1. Environment Set Up

Values of RMarkdown parameters

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, collapse = TRUE)
for (i in 1:length(params))
  print(paste('Parameter:', names(params)[i], ' - Value:', params[[i]], '- Class:', class(params[[i]])))
## [1] "Parameter: DEA_CTL04  - Value: ~/DataDir/bulkRNASeq/5.DifferentialExpression/CTL04/DEARes.rds - Class: character"
## [1] "Parameter: DEA_CTL08  - Value: ~/DataDir/bulkRNASeq/5.DifferentialExpression/CTL08/DEARes.rds - Class: character"
## [1] "Parameter: OutputFolder  - Value: ~/DataDir/bulkRNASeq/9.DEGsCharacterization/BothLines/ - Class: character"
library(RNASeqBulkExploratory)  #Our Package
library(ggplot2)
library(gridExtra)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:gridExtra':
## 
##     combine
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
DEA_CTL08 <- readRDS(params$DEA_CTL08)
DEA_CTL04 <- readRDS(params$DEA_CTL04)

Saving DEGs into excel files with DEGs from each comparison (AgvsInh) in separate sheets (ordered by adjusted p-value).

list_DEGs_CTL04 <- vector("list", length(names(DEA_CTL04))) %>% setNames(names(DEA_CTL04))

for (Cond in names(list_DEGs_CTL04)) {
  list_DEGs_CTL04[[Cond]] <- DEA_CTL04[[Cond]]$AgvsInh$DEGs %>% arrange(padj)
}

list_DEGs_CTL04 <- list_DEGs_CTL04 %>% setNames(paste0("DEGs_", names(list_DEGs_CTL04), "_AgvsInh"))

openxlsx::write.xlsx(list_DEGs_CTL04, paste0(params$OutputFolder, "DEGs_CTL04_AgvsInh.xlsx"), rowNames = TRUE, headerStyle = openxlsx::createStyle(textDecoration = "Bold"))
list_DEGs_CTL08 <- vector("list", length(names(DEA_CTL04))) %>% setNames(names(DEA_CTL04)) #keep CTL04 to have the same order in the final file

for (Cond in names(list_DEGs_CTL08)[names(list_DEGs_CTL08) != "Estr"]) {
  list_DEGs_CTL08[[Cond]] <- DEA_CTL08[[Cond]]$AgvsInh$DEGs %>% arrange(padj)
}

list_DEGs_CTL08 <- list_DEGs_CTL08 %>% setNames(paste0("DEGs_", names(list_DEGs_CTL08), "_AgvsInh"))

#We do not have Estr Inh so we will save only DEGs AgvsDMSO
list_DEGs_CTL08[[grep("Estr", names(list_DEGs_CTL08))]] <- DEA_CTL08[["Estr"]]$Agonist$DEGs %>% arrange(padj)
names(list_DEGs_CTL08)[[grep("Estr", names(list_DEGs_CTL08))]] <- "DEGs_Estr_AgvsDMSO"

openxlsx::write.xlsx(list_DEGs_CTL08, paste0(params$OutputFolder, "DEGs_CTL08_AgvsInh.xlsx"), rowNames = TRUE, headerStyle = openxlsx::createStyle(textDecoration = "Bold"))

2. Retinoic acid overlap CTL04 vs CTL08

agonist

computeCompResFC(DEA_CTL08$Ret$Agonist$Res, DEA_CTL04$Ret$Agonist$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000, padjceil = 0)$topGenes
## Loading required package: DESeq2
## Loading required package: S4Vectors
## Loading required package: stats4
## Loading required package: BiocGenerics
## 
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:dplyr':
## 
##     combine, intersect, setdiff, union
## The following object is masked from 'package:gridExtra':
## 
##     combine
## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
## 
##     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
##     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
##     get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
##     match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
##     Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
##     table, tapply, union, unique, unsplit, which.max, which.min
## 
## Attaching package: 'S4Vectors'
## The following objects are masked from 'package:dplyr':
## 
##     first, rename
## The following objects are masked from 'package:base':
## 
##     expand.grid, I, unname
## Loading required package: IRanges
## 
## Attaching package: 'IRanges'
## The following objects are masked from 'package:dplyr':
## 
##     collapse, desc, slice
## Loading required package: GenomicRanges
## Loading required package: GenomeInfoDb
## Loading required package: SummarizedExperiment
## Loading required package: MatrixGenerics
## Loading required package: matrixStats
## 
## Attaching package: 'matrixStats'
## The following object is masked from 'package:dplyr':
## 
##     count
## 
## Attaching package: 'MatrixGenerics'
## The following objects are masked from 'package:matrixStats':
## 
##     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
##     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
##     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
##     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
##     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
##     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
##     colWeightedMeans, colWeightedMedians, colWeightedSds,
##     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
##     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
##     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
##     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
##     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
##     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
##     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
##     rowWeightedSds, rowWeightedVars
## Loading required package: Biobase
## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.
## 
## Attaching package: 'Biobase'
## The following object is masked from 'package:MatrixGenerics':
## 
##     rowMedians
## The following objects are masked from 'package:matrixStats':
## 
##     anyMissing, rowMedians
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
##  [1] "HOPX"    "HOXB3"   "NXPH2"   "SKAP2"   "SLC6A5"  "CDH1"    "RASGRP3"
##  [8] "HTR2C"   "SLC30A3" "DMRT3"  
## 
## $Down_Down
##  [1] "LHX9"  "RSPO1" "ZIC5"  "BMP5"  "ZIC2"  "ZIC4"  "EN2"   "ZIC3"  "CNPY1"
## [10] "GSX1" 
## 
## $Up_Down
## [1] "FOXA1"   "COL21A1" "HK2"    
## 
## $Down_Up
## [1] "APOE"  "PGAM2" "PYGM"
compResFScatter(DEA_CTL08$Ret$Agonist$Res, DEA_CTL04$Ret$Agonist$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 8, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

agonist vs inhibitor

computeCompResFC(DEA_CTL08$Ret$AgvsInh$Res, DEA_CTL04$Ret$AgvsInh$Res, LogFCth = 1, padjth = 0.01 , LogFCceil = 1000, padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
##  [1] "HOPX"     "HTR2C"    "CSMD1"    "VSNL1"    "NXPH2"    "SPHKAP"  
##  [7] "NOS1"     "SYTL5"    "PLPP4"    "SYNDIG1L"
## 
## $Down_Down
##  [1] "RSPO1"  "ZIC5"   "ZIC2"   "EN2"    "ZIC3"   "ZIC4"   "BMP5"   "GSX1"  
##  [9] "CNPY1"  "TFAP2B"
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## [1] "C1QL3"
compResFScatter(DEA_CTL08$Ret$AgvsInh$Res, DEA_CTL04$Ret$AgvsInh$Res, LogFCth = 1, padjth = 0.01 , LogFCceil = 8, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

3. Thyroid overlap CTL04 vs CTL08

agonist

computeCompResFC(DEA_CTL08$Thyr$Agonist$Res, DEA_CTL04$Thyr$Agonist$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000,padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
##  [1] "HTR2C"    "WFIKKN2"  "OTX1"     "C11orf87" "SPARCL1"  "RGS6"    
##  [7] "DRGX"     "HCN1"     "ISLR2"    "SPOCK1"  
## 
## $Down_Down
## [1] "HSPA6"
## 
## $Up_Down
## [1] "SP5"
## 
## $Down_Up
## character(0)
compResFScatter(DEA_CTL08$Thyr$Agonist$Res, DEA_CTL04$Thyr$Agonist$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

agonist vs inhibitor

computeCompResFC(DEA_CTL08$Thyr$AgvsInh$Res, DEA_CTL04$Thyr$AgvsInh$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000,padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
##  [1] "TREX1"      "CABCOCO1"   "APLN"       "TNC"        "SLA"       
##  [6] "PBLD"       "MATN2"      "ZSCAN1"     "RGS6"       "CSGALNACT1"
## 
## $Down_Down
##  [1] "HSPA6"      "NPC1L1"     "CPT1A"      "H2BC8"      "H4C8"      
##  [6] "SNX31"      "AL031777.2" "HSPA1B"     "H1-2"       "TNFRSF14"  
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## character(0)
compResFScatter(DEA_CTL08$Thyr$AgvsInh$Res, DEA_CTL04$Thyr$AgvsInh$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

4. Estrogen overlap CTL04 vs CTL08

agonist

computeCompResFC(DEA_CTL08$Estr$Agonist$Res, DEA_CTL04$Estr$Agonist$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000,padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
## character(0)
## 
## $Down_Down
## character(0)
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## character(0)
compResFScatter(DEA_CTL08$Estr$Agonist$Res, DEA_CTL04$Estr$Agonist$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

Absent/low quality samples for estrogen inhibitor CTL08

5. Androgen overlap CTL04 vs CTL08

agonist

computeCompResFC(DEA_CTL08$Andr$Agonist$Res, DEA_CTL04$Andr$Agonist$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000,padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
## character(0)
## 
## $Down_Down
## character(0)
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## [1] "SNX31"
compResFScatter(DEA_CTL08$Andr$Agonist$Res, DEA_CTL04$Andr$Agonist$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

agonist vs inhibitor

computeCompResFC(DEA_CTL08$Andr$AgvsInh$Res, DEA_CTL04$Andr$AgvsInh$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000, padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
## [1] "H3C6"
## 
## $Down_Down
## character(0)
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## character(0)
compResFScatter(DEA_CTL08$Andr$AgvsInh$Res, DEA_CTL04$Andr$AgvsInh$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

6. Glucocorticoid overlap CTL04 vs CTL08

agonist

computeCompResFC(DEA_CTL08$GC$Agonist$Res, DEA_CTL04$GC$Agonist$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000, padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
## character(0)
## 
## $Down_Down
## character(0)
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## character(0)
compResFScatter(DEA_CTL08$GC$Agonist$Res, DEA_CTL04$GC$Agonist$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

agonist vs inhibitor

computeCompResFC(DEA_CTL08$GC$AgvsInh$Res, DEA_CTL04$GC$AgvsInh$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, LogFCceil = 1000)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
##  [1] "NPY"     "PROKR1"  "ATP1A2"  "FGF1"    "RHOJ"    "ALDH1L1" "IFI44"  
##  [8] "HSD17B8" "FBLN5"   "CD9"    
## 
## $Down_Down
##  [1] "HSPE1-MOB4" "CPT1A"      "HSPA1B"     "HSPH1"      "DNAJB1"    
##  [6] "CHORDC1"    "HSPA6"      "H2BC5"      "H4C9"       "AL031777.2"
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## character(0)
compResFScatter(DEA_CTL08$GC$AgvsInh$Res, DEA_CTL04$GC$AgvsInh$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, LogFCceil = 6, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

7. AhHyd overlap CTL04 vs CTL08

agonist

computeCompResFC(DEA_CTL08$AhHyd$Agonist$Res, DEA_CTL04$AhHyd$Agonist$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000, padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
## character(0)
## 
## $Down_Down
## character(0)
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## character(0)
compResFScatter(DEA_CTL08$AhHyd$Agonist$Res, DEA_CTL04$AhHyd$Agonist$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

agonist vs inhibitor

computeCompResFC(DEA_CTL08$AhHyd$AgvsInh$Res, DEA_CTL04$AhHyd$AgvsInh$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000, padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
## character(0)
## 
## $Down_Down
##  [1] "PPP1R16B"  "VGF"       "NKX1-2"    "SPRY4"     "NEFL"      "C4orf50"  
##  [7] "CNTN1"     "ONECUT2"   "FRRS1L"    "PABPC1L2A"
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## [1] "HSPA6" "FLNC"
compResFScatter(DEA_CTL08$AhHyd$AgvsInh$Res, DEA_CTL04$AhHyd$AgvsInh$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

8. LiverX overlap CTL04 vs CTL08

agonist

computeCompResFC(DEA_CTL08$LivX$Agonist$Res, DEA_CTL04$LivX$Agonist$Res, LogFCth = 1, padjth = 0.01, LogFCceil = 1000, padjceil = 0)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
## [1] "ABCA1"  "ABCG1"  "SREBF1" "MYLIP" 
## 
## $Down_Down
## character(0)
## 
## $Up_Down
## character(0)
## 
## $Down_Up
## character(0)
compResFScatter(DEA_CTL08$LivX$Agonist$Res, DEA_CTL04$LivX$Agonist$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150

agonist vs inhibitor

computeCompResFC(DEA_CTL08$LivX$AgvsInh$Res, DEA_CTL04$LivX$AgvsInh$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, LogFCceil = 1000)$topGenes
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
## $Up_Up
##  [1] "ABCA1"   "ABCG1"   "SREBF1"  "MYLIP"   "LPCAT3"  "PROKR1"  "ADAMTS3"
##  [8] "ACSL3"   "CASQ1"   "SHFL"   
## 
## $Down_Down
## [1] "AC099489.1" "DERL3"      "ARHGAP45"  
## 
## $Up_Down
##  [1] "H4C8"       "HSPH1"      "SNX31"      "H2BC8"      "H2AC6"     
##  [6] "FOS"        "AL031777.2" "H2BC7"      "H2BC5"      "H2BC6"     
## 
## $Down_Up
##  [1] "HSD17B8" "PYCR3"   "GGACT"   "PRODH"   "PCK2"    "DNPH1"   "GDF15"  
##  [8] "ART5"    "TMEM129" "HINT2"
compResFScatter(DEA_CTL08$LivX$AgvsInh$Res, DEA_CTL04$LivX$AgvsInh$Res, LogFCth = 1, padjth = 0.01, padjceil = 0, Interactive = TRUE)
## The number of genes in the first data set is 14850
## The number of genes in the second data set is 14305
## The number of common genes that will be examined is 14150
date()
## [1] "Mon Jul 21 23:14:32 2025"
sessionInfo()
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## 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] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] DESeq2_1.38.3               SummarizedExperiment_1.28.0
##  [3] Biobase_2.58.0              MatrixGenerics_1.10.0      
##  [5] matrixStats_0.63.0          GenomicRanges_1.50.2       
##  [7] GenomeInfoDb_1.34.9         IRanges_2.32.0             
##  [9] S4Vectors_0.36.1            BiocGenerics_0.44.0        
## [11] dplyr_1.1.0                 gridExtra_2.3              
## [13] ggplot2_3.4.1               RNASeqBulkExploratory_0.2.1
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7           bit64_4.0.5            RColorBrewer_1.1-3    
##  [4] httr_1.4.5             tools_4.2.1            bslib_0.4.2           
##  [7] utf8_1.2.3             R6_2.5.1               DT_0.27               
## [10] DBI_1.1.3              lazyeval_0.2.2         colorspace_2.1-0      
## [13] withr_2.5.0            tidyselect_1.2.0       bit_4.0.5             
## [16] compiler_4.2.1         cli_3.6.1              DelayedArray_0.24.0   
## [19] plotly_4.10.1          labeling_0.4.2         sass_0.4.5            
## [22] scales_1.2.1           digest_0.6.31          rmarkdown_2.20        
## [25] XVector_0.38.0         pkgconfig_2.0.3        htmltools_0.5.4       
## [28] fastmap_1.1.1          htmlwidgets_1.6.1      rlang_1.1.1           
## [31] rstudioapi_0.14        RSQLite_2.3.0          jquerylib_0.1.4       
## [34] generics_0.1.3         jsonlite_1.8.4         crosstalk_1.2.0       
## [37] BiocParallel_1.32.5    zip_2.2.2              RCurl_1.98-1.10       
## [40] magrittr_2.0.3         GenomeInfoDbData_1.2.9 Matrix_1.5-3          
## [43] Rcpp_1.0.10            munsell_0.5.0          fansi_1.0.4           
## [46] lifecycle_1.0.3        stringi_1.7.12         yaml_2.3.7            
## [49] zlibbioc_1.44.0        grid_4.2.1             blob_1.2.3            
## [52] parallel_4.2.1         crayon_1.5.2           lattice_0.20-45       
## [55] Biostrings_2.66.0      annotate_1.76.0        KEGGREST_1.38.0       
## [58] locfit_1.5-9.7         knitr_1.42             pillar_1.8.1          
## [61] geneplotter_1.76.0     codetools_0.2-19       XML_3.99-0.13         
## [64] glue_1.6.2             evaluate_0.20          data.table_1.14.8     
## [67] vctrs_0.6.2            png_0.1-8              gtable_0.3.1          
## [70] purrr_1.0.1            tidyr_1.3.0            cachem_1.0.7          
## [73] xfun_0.37              openxlsx_4.2.5.2       xtable_1.8-4          
## [76] viridisLite_0.4.1      tibble_3.2.1           AnnotationDbi_1.60.0  
## [79] memoise_2.0.1          ellipsis_0.3.2