Figure 3
ERM
2025-08-07
Last updated: 2025-08-07
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Knit directory: ATAC_learning/
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packages
library(tidyverse)
library(cowplot)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(edgeR)
library(ggfortify)
library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(Cormotif)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(BSgenome.Hsapiens.UCSC.hg38)
library(data.table)
library(eulerr)
library(plyranges)Figure 3: Early drug-dependent responsive chromatin regions are enriched in TOP2B-bound regions near transcription start sites.
knitr::include_graphics("assets/Figure\ 3.png", error=FALSE)
knitr::include_graphics("docs/assets/Figure\ 3.png",error = FALSE)
Figure 3.A. TOP2B ChIP-seq track alongside VEH treated ATAC-seq tracks at a known heart gene
**see image above.
Figure 3.B. Gene feature distribution across DARs
link to how ChipSeeker was used to create this plot
(may need to scroll down further on link above to see the code and notes)
# Top2b_peaks <- import(con="data/other_papers/ChIP3_TOP2B_CM_87-1.bed",format = "bed",genome="hg38")
filt_peakAnnoList_Top2b_DAR <- readRDS("data/Final_four_data/re_analysis/filt_Top2B_DAR_annotated_peaks_chipannno.RDS")
ChIPseeker::plotAnnoBar(filt_peakAnnoList_Top2b_DAR[c(10,4,6,2,8,3,5,1,7)])+
ggtitle ("Genomic Feature Distribution, Significant regions with Top2B \n using adj.P.Val <0.05")
| Version | Author | Date |
|---|---|---|
| e077582 | reneeisnowhere | 2025-08-07 |
Figure 3.C. Overlap of Accessible regions and TOP2B regions
Top2b_peaks <- import(con="data/other_papers/ChIP3_TOP2B_CM_87-1.bed",format = "bed",genome="hg38")
Motif_list_gr <- readRDS( "data/Final_four_data/re_analysis/Motif_list_granges.RDS")
list2env(Motif_list_gr[10],envir = .GlobalEnv)<environment: R_GlobalEnv>top2b_overlap <- join_overlap_inner(all_regions,Top2b_peaks)
AR_total <- length(unique(all_regions$Peakid))
Top2B_total <- length(unique(Top2b_peaks$name))
overlap_n <- length(unique(top2b_overlap$Peakid))
fit_top2b <- euler(c(
"ARs" = AR_total-overlap_n,
"Top2B" = Top2B_total-length(unique(top2b_overlap$name)),
"ARs&Top2B" = overlap_n
))
plot(fit_top2b, fills = list(fill = c("skyblue", "lightcoral"), alpha = 0.6),
labels = FALSE, edges = TRUE, quantities = TRUE,
main = "Overlap between AR and TOP2B peaks")
| Version | Author | Date |
|---|---|---|
| e077582 | reneeisnowhere | 2025-08-07 |
Figure 3.D. Enrichment of TOP2B regions in DARs
Allregion_ol <- join_overlap_intersect(all_regions,Top2b_peaks)%>%
as.data.frame() %>%
distinct(Peakid,.keep_all = TRUE)
toptable_results <- readRDS("data/Final_four_data/re_analysis/Toptable_results.RDS")
all_results <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y)) %>%
bind_rows()
annotated_DARs<- readRDS("data/Final_four_data/re_analysis/DOX_DAR_annotated_peaks_chipannno.RDS")
gene_N_peak <-
annotated_DARs$DOX_3 %>%
as.data.frame() %>%
dplyr::select(mcols.genes,annotation, geneId:distanceToTSS)my_DOX_data <- all_results %>%
dplyr::filter(source=="DOX_3"|source=="DOX_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))
my_EPI_data <- all_results %>%
dplyr::filter(source=="EPI_3"|source=="EPI_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))
my_DNR_data <- all_results %>%
dplyr::filter(source=="DNR_3"|source=="DNR_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))
my_MTX_data <- all_results %>%
dplyr::filter(source=="MTX_3"|source=="MTX_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))Making the annotation dataframe to create 2x2 matrices
TSS_listed_df <- all_results %>%
mutate(top_2b_ol=case_when(genes %in% Allregion_ol$Peakid~"TOP2b_peak",
TRUE ~"not_TOP2b_peak")) %>%
left_join(.,my_DOX_data,by=c("genes"="genes")) %>%
mutate(DOX_sig_3=if_else(adj.P.Val_DOX_3<0.05,"sig","not_sig"),
DOX_sig_24=if_else(adj.P.Val_DOX_24<0.05,"sig","not_sig")) %>%
mutate(DOX_sig_3=factor(DOX_sig_3,levels=c("sig","not_sig")),
DOX_sig_24=factor(DOX_sig_24,levels=c("sig","not_sig"))) %>%
left_join(.,my_EPI_data,by=c("genes"="genes")) %>%
mutate(EPI_sig_3=if_else(adj.P.Val_EPI_3<0.05,"sig","not_sig"),
EPI_sig_24=if_else(adj.P.Val_EPI_24<0.05,"sig","not_sig")) %>%
mutate(EPI_sig_3=factor(EPI_sig_3,levels=c("sig","not_sig")),
EPI_sig_24=factor(EPI_sig_24,levels=c("sig","not_sig"))) %>%
left_join(.,my_DNR_data,by=c("genes"="genes")) %>%
mutate(DNR_sig_3=if_else(adj.P.Val_DNR_3<0.05,"sig","not_sig"),
DNR_sig_24=if_else(adj.P.Val_DNR_24<0.05,"sig","not_sig")) %>%
mutate(DNR_sig_3=factor(DNR_sig_3,levels=c("sig","not_sig")),
DNR_sig_24=factor(DNR_sig_24,levels=c("sig","not_sig"))) %>%
left_join(.,my_MTX_data,by=c("genes"="genes")) %>%
mutate(MTX_sig_3=if_else(adj.P.Val_MTX_3<0.05,"sig","not_sig"),
MTX_sig_24=if_else(adj.P.Val_MTX_24<0.05,"sig","not_sig")) %>%
mutate(MTX_sig_3=factor(MTX_sig_3,levels=c("sig","not_sig")),
MTX_sig_24=factor(MTX_sig_24,levels=c("sig","not_sig"))) %>%
left_join(., gene_N_peak, by= c("genes"="mcols.genes"))make the odds ratio dataframe
DOX_top2b_3hr_or <-
TSS_listed_df %>%
group_by(DOX_sig_3,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=DOX_sig_3, names_from = top_2b_ol, values_from = n) %>% column_to_rownames("DOX_sig_3") %>%
as.matrix() %>%
fisher.test(.)
DOX_top2b_24hr_or <- TSS_listed_df %>%
group_by(DOX_sig_24,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=DOX_sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("DOX_sig_24") %>%
as.matrix() %>%
fisher.test(.)
EPI_top2b_3hr_or <-
TSS_listed_df %>%
group_by(EPI_sig_3,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=EPI_sig_3, names_from = top_2b_ol, values_from = n) %>% column_to_rownames("EPI_sig_3") %>%
as.matrix() %>%
fisher.test(.)
EPI_top2b_24hr_or <- TSS_listed_df %>%
group_by(EPI_sig_24,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=EPI_sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("EPI_sig_24") %>%
as.matrix() %>%
fisher.test(.)
DNR_top2b_3hr_or <-
TSS_listed_df %>%
group_by(DNR_sig_3,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=DNR_sig_3, names_from = top_2b_ol, values_from = n) %>% column_to_rownames("DNR_sig_3") %>%
as.matrix() %>%
fisher.test(.)
DNR_top2b_24hr_or <- TSS_listed_df %>%
group_by(DNR_sig_24,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=DNR_sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("DNR_sig_24") %>%
as.matrix() %>%
fisher.test(.)
MTX_top2b_3hr_or <-
TSS_listed_df %>%
group_by(MTX_sig_3,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=MTX_sig_3, names_from = top_2b_ol, values_from = n) %>% column_to_rownames("MTX_sig_3") %>%
as.matrix() %>%
fisher.test(.)
MTX_top2b_24hr_or <- TSS_listed_df %>%
group_by(MTX_sig_24,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=MTX_sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("MTX_sig_24") %>%
as.matrix() %>%
fisher.test(.)# Define the variable names
var_names <- c("DOX_top2b_3hr_or", "DOX_top2b_24hr_or",
"EPI_top2b_3hr_or", "EPI_top2b_24hr_or",
"DNR_top2b_3hr_or", "DNR_top2b_24hr_or",
"MTX_top2b_3hr_or", "MTX_top2b_24hr_or")
# Optional: label for grouping
group_labels <- c("DOX_3hr", "DOX_24hr", "EPI_3hr", "EPI_24hr", "DNR_3hr", "DNR_24hr", "MTX_3hr", "MTX_24hr")
# Build the data frame
OR_all_trt_result_df <- do.call(rbind, lapply(seq_along(var_names), function(i) {
var <- get(var_names[i])
data.frame(
or_value = unname(var$estimate), # remove the name "odds ratio"
lower_ci = var$conf.int[1],
upper_ci = var$conf.int[2],
p_value = var$p.value,
group = group_labels[i]
)
}))OR_all_trt_result_df %>%
separate_wider_delim(.,cols="group", names = c("trt","time"), delim = "_",cols_remove = FALSE) %>%
mutate(time= factor(time, levels =c("3hr","24hr")),
trt=factor(trt, levels= c("DOX", "EPI", "DNR", "MTX"))) %>%
# mutate(significant=if_else(p_value <0.05,"TRUE","FALSE")) %>%
mutate(
significant = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
) %>%
ggplot(., aes(x = trt, y = or_value)) +
geom_point(aes(color = trt), size=4)+
geom_errorbar(aes(ymin = lower_ci, ymax = upper_ci), width = 0.2) +
geom_hline(yintercept = 1, linetype = "dashed", color = "gray40") +
geom_text(
aes(y = upper_ci + 0.1 * or_value, label = significant),
hjust = 0, # aligns text to the left of the y point
size = 4,
color = "black"
)+
labs(
title = "Odds Ratio of TOP2b Peak Overlap",
y = "Odds Ratio (95% confidence interval)",
x = "treatment"
) +
# coord_flip()+
theme_classic() +
facet_wrap(~time)+
theme(
text = element_text(size = 12),
plot.title = element_text(hjust = 0.5)
)
| Version | Author | Date |
|---|---|---|
| e077582 | reneeisnowhere | 2025-08-07 |
Figure 3.E. TOP2B RNA expression
(Uses log2cpm values from my previous paper (Matthews, et al. PLOS genet. 2024))
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
RNA_counts <- readRDS("data/other_papers/cpmcount.RDS") %>%
dplyr::rename_with(.,~gsub(pattern="Da",replacement="DNR",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Do",replacement="DOX",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Ep",replacement="EPI",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Mi",replacement="MTX",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Tr",replacement="TRZ",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Ve",replacement="VEH",.)) %>%
rownames_to_column("ENTREZID")
RNA_counts %>%
dplyr::filter(ENTREZID =="7155") %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
# facet_wrap(~SYMBOL, scales="free_y")+
scale_fill_manual(values = drug_pal)+
ggtitle("RNA Log2cpm of TOP2b")+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| e077582 | reneeisnowhere | 2025-08-07 |
Figure 3.F. Enrichment of sequence features in DARs
For direct links in how all numbers were derived:
Analysis of TSS and CpG islands enrichment
Enrichment of cis Regulatory Elements
library(tidyverse)
library(ComplexHeatmap)
library(circlize)top_df <- readRDS("data/Final_four_data/re_analysis/OR_results_TE_df_1bp_alltrt.RDS")
mid_df <- readRDS("data/Final_four_data/re_analysis/OR_results_TSS_CpG_df_1bp_alltrt.RDS")
bot_df <- readRDS("data/Final_four_data/re_analysis/OR_results_cREs_df_1bp_alltrt.RDS")results_order <- top_df %>%
bind_rows(mid_df) %>%
bind_rows(bot_df) %>%
mutate(status=factor(status,
levels=c("TE_status",
"SINE_status",
"LINE_status",
"DNA_status","LTR_status",
"SVA_status","CpG_status",
"TSS_status","cRE_status",
"PLS_status","dELS_status","pELS_status",
"CTCF_status"))) %>%
arrange(status) %>%
group_by(source) %>%
mutate(rank_val=rank(chi_sq_p, ties.method = "first")) %>%
mutate(BH_correction= p.adjust(chi_sq_p,method= "BH")) %>%
mutate(sig=chi_sq_p<BH_correction) %>%
mutate(source=factor(source,levels = c("DOX_3hr", "EPI_3hr",
"DNR_3hr","MTX_3hr",
"DOX_24hr","EPI_24hr",
"DNR_24hr","MTX_24hr")))
critical_value <- max(results_order$chi_sq_p[results_order$sig])
col_fun_OR = colorRamp2(c(0,1,1.5,3,4), c("#BC9BFF","white","lightgreen","green3","green3" ))
sig_mat_OR <-
results_order %>%
as.data.frame() %>%
dplyr::select( status,source,BH_correction) %>%
arrange(status) %>%
pivot_wider(., id_cols = status, names_from = source, values_from = BH_correction) %>%
# dplyr::select(Matrix_Name,EAR_open,ESR_open,LR_open,ESR_opcl,EAR_close,ESR_close,LR_close,ESR_clop) %>%
column_to_rownames("status") %>%
as.matrix()
results_mat <- results_order %>%
as.data.frame() %>%
dplyr::select( status,source,odds_ratio) %>%
arrange(status) %>%
pivot_wider(., id_cols = status, names_from = source, values_from = odds_ratio) %>%
# dplyr::select(status,EAR_open,ESR_open,LR_open,ESR_opcl,EAR_close,ESR_close,LR_close,ESR_clop) %>%
column_to_rownames("status") %>%
as.matrix()ComplexHeatmap::Heatmap(results_mat ,col = col_fun_OR,
cluster_rows=FALSE,
cluster_columns=FALSE,
column_names_side = "top",
column_names_rot = 45,
column_order=c("DOX_3hr","EPI_3hr","DNR_3hr","MTX_3hr","DOX_24hr","EPI_24hr","DNR_24hr","MTX_24hr"),
# na_col = "black",
cell_fun = function(j, i, x, y, width, height, fill) {
if (!is.na(sig_mat_OR[i, j]) && sig_mat_OR[i, j] <0.05 && results_mat[i, j] > 1) {
grid.text("*", x, y, gp = gpar(fontsize = 20)) # Add star if significant
} })
Warning! The custom
fig.path you set was
ignored by workflowr.
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] circlize_0.4.16
[2] ComplexHeatmap_2.22.0
[3] plyranges_1.26.0
[4] eulerr_7.0.2
[5] data.table_1.17.6
[6] BSgenome.Hsapiens.UCSC.hg38_1.4.5
[7] BSgenome_1.74.0
[8] BiocIO_1.16.0
[9] Biostrings_2.74.1
[10] XVector_0.46.0
[11] devtools_2.4.5
[12] usethis_3.1.0
[13] ggpubr_0.6.1
[14] BiocParallel_1.40.2
[15] Cormotif_1.52.0
[16] affy_1.84.0
[17] scales_1.4.0
[18] VennDiagram_1.7.3
[19] futile.logger_1.4.3
[20] gridExtra_2.3
[21] ggfortify_0.4.18
[22] edgeR_4.4.2
[23] limma_3.62.2
[24] rtracklayer_1.66.0
[25] org.Hs.eg.db_3.20.0
[26] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[27] GenomicFeatures_1.58.0
[28] AnnotationDbi_1.68.0
[29] Biobase_2.66.0
[30] GenomicRanges_1.58.0
[31] GenomeInfoDb_1.42.3
[32] IRanges_2.40.1
[33] S4Vectors_0.44.0
[34] BiocGenerics_0.52.0
[35] ChIPseeker_1.42.1
[36] RColorBrewer_1.1-3
[37] broom_1.0.8
[38] kableExtra_1.4.0
[39] cowplot_1.1.3
[40] lubridate_1.9.4
[41] forcats_1.0.0
[42] stringr_1.5.1
[43] dplyr_1.1.4
[44] purrr_1.0.4
[45] readr_2.1.5
[46] tidyr_1.3.1
[47] tibble_3.3.0
[48] ggplot2_3.5.2
[49] tidyverse_2.0.0
[50] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.6
[2] matrixStats_1.5.0
[3] bitops_1.0-9
[4] enrichplot_1.26.6
[5] doParallel_1.0.17
[6] httr_1.4.7
[7] profvis_0.4.0
[8] tools_4.4.2
[9] backports_1.5.0
[10] R6_2.6.1
[11] lazyeval_0.2.2
[12] GetoptLong_1.0.5
[13] urlchecker_1.0.1
[14] withr_3.0.2
[15] preprocessCore_1.68.0
[16] cli_3.6.5
[17] textshaping_1.0.1
[18] formatR_1.14
[19] Cairo_1.6-2
[20] labeling_0.4.3
[21] sass_0.4.10
[22] Rsamtools_2.22.0
[23] systemfonts_1.2.3
[24] yulab.utils_0.2.0
[25] DOSE_4.0.1
[26] svglite_2.2.1
[27] R.utils_2.13.0
[28] dichromat_2.0-0.1
[29] sessioninfo_1.2.3
[30] plotrix_3.8-4
[31] rstudioapi_0.17.1
[32] RSQLite_2.4.1
[33] shape_1.4.6.1
[34] generics_0.1.4
[35] gridGraphics_0.5-1
[36] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[37] gtools_3.9.5
[38] car_3.1-3
[39] GO.db_3.20.0
[40] Matrix_1.7-3
[41] abind_1.4-8
[42] R.methodsS3_1.8.2
[43] lifecycle_1.0.4
[44] whisker_0.4.1
[45] yaml_2.3.10
[46] carData_3.0-5
[47] SummarizedExperiment_1.36.0
[48] gplots_3.2.0
[49] qvalue_2.38.0
[50] SparseArray_1.6.2
[51] blob_1.2.4
[52] promises_1.3.3
[53] crayon_1.5.3
[54] miniUI_0.1.2
[55] ggtangle_0.0.7
[56] lattice_0.22-7
[57] KEGGREST_1.46.0
[58] magick_2.8.7
[59] pillar_1.11.0
[60] knitr_1.50
[61] fgsea_1.32.4
[62] rjson_0.2.23
[63] boot_1.3-31
[64] codetools_0.2-20
[65] fastmatch_1.1-6
[66] glue_1.8.0
[67] getPass_0.2-4
[68] ggfun_0.1.9
[69] remotes_2.5.0
[70] vctrs_0.6.5
[71] png_0.1-8
[72] treeio_1.30.0
[73] gtable_0.3.6
[74] cachem_1.1.0
[75] xfun_0.52
[76] S4Arrays_1.6.0
[77] mime_0.13
[78] iterators_1.0.14
[79] statmod_1.5.0
[80] ellipsis_0.3.2
[81] nlme_3.1-168
[82] ggtree_3.14.0
[83] bit64_4.6.0-1
[84] rprojroot_2.0.4
[85] bslib_0.9.0
[86] affyio_1.76.0
[87] KernSmooth_2.23-26
[88] colorspace_2.1-1
[89] DBI_1.2.3
[90] tidyselect_1.2.1
[91] processx_3.8.6
[92] bit_4.6.0
[93] compiler_4.4.2
[94] curl_6.4.0
[95] git2r_0.36.2
[96] xml2_1.3.8
[97] DelayedArray_0.32.0
[98] caTools_1.18.3
[99] callr_3.7.6
[100] digest_0.6.37
[101] rmarkdown_2.29
[102] htmltools_0.5.8.1
[103] pkgconfig_2.0.3
[104] MatrixGenerics_1.18.1
[105] fastmap_1.2.0
[106] GlobalOptions_0.1.2
[107] rlang_1.1.6
[108] htmlwidgets_1.6.4
[109] UCSC.utils_1.2.0
[110] shiny_1.11.1
[111] farver_2.1.2
[112] jquerylib_0.1.4
[113] jsonlite_2.0.0
[114] GOSemSim_2.32.0
[115] R.oo_1.27.1
[116] RCurl_1.98-1.17
[117] magrittr_2.0.3
[118] Formula_1.2-5
[119] GenomeInfoDbData_1.2.13
[120] ggplotify_0.1.2
[121] patchwork_1.3.1
[122] Rcpp_1.1.0
[123] ape_5.8-1
[124] stringi_1.8.7
[125] zlibbioc_1.52.0
[126] plyr_1.8.9
[127] pkgbuild_1.4.8
[128] parallel_4.4.2
[129] ggrepel_0.9.6
[130] splines_4.4.2
[131] hms_1.1.3
[132] polylabelr_0.3.0
[133] locfit_1.5-9.12
[134] ps_1.9.1
[135] igraph_2.1.4
[136] ggsignif_0.6.4
[137] reshape2_1.4.4
[138] pkgload_1.4.0
[139] futile.options_1.0.1
[140] XML_3.99-0.18
[141] evaluate_1.0.4
[142] lambda.r_1.2.4
[143] BiocManager_1.30.26
[144] foreach_1.5.2
[145] tzdb_0.5.0
[146] httpuv_1.6.16
[147] polyclip_1.10-7
[148] clue_0.3-66
[149] xtable_1.8-4
[150] restfulr_0.0.16
[151] tidytree_0.4.6
[152] rstatix_0.7.2
[153] later_1.4.2
[154] viridisLite_0.4.2
[155] aplot_0.2.8
[156] memoise_2.0.1
[157] GenomicAlignments_1.42.0
[158] cluster_2.1.8.1
[159] timechange_0.3.0