Figure_2
Renee Matthews
2025-02-21
Last updated: 2025-05-01
Checks: 7 0
Knit directory: ATAC_learning/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20231016) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version cfc813a. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .RData
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data/ACresp_SNP_table.csv
Ignored: data/ARR_SNP_table.csv
Ignored: data/All_merged_peaks.tsv
Ignored: data/CAD_gwas_dataframe.RDS
Ignored: data/CTX_SNP_table.csv
Ignored: data/Collapsed_expressed_NG_peak_table.csv
Ignored: data/DEG_toplist_sep_n45.RDS
Ignored: data/FRiP_first_run.txt
Ignored: data/Final_four_data/
Ignored: data/Frip_1_reads.csv
Ignored: data/Frip_2_reads.csv
Ignored: data/Frip_3_reads.csv
Ignored: data/Frip_4_reads.csv
Ignored: data/Frip_5_reads.csv
Ignored: data/Frip_6_reads.csv
Ignored: data/GO_KEGG_analysis/
Ignored: data/HF_SNP_table.csv
Ignored: data/Ind1_75DA24h_dedup_peaks.csv
Ignored: data/Ind1_TSS_peaks.RDS
Ignored: data/Ind1_firstfragment_files.txt
Ignored: data/Ind1_fragment_files.txt
Ignored: data/Ind1_peaks_list.RDS
Ignored: data/Ind1_summary.txt
Ignored: data/Ind2_TSS_peaks.RDS
Ignored: data/Ind2_fragment_files.txt
Ignored: data/Ind2_peaks_list.RDS
Ignored: data/Ind2_summary.txt
Ignored: data/Ind3_TSS_peaks.RDS
Ignored: data/Ind3_fragment_files.txt
Ignored: data/Ind3_peaks_list.RDS
Ignored: data/Ind3_summary.txt
Ignored: data/Ind4_79B24h_dedup_peaks.csv
Ignored: data/Ind4_TSS_peaks.RDS
Ignored: data/Ind4_V24h_fraglength.txt
Ignored: data/Ind4_fragment_files.txt
Ignored: data/Ind4_fragment_filesN.txt
Ignored: data/Ind4_peaks_list.RDS
Ignored: data/Ind4_summary.txt
Ignored: data/Ind5_TSS_peaks.RDS
Ignored: data/Ind5_fragment_files.txt
Ignored: data/Ind5_fragment_filesN.txt
Ignored: data/Ind5_peaks_list.RDS
Ignored: data/Ind5_summary.txt
Ignored: data/Ind6_TSS_peaks.RDS
Ignored: data/Ind6_fragment_files.txt
Ignored: data/Ind6_peaks_list.RDS
Ignored: data/Ind6_summary.txt
Ignored: data/Knowles_4.RDS
Ignored: data/Knowles_5.RDS
Ignored: data/Knowles_6.RDS
Ignored: data/LiSiLTDNRe_TE_df.RDS
Ignored: data/MI_gwas.RDS
Ignored: data/SNP_GWAS_PEAK_MRC_id
Ignored: data/SNP_GWAS_PEAK_MRC_id.csv
Ignored: data/SNP_gene_cat_list.tsv
Ignored: data/SNP_supp_schneider.RDS
Ignored: data/TE_info/
Ignored: data/TFmapnames.RDS
Ignored: data/all_TSSE_scores.RDS
Ignored: data/all_four_filtered_counts.txt
Ignored: data/aln_run1_results.txt
Ignored: data/anno_ind1_DA24h.RDS
Ignored: data/anno_ind4_V24h.RDS
Ignored: data/annotated_gwas_SNPS.csv
Ignored: data/background_n45_he_peaks.RDS
Ignored: data/cardiac_muscle_FRIP.csv
Ignored: data/cardiomyocyte_FRIP.csv
Ignored: data/col_ng_peak.csv
Ignored: data/cormotif_full_4_run.RDS
Ignored: data/cormotif_full_4_run_he.RDS
Ignored: data/cormotif_full_6_run.RDS
Ignored: data/cormotif_full_6_run_he.RDS
Ignored: data/cormotif_probability_45_list.csv
Ignored: data/cormotif_probability_45_list_he.csv
Ignored: data/cormotif_probability_all_6_list.csv
Ignored: data/cormotif_probability_all_6_list_he.csv
Ignored: data/datasave.RDS
Ignored: data/embryo_heart_FRIP.csv
Ignored: data/enhancer_list_ENCFF126UHK.bed
Ignored: data/enhancerdata/
Ignored: data/filt_Peaks_efit2.RDS
Ignored: data/filt_Peaks_efit2_bl.RDS
Ignored: data/filt_Peaks_efit2_n45.RDS
Ignored: data/first_Peaksummarycounts.csv
Ignored: data/first_run_frag_counts.txt
Ignored: data/full_bedfiles/
Ignored: data/gene_ref.csv
Ignored: data/gwas_1_dataframe.RDS
Ignored: data/gwas_2_dataframe.RDS
Ignored: data/gwas_3_dataframe.RDS
Ignored: data/gwas_4_dataframe.RDS
Ignored: data/gwas_5_dataframe.RDS
Ignored: data/high_conf_peak_counts.csv
Ignored: data/high_conf_peak_counts.txt
Ignored: data/high_conf_peaks_bl_counts.txt
Ignored: data/high_conf_peaks_counts.txt
Ignored: data/hits_files/
Ignored: data/hyper_files/
Ignored: data/hypo_files/
Ignored: data/ind1_DA24hpeaks.RDS
Ignored: data/ind1_TSSE.RDS
Ignored: data/ind2_TSSE.RDS
Ignored: data/ind3_TSSE.RDS
Ignored: data/ind4_TSSE.RDS
Ignored: data/ind4_V24hpeaks.RDS
Ignored: data/ind5_TSSE.RDS
Ignored: data/ind6_TSSE.RDS
Ignored: data/initial_complete_stats_run1.txt
Ignored: data/left_ventricle_FRIP.csv
Ignored: data/median_24_lfc.RDS
Ignored: data/median_3_lfc.RDS
Ignored: data/mergedPeads.gff
Ignored: data/mergedPeaks.gff
Ignored: data/motif_list_full
Ignored: data/motif_list_n45
Ignored: data/motif_list_n45.RDS
Ignored: data/multiqc_fastqc_run1.txt
Ignored: data/multiqc_fastqc_run2.txt
Ignored: data/multiqc_genestat_run1.txt
Ignored: data/multiqc_genestat_run2.txt
Ignored: data/my_hc_filt_counts.RDS
Ignored: data/my_hc_filt_counts_n45.RDS
Ignored: data/n45_bedfiles/
Ignored: data/n45_files
Ignored: data/other_papers/
Ignored: data/peakAnnoList_1.RDS
Ignored: data/peakAnnoList_2.RDS
Ignored: data/peakAnnoList_24_full.RDS
Ignored: data/peakAnnoList_24_n45.RDS
Ignored: data/peakAnnoList_3.RDS
Ignored: data/peakAnnoList_3_full.RDS
Ignored: data/peakAnnoList_3_n45.RDS
Ignored: data/peakAnnoList_4.RDS
Ignored: data/peakAnnoList_5.RDS
Ignored: data/peakAnnoList_6.RDS
Ignored: data/peakAnnoList_Eight.RDS
Ignored: data/peakAnnoList_full_motif.RDS
Ignored: data/peakAnnoList_n45_motif.RDS
Ignored: data/siglist_full.RDS
Ignored: data/siglist_n45.RDS
Ignored: data/summarized_peaks_dataframe.txt
Ignored: data/summary_peakIDandReHeat.csv
Ignored: data/test.list.RDS
Ignored: data/testnames.txt
Ignored: data/toplist_6.RDS
Ignored: data/toplist_full.RDS
Ignored: data/toplist_full_DAR_6.RDS
Ignored: data/toplist_n45.RDS
Ignored: data/trimmed_seq_length.csv
Ignored: data/unclassified_full_set_peaks.RDS
Ignored: data/unclassified_n45_set_peaks.RDS
Ignored: data/xstreme/
Untracked files:
Untracked: analysis/Expressed_RNA_associations.Rmd
Untracked: analysis/LFC_corr.Rmd
Untracked: analysis/SVA.Rmd
Untracked: analysis/Tan2020.Rmd
Untracked: analysis/my_hc_filt_counts.csv
Untracked: code/IGV_snapshot_code.R
Untracked: code/LongDARlist.R
Untracked: code/just_for_Fun.R
Untracked: output/cormotif_probability_45_list.csv
Untracked: output/cormotif_probability_all_6_list.csv
Untracked: setup.RData
Unstaged changes:
Modified: ATAC_learning.Rproj
Modified: analysis/Correlation_of_SNPnPEAK.Rmd
Modified: analysis/GO_KEGG_analysis.Rmd
Modified: analysis/Raodah_mycount.Rmd
Modified: analysis/TE_analysis_ff.Rmd
Modified: analysis/final_plot_attempt.Rmd
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/Figure_2.Rmd) and HTML
(docs/Figure_2.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 |
|---|---|---|---|---|
| html | b735dc1 | E. Renee Matthews | 2025-02-24 | Build site. |
| Rmd | d88843c | E. Renee Matthews | 2025-02-24 | wflow_publish("analysis/Figure_2.Rmd") |
| Rmd | 8161256 | E. Renee Matthews | 2025-02-21 | next figure |
package loading
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(ggfortify)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(scales)
library(BiocParallel)
library(ggpubr)
# library(edgeR)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)
library(readxl)
library(circlize)
library(epitools)Figure 2
knitr::include_graphics("assets/Figure\ 2.png", error=FALSE)
| Version | Author | Date |
|---|---|---|
| 50f3de9 | E. Renee Matthews | 2025-02-21 |
knitr::include_graphics("docs/assets/Figure\ 2.png",error = FALSE)
Figure 2.A. Transposable elements:
repeatmasker
repeatmasker <- read.delim("data/other_papers/repeatmasker.tsv")# TSS_NG_data <- read_delim("data/Final_four_data/TSS_assigned_NG.txt",
# delim = "\t", escape_double = FALSE,
# trim_ws = TRUE)
Collapsed_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt",
delim = "\t",
escape_double = FALSE,
trim_ws = TRUE)
reClass_list <- repeatmasker %>%
distinct(repClass)
Line_repeats <- repeatmasker %>%
dplyr::filter(repClass == "LINE") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
Sine_repeats <- repeatmasker %>%
dplyr::filter(repClass == "SINE") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
LTR_repeats <- repeatmasker %>%
dplyr::filter(repClass == "LTR") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
DNA_repeats <- repeatmasker %>%
dplyr::filter(repClass == "DNA") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
retroposon_repeats <- repeatmasker %>%
dplyr::filter(repClass == "Retroposon") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
all_TEs_gr <- repeatmasker %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
Nine_group_list <- readRDS("data/Final_four_data/Nine_group_list.RDS")
list2env(Nine_group_list, envir = .GlobalEnv)<environment: R_GlobalEnv>peakAnnoList_ff_motif <- readRDS("data/Final_four_data/peakAnnoList_ff_motif.RDS")
background_peaks <- as.data.frame(peakAnnoList_ff_motif$background)
Col_TSS_data_gr <- Collapsed_peaks %>%
dplyr::filter(chr != "chrY") %>%
dplyr::filter(Peakid %in% background_peaks$Peakid) %>%
GRanges()
all_TEs_gr$TE_width <- width(all_TEs_gr)
Col_TSS_data_gr$peak_width <- width(Col_TSS_data_gr)
Col_fullDF_overlap <- join_overlap_intersect(Col_TSS_data_gr,all_TEs_gr)
cpgislands_df <- read.delim("data/other_papers/cpg_islands.tsv")
cpg_island_gr <- cpgislands_df %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "chrom", start.field = "chromStart", end.field = "chromEnd",starts.in.df.are.0based=TRUE)
Col_TSS_data_gr$peak_width <- width(Col_TSS_data_gr)
cpg_island_gr$cpg_width <- width(cpg_island_gr)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
transcripts <- transcripts(txdb)
tss <- resize(transcripts, width = 1, fix = "start")
overlaps <- findOverlaps(Col_TSS_data_gr,tss)
overlapping_regions <- Col_TSS_data_gr[queryHits(overlaps)]
TSS_overlap_list <- overlapping_regions %>% as.data.frame() %>%
distinct(Peakid)
Col_fullDF_cug_overlap <- join_overlap_intersect(Col_TSS_data_gr,cpg_island_gr)
Col_fullDF_cug_overlap <-Col_fullDF_cug_overlap %>%
as.data.frame %>%
mutate(per_ol=width/cpg_width)Nine_te_df <-
Col_TSS_data_gr %>%
as.data.frame %>%
distinct(Peakid) %>%
left_join(.,(Col_fullDF_overlap %>% as.data.frame)) %>%
dplyr::select(Peakid, repName:repFamily,TE_width,width) %>%
mutate(mrc = case_when(
Peakid %in% EAR_open$Peakid ~ "EAR_open",
Peakid %in% EAR_close$Peakid ~ "EAR_close",
Peakid %in% ESR_open$Peakid ~ "ESR_open",
Peakid %in% ESR_close$Peakid ~ "ESR_close",
Peakid %in% ESR_opcl$Peakid ~ "ESR_opcl",
Peakid %in% LR_open$Peakid ~ "LR_open",
Peakid %in% LR_close$Peakid ~ "LR_close",
Peakid %in% NR$Peakid ~ "NR",
Peakid %in% ESR_clop$Peakid ~ "ESR_clop",
TRUE ~ "not_mrc"
)) %>%
mutate(per_ol= width/TE_width) %>%
mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
mutate(repClass_org=repClass) %>%
mutate(repClass=factor(repClass)) %>%
mutate(repClass=if_else(repClass_org=="LINE", repClass_org,
if_else(repClass_org=="SINE",repClass_org,
if_else(repClass_org=="LTR", repClass_org,
if_else(repClass_org=="DNA", repClass_org,
if_else(repClass_org=="Retroposon",repClass_org,"Other")))))) %>%
mutate(Sine_status = if_else(is.na(repClass),"not_sine",
if_else(repClass=="SINE","sine_peak", "not_sine"))) %>%
mutate(Line_status = if_else(is.na(repClass),"not_line",
if_else(repClass=="LINE","line_peak", "not_line"))) %>%
mutate(LTR_status = if_else(is.na(repClass),"not_LTR",
if_else(repClass=="LTR","LTR_peak", "not_LTR"))) %>%
mutate(DNA_status = if_else(is.na(repClass),"not_DNA",
if_else(repClass=="DNA","DNA_peak", "not_DNA"))) %>%
mutate(Retro_status = if_else(is.na(repClass)&is.na(per_ol),"not_Retro",
if_else(repClass=="Retroposon","Retro_peak", "not_Retro"))) %>%
mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>%
mutate(Sine_status=factor(Sine_status, levels = c("sine_peak","not_sine")),
Line_status=factor(Line_status, levels =c("line_peak","not_line")),
LTR_status=factor(LTR_status, levels =c("LTR_peak","not_LTR")),
DNA_status=factor(DNA_status, levels =c("DNA_peak","not_DNA")),
Retro_status=factor(Retro_status, levels =c("Retro_peak","not_Retro")))
CUG_mrc_nine_list <-
Col_TSS_data_gr%>% as.data.frame() %>%
left_join(., (Col_fullDF_cug_overlap %>%
as.data.frame(.)), by=c("seqnames"="seqnames", "start"="start", "end"="end", "Peakid"="Peakid", "NCBI_gene"="NCBI_gene", "dist_to_NG"="dist_to_NG", "SYMBOL" = "SYMBOL", "peak_width"="peak_width")) %>%
dplyr::select(Peakid, name,cpgNum:per_ol) %>%
mutate(cugstatus=if_else(is.na(cpgNum),"not_CGi_peak","CGi_peak")) %>%
mutate(mrc = case_when(
Peakid %in% EAR_open$Peakid ~ "EAR_open",
Peakid %in% EAR_close$Peakid ~ "EAR_close",
Peakid %in% ESR_open$Peakid ~ "ESR_open",
Peakid %in% ESR_close$Peakid ~ "ESR_close",
Peakid %in% ESR_opcl$Peakid ~ "ESR_opcl",
Peakid %in% LR_open$Peakid ~ "LR_open",
Peakid %in% LR_close$Peakid ~ "LR_close",
Peakid %in% NR$Peakid ~ "NR",
Peakid %in% ESR_clop$Peakid ~ "ESR_clop",
TRUE ~ "not_mrc"
)) %>%
distinct()making contingency matrices ##### contingency matrices for TE/TSS/CGI
TE_mat<- Nine_te_df %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(TEstatus, mrc) %>%
tally %>%
pivot_wider(id_cols = mrc, names_from = TEstatus,values_from = n) %>%
column_to_rownames("mrc") %>%
as.matrix(.)
Sine_mat<- Nine_te_df %>%
dplyr::filter(mrc != "not_mrc") %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
group_by(Sine_status, mrc) %>%
tally %>%
pivot_wider(id_cols = mrc, names_from = Sine_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
Line_mat<- Nine_te_df %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(Line_status, mrc) %>%
tally %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
pivot_wider(id_cols = mrc, names_from = Line_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
LTR_mat<- Nine_te_df %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(LTR_status, mrc) %>%
tally %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
pivot_wider(id_cols = mrc, names_from = LTR_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
Retro_mat<- Nine_te_df %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(Retro_status, mrc) %>%
tally %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
pivot_wider(id_cols = mrc, names_from = Retro_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
DNA_mat<- Nine_te_df %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(DNA_status, mrc) %>%
tally %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
pivot_wider(id_cols = mrc, names_from = DNA_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
CUG_mat <- CUG_mrc_nine_list %>%
distinct(Peakid,.keep_all = TRUE) %>%
dplyr::filter(mrc != "not_mrc") %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
group_by(cugstatus, mrc) %>%
tally %>%
pivot_wider(id_cols = mrc, names_from = cugstatus,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit() %>%
as.matrix(.)
TSS_mat <-Col_TSS_data_gr %>%
as.data.frame() %>%
dplyr::select(Peakid) %>%
mutate(TSS_status= if_else(Peakid %in% TSS_overlap_list$Peakid,"TSS_peak","not_TSS_peak")) %>%
mutate(mrc = case_when(
Peakid %in% EAR_open$Peakid ~ "EAR_open",
Peakid %in% EAR_close$Peakid ~ "EAR_close",
Peakid %in% ESR_open$Peakid ~ "ESR_open",
Peakid %in% ESR_close$Peakid ~ "ESR_close",
Peakid %in% ESR_opcl$Peakid ~ "ESR_opcl",
Peakid %in% LR_open$Peakid ~ "LR_open",
Peakid %in% LR_close$Peakid ~ "LR_close",
Peakid %in% NR$Peakid ~ "NR",
Peakid %in% ESR_clop$Peakid ~ "ESR_clop",
TRUE ~ "not_mrc"
)) %>%
group_by(TSS_status, mrc) %>%
dplyr::filter(mrc !="not_mrc") %>%
tally %>%
pivot_wider(id_cols = mrc, names_from = TSS_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)odds ratio TE/TSS/CGI
matrix_list <- list("TE"=TE_mat, "Sines"=Sine_mat, "Lines"=Line_mat,"DNA"= DNA_mat,"Retro"= Retro_mat,"LTR"= LTR_mat,"CGI"=CUG_mat,"TSS"=TSS_mat)
results_or <- data.frame(Matrix_Name = character(),
Row_Compared = character(),
Odds_Ratio = numeric(),
Lower_CI = numeric(),
Upper_CI = numeric(),
P_Value = numeric(),
stringsAsFactors = FALSE)
# Loop through each matrix in the list
for (matrix_name in names(matrix_list)) {
current_matrix <- matrix_list[[matrix_name]]
n_rows <- nrow(current_matrix)
# Loop through each row of the current matrix (except the last row)
for (i in 1:(n_rows - 1)) {
# Perform odds ratio test between row i and the last row using epitools
test_result <- tryCatch({
contingency_table <- rbind(current_matrix[i, ], current_matrix[n_rows, ])
# Check if any row in the contingency table contains only zeros
if (any(rowSums(contingency_table) == 0)) {
stop("Contingency table contains empty rows.")
}
oddsratio_result <- oddsratio(contingency_table)
# Ensure the oddsratio result has at least 2 rows
if (nrow(oddsratio_result$measure) < 2) {
stop("oddsratio result does not have enough data.")
}
list(oddsratio = oddsratio_result, p.value = oddsratio_result$p.value[2,"chi.square"])
}, error = function(e) {
cat("Error in odds ratio test for row", i, "in matrix", matrix_name, ":", e$message, "\n")
return(NULL)
})
# Only store the result if test_result is valid (i.e., not NULL)
if (!is.null(test_result)) {
or_value <- test_result$oddsratio$measure[2, "estimate"]
lower_ci <- test_result$oddsratio$measure[2, "lower"]
upper_ci <- test_result$oddsratio$measure[2, "upper"]
p_value <- test_result$oddsratio$p.value[2,"chi.square"]
# Check if the values are numeric and valid (not NA)
if (!is.na(or_value) && !is.na(lower_ci) && !is.na(upper_ci) && !is.na(p_value)) {
# Store the results in the dataframe
results_or <- rbind(results_or, data.frame(Matrix_Name = matrix_name,
Row_Compared = rownames(current_matrix)[i],
Odds_Ratio = or_value,
Lower_CI = lower_ci,
Upper_CI = upper_ci,
P_Value = p_value))
}
}
}
}
results_or <- results_or %>%
mutate(Matrix_Name=factor(Matrix_Name, levels=c("TE","Sines","Lines", "DNA","LTR","Retro","CGI","TSS"))) %>%
mutate(Row_Compared=factor(Row_Compared, levels = c("EAR_open","ESR_open", "LR_open","ESR_opcl", "EAR_close", "ESR_close", "LR_close", "ESR_clop"))) %>%
arrange(Matrix_Name,Row_Compared)
col_fun_OR = colorRamp2(c(0,1,1.5,3,4), c("#BC9BFF","white","lightgreen","green3","green3" ))
sig_mat_OR <- results_or %>%
as.data.frame() %>%
dplyr::select( Matrix_Name,Row_Compared,P_Value) %>%
pivot_wider(., id_cols = Matrix_Name, names_from = Row_Compared, values_from = P_Value) %>%
column_to_rownames("Matrix_Name") %>%
as.matrix()
# results_or %>%
# as.data.frame() %>%
# dplyr::select( Matrix_Name,Row_Compared,Odds_Ratio) %>%
# pivot_wider(., id_cols = Matrix_Name, names_from = Row_Compared, values_from = Odds_Ratio) %>%
# column_to_rownames("Matrix_Name") %>%
# as.matrix() %>%
# ComplexHeatmap::Heatmap(. ,col = col_fun_OR,
# cluster_rows=FALSE,
# cluster_columns=FALSE,
# column_names_side = "top",
# column_names_rot = 45,
# # 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)
# grid.text("*", x, y, gp = gpar(fontsize = 20))})cRE dataframes
cREs_HLV_46F <- genomation::readBed("data/enhancerdata/ENCFF867HAD_ENCFF152PBB_ENCFF352YYH_ENCFF252IVK.7group.bed")
NR_gr <- NR %>% GRanges()
LR_open_gr <- LR_open %>% GRanges()
LR_close_gr <- LR_close%>% GRanges()
EAR_open_gr <- EAR_open%>% GRanges()
EAR_close_gr <- EAR_close%>% GRanges()
ESR_open_gr <- ESR_open%>% GRanges()
ESR_close_gr <- ESR_close%>% GRanges()
ESR_opcl_gr <- ESR_opcl%>% GRanges()
ESR_clop_gr <- ESR_clop%>% GRanges()
NR_cREs <- join_overlap_intersect(NR_gr,cREs_HLV_46F)
LR_open_cREs <- join_overlap_intersect(LR_open_gr,cREs_HLV_46F)
LR_close_cREs <- join_overlap_intersect(LR_close_gr,cREs_HLV_46F)
ESR_open_cREs <- join_overlap_intersect(ESR_open_gr,cREs_HLV_46F)
ESR_close_cREs <- join_overlap_intersect(ESR_close_gr,cREs_HLV_46F)
ESR_opcl_cREs <- join_overlap_intersect(ESR_opcl_gr, cREs_HLV_46F)
ESR_clop_cREs <- join_overlap_intersect(ESR_clop_gr, cREs_HLV_46F)
EAR_open_cREs <- join_overlap_intersect(EAR_open_gr,cREs_HLV_46F)
EAR_close_cREs <- join_overlap_intersect(EAR_close_gr,cREs_HLV_46F)
Whole_peaks <- join_overlap_intersect(Col_TSS_data_gr, cREs_HLV_46F)
keep_cRE_names <- c("CTCF-only,CTCF-bound" ,"PLS,CTCF-bound","PLS","dELS,CTCF-bound", "pELS","pELS,CTCF-bound","dELS")
is_cRE <- Whole_peaks %>%
as.data.frame() %>%
dplyr::filter(blockCount %in% keep_cRE_names) %>%
distinct(Peakid,blockCount)
is_CTCF <- Whole_peaks %>%
as.data.frame() %>%
dplyr::filter(blockCount == "CTCF-only,CTCF-bound") %>%
distinct(Peakid,blockCount)
is_dELS <- Whole_peaks %>%
as.data.frame() %>%
dplyr::filter(blockCount == "dELS,CTCF-bound"|blockCount == "dELS") %>%
distinct(Peakid,blockCount)
is_pELS <- Whole_peaks %>%
as.data.frame() %>%
dplyr::filter(blockCount == "pELS,CTCF-bound"|blockCount == "pELS") %>%
distinct(Peakid,blockCount)
is_PLS <- Whole_peaks %>%
as.data.frame() %>%
dplyr::filter(blockCount == "PLS,CTCF-bound"|blockCount == "PLS") %>%
distinct(Peakid,blockCount)
CRE_summary <- Collapsed_peaks %>%
mutate(cRE_status=if_else(Peakid %in% is_cRE$Peakid,"cRE_peak","not_cRE_peak")) %>%
mutate(CTCF_status=if_else(Peakid %in% is_CTCF$Peakid,"CTCF_peak","not_CTCF_peak")) %>%
mutate(dELS_status=if_else(Peakid %in% is_dELS$Peakid,"dELS_peak","not_dELS_peak")) %>%
mutate(pELS_status=if_else(Peakid %in% is_pELS$Peakid,"pELS_peak","not_pELS_peak")) %>%
mutate(PLS_status=if_else(Peakid %in% is_PLS$Peakid,"PLS_peak","not_PLS_peak")) %>%
mutate(mrc = case_when(
Peakid %in% EAR_open$Peakid ~ "EAR_open",
Peakid %in% EAR_close$Peakid ~ "EAR_close",
Peakid %in% ESR_open$Peakid ~ "ESR_open",
Peakid %in% ESR_close$Peakid ~ "ESR_close",
Peakid %in% ESR_opcl$Peakid ~ "ESR_opcl",
Peakid %in% LR_open$Peakid ~ "LR_open",
Peakid %in% LR_close$Peakid ~ "LR_close",
Peakid %in% NR$Peakid ~ "NR",
Peakid %in% ESR_clop$Peakid ~ "ESR_clop",
TRUE ~ "not_mrc"
)) cRE contingency matricies
cRE_mat<- CRE_summary %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(cRE_status, mrc) %>%
tally %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
pivot_wider(id_cols = mrc, names_from = cRE_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
CTCF_mat<- CRE_summary %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(CTCF_status, mrc) %>%
tally %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
pivot_wider(id_cols = mrc, names_from = CTCF_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
dELS_mat<- CRE_summary %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(dELS_status, mrc) %>%
tally %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
pivot_wider(id_cols = mrc, names_from = dELS_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
pELS_mat<- CRE_summary %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(pELS_status, mrc) %>%
tally %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
pivot_wider(id_cols = mrc, names_from = pELS_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)
PLS_mat<- CRE_summary %>%
dplyr::filter(mrc != "not_mrc") %>%
group_by(PLS_status, mrc) %>%
tally %>%
mutate(mrc= factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close", "ESR_opcl", "ESR_clop","LR_open","LR_close","NR"))) %>%
pivot_wider(id_cols = mrc, names_from = PLS_status,values_from = n) %>%
column_to_rownames("mrc") %>%
na.omit(.) %>%
as.matrix(.)cRE odds ratio
matrix_list_cre <- list("PLS"=PLS_mat, "dELS"=dELS_mat, "pELS"=pELS_mat,"CTCF"= CTCF_mat,"All cREs"= cRE_mat)
results_or_cre <- data.frame(Matrix_Name = character(),
Row_Compared = character(),
Odds_Ratio = numeric(),
Lower_CI = numeric(),
Upper_CI = numeric(),
P_Value = numeric(),
stringsAsFactors = FALSE)
# Loop through each matrix in the list
for (matrix_name in names(matrix_list_cre)) {
current_matrix <- matrix_list_cre[[matrix_name]]
n_rows <- nrow(current_matrix)
# Loop through each row of the current matrix (except the last row)
for (i in 1:(n_rows - 1)) {
# Perform odds ratio test between row i and the last row using epitools
test_result <- tryCatch({
contingency_table <- rbind(current_matrix[i, ], current_matrix[n_rows, ])
# Check if any row in the contingency table contains only zeros
if (any(rowSums(contingency_table) == 0)) {
stop("Contingency table contains empty rows.")
}
oddsratio_result <- oddsratio(contingency_table)
# Ensure the oddsratio result has at least 2 rows
if (nrow(oddsratio_result$measure) < 2) {
stop("oddsratio result does not have enough data.")
}
list(oddsratio = oddsratio_result, p.value = oddsratio_result$p.value[2,"chi.square"])
}, error = function(e) {
cat("Error in odds ratio test for row", i, "in matrix", matrix_name, ":", e$message, "\n")
return(NULL)
})
# Only store the result if test_result is valid (i.e., not NULL)
if (!is.null(test_result)) {
or_value <- test_result$oddsratio$measure[2, "estimate"]
lower_ci <- test_result$oddsratio$measure[2, "lower"]
upper_ci <- test_result$oddsratio$measure[2, "upper"]
p_value <- test_result$oddsratio$p.value[2,"chi.square"]
# Check if the values are numeric and valid (not NA)
if (!is.na(or_value) && !is.na(lower_ci) && !is.na(upper_ci) && !is.na(p_value)) {
# Store the results in the dataframe
results_or_cre <- rbind(results_or_cre, data.frame(Matrix_Name = matrix_name,
Row_Compared = rownames(current_matrix)[i],
Odds_Ratio = or_value,
Lower_CI = lower_ci,
Upper_CI = upper_ci,
P_Value = p_value))
}
}
}
}
results_or_cre <- results_or_cre %>%
mutate(Matrix_Name=factor(Matrix_Name, levels=c("All cREs","PLS","dELS", "pELS","CTCF"))) %>%
mutate(Row_Compared=factor(Row_Compared, levels = c("EAR_open","ESR_open", "LR_open","ESR_opcl", "EAR_close", "ESR_close", "LR_close", "ESR_clop"))) %>%
arrange(Matrix_Name,Row_Compared)
col_fun_cre = colorRamp2(c(0,1,1.5,3,4), c("#BC9BFF","white","lightgreen","green3","green3" ))
sig_mat_cre <- results_or_cre %>%
as.data.frame() %>%
dplyr::select( Matrix_Name,Row_Compared,P_Value) %>%
pivot_wider(., id_cols = Matrix_Name, names_from = Row_Compared, values_from = P_Value) %>%
column_to_rownames("Matrix_Name") %>%
as.matrix()
# results_or_cre %>%
# as.data.frame() %>%
# dplyr::select( Matrix_Name,Row_Compared,Odds_Ratio) %>%
# pivot_wider(., id_cols = Matrix_Name, names_from = Row_Compared, values_from = Odds_Ratio) %>%
# column_to_rownames("Matrix_Name") %>%
# as.matrix() %>%
# ComplexHeatmap::Heatmap(. ,col = col_fun_cre,
# cluster_rows=FALSE,
# cluster_columns=FALSE,
# column_names_side = "top",
# column_names_rot = 45,
# cell_fun = function(j, i, x, y, width, height, fill) {if (!is.na(sig_mat_cre[i, j]) && sig_mat_cre[i, j] < 0.05)
# grid.text("*", x, y, gp = gpar(fontsize = 20))})Now to adjust P-values BH style:
bot_df <- results_or_cre
top_df <- results_or
results_order <- top_df %>%
rbind(bot_df) %>%
mutate(Matrix_Name=factor(Matrix_Name,
levels=c("TE",
"Sines",
"Lines",
"DNA","LTR",
"Retro","CGI",
"TSS","All cREs",
"PLS","dELS","pELS",
"CTCF"))) %>%
arrange(Matrix_Name) %>%
group_by(Row_Compared) %>%
mutate(rank_val=rank(P_Value, ties.method = "first")) %>%
mutate(BH_correction= p.adjust(P_Value,method= "BH")) %>%
mutate(sig=P_Value<BH_correction) %>%
mutate(Row_Compared=factor(Row_Compared,levels = c("EAR_open", "ESR_open", "LR_open","ESR_opcl",
"EAR_close","ESR_close","LR_close","ESR_clop")))
critical_value <- max(results_order$P_Value[results_order$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( Matrix_Name,Row_Compared,BH_correction) %>%
arrange(Matrix_Name) %>%
pivot_wider(., id_cols = Matrix_Name, names_from = Row_Compared, 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("Matrix_Name") %>%
as.matrix()
results_mat <- results_order %>%
as.data.frame() %>%
dplyr::select( Matrix_Name,Row_Compared,Odds_Ratio) %>%
arrange(Matrix_Name) %>%
pivot_wider(., id_cols = Matrix_Name, names_from = Row_Compared, values_from = Odds_Ratio) %>%
dplyr::select(Matrix_Name,EAR_open,ESR_open,LR_open,ESR_opcl,EAR_close,ESR_close,LR_close,ESR_clop) %>%
column_to_rownames("Matrix_Name") %>%
as.matrix()
#%>%
ComplexHeatmap::Heatmap(results_mat ,col = col_fun_OR,
cluster_rows=FALSE,
cluster_columns=FALSE,
column_names_side = "top",
column_names_rot = 45,
# 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) {
grid.text("*", x, y, gp = gpar(fontsize = 20)) # Add star if significant
} })
| Version | Author | Date |
|---|---|---|
| b735dc1 | E. Renee Matthews | 2025-02-24 |
Figure 2.B: SVAs (Retrotransposons in repeatmasker)
retroposon_repeats <- repeatmasker %>%
dplyr::filter(repClass == "Retroposon") %>%
makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)
retroposon_df <- retroposon_repeats %>%
as.data.frame() %>%
mutate(repName=factor(repName))
scale_fill_retroposons <- function(...){
ggplot2:::manual_scale(
'fill',
values = setNames(c( "#8DD3C7",
"#FFFFB3",
"#BEBADA" ,
"#FB8072",
"#80B1D3",
"#FDB462",
"#B3DE69",
"#FCCDE5",
"#D9D9D9",
"#BC80BD",
"#CCEBC5",
"pink4",
"cornflowerblue",
"chocolate",
"brown",
"green",
"yellow4",
"purple",
"darkorchid4",
"coral4",
"darkolivegreen4",
"darkorange"), unique(retroposon_df$repName)),
...
)
}
h.genome_df <- repeatmasker %>%
mutate(repClass_org = repClass) %>% #copy repClass for storage
mutate(repClass=if_else(##relable repClass with other
repClass_org=="LINE", repClass_org,if_else(repClass_org=="SINE",repClass_org,if_else(repClass_org=="LTR", repClass_org, if_else(repClass_org=="DNA", repClass_org, if_else(repClass_org=="Retroposon",repClass_org,"Other")))))) %>%
mutate(Peakid=paste0(rownames(.),"_TE")) %>%
dplyr::select(Peakid,repName,repClass, repFamily,repClass_org) %>%
mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA", width="NA")
ggretroposon_df <-Nine_te_df %>%
dplyr::filter(repClass=="Retroposon") %>%
distinct(Peakid, TEstatus,repClass,.keep_all = TRUE) %>%
mutate(mrc="all_peaks")
h.genome_SVA <-h.genome_df %>%
dplyr::filter(repClass=="Retroposon") %>%
rbind(., (ggretroposon_df %>% dplyr::select(Peakid:repFamily,width, mrc, per_ol,TEstatus,repClass_org))) %>%
rbind(., (Nine_te_df %>%
dplyr::filter(repClass=="Retroposon") %>%
dplyr::select(Peakid:repFamily,width, mrc, per_ol,TEstatus,repClass_org) %>%
dplyr::filter(mrc != "not_mrc")))%>%
mutate(repName=factor(repName)) %>%
mutate(mrc=factor(mrc, levels = c("h.genome", "all_peaks", "EAR_open","ESR_open","LR_open","ESR_opcl", "EAR_close","ESR_close","LR_close","ESR_clop","NR")))
h.genome_SVA %>%
dplyr::filter(TEstatus=="TE_peak") %>%
ggplot(., aes(x=mrc, fill= repName))+
geom_bar(position="fill", col="black")+
theme_bw()+
ggtitle("Repeat breakdown across response classes")+
scale_fill_retroposons()
| Version | Author | Date |
|---|---|---|
| b735dc1 | E. Renee Matthews | 2025-02-24 |
Figure 2.C: Gene elements distribution
# mylist_new <- list("EAR_open"=EAR_open_gr,
# "EAR_close"=EAR_close_gr,
# "ESR_open"=ESR_open_gr,
# "ESR_close"=ESR_close_gr,
# "ESR_opcl"=ESR_opcl_gr,
# "ESR_clop"=ESR_clop_gr,
# "LR_open"=LR_open_gr,
# "LR_close"=LR_close_gr,
# "NR"=NR_gr,
# "background"=background_gr)
#
# peakAnnoList<- lapply(mylist_new, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# names(peakAnnoList) <- c("EAR_open", "EAR_close","ESR_open", "ESR_close","ESR_opcl","ESR_clop", "LR_open", "LR_close","NR","background")
# saveRDS(peakAnnoList, "data/Final_four_data/peakAnnoList_ff_9motif.RDS")
peakAnnoList_9_motif <- readRDS("data/Final_four_data/peakAnnoList_ff_9motif.RDS")
plotAnnoBar(peakAnnoList_9_motif[c(1,3,7,5,2,4,8,6,9)])+
ggtitle ("Genomic Feature Distribution, nine groups")
| Version | Author | Date |
|---|---|---|
| b735dc1 | E. Renee Matthews | 2025-02-24 |
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] ChIPseeker_1.42.1
[2] epitools_0.5-10.1
[3] circlize_0.4.16
[4] readxl_1.4.5
[5] smplot2_0.2.5
[6] cowplot_1.1.3
[7] ComplexHeatmap_2.22.0
[8] ggrepel_0.9.6
[9] plyranges_1.26.0
[10] ggsignif_0.6.4
[11] genomation_1.38.0
[12] ggpubr_0.6.0
[13] BiocParallel_1.40.0
[14] scales_1.3.0
[15] gridExtra_2.3
[16] ggfortify_0.4.17
[17] rtracklayer_1.66.0
[18] org.Hs.eg.db_3.20.0
[19] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[20] GenomicFeatures_1.58.0
[21] AnnotationDbi_1.68.0
[22] Biobase_2.66.0
[23] GenomicRanges_1.58.0
[24] GenomeInfoDb_1.42.3
[25] IRanges_2.40.1
[26] S4Vectors_0.44.0
[27] BiocGenerics_0.52.0
[28] RColorBrewer_1.1-3
[29] broom_1.0.7
[30] kableExtra_1.4.0
[31] lubridate_1.9.4
[32] forcats_1.0.0
[33] stringr_1.5.1
[34] dplyr_1.1.4
[35] purrr_1.0.4
[36] readr_2.1.5
[37] tidyr_1.3.1
[38] tibble_3.2.1
[39] ggplot2_3.5.1
[40] tidyverse_2.0.0
[41] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.5
[2] matrixStats_1.5.0
[3] bitops_1.0-9
[4] enrichplot_1.26.6
[5] httr_1.4.7
[6] doParallel_1.0.17
[7] tools_4.4.2
[8] backports_1.5.0
[9] R6_2.6.1
[10] lazyeval_0.2.2
[11] GetoptLong_1.0.5
[12] withr_3.0.2
[13] cli_3.6.4
[14] labeling_0.4.3
[15] sass_0.4.9
[16] Rsamtools_2.22.0
[17] systemfonts_1.2.1
[18] yulab.utils_0.2.0
[19] foreign_0.8-88
[20] DOSE_4.0.0
[21] svglite_2.1.3
[22] R.utils_2.13.0
[23] plotrix_3.8-4
[24] BSgenome_1.74.0
[25] pwr_1.3-0
[26] rstudioapi_0.17.1
[27] impute_1.80.0
[28] RSQLite_2.3.9
[29] generics_0.1.3
[30] gridGraphics_0.5-1
[31] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[32] shape_1.4.6.1
[33] BiocIO_1.16.0
[34] gtools_3.9.5
[35] vroom_1.6.5
[36] car_3.1-3
[37] GO.db_3.20.0
[38] Matrix_1.7-3
[39] abind_1.4-8
[40] R.methodsS3_1.8.2
[41] lifecycle_1.0.4
[42] whisker_0.4.1
[43] yaml_2.3.10
[44] carData_3.0-5
[45] SummarizedExperiment_1.36.0
[46] gplots_3.2.0
[47] qvalue_2.38.0
[48] SparseArray_1.6.2
[49] blob_1.2.4
[50] promises_1.3.2
[51] crayon_1.5.3
[52] ggtangle_0.0.6
[53] lattice_0.22-6
[54] KEGGREST_1.46.0
[55] magick_2.8.5
[56] pillar_1.10.1
[57] knitr_1.49
[58] fgsea_1.32.2
[59] rjson_0.2.23
[60] boot_1.3-31
[61] codetools_0.2-20
[62] fastmatch_1.1-6
[63] glue_1.8.0
[64] getPass_0.2-4
[65] ggfun_0.1.8
[66] data.table_1.17.0
[67] vctrs_0.6.5
[68] png_0.1-8
[69] treeio_1.30.0
[70] cellranger_1.1.0
[71] gtable_0.3.6
[72] cachem_1.1.0
[73] xfun_0.51
[74] S4Arrays_1.6.0
[75] iterators_1.0.14
[76] nlme_3.1-167
[77] ggtree_3.14.0
[78] bit64_4.6.0-1
[79] rprojroot_2.0.4
[80] bslib_0.9.0
[81] KernSmooth_2.23-26
[82] rpart_4.1.24
[83] colorspace_2.1-1
[84] DBI_1.2.3
[85] Hmisc_5.2-2
[86] seqPattern_1.38.0
[87] nnet_7.3-20
[88] tidyselect_1.2.1
[89] processx_3.8.6
[90] bit_4.6.0
[91] compiler_4.4.2
[92] curl_6.2.1
[93] git2r_0.35.0
[94] htmlTable_2.4.3
[95] xml2_1.3.7
[96] DelayedArray_0.32.0
[97] caTools_1.18.3
[98] checkmate_2.3.2
[99] callr_3.7.6
[100] digest_0.6.37
[101] rmarkdown_2.29
[102] XVector_0.46.0
[103] htmltools_0.5.8.1
[104] pkgconfig_2.0.3
[105] base64enc_0.1-3
[106] MatrixGenerics_1.18.1
[107] fastmap_1.2.0
[108] rlang_1.1.5
[109] GlobalOptions_0.1.2
[110] htmlwidgets_1.6.4
[111] UCSC.utils_1.2.0
[112] farver_2.1.2
[113] jquerylib_0.1.4
[114] zoo_1.8-13
[115] jsonlite_1.9.1
[116] GOSemSim_2.32.0
[117] R.oo_1.27.0
[118] RCurl_1.98-1.16
[119] magrittr_2.0.3
[120] Formula_1.2-5
[121] GenomeInfoDbData_1.2.13
[122] ggplotify_0.1.2
[123] patchwork_1.3.0
[124] munsell_0.5.1
[125] Rcpp_1.0.14
[126] ape_5.8-1
[127] stringi_1.8.4
[128] zlibbioc_1.52.0
[129] plyr_1.8.9
[130] parallel_4.4.2
[131] Biostrings_2.74.1
[132] splines_4.4.2
[133] hms_1.1.3
[134] ps_1.9.0
[135] igraph_2.1.4
[136] reshape2_1.4.4
[137] XML_3.99-0.18
[138] evaluate_1.0.3
[139] tzdb_0.4.0
[140] foreach_1.5.2
[141] httpuv_1.6.15
[142] clue_0.3-66
[143] gridBase_0.4-7
[144] restfulr_0.0.15
[145] tidytree_0.4.6
[146] rstatix_0.7.2
[147] later_1.4.1
[148] viridisLite_0.4.2
[149] aplot_0.2.5
[150] memoise_2.0.1
[151] GenomicAlignments_1.42.0
[152] cluster_2.1.8.1
[153] timechange_0.3.0