RNA_CorrelationHeatMap
2026-01-14
Last updated: 2026-01-28
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Knit directory: CrossSpecies_CM_Diff_RNA/
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####Library Loading####
library("edgeR")Loading required package: limmalibrary("ggplot2")
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary("workflowr")
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library("readxl")
library("ggfortify")Warning: package 'ggfortify' was built under R version 4.5.2library("ComplexHeatmap")Loading required package: grid
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========================================
ComplexHeatmap version 2.24.1
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
If you use it in published research, please cite either one:
- Gu, Z. Complex Heatmap Visualization. iMeta 2022.
- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
genomic data. Bioinformatics 2016.
The new InteractiveComplexHeatmap package can directly export static
complex heatmaps into an interactive Shiny app with zero effort. Have a try!
This message can be suppressed by:
suppressPackageStartupMessages(library(ComplexHeatmap))
========================================
! pheatmap() has been masked by ComplexHeatmap::pheatmap(). Most of the arguments
in the original pheatmap() are identically supported in the new function. You
can still use the original function by explicitly calling pheatmap::pheatmap().
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pheatmap# BiocManager::install("SummarizedExperiment")
RNA_fc_df <- readRDS("data/Raw_Data/RNA_fc_df.RDS")
RNA_Metadata <- readRDS("data/Raw_Data/RNA_Metadata.RDS")
RNA_fc <- readRDS("data/QC/RNA_fc.RDS")
RNA_log2cpm <- ("data/QC/RNA_log2cpm.RDS")
Filt_RMG0_RNA_fc <- readRDS("data/QC/Filt_RMG0_RNA_fc.RDS")
Filt_RMG0_RNA_log2cpm <-("data/QC/RNA_log2cpm_RMG0.RDS")
Cor_Filt_RMG0_RNA_log2cpm <- readRDS("data/QC/Cor_Filt_RMG0_RNA_log2cpm.RDS")
Cor_metadata <- readRDS("data/QC/Cor_RNA_metadata.RDS")
ann_colors <- readRDS("data/QC/ann_colors.RDS")
RNA_Metadata_No4 <- readRDS("data/QC/RNA_Metatdata_No4.RDS")
Filt_RMG0_RNA_fc_NoD4 <- readRDS("data/QC/Filt_RMG0_RNA_fc_NoD4.RDS")
Filt_RMG0_RNA_log2cpm_NoD4 <- readRDS("data/QC/Filt_RMG0_RNA_log2cpm_NoD4.RDS")
Cor_metadata_No4 <- readRDS("data/QC/Cor_metadata_No4.RDS")
Cor_Filt_RMG0_RNA_log2cpm_NoD4 <- readRDS("data/QC/Cor_Filt_RMG0_RNA_log2cpm_NoD4.RDS")
ann_colors_No4 <- readRDS("data/QC/ann_colors_no4.RDS")# #To keep only OrthoGenes and sample columns
# RNA_fc <- RNA_joined_fc[ , !(names(RNA_joined_fc) %in% c("gene","Chr.x","Start.x","End.x","Strand.x","Length.x","Geneid.x","Chr.y","Start.y","End.y","Strand.y","Length.y","Geneid.y"))]
# # 89 columns; 7 x 6 = 42 Human Exp, 7 x 6 = 42 Chimp Exp, 4 Human Replicate
# rownames(RNA_fc) <- RNA_joined_fc$gene
#
# #Rename column Names to More Useful Info
col_names <- c("H28126_D0",
"H28126_D2",
"H28126_D4",
"H28126_D5",
"H28126_D15",
"H28126_D30",
"H17_D0",
"H17_D2",
"H17_D4",
"H17_D5",
"H17_D15",
"H17_D30",
"H78_D0",
"H78_D2",
"H78_D4",
"H78_D5",
"H78_D15",
"H78_D30",
"H20682_D0",
"H20682_D2",
"H20682_D4",
"H20682_D5",
"H20682_D15",
"H20682_D30",
"H22422_D0",
"H22422_D2",
"H22422_D4",
"H22422_D5",
"H22422_D15",
"H22422_D30",
"H21792_D0",
"H21792_D2",
"H21792_D4",
"H21792_D5",
"H21792_D15",
"H21792_D30",
"H24280_D0",
"H24280_D2",
"H24280_D4",
"H24280_D5",
"H24280_D15",
"H24280_D30",
"H20682R_D0",
"H20682R_D2",
"H20682R_D5",
"H20682R_D30",
"C3649_D0",
"C3649_D2",
"C3649_D4",
"C3649_D5",
"C3649_D15",
"C3649_D30",
"C4955_D0",
"C4955_D2",
"C4955_D4",
"C4955_D5",
"C4955_D15",
"C4955_D30",
"C3651_D0",
"C3651_D2",
"C3651_D4",
"C3651_D5",
"C3651_D15",
"C3651_D30",
"C40210_D0",
"C40210_D2",
"C40210_D4",
"C40210_D5",
"C40210_D15",
"C40210_D30",
"C8861_D0",
"C8861_D2",
"C8861_D4",
"C8861_D5",
"C8861_D15",
"C8861_D30",
"C40280_D0",
"C40280_D2",
"C40280_D4",
"C40280_D5",
"C40280_D15",
"C40280_D30",
"C3647_D0",
"C3647_D2",
"C3647_D4",
"C3647_D5",
"C3647_D15",
"C3647_D30"
)
# colnames(RNA_fc) <- col_names
# dim(RNA_fc)
#
# sum(duplicated(rownames(RNA_fc)))
# ensembl_ids_unfilt <- rownames(RNA_fc)
# entrez_ids_unfilt <- mapIds(org.Hs.eg.db,
# keys = ensembl_ids_unfilt,
# column = "ENTREZID",
# keytype = "ENSEMBL",
# multiVals = "first")
# symbol_ids_unfilt <- mapIds(org.Hs.eg.db,
# keys = ensembl_ids_unfilt,
# column = "SYMBOL",
# keytype = "ENSEMBL",
# multiVals = "first")
#
# RNA_fc_df <- as.data.frame(RNA_fc)
# RNA_fc_df <- RNA_fc_df %>%
# rownames_to_column(var = "Ensemble") %>%
# dplyr::mutate(
# Entrez_ID = entrez_ids_unfilt,
# Symbol = symbol_ids_unfilt
# ) %>%
# dplyr::select(
# Ensemble, # 1st column
# Entrez_ID, # 2nd column
# Symbol, # 3rd column
# everything() # rest unchanged
# )
#
# # saveRDS(RNA_fc_df,"data/Data_Frames/RNA_fc_df.RDS")
#
# RNA_Metadata <- read_excel("~/diff_timeline_tes/RNA/RNA_Metadata.xlsx")
#
# # saveRDS(RNA_Metadata,"data/Data_Frames/RNA_Metadata.RDS")#####Unfiltered####
RNA_fc <- RNA_fc_df %>%
dplyr::select(c(-"Entrez_ID", -"Symbol")) %>%
column_to_rownames("Ensemble")
# saveRDS(RNA_fc,"data/QC/RNA_fc.RDS")
RNA_log2cpm <- cpm(RNA_fc,log=TRUE)
print(hist(RNA_log2cpm, main = "Histogram of all counts (unfiltered)",
xlab =expression("Log"[2]*" counts-per-million"), col =4 ))
$breaks
[1] -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
$counts
[1] 2026734 332340 206543 159084 134510 130097 148151 195453 219757
[10] 174507 94391 39658 15034 5070 1267 310 70 21
[19] 3
$density
[1] 5.219506e-01 8.558846e-02 5.319160e-02 4.096935e-02 3.464074e-02
[6] 3.350425e-02 3.815375e-02 5.033557e-02 5.659464e-02 4.494128e-02
[11] 2.430878e-02 1.021324e-02 3.871749e-03 1.305691e-03 3.262941e-04
[16] 7.983518e-05 1.802730e-05 5.408190e-06 7.725985e-07
$mids
[1] -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5
[16] 12.5 13.5 14.5 15.5
$xname
[1] "RNA_log2cpm"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"boxplot(RNA_log2cpm, main = "Boxplots of log cpm per sample
(unfiltered)", xaxt = "n", xlab= "")
axis(1,
at = 1:length(col_names), # positions (one per sample)
labels = col_names, # your labels vector
las = 2, # rotate text vertically (like srt=90)
cex.axis = 0.3) # shrink label size
# saveRDS(RNA_log2cpm,"data/QC/RNA_log2cpm.RDS")#####RowMu>0####
row_means <- rowMeans(RNA_log2cpm)
Filt_RMG0_RNA_fc <- RNA_fc[row_means >0,]
# saveRDS(Filt_RMG0_RNA_fc,"data/QC/Filt_RMG0_RNA_fc.RDS")
Filt_RMG0_RNA_log2cpm <- cpm(Filt_RMG0_RNA_fc,log=TRUE)
# saveRDS(Filt_RMG0_RNA_log2cpm,"data/QC/RNA_log2cpm_RMG0.RDS")
hist(Filt_RMG0_RNA_log2cpm, main = "Histogram of filtered counts using rowMeans > 0 method",
xlab =expression("Log"[2]*" counts-per-million"), col =5 )
boxplot(Filt_RMG0_RNA_log2cpm, main = "Boxplots of log cpm per sample (RowMeans>0)",xaxt = "n", xlab= "")
axis(1,
at = 1:length(col_names), # positions (one per sample)
labels = col_names, # your labels vector
las = 2, # rotate text vertically (like srt=90)
cex.axis = 0.3) # shrink label size
######Cor_HeatMap####
Cor_Filt_RMG0_RNA_log2cpm <- cor(Filt_RMG0_RNA_log2cpm, method = "spearman")
individual <- RNA_Metadata$Individual
species <- RNA_Metadata$Species
timepoint <- RNA_Metadata$Timepoint
timepoint <- factor(timepoint,levels = c("Day0","Day2","Day4","Day5","Day15","Day30"))
Cor_metadata <- data.frame(
sample_cor = colnames(Filt_RMG0_RNA_log2cpm),
species_cor = species,
timepoint_cor = timepoint,
individual_cor = individual
)
ann_colors <- list(
timepoint_cor = c(
"Day0" = "#883268", # Purple
"Day2" = "#3E7274", # blue
"Day4" = "#5AAA464D", # light green
"Day5" = "#94C47D", # Green
"Day15" = "#C03830", # red
"Day30" = "#830C05" # dark red
),
species_cor = c(
"H" = "#171717", # black
"C" = "#17171717" # light grey
),
individual_cor = c(
H1 = "#091638", #Blue-Green Darkest
H2 = "#11185B",
H3 = "#0F2C71",
H4 = "#0D568F",
H4R = "#0D568F",
H5 = "#1D8296",
H6 = "#46A389",
H7 = "#9DD484", #Blue-Green Lightest
C1 = "#340702", #Brown-Orange darkest
C2 = "#5D0B02",
C3 = "#951302",
C4 = "#D32804",
C5 = "#F74019",
C6 = "#FA7A38",
C7 = "#FCC598"
)
)
rownames(Cor_metadata) <- Cor_metadata$sample_cor
# saveRDS(Cor_Filt_RMG0_RNA_log2cpm, "data/QC/Cor_Filt_RMG0_RNA_log2cpm.RDS")
# saveRDS(Cor_metadata, "data/QC/Cor_RNA_metadata.RDS")
# saveRDS(ann_colors,"data/QC/ann_colors.RDS")print(
pheatmap(Cor_Filt_RMG0_RNA_log2cpm,
fontsize_row = 5,
fontsize_col = 5,
annotation_col = Cor_metadata[, c("species_cor", "timepoint_cor","individual_cor")],
annotation_colors = ann_colors,
clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation",
main = "Sample-Sample Correlation \n(Spearman-log2CPM-RowMeans>0)")
)
####Subset####
RNA_Metadata_No4 <- RNA_Metadata %>%
filter(timepoint != "Day4")
RNA_fc_NoD4 <- RNA_fc %>%
dplyr::select(-ends_with("_D4"))
RNA_log2cpm_NoD4 <- cpm(RNA_fc_NoD4,log=TRUE)
dim(RNA_log2cpm_NoD4)[1] 44125 74dim(RNA_fc)[1] 44125 88row_means_NoD4 <- rowMeans(RNA_log2cpm_NoD4)
Filt_RMG0_RNA_fc_NoD4 <- RNA_fc_NoD4[row_means_NoD4 >0,]
dim(Filt_RMG0_RNA_fc_NoD4)[1] 14838 74Filt_RMG0_RNA_log2cpm_NoD4 <- cpm(Filt_RMG0_RNA_fc_NoD4,log=TRUE)
# saveRDS(RNA_Metadata_No4,"data/QC/RNA_Metatdata_No4.RDS")
# saveRDS(Filt_RMG0_RNA_fc_NoD4,"data/QC/Filt_RMG0_RNA_fc_NoD4.RDS")
# saveRDS(Filt_RMG0_RNA_log2cpm_NoD4, "data/QC/Filt_RMG0_RNA_log2cpm_NoD4.RDS")######Cor_HeatMap####
Cor_Filt_RMG0_RNA_log2cpm_NoD4 <- cor(Filt_RMG0_RNA_log2cpm_NoD4, method = "spearman")
Cor_metadata_No4 <- Cor_metadata %>%
dplyr::filter(timepoint_cor !="Day4")
ann_colors_No4 <- ann_colors
ann_colors_No4$timepoint_cor <- ann_colors$timepoint_cor[
names(ann_colors$timepoint_cor) != "Day4"
]
# saveRDS(Cor_metadata_No4, "data/QC/Cor_metadata_No4.RDS")
# saveRDS(Cor_Filt_RMG0_RNA_log2cpm_NoD4, "data/QC/Cor_Filt_RMG0_RNA_log2cpm_NoD4.RDS")
# saveRDS(ann_colors_No4,"data/QC/ann_colors_no4.RDS")print(
pheatmap(Cor_Filt_RMG0_RNA_log2cpm_NoD4,
fontsize_row = 5,
fontsize_col = 5,
annotation_col = Cor_metadata_No4[, c("species_cor", "timepoint_cor","individual_cor")],
annotation_colors = ann_colors_No4,
clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation",
main = "Sample-Sample Correlation (Spearman) \n (log2CPM-RowMeans>0-NoDay4)")
)
filt_gene_list <- rownames(Filt_RMG0_RNA_log2cpm_NoD4)
#14838
length(filt_gene_list)[1] 14838# # in order to make this match with annot later down the line, change the col names for counts_raw_matrix to match final_sample_names in annot
#
# # i'll also want to make sure I keep the replicate for this set
#
# Ind_RUV <- c(rep("H1",5),
# rep("H2",5),
# rep("H3",5),
# rep("H4",5),
# rep("H5",5),
# rep("H6",5),
# rep("H7",5),
# rep("H4R",4),
# rep("C1",5),
# rep("C2",5),
# rep("C3",5),
# rep("C4",5),
# rep("C5",5),
# rep("C6",5),
# rep("C7",5)
# )
# RNA_Metadata_No4$Ind_RUV <- Ind_RUV
# RNA_Metadata_No4_RUV <- RNA_Metadata_No4 %>%
# mutate(
# Ind_RUV = factor(Ind_RUV,
# levels = c("H1","H2","H3","H4","H5","H6","H7","H4R","C1","C2","C3","C4","C5","C6","C7"),ordered=TRUE),
# Timepoint = factor(Timepoint,
# levels = c("Day0","Day2","Day5","Day15","Day30"),ordered=TRUE),
# Species = factor(Species,
# levels = c("H","C"),ordered=TRUE),
# Condition = factor(
# Condition,
# levels = expand.grid(
# Ind_RUV = levels(Ind_RUV),
# Timepoint = levels(Timepoint),
# Species = levels(Species)
# ) %>%
# transmute(Condition=paste(Species,Timepoint,Ind_RUV,sep="_")) %>%
# pull(Condition),
# ordered=TRUE
# )
# )
#
# # saveRDS(RNA_Metadata_No4_RUV,"data/QC/RNA_MetaData_NoD4_RUV.RDS")
# RNA_Metadata_No4_RUV <- readRDS("data/QC/RNA_MetaData_NoD4_RUV.RDS")
#
# RNA_fc_NoD4_RUV <- RNA_fc_NoD4
# colnames(RNA_fc_NoD4_RUV) <- RNA_Metadata_No4_RUV$Condition
#
# RUV_filt_counts <- RNA_fc_NoD4_RUV %>%
# as.data.frame() %>%
# dplyr::filter(., row.names(.)%in% filt_gene_list)
# # saveRDS(RUV_filt_counts, "data/DGE/RUV_filt_counts.RDS")
# dim(RUV_filt_counts)
#
# #add in the annotation files
# ind_num <- RNA_Metadata_No4_RUV$Ind_RUV
# type(ind_num)
# length(ind_num)
#
# annot <- as.data.frame(RNA_Metadata_No4_RUV)
# type(annot)
# is.data.frame(annot)
# #same as Metadata
#
#
# # counts need to be integer values and in a numeric matrix
# # note: the log transformation needs to be accounted for in the isLog argument in RUVs function.
# counts <- as.matrix(RUV_filt_counts)
#
# # saveRDS(counts, "data/QC/filt_counts_matrix.RDS")
#
# # Create a DataFrame for the phenoData
# phenoData <- DataFrame(annot)
#
# set <- SummarizedExperiment(assays = counts, metadata = phenoData)
#
# scIdx <- RUVSeq::makeGroups(phenoData$Cond)
#
# # # Generate a background matrix
# # # The column "Cond" holds the comparisons that you actually want to make. DOX_24, DMSO_24,5FU_24, DOX_3,etc.
# # Day0 <- c(16,36)
# # Day2 <- c(17,37)
# # Day5 <- c(18,38)
# # Day30 <- c(20,39)
# # scIdx <- rbind(Day0,Day2,Day5,Day30)
# # rownames(scIdx) <- c("[1,]","[2,]","[3,]","[4,]")
# # colnames(scIdx) <- c("[,1]","[,2]")
# ```
#
#
#
# ```{r}
# #now I've made all of the data I need for this - they are located in each section for k values
#
# #DO NOT USE THESE COUNTS FOR LINEAR MODELING
#
# #colors for all of the plots
# # txtime_col
# # ind_col
# # time_col
# # tx_col
# log2cpm <- cpm(counts,log=TRUE)
# # before ruv (counts PCA)
# prcomp_res_log2 <- prcomp(t(Filt_RMG0_RNA_log2cpm_NoD4), scale=FALSE, center = TRUE)
# annot_prcomp_res <- prcomp_res_log2$x %>% cbind(., annot)
# #
# # group_2 <- annot$dgelist
# # dgelist_col <- annot$dgelist
# # individual_list <- annot$Individual#
# #now plot my PCA for filtered counts
# ####PC1/PC2####
# individual_list <- RNA_Metadata_No4_RUV$Individual
#
# suppressWarnings(
# ggplot2::autoplot(prcomp_res_log2,
# data = annot,
# colour = "Timepoint",
# shape = "Species",
# size = 4,
# x = 1,
# y = 2) +
# scale_color_manual(values = ann_colors_No4$timepoint_cor) +
# ggrepel::geom_text_repel(
# label = individual_list,
# vjust = -0.5,
# max.overlaps = 50
# ) +
# ggtitle("RNA log2cpm RMG0")
# )# set1 <- RUVSeq::RUVs(x = counts, k =1, scIdx = scIdx, isLog = FALSE)
#
# # Get the RUV factors (weights) for modeling
# RUV_df1 <- set1$W %>% as.data.frame()
# RUV_df1$Condition <- rownames(RUV_df1)
#
# RUV_df_rm1 <- RUV_df1[RUV_df1$Condition %in% annot$Condition, ]
# View(RUV_df_rm1)
# RUV_1 <- RUV_df_rm1$W_1
#
# saveRDS(RUV_df_rm1, "data/QC/RUV_df_rm1.RDS")
# saveRDS(RUV_1, "data/QC/RUV_1.RDS")
#
#
# log2cpm_k1 <- cpm(set1$normalizedCounts, log = TRUE)
#
# prcomp_res_log2_k1 <- prcomp(t(log2cpm_k1), scale=FALSE, center = TRUE)
# annot_prcomp_res_k1 <- prcomp_res_log2_k1$x %>% cbind(., RNA_Metadata_No4)
# #PCA checks
# #k=1
#
# ggplot2::autoplot(prcomp_res_log2_k1,
# data = annot_prcomp_res_k1,
# colour = "Timepoint",
# shape = "Species",
# size = 4,
# x=1,
# y=2) +
# scale_color_manual(values=ann_colors_No4$timepoint_cor)+
# ggrepel::geom_text_repel(label = individual_list,
# vjust = -0.5,
# max.overlaps = 50)+
# ggtitle("RUV Correction k=1 log2cpm")# git -> commit all changes
# git -> push
# wflow_publish("analysis/RNA_CorrelationHeatMap_Ensemble.Rmd")
sessionInfo()R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
LAPACK version 3.12.1
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] ComplexHeatmap_2.24.1 ggfortify_0.4.19
[3] readxl_1.4.5 RUVSeq_1.42.0
[5] EDASeq_2.42.0 ShortRead_1.66.0
[7] GenomicAlignments_1.44.0 SummarizedExperiment_1.38.1
[9] MatrixGenerics_1.20.0 matrixStats_1.5.0
[11] Rsamtools_2.24.0 GenomicRanges_1.60.0
[13] Biostrings_2.76.0 GenomeInfoDb_1.44.3
[15] XVector_0.48.0 BiocParallel_1.42.1
[17] lubridate_1.9.4 forcats_1.0.1
[19] stringr_1.5.2 purrr_1.1.0
[21] tidyr_1.3.1 tidyverse_2.0.0
[23] Cormotif_1.54.0 affy_1.86.0
[25] pheatmap_1.0.13 org.Hs.eg.db_3.21.0
[27] AnnotationDbi_1.70.0 IRanges_2.42.0
[29] S4Vectors_0.46.0 Biobase_2.68.0
[31] BiocGenerics_0.54.0 generics_0.1.4
[33] readr_2.1.5 ggrepel_0.9.6
[35] dplyr_1.1.4 tibble_3.3.0
[37] ggplot2_4.0.0 edgeR_4.6.3
[39] limma_3.64.3 workflowr_1.7.2
loaded via a namespace (and not attached):
[1] later_1.4.4 BiocIO_1.18.0 bitops_1.0-9
[4] filelock_1.0.3 R.oo_1.27.1 cellranger_1.1.0
[7] preprocessCore_1.70.0 XML_3.99-0.20 lifecycle_1.0.5
[10] httr2_1.2.2 pwalign_1.4.0 doParallel_1.0.17
[13] rprojroot_2.1.1 processx_3.8.6 lattice_0.22-7
[16] MASS_7.3-65 magrittr_2.0.3 sass_0.4.10
[19] rmarkdown_2.30 jquerylib_0.1.4 yaml_2.3.10
[22] httpuv_1.6.16 otel_0.2.0 DBI_1.2.3
[25] RColorBrewer_1.1-3 abind_1.4-8 R.utils_2.13.0
[28] RCurl_1.98-1.17 rappdirs_0.3.4 git2r_0.36.2
[31] circlize_0.4.17 GenomeInfoDbData_1.2.14 codetools_0.2-20
[34] DelayedArray_0.34.1 xml2_1.5.1 tidyselect_1.2.1
[37] shape_1.4.6.1 UCSC.utils_1.4.0 farver_2.1.2
[40] BiocFileCache_2.16.2 jsonlite_2.0.0 GetoptLong_1.1.0
[43] iterators_1.0.14 foreach_1.5.2 tools_4.5.1
[46] progress_1.2.3 Rcpp_1.1.0 glue_1.8.0
[49] gridExtra_2.3 SparseArray_1.8.1 xfun_0.53
[52] withr_3.0.2 BiocManager_1.30.27 fastmap_1.2.0
[55] latticeExtra_0.6-31 callr_3.7.6 digest_0.6.37
[58] timechange_0.3.0 R6_2.6.1 colorspace_2.1-2
[61] Cairo_1.7-0 jpeg_0.1-11 biomaRt_2.64.0
[64] RSQLite_2.4.3 R.methodsS3_1.8.2 rtracklayer_1.68.0
[67] prettyunits_1.2.0 httr_1.4.7 S4Arrays_1.8.1
[70] whisker_0.4.1 pkgconfig_2.0.3 gtable_0.3.6
[73] blob_1.3.0 S7_0.2.0 hwriter_1.3.2.1
[76] htmltools_0.5.8.1 clue_0.3-66 scales_1.4.0
[79] png_0.1-8 knitr_1.51 rstudioapi_0.18.0
[82] tzdb_0.5.0 rjson_0.2.23 curl_7.0.0
[85] cachem_1.1.0 GlobalOptions_0.1.3 parallel_4.5.1
[88] restfulr_0.0.16 pillar_1.11.1 vctrs_0.6.5
[91] promises_1.3.3 dbplyr_2.5.1 cluster_2.1.8.1
[94] evaluate_1.0.5 GenomicFeatures_1.60.0 cli_3.6.5
[97] locfit_1.5-9.12 compiler_4.5.1 rlang_1.1.6
[100] crayon_1.5.3 interp_1.1-6 aroma.light_3.38.0
[103] ps_1.9.1 getPass_0.2-4 fs_1.6.6
[106] stringi_1.8.7 deldir_2.0-4 Matrix_1.7-3
[109] hms_1.1.4 bit64_4.6.0-1 KEGGREST_1.48.1
[112] statmod_1.5.0 memoise_2.0.1 affyio_1.78.0
[115] bslib_0.9.0 bit_4.6.0