom <- imap(tracks_pred, ~ trackplot_bw(
bw = .x,
region = region_zoom,
facet_label = .y,
track_label = "Pred (bias-corrected)",
color = cmap_cluster[.y],
tile_width = 1))## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...# plot all the uncorrected predictions in the wide region
track_preds_un_broad <- imap(tracks_pred_un, ~ trackplot_bw(
bw = .x,
region = region,
facet_label = .y,
track_label = "Pred (uncorrected)",
color = cmap_cluster[.y],
tile_width = 5) + highlight_region(region_zoom, alpha = 0.4, color = "yellow"))## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...## @ preparing data...## @ plotting in region with width 2432## @ binning data with tile width 5## @ plotting...# plot all the predictions in the zoomed region
track_preds_un_zoom <- imap(tracks_pred_un, ~ trackplot_bw(
bw = .x,
region = region_zoom,
facet_label = .y,
track_label = "Pred (uncorrected)",
color = cmap_cluster[.y],
tile_width = 1))## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...## @ preparing data...## @ plotting in region with width 305## @ plotting without binning.## @ plotting...# plot all the contributions in the zoomed region
# this version prints the ymin-ymax scale
track_contribs <- imap(tracks_contrib, ~ trackplot_contribs_BL(
.x,
region = region_zoom,
facet_label = .y,
track_label = "Contributions",
genome = BSgenome.Hsapiens.UCSC.hg38,
# ymax = 0.25, ymin = -0.039,
rel_height = 1) + xlab(NULL))## @ plotting basepair-level contribs for width 305## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## @ plotting basepair-level contribs for width 305
##
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## @ plotting basepair-level contribs for width 305
##
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## @ plotting basepair-level contribs for width 305
##
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## @ plotting basepair-level contribs for width 305
##
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## @ plotting basepair-level contribs for width 305
##
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.# gene annotation
track_genes <- trackplot_gene(global_bp_obj$transcripts, region) +
ggplot2::guides(color = "none") +
highlight_region(region_zoom, alpha = 0.4, color = "yellow")
# hits
hits <- rtracklayer::import.bed(
glue("{chrombpnet_dir}/02-compendium/hits_unified_motifs/reconciled_per_celltype_peaks/Muscle_c5/{finemo_param}/Muscle_c5__hits_unified.{finemo_param}.reconciled.bed.gz"),
extraCols = c("pattern_class" = "character"))
track_hits <- trackplot_genome_annotation(loci = hits,
region = region_zoom,
color_by = "score",
show_strand = TRUE,
colors = c("white", "red"),
label_size = 3,
label_by = "name",
track_label = "Instances")
# scale bar for the top
scale_plot <- trackplot_scalebar(region)trackplot_combine2(c(list(scale_plot), list(track_cov),
track_preds_un_broad,
track_preds_broad,
list(track_genes),
track_preds_zoom,
track_contribs,
list(track_hits)),
title = str_to_pretty(region)) &
ggplot2::theme(legend.direction = "vertical")
8 Session info
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /share/software/user/open/openblas/0.3.10/lib/libopenblas_haswellp-r0.3.10.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggthemes_4.2.4 RColorBrewer_1.1-3
## [3] magrittr_2.0.3 GenomicFeatures_1.46.5
## [5] AnnotationDbi_1.56.2 Biobase_2.54.0
## [7] BSgenome.Hsapiens.UCSC.hg38_1.4.4 BSgenome_1.62.0
## [9] rtracklayer_1.54.0 Biostrings_2.62.0
## [11] XVector_0.34.0 GenomicRanges_1.46.1
## [13] GenomeInfoDb_1.30.1 IRanges_2.28.0
## [15] S4Vectors_0.32.4 BiocGenerics_0.40.0
## [17] ggrastr_1.0.1 patchwork_1.2.0
## [19] BPCells_0.2.0 ggseqlogo_0.1
## [21] ggrepel_0.9.3 stringr_1.5.0
## [23] purrr_1.0.2 glue_1.8.0
## [25] scales_1.3.0 readr_2.1.4
## [27] ggplot2_3.5.1 tidyr_1.3.0
## [29] dplyr_1.1.3 here_1.0.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 matrixStats_1.0.0
## [3] bit64_4.0.5 filelock_1.0.2
## [5] progress_1.2.2 httr_1.4.7
## [7] rprojroot_2.0.3 tools_4.1.2
## [9] bslib_0.5.1 utf8_1.2.3
## [11] R6_2.5.1 vipor_0.4.5
## [13] DBI_1.1.3 colorspace_2.1-0
## [15] withr_2.5.0 prettyunits_1.1.1
## [17] tidyselect_1.2.0 curl_5.0.2
## [19] bit_4.0.5 compiler_4.1.2
## [21] cli_3.6.1 xml2_1.3.5
## [23] DelayedArray_0.20.0 labeling_0.4.3
## [25] sass_0.4.7 rappdirs_0.3.3
## [27] digest_0.6.33 Rsamtools_2.10.0
## [29] rmarkdown_2.24 pkgconfig_2.0.3
## [31] htmltools_0.5.6 MatrixGenerics_1.6.0
## [33] highr_0.10 dbplyr_2.3.3
## [35] fastmap_1.1.1 rlang_1.1.1
## [37] rstudioapi_0.15.0 RSQLite_2.3.1
## [39] farver_2.1.1 jquerylib_0.1.4
## [41] BiocIO_1.4.0 generics_0.1.3
## [43] jsonlite_1.8.7 BiocParallel_1.28.3
## [45] vroom_1.6.3 RCurl_1.98-1.12
## [47] GenomeInfoDbData_1.2.7 Matrix_1.6-3
## [49] Rcpp_1.0.11 ggbeeswarm_0.7.2
## [51] munsell_0.5.0 fansi_1.0.4
## [53] lifecycle_1.0.3 stringi_1.7.12
## [55] yaml_2.3.7 SummarizedExperiment_1.24.0
## [57] zlibbioc_1.40.0 BiocFileCache_2.2.1
## [59] blob_1.2.4 parallel_4.1.2
## [61] crayon_1.5.2 lattice_0.20-45
## [63] hms_1.1.3 KEGGREST_1.34.0
## [65] knitr_1.43 pillar_1.9.0
## [67] rjson_0.2.21 biomaRt_2.50.3
## [69] XML_3.99-0.14 evaluate_0.21
## [71] vctrs_0.6.3 png_0.1-8
## [73] tzdb_0.4.0 gtable_0.3.4
## [75] cachem_1.0.8 xfun_0.40
## [77] restfulr_0.0.15 tibble_3.2.1
## [79] GenomicAlignments_1.30.0 beeswarm_0.4.0
## [81] memoise_2.0.1