Cross-species comparisons for genes with peaks
lecook
2022-02-23
Last updated: 2022-04-11
Checks: 7 0
Knit directory: chipseq-cross-species/
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Unstaged changes:
Modified: analysis/dunnart_peak_characterisation.Rmd
Modified: analysis/tcseq_expression_analysis.Rmd
Modified: code/basic_wrapper.slurm
Modified: output/qc/A-1_input.PPq30.flagstat.qc
Modified: output/qc/A-1_input.dedup.flagstat.qc
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Modified: output/qc/A-3_H3K27ac.unfiltered.flagstat.qc
Modified: output/qc/B-1_input.PPq30.flagstat.qc
Modified: output/qc/B-1_input.dedup.flagstat.qc
Modified: output/qc/B-1_input.dupmark.flagstat.qc
Modified: output/qc/B-1_input.unfiltered.flagstat.qc
Modified: output/qc/H3K4me3_overlap_default.frip
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/gene_level_comparisons.Rmd) and HTML (docs/gene_level_comparisons.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 1b05cda | lecook | 2022-04-11 | wflow_publish(“analysis/gene_level_comparisons.Rmd”) |
| Rmd | a97d3c5 | lecook | 2022-03-01 | first commit |
Set-up
# Load in libraries
library(data.table)
library(tidyverse)
library(ggridges)
library(ggpubr)
library(reshape2)
library(RColorBrewer)
library(ggplot2)
library(VennDiagram)
library(viridis)
library(hrbrthemes)
library(gghalves)
library(dplyr)
library(UpSetR)
library(GOSemSim)
library(circlize)
library(simplifyEnrichment)
library(clusterProfiler)
library(enrichplot)
library(org.Mm.eg.db)
library(tools)
library(ComplexHeatmap)
library('BiocParallel')
library(stringi)
library(gridtext)
library(tools)
library(devtools)
library(clusterProfiler)
library(ChIPseeker)
plot_dir <- "output/plots/"
fullPeak_dir <- "output/peaks/"
annot_dir <- "output/annotations/"
filterPeaks_dir <- "output/filtered_peaks/"
## Set the fonts up so that each plot is the saved the same way.
font <- theme(axis.text.x = element_text(size = 25),
axis.text.y = element_text(size = 25),
axis.title.x = element_text(size = 25),
axis.title.y = element_text(size = 25),
legend.title = element_text(size = 25), legend.text = element_text(size = 25))Compare genes with peaks between species
Gene intersections
files =list.files(annot_dir, pattern= "dunnart_enhancer_annotationConvertedIDs.txt|cluster1_promoter_annotationConvertedIDs.txt|*_cluster1_annotation.txt|*.5_enhancer_annotation.txt", full.names=T)
files = as.list(files)
data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA")))
df1 = Map(mutate, data[c(1,3,5,7,9,11,13)], cre = "promoter")
df2 = Map(mutate, data[c(2,4,6,8,10,12,14)], cre = "enhancer")
data = append(df1, df2)
df1 = Map(mutate, data[c(1,8)], group = "dunnart")
df2 = Map(mutate, data[c(2,9)], group = "E10.5")
df3 = Map(mutate, data[c(3,10)], group = "E11.5")
df4 = Map(mutate, data[c(4,11)], group = "E12.5")
df5 = Map(mutate, data[c(5,12)], group = "E13.5")
df6 = Map(mutate, data[c(6,13)], group = "E14.5")
df7 = Map(mutate, data[c(7,14)], group = "E15.5")
data <- append(df1, df2)
data <- append(data, df3)
data = append(data, df4)
data = append(data, df5)
data = append(data, df6)
data = append(data, df7)
colnames(data[[1]])[19] <- "geneName"
colnames(data[[2]])[19] <- "geneName"
data = lapply(data, function(x) x=setnames(x, old="geneId", new="mouseensembl", skip_absent=TRUE) %>% as.data.table())
subset = rbindlist(
lapply(
data,
function(x)
x %>% dplyr::select(mouseensembl,
cre,
group)
%>% as.data.table()
%>% unique()
),
)
promoters <- split(
dplyr::select(
filter(subset
,cre == "promoter")
,mouseensembl
,group)
,by = "group"
)
enhancers <- split(
dplyr::select(
filter(subset
,cre == "enhancer")
,mouseensembl
,group)
,by = "group"
)
merged_promoters <- promoters %>%
purrr::reduce(full_join
,by = "mouseensembl"
)
merged_enhancers <- enhancers %>%
purrr::reduce(full_join
,by = "mouseensembl"
)
merged_promoters[is.na(merged_promoters)] <- 0
merged_enhancers[is.na(merged_enhancers)] <- 0
merged_promoters = as.data.frame(
merged_promoters)
merged_enhancers = as.data.frame(
merged_enhancers)
colnames(merged_promoters) = c("geneId",
"dunnart",
"E10.5",
"E11.5",
"E12.5",
"E13.5",
"E14.5",
"E15.5")
colnames(merged_enhancers) = c("geneId",
"dunnart",
"E10.5",
"E11.5",
"E12.5",
"E13.5",
"E14.5",
"E15.5")
x = merged_promoters
create_upset_df <- function(x, y){
geneId = x$geneId
upset.df = data.frame(
lapply(
x[,2:8],
function(x)
as.numeric(x!="0")
)
)
rownames(upset.df) = geneId
write.table(upset.df,
paste0(annot_dir,
y,
sep=''),
sep="\t", quote=F,
row.names=T,
col.names = T)
return(upset.df)
}
promoter_upset = create_upset_df(
x = merged_promoters,
y = "mouse_dunnart_promoters_upsetData.txt"
)
enhancer_upset = create_upset_df(
x = merged_enhancers,
y = "mouse_dunnart_enhancers_upsetData.txt"
)
plot_upset <- function(x){
upset(x,
set_size.angles = 45,
order.by="freq",
text.scale = 2,
sets = c("dunnart",
"E10.5","E11.5",
"E12.5","E13.5",
"E14.5", "E15.5"),
nsets=7,
keep.order = T,
mainbar.y.label = "number of genes")
}
p <- plot_upset(promoter_upset)
pdf(paste0(plot_dir,"mouse_dunnart_promoters_upsetPlot.pdf", sep=''))
print(p)
dev.off()svg
2 p <- plot_upset(enhancer_upset)
pdf(paste0(plot_dir,"mouse_dunnart_enhancers_upsetPlot.pdf", sep=''))
print(p)
dev.off()svg
2 GO semantic similarity between stages and species
mmGO = godata('org.Mm.eg.db', ont="BP")
backg = fread("output/annotations/mart_export.txt", header = FALSE)
backg = unlist(backg$V1)
files =list.files(annot_dir, pattern= "dunnart_promoter_cluster1_annotationConvertedIDs.txt|*_cluster1_annotation.txt|*.5_enhancer_annotation.txt",
full.names=T)
files <- as.list(files)
data = lapply(files, function(x) fread(x,
header=TRUE, sep="\t",
quote = "", na.strings=c("",
"NA")))
colnames(data[[1]])[19] <- "gene"
colnames(data[[1]])[26] <- "geneId"
go <- lapply(data,
function(x)
enrichGO(gene = unlist(x$geneId),
keyType = "ENSEMBL",
OrgDb = org.Mm.eg.db,
ont = "BP",
universe = backg,
pAdjustMethod = "fdr",
readable = TRUE))
names(go) <- c("dunnart_cluster1",
"E10.5_cluster1", "E10.5_enhancer",
"E11.5_cluster1", "E11.5_enhancer",
"E12.5_cluster1", "E12.5_enhancer",
"E13.5_cluster1", "E13.5_enhancer",
"E14.5_cluster1", "E14.5_enhancer",
"E15.5_cluster1", "E15.5_enhancer")
lapply(names(go), function(x) write.table(go[[x]], file=paste0(annot_dir,x,"_mm10GOenrich"), sep="\t", quote=FALSE, col.names=TRUE, row.names=FALSE))[[1]]
NULL
[[2]]
NULL
[[3]]
NULL
[[4]]
NULL
[[5]]
NULL
[[6]]
NULL
[[7]]
NULL
[[8]]
NULL
[[9]]
NULL
[[10]]
NULL
[[11]]
NULL
[[12]]
NULL
[[13]]
NULLsource("code/go_semantic_similarity.R")
#dunnart versus all mouse stages
dunnartvsmouse <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "dunnart_cluster1_mm10GOenrich"),
file_pattern = "*.5_cluster1_mm10GOenrich")
e10vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E10.5_cluster1_mm10GOenrich"),
file_pattern = "*.5_cluster1_mm10GOenrich")
e11vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E11.5_cluster1_mm10GOenrich"),
file_pattern = "*.5_cluster1_mm10GOenrich")
e12vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E12.5_cluster1_mm10GOenrich"),
file_pattern = "*.5_cluster1_mm10GOenrich")
e13vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E13.5_cluster1_mm10GOenrich"),
file_pattern = "*.5_cluster1_mm10GOenrich")
e14vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E14.5_cluster1_mm10GOenrich"),
file_pattern = "*.5_cluster1_mm10GOenrich")
vector_of_scores <- c(
dunnartvsmouse$'10', dunnartvsmouse$'11', dunnartvsmouse$'12', dunnartvsmouse$'13', dunnartvsmouse$'14',dunnartvsmouse$'15',
e10vs$'11',e10vs$'12',e10vs$'13',e10vs$'14',e10vs$'15',
e11vs$'12',e11vs$'13',e11vs$'14',e11vs$'15',
e12vs$'13',e12vs$'14',e12vs$'15',
e13vs$'14',e13vs$'15',
e14vs$'15'
)
my_matrix <- matrix(0,7,7) ## creates a n x n square 0 matrix
rownames(my_matrix) = c('dunnart','E10','E11','E12','E13','E14','E15')
colnames(my_matrix) = c('dunnart','E10','E11','E12','E13','E14','E15')
my_matrix[ col(my_matrix) < row(my_matrix) ] <- vector_of_scores
my_matrix <- my_matrix + t(my_matrix)
diag(my_matrix) <- 1
my_matrix dunnart E10 E11 E12 E13 E14 E15
dunnart 1.000 0.399 0.203 0.482 0.449 0.416 0.449
E10 0.399 1.000 0.254 0.459 0.410 0.388 0.479
E11 0.203 0.254 1.000 0.348 0.321 0.366 0.367
E12 0.482 0.459 0.348 1.000 0.896 0.832 0.891
E13 0.449 0.410 0.321 0.896 1.000 0.839 0.895
E14 0.416 0.388 0.366 0.832 0.839 1.000 0.782
E15 0.449 0.479 0.367 0.891 0.895 0.782 1.000p <- Heatmap(my_matrix, name = "score", col=c('#a8ddb5','#7bccc4','#4eb3d3','#2b8cbe','#08589e'), heatmap_legend_param = list(
title = "GO similarity score", at = c(0,0.2,0.4,0.6,0.8,1)))
p
pdf(paste(plot_dir,'promoter_GO_semantic_scores.pdf',sep=''),width=10,height = 7)
print(p)
dev.off()svg
2 dunnartvsmouse <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir,
"dunnart_enhancer_mm10GOenrich"),
file_pattern = "*.5_enhancer_mm10GOenrich")
e10vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E10.5_enhancer_mm10GOenrich"),
file_pattern = "*.5_enhancer_mm10GOenrich")
e11vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E11.5_enhancer_mm10GOenrich"),
file_pattern = "*.5_enhancer_mm10GOenrich")
e12vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E12.5_enhancer_mm10GOenrich"),
file_pattern = "*.5_enhancer_mm10GOenrich")
e13vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E13.5_enhancer_mm10GOenrich"),
file_pattern = "*.5_enhancer_mm10GOenrich")
e14vs <- go_semantic_similarity(fileList = annot_dir,
file_to_compare_to = paste0(annot_dir, "E14.5_enhancer_mm10GOenrich"),
file_pattern = "*.5_enhancer_mm10GOenrich")
vector_of_scores <- c(
dunnartvsmouse$'10', dunnartvsmouse$'11', dunnartvsmouse$'12', dunnartvsmouse$'13', dunnartvsmouse$'14',dunnartvsmouse$'15',
e10vs$'11',e10vs$'12',e10vs$'13',e10vs$'14',e10vs$'15',
e11vs$'12',e11vs$'13',e11vs$'14',e11vs$'15',
e12vs$'13',e12vs$'14',e12vs$'15',
e13vs$'14',e13vs$'15',
e14vs$'15'
)
my_matrix <- matrix(0,7,7) ## creates a n x n square 0 matrix
rownames(my_matrix) = c('dunnart','E10','E11','E12','E13','E14','E15')
colnames(my_matrix) = c('dunnart','E10','E11','E12','E13','E14','E15')
my_matrix[ col(my_matrix) < row(my_matrix) ] <- vector_of_scores
my_matrix <- my_matrix + t(my_matrix)
diag(my_matrix) <- 1
my_matrix dunnart E10 E11 E12 E13 E14 E15
dunnart 1.000 0.740 0.652 0.789 0.771 0.784 0.797
E10 0.740 1.000 0.833 0.899 0.902 0.906 0.897
E11 0.652 0.833 1.000 0.814 0.811 0.805 0.792
E12 0.789 0.899 0.814 1.000 0.920 0.930 0.925
E13 0.771 0.902 0.811 0.920 1.000 0.933 0.926
E14 0.784 0.906 0.805 0.930 0.933 1.000 0.943
E15 0.797 0.897 0.792 0.925 0.926 0.943 1.000p <- Heatmap(my_matrix, name = "score", col=c('#a8ddb5','#7bccc4','#4eb3d3','#2b8cbe','#08589e'), heatmap_legend_param = list(
title = "GO similarity score", at = c(0,0.2,0.4,0.6,0.8,1)))
p
pdf(paste(plot_dir,'enhancer_GO_semantic_scores.pdf',sep=''),width=10,height = 7)
print(p)
dev.off()svg
2 GO cluster comparisons
files =list.files(annot_dir, pattern= "dunnart_enhancer_annotationConvertedIDs.txt|dunnart_promoter_cluster1_annotationConvertedIDs.txt|*_cluster1_annotation.txt|*.5_enhancer_annotation.txt",
full.names=T)
files <- as.list(files)
data = lapply(files, function(x) fread(x,
header=TRUE, sep="\t",
quote = "", na.strings=c("",
"NA")))
colnames(data[[1]])[19] <- "geneID"
colnames(data[[2]])[19] <- "geneID"
data = lapply(data, function(x) x=setnames(x, old="geneId", new="mouseensembl", skip_absent=TRUE) %>% as.data.table())
promoter = list(data[[2]]$mouseensembl, data[[3]]$mouseensembl,
data[[5]]$mouseensembl,
data[[7]]$mouseensembl,
data[[9]]$mouseensembl,
data[[11]]$mouseensembl,
data[[13]]$mouseensembl)
enhancer = list(data[[1]]$mouseensembl, data[[4]]$mouseensembl,
data[[6]]$mouseensembl,
data[[8]]$mouseensembl,
data[[10]]$mouseensembl,
data[[12]]$mouseensembl,
data[[14]]$mouseensembl)
names(enhancer) = c("dunnart","E10.5","E11.5","E12.5", "E13.5","E14.5", "E15.5")
names(promoter) = c("dunnart","E10.5","E11.5","E12.5", "E13.5","E14.5", "E15.5")
compare_go_cluster <- function(x, dotplot){
df <- lapply(x, function(i) unique(i))
go_cluster = simplify(setReadable(
compareCluster(
geneCluster = df,
fun = enrichGO,
ont="BP",
universe = backg,
keyType="ENSEMBL",
pvalueCutoff = 0.001,
OrgDb = org.Mm.eg.db),
OrgDb = org.Mm.eg.db,
keyType="ENSEMBL"))
ck = pairwise_termsim(go_cluster, method = "Wang", semData=mmGO)
p <- dotplot(ck, showCategory = 5) +
scale_color_viridis() +
theme(axis.text.x = element_text(angle = 45, hjust=1))
## Dotplot
pdf(paste0(plot_dir, dotplot, sep=''), width = 9, height = 9)
print(p)
dev.off()
return(list(p,go_cluster))
}
enhancer_plot = compare_go_cluster(enhancer, "enhancer_simplifyGO_heatmap.pdf")
promoter_plot = compare_go_cluster(promoter, "promoter_simplifyGO_heatmap.pdf")
enhancer-associated peaks
enhancer_plot[[1]]
promoter-associated peaks
promoter_plot[[1]]
files =list.files(annot_dir, pattern= "dunnart_enhancer_mm10GOenrich|*.5_enhancer_mm10GOenrich",
full.names=T)
files <- as.list(files)
enhancers = lapply(files, function(x) fread(x,
header=TRUE, sep="\t",
quote = "", na.strings=c("",
"NA")))
files =list.files(annot_dir, pattern= "dunnart_cluster1_mm10GOenrich|*5_cluster1_mm10GOenrich",
full.names=T)
files <- as.list(files)
promoters = lapply(files, function(x) fread(x,
header=TRUE, sep="\t",
quote = "", na.strings=c("",
"NA")))Enhancer-associated peaks
go_data_filtered = lapply(enhancers, function(x) x$ID[x$p.adjust < 0.001])
names(go_data_filtered) = c( "dunnart","E10.5", "E11.5", "E12.5", "E13.5", "E14.5", "E15.5")
simplifyGOFromMultipleLists(go_data_filtered, db = org.Mm.eg.db, measure = "Wang", method = "binary_cut")476/476 GO IDs left for clustering.
Cluster 476 terms by 'binary_cut'... 18 clusters, used 0.1873841 secs.
Promoter-associated peaks
go_data_filtered = lapply(promoters, function(x) x$ID[x$p.adjust < 0.001])
names(go_data_filtered) = c( "dunnart","E10.5", "E11.5", "E12.5", "E13.5", "E14.5", "E15.5")
simplifyGOFromMultipleLists(go_data_filtered, db = org.Mm.eg.db, measure = "Wang", method = "binary_cut")324/324 GO IDs left for clustering.
Cluster 324 terms by 'binary_cut'... 5 clusters, used 0.07666135 secs.
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux
Matrix products: default
BLAS/LAPACK: /usr/local/easybuild-2019/easybuild/software/compiler/gcc/10.2.0/openblas/0.3.12/lib/libopenblas_haswellp-r0.3.12.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] tools stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] org.Hs.eg.db_3.14.0 ChIPseeker_1.30.3 devtools_2.4.1
[4] usethis_2.0.1 gridtext_0.1.4 stringi_1.6.2
[7] BiocParallel_1.28.3 ComplexHeatmap_2.10.0 org.Mm.eg.db_3.14.0
[10] AnnotationDbi_1.56.2 IRanges_2.28.0 S4Vectors_0.32.4
[13] Biobase_2.54.0 enrichplot_1.14.2 clusterProfiler_4.2.2
[16] simplifyEnrichment_1.4.0 BiocGenerics_0.40.0 circlize_0.4.12
[19] GOSemSim_2.20.0 UpSetR_1.4.0 gghalves_0.1.1
[22] hrbrthemes_0.8.0 viridis_0.6.2 viridisLite_0.4.0
[25] VennDiagram_1.7.1 futile.logger_1.4.3 RColorBrewer_1.1-2
[28] reshape2_1.4.4 ggpubr_0.4.0 ggridges_0.5.3
[31] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.8
[34] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[37] tibble_3.1.2 ggplot2_3.3.3 tidyverse_1.3.1
[40] data.table_1.14.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3
[2] rtracklayer_1.54.0
[3] bit64_4.0.5
[4] knitr_1.33
[5] DelayedArray_0.20.0
[6] KEGGREST_1.34.0
[7] RCurl_1.98-1.3
[8] doParallel_1.0.16
[9] generics_0.1.0
[10] GenomicFeatures_1.46.5
[11] callr_3.7.0
[12] lambda.r_1.2.4
[13] RSQLite_2.2.7
[14] shadowtext_0.1.1
[15] bit_4.0.4
[16] xml2_1.3.2
[17] lubridate_1.7.10
[18] httpuv_1.6.1
[19] SummarizedExperiment_1.24.0
[20] assertthat_0.2.1
[21] xfun_0.23
[22] hms_1.1.0
[23] jquerylib_0.1.4
[24] evaluate_0.14
[25] promises_1.2.0.1
[26] restfulr_0.0.13
[27] progress_1.2.2
[28] fansi_0.5.0
[29] caTools_1.18.2
[30] dbplyr_2.1.1
[31] readxl_1.3.1
[32] igraph_1.2.6
[33] DBI_1.1.1
[34] ellipsis_0.3.2
[35] backports_1.2.1
[36] MatrixGenerics_1.6.0
[37] biomaRt_2.50.3
[38] RcppParallel_5.1.4
[39] vctrs_0.3.8
[40] remotes_2.4.0
[41] abind_1.4-5
[42] cachem_1.0.5
[43] withr_2.4.2
[44] ggforce_0.3.3
[45] GenomicAlignments_1.30.0
[46] treeio_1.18.1
[47] prettyunits_1.1.1
[48] cluster_2.1.2
[49] DOSE_3.20.1
[50] ape_5.5
[51] lazyeval_0.2.2
[52] crayon_1.4.1
[53] labeling_0.4.2
[54] pkgconfig_2.0.3
[55] slam_0.1-48
[56] tweenr_1.0.2
[57] GenomeInfoDb_1.30.1
[58] nlme_3.1-152
[59] pkgload_1.2.1
[60] rlang_1.0.2
[61] lifecycle_1.0.1
[62] downloader_0.4
[63] filelock_1.0.2
[64] extrafontdb_1.0
[65] BiocFileCache_2.2.1
[66] modelr_0.1.8
[67] cellranger_1.1.0
[68] rprojroot_2.0.2
[69] polyclip_1.10-0
[70] matrixStats_0.61.0
[71] Matrix_1.3-4
[72] aplot_0.1.3
[73] carData_3.0-4
[74] boot_1.3-28
[75] reprex_2.0.0
[76] whisker_0.4
[77] GlobalOptions_0.1.2
[78] processx_3.5.2
[79] png_0.1-7
[80] rjson_0.2.20
[81] bitops_1.0-7
[82] getPass_0.2-2
[83] KernSmooth_2.23-20
[84] Biostrings_2.62.0
[85] blob_1.2.1
[86] shape_1.4.6
[87] qvalue_2.26.0
[88] rstatix_0.7.0
[89] gridGraphics_0.5-1
[90] ggsignif_0.6.1
[91] scales_1.1.1
[92] memoise_2.0.0
[93] magrittr_2.0.1
[94] plyr_1.8.6
[95] gplots_3.1.1
[96] zlibbioc_1.40.0
[97] compiler_4.1.0
[98] scatterpie_0.1.7
[99] BiocIO_1.4.0
[100] plotrix_3.8-1
[101] clue_0.3-59
[102] Rsamtools_2.10.0
[103] cli_2.5.0
[104] XVector_0.34.0
[105] patchwork_1.1.1
[106] ps_1.6.0
[107] formatR_1.11
[108] MASS_7.3-54
[109] tidyselect_1.1.1
[110] highr_0.9
[111] yaml_2.2.1
[112] ggrepel_0.9.1
[113] sass_0.4.0
[114] fastmatch_1.1-0
[115] parallel_4.1.0
[116] rio_0.5.26
[117] rstudioapi_0.13
[118] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[119] foreach_1.5.1
[120] foreign_0.8-81
[121] git2r_0.28.0
[122] gridExtra_2.3
[123] farver_2.1.0
[124] ggraph_2.0.5
[125] proxyC_0.2.4
[126] digest_0.6.27
[127] Rcpp_1.0.8.3
[128] GenomicRanges_1.46.1
[129] car_3.0-10
[130] broom_0.7.6
[131] later_1.2.0
[132] httr_1.4.2
[133] gdtools_0.2.4
[134] colorspace_2.0-1
[135] XML_3.99-0.6
[136] rvest_1.0.0
[137] fs_1.5.0
[138] splines_4.1.0
[139] yulab.utils_0.0.4
[140] tidytree_0.3.9
[141] graphlayouts_0.7.1
[142] ggplotify_0.1.0
[143] sessioninfo_1.1.1
[144] systemfonts_1.0.4
[145] jsonlite_1.7.2
[146] ggtree_3.2.1
[147] futile.options_1.0.1
[148] tidygraph_1.2.0
[149] NLP_0.2-1
[150] ggfun_0.0.6
[151] testthat_3.0.2
[152] R6_2.5.0
[153] tm_0.7-8
[154] pillar_1.6.1
[155] htmltools_0.5.1.1
[156] glue_1.4.2
[157] fastmap_1.1.0
[158] codetools_0.2-18
[159] fgsea_1.20.0
[160] pkgbuild_1.2.0
[161] utf8_1.2.1
[162] lattice_0.20-44
[163] bslib_0.2.5.1
[164] curl_4.3.1
[165] gtools_3.8.2
[166] magick_2.7.2
[167] zip_2.2.0
[168] GO.db_3.14.0
[169] openxlsx_4.2.3
[170] Rttf2pt1_1.3.8
[171] rmarkdown_2.8
[172] desc_1.3.0
[173] munsell_0.5.0
[174] DO.db_2.9
[175] GetoptLong_1.0.5
[176] GenomeInfoDbData_1.2.7
[177] iterators_1.0.13
[178] haven_2.4.1
[179] gtable_0.3.0
[180] extrafont_0.17