Supplemental Figures 1-11
Renee Matthews
2025-02-26
Last updated: 2025-05-01
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Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | b5ac214 | reneeisnowhere | 2025-03-20 | Build site. |
| Rmd | 58be8ae | reneeisnowhere | 2025-03-20 | updates to supplementary files |
| Rmd | ea368e6 | reneeisnowhere | 2025-03-20 | updates to supplementary files |
| html | 35ff04f | E. Renee Matthews | 2025-03-05 | Build site. |
| html | 6b0cfc3 | E. Renee Matthews | 2025-02-27 | Build site. |
| Rmd | bb8d8a8 | E. Renee Matthews | 2025-02-27 | updates to plot |
| Rmd | 634732c | E. Renee Matthews | 2025-02-27 | updates to volcano plots |
| html | e446dec | E. Renee Matthews | 2025-02-26 | Build site. |
| Rmd | 785ca3a | E. Renee Matthews | 2025-02-26 | updating supplemental figures |
| Rmd | faa2861 | E. Renee Matthews | 2025-02-26 | end of day |
| Rmd | 66d9e61 | E. Renee Matthews | 2025-02-26 | first open commit |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(ggfortify)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(ggVennDiagram)
library(BiocParallel)
library(ggpubr)
library(edgeR)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)
library(readxl)
library(devtools)
library(vargen)
library(eulerr)Figure S1: Read numbers are similar across time and drug treatments.
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
read_summary <- read_delim(file="data/Final_four_data/reads_summary_FF.txt",delim="\t")
read_summary %>%
pivot_longer(., cols=c(total_reads:unique_mapped_reads), names_to = "read_type",values_to = "counts") %>%
dplyr::mutate(trt=factor(trt, levels = c("DOX", "EPI","DNR", "MTX","TRZ","VEH"))) %>%
mutate(time=factor(time, levels =c("3h","24h"))) %>%
mutate(indv=gsub("1","D",indv), indv=gsub("2","A",indv), indv=gsub("3","B",indv), indv=gsub("6","C",indv))%>%
mutate(indv=factor(indv, levels=c("IndD","IndA","IndB","IndC"))) %>%
mutate(read_type=factor(read_type, levels =c("total_reads","total_mapped_reads","nuclear_mapped_reads","unique_mapped_reads","nodup_mapped_reads"))) %>%
ggplot(., aes(x=read_type, y=counts))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv))+
theme_bw()+
facet_wrap(~trt+time,nrow = 3, ncol = 6 )+
scale_fill_manual(values=drug_pal)+
scale_color_brewer(palette = "Dark2")+
theme(strip.text = element_text(face = "bold", hjust = 0, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'),
axis.text.x=element_text(angle = 90, vjust = 0.5, hjust=1))
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Figure 2: Peak numbers are similar across time and drug treatments.
Figure S2A: Read numbers across treatment and time
read_summary %>%
dplyr::select(sample:time, nodup_mapped_reads) %>%
dplyr::mutate(trt=factor(trt, levels = c("DOX", "EPI","DNR", "MTX","TRZ","VEH"))) %>%
mutate(time=factor(time, levels =c("3h","24h"))) %>%
mutate(indv=gsub("1","D",indv),
indv=gsub("2","A",indv),
indv=gsub("3","B",indv),
indv=gsub("6","C",indv))%>%
mutate(indv=factor(indv, levels=c("IndD","IndA","IndB","IndC"))) %>%
ggplot(., aes(x=trt,y=nodup_mapped_reads,group=(interaction(time,trt))))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Reads across treatment and time")+
theme_bw()+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'))
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Figure S2B: Peak number across treatment and time
peakcount_ff <- read_delim("data/Final_four_data/Peak_count_ff.txt",delim= "\t")
peakcount_ff %>%
mutate(time = factor(time, levels = c("3h", "24h"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH"))) %>%
mutate(indv=gsub("1","D",indv),
indv=gsub("2","A",indv),
indv=gsub("3","B",indv),
indv=gsub("6","C",indv))%>%
mutate(indv=factor(indv, levels=c("D","A","B","C"))) %>%
ggplot(., aes(x=trt,y=peak_number))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Peaks across treatment and time")+
theme_bw()+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'))
Figure S3: Samples have a high fraction of read-fragments in high-confidence open chromatin regions.
frip_newpeaks <- c(38.8,36.3,46.0,38.9,49.6,40.0,39.2,30.2,52.1,39.8,51.1,28.0,
42.3,40.3,39.7,38.7,37.9,36.6,36.0,48.7,50.4,44.2,52.0,31.9,
40.5,34.1,41.2,33.7,43.5,28.6,34.7,42.8,38.1,40.3,44.6,26.4,
46.5,23.9,46.9,25.8,46.7,23.8,21.8,39.2,33.2,22.8,36.8,34.8)
peakcount_ff$frip_newpeaks <- frip_newpeaks
peakcount_ff %>%
mutate(time = factor(time, levels = c("3h", "24h"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH"))) %>%
mutate(indv=gsub("1","D",indv),
indv=gsub("2","A",indv),
indv=gsub("3","B",indv),
indv=gsub("6","C",indv))%>%
mutate(indv=factor(indv, levels=c("D","A","B","C"))) %>%
ggplot(., aes(x=trt,y=frip_newpeaks))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
geom_hline(aes(yintercept = 20), linetype=2, color="red")+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Fraction of fragments in high-confidence regions")+
theme_bw()+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'))+
coord_cartesian(ylim = c(0,100))
| Version | Author | Date |
|---|---|---|
| 6b0cfc3 | E. Renee Matthews | 2025-02-27 |
Figure S4: iPSC-CM open chromatin regions are shared with human heart-left ventricle open chromatin regions.
Snyder_41peaks <- read.delim("data/other_papers/ENCFF966JZT_bed_Snyder_41peaks.bed",header=TRUE) %>%
GRanges()
filtered_hc_regions <- read_delim("data/Final_four_data/LCPM_matrix_ff.txt",delim = "/") %>%
dplyr::select(Peakid) %>%
separate_wider_delim(., cols =Peakid,
names=c("chr","start","end"),
delim = ".",
cols_remove = FALSE)
filtered_hc_regions_gr <- filtered_hc_regions %>%
dplyr::filter(chr!="chrY") %>%
GRanges() %>%
keepStandardChromosomes(., pruning.mode = "coarse")
heart_overlap <- join_overlap_intersect(Snyder_41peaks,filtered_hc_regions_gr)
length(unique(heart_overlap$Peakid))[1] 66927fit <- euler(c("This study" = length(filtered_hc_regions_gr) - length(unique(heart_overlap$Peakid)),
"Snyder study" = length(Snyder_41peaks) - length(unique(heart_overlap$name)),
"This study&Snyder study" = 66927))
plot(fit, fills = list(fill = c("skyblue", "lightcoral"), alpha = 0.6),
labels = FALSE, edges = TRUE, quantities = TRUE,
main = "Euler diagram between this study and Snyder's study")
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Figure S5: Open chromatin regions are enriched at transcription start sites.
Figure S5A: Enrichment of accessible chromatin at TSS
## What I did here: I called all my narrowpeak files made by MACS2 callpeaks
# peakfiles1 <- choose.files()
#
# ##This loop first established a list then (because I already knew the list had 12 files)
# ## I then imported each of these onto that list. Once I had the list, I stored it as
# ## an R object,
# Ind1_peaks <- list()
# for (file in 1:12){
# testname <- basename(peakfiles1[file])
# banana_peel <- str_split_i(testname, "_",3)
# Ind1_peaks[[banana_peel]] <- readPeakFile(peakfiles1[file])
# }
# saveRDS(Ind4_peaks, "data/Ind4_peaks_list.RDS")
# I then called annotatePeak on that list object, and stored that as a R object for later retrieval.)
# peakAnnoList_1 <- lapply(Ind1_peaks, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# saveRDS(peakAnnoList_1, "data/peakAnnoList_1.RDS")
IndD_TSS_peaks_plot <- readRDS("data/Ind1_TSS_peaks.RDS")
IndA_TSS_peaks_plot <- readRDS("data/Ind2_TSS_peaks.RDS")
IndB_TSS_peaks_plot <- readRDS("data/Ind3_TSS_peaks.RDS")
IndC_TSS_peaks_plot <- readRDS("data/Ind6_TSS_peaks.RDS")
d1<- plotAvgProf(IndD_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual D" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-01 10:38:46 AM a1 <- plotAvgProf(IndA_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual A" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-01 10:38:47 AM b1 <- plotAvgProf(IndB_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual B" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-01 10:38:48 AM c1 <- plotAvgProf(IndC_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual C" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-01 10:38:49 AM d2 <- plotAvgProf(IndD_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual D" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-01 10:38:50 AM a2 <- plotAvgProf(IndA_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual A" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-01 10:38:52 AM b2 <- plotAvgProf(IndB_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual B" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-01 10:38:53 AM c2 <- plotAvgProf(IndC_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual C" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-01 10:38:54 AM plot_grid(a1,a2, b1,b2,c1,c2,d1,d2, axis="l",align = "hv",nrow=4, ncol=2)
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Code used to calculate fig S5B enrichment scores
library(GenomicRanges)
library(ATACseqQC)
bamfilelist <- choose.files()
list1 <- lapply(bamfilelist, readBamFile,bigFile=TRUE)
# bamfilenames <- lapply(bamfilelist, basename)
# gal1 <- readBamFile(bamFile=bamfile, tag=character(0),
# asMates=FALSE)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txs <- transcripts(TxDb.Hsapiens.UCSC.hg38.knownGene)
# tsse <- TSSEscore(gal1, txs)
indA_TSSE <- lapply(list1,TSSEscore, txs=txs)
# saveRDS(indC_TSSE, "data/Final_four_data/H3K27ac_files/indC_TSSE.RDS")
# saveRDS(indB_TSSE, "data/Final_four_data/H3K27ac_files/indB_TSSE.RDS")
# saveRDS(indA_TSSE, "data/Final_four_data/H3K27ac_files/indA_TSSE.RDS")
# saveRDS(ind6_TSSE, "data/ind6_TSSE.RDS")
# saveRDS(ind4_TSSE, "data/ind4_TSSE.RDS")
# saveRDS(ind5_TSSE, "data/ind5_TSSE.RDS")
# saveRDS(ind2_TSSE, "data/ind2_TSSE.RDS")
# saveRDS(ind3_TSSE, "data/ind3_TSSE.RDS")
# saveRDS(ind1_TSSE,"data/ind1_TSSE.RDS")
# ind1_TSSE <- tribble(
# ~sample, ~TSSE,
# "1_DNR_3",16.89282,
# "1_DOX_3",19.43605,
# "1_EPI_3",18.97398,
# "1_MTX_3",14.93388,
# "1_TRZ_3",21.0788,
# "1_VEH_3",12.46743,
# "1_DNR_24",16.56416,
# "1_DOX_24",21.6031,
# "1_EPI_24", 21.75785,
# "1_MTX_24",17.63624,
# "1_TRZ_24", 28.37166,
# "1_VEH_24",34.34781)
##now I can ccombine them all!
ind1_TSSE <- readRDS("data/ind1_TSSE.RDS")
ind2_TSSE <- readRDS("data/ind2_TSSE.RDS")
ind3_TSSE <- readRDS("data/ind3_TSSE.RDS")
ind4_TSSE <- readRDS("data/ind4_TSSE.RDS")
ind5_TSSE <- readRDS("data/ind5_TSSE.RDS")
ind6_TSSE <- readRDS("data/ind6_TSSE.RDS")
ind1 <- lapply(ind1_TSSE, '[[',2)
names(ind1) <- c("1_DNR_3", "1_DNR_24","1_DOX_3",
"1_DOX_24","1_EPI_3","1_EPI_24","1_MTX_3",
"1_MTX_24","1_TRZ_3" , "1_TRZ_24","1_VEH_3","1_VEH_24")
ind1 <- lapply(ind1_TSSE, '[[',2)
names(ind1) <- c("1_DNR_3", "1_DNR_24","1_DOX_3",
"1_DOX_24","1_EPI_3","1_EPI_24","1_MTX_3",
"1_MTX_24","1_TRZ_3" , "1_TRZ_24","1_VEH_3","1_VEH_24")
ind2 <- lapply(ind2_TSSE, '[[',2)
names(ind2) <- c("2_DNR_3", "2_DNR_24","2_DOX_3",
"2_DOX_24","2_EPI_3","2_EPI_24","2_MTX_3",
"2_MTX_24","2_TRZ_3" , "2_TRZ_24","2_VEH_3","2_VEH_24")
ind3 <- lapply(ind3_TSSE, '[[',2)
names(ind3) <- c("3_DNR_3", "3_DNR_24","3_DOX_3",
"3_DOX_24","3_EPI_3","3_EPI_24","3_MTX_3",
"3_MTX_24","3_TRZ_3" , "3_TRZ_24","3_VEH_3","3_VEH_24")
ind4 <- lapply(ind4_TSSE, '[[',2)
names(ind4) <- c("4_DNR_3", "4_DNR_24","4_DOX_3",
"4_DOX_24","4_EPI_3","4_EPI_24","4_MTX_3",
"4_MTX_24","4_TRZ_3" , "4_TRZ_24","4_VEH_3","4_VEH_24")
ind5 <- lapply(ind5_TSSE, '[[',2)
names(ind5) <- c("5_DNR_3", "5_DNR_24","5_DOX_3",
"5_DOX_24","5_EPI_3","5_EPI_24","5_MTX_3",
"5_MTX_24","5_TRZ_3" , "5_TRZ_24","5_VEH_3","5_VEH_24")
ind6 <- lapply(ind6_TSSE, '[[',2)
names(ind6) <- c("6_DNR_3", "6_DNR_24","6_DOX_3",
"6_DOX_24","6_EPI_3","6_EPI_24","6_MTX_3",
"6_MTX_24","6_TRZ_3" , "6_TRZ_24","6_VEH_3","6_VEH_24")
allTSSE <- c(ind1, ind2, ind3, ind4, ind5, ind6)
allTSSE <- do.call(rbind, allTSSE)
saveRDS(allTSSE, "data/all_TSSE_scores.RDS")
############################################################
###Adding H3K27 combos
indC_TSSE <- readRDS("data/Final_four_data/H3K27ac_files/indC_TSSE.RDS")
indB_TSSE <- readRDS("data/Final_four_data/H3K27ac_files/indB_TSSE.RDS")
indA_TSSE <- readRDS("data/Final_four_data/H3K27ac_files/indA_TSSE.RDS")
indA <- lapply(indA_TSSE, '[[',2)
names(indA) <- c("A_DNR_3", "A_DNR_24","A_DOX_3",
"A_DOX_24","A_MTX_3",
"A_MTX_24","A_VEH_3","A_VEH_24")
indB <- lapply(indB_TSSE, '[[',2)
names(indB) <- c("B_DNR_3", "B_DNR_24","B_DOX_3","B_EPI_3",
"B_EPI_24","B_MTX_24","B_VEH_3","B_VEH_24")
indC <- lapply(indC_TSSE, '[[',2)
names(indC) <- c("C_DNR_3", "C_DNR_24","C_DOX_24","C_EPI_3",
"C_EPI_24","C_MTX_3","C_MTX_24","C_VEH_3","C_VEH_24")
allTSSE_ac <- c(indA, indB, indC)
allTSSE_ac <- do.call(rbind, allTSSE_ac)
saveRDS(allTSSE_ac, "data/Final_four_data/H3K27ac_files/H3K27ac_TSSE_scores.RDS")Figure S5B: TSS enrichement scores
allTSSE <- readRDS( "data/all_TSSE_scores.RDS")
allTSSE %>% as.data.frame() %>%
rownames_to_column("sample") %>%
separate(sample, into = c("indv","trt","time"), sep= "_") %>%
mutate(trt= factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(time = factor(time, levels = c("3","24"),labels = c("3 hours","24 hours"))) %>%
dplyr::filter(indv !=4 &indv !=5) %>%
mutate(indv=gsub("1","D",indv),
indv=gsub("2","A",indv),
indv=gsub("3","B",indv),
indv=gsub("6","C",indv))%>%
ggplot(., aes(x= time, y= V1, group = indv))+
geom_jitter(aes(col = trt, size = 1.5, alpha = 0.5) , position=position_jitter(0.25))+
geom_hline(yintercept=5, linetype = 3)+
geom_hline(yintercept=7, col = "blue")+
facet_wrap(~indv)+
theme_bw()+
ylab("score")+
ggtitle("TSS enrichment scores")+
scale_color_manual(values=drug_pal)+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1))
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Figure S6: Genome coverage is similar across samples at the TSS of the cardiac gene TNNT2.
knitr::include_graphics("assets/Fig\ S6.png", error=FALSE)
| Version | Author | Date |
|---|---|---|
| 50f3de9 | E. Renee Matthews | 2025-02-21 |
knitr::include_graphics("docs/assets/Fig\ S6.png",error = FALSE)
### Figure S7: ATAC-seq samples cluster by time and treatment.
ATAC_counts <- readRDS("data/Final_four_data/ATAC_filtered_raw_counts_allsamples.RDS") %>% as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "D_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "A_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "B_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "C_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
cpm(., log = TRUE)
FCmatrix_full <- ATAC_counts %>%
as.matrix() %>%
cor()
filmat_groupmat_col <- data.frame(timeset = colnames(FCmatrix_full))
counts_corr_mat <-filmat_groupmat_col %>%
# mutate(sample = timeset) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(class = if_else(trt == "DNR", "AC",
if_else(trt == "DOX", "AC",
if_else(trt == "EPI", "AC", "nAC")))) %>%
mutate(TOP2i = if_else(trt == "DNR", "yes",
if_else(trt == "DOX", "yes",
if_else(trt == "EPI", "yes",
if_else(trt == "MTX", "yes", "no")))))
mat_colors <- list(
trt= c("#F1B72B","#8B006D","#DF707E","#3386DD","#707031","#41B333"),
indv=c("#1B9E77", "#D95F02" ,"#7570B3", "#E6AB02"),
time=c("pink", "chocolate4"),
class=c("yellow1","darkorange1"),
TOP2i =c("darkgreen","lightgreen"))
names(mat_colors$trt) <- unique(counts_corr_mat$trt)
names(mat_colors$indv) <- unique(counts_corr_mat$indv)
names(mat_colors$time) <- unique(counts_corr_mat$time)
names(mat_colors$class) <- unique(counts_corr_mat$class)
names(mat_colors$TOP2i) <- unique(counts_corr_mat$TOP2i)
htanno_full <- ComplexHeatmap::HeatmapAnnotation(df = counts_corr_mat, col = mat_colors)
Heatmap(FCmatrix_full, top_annotation = htanno_full)
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Figure S8: PC1 associates with drug treatment and PC2 associates with individual.
pca_final_four <- (prcomp(t(ATAC_counts), scale. = TRUE))
pca_final_four_anno <- pca_final_four$x %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
separate_wider_delim(., cols =sample,
names=c("indv","trt","time"),
delim = "_",
cols_remove = FALSE) %>%
mutate(time = factor(time, levels = c("3h", "24h"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH")))
pca_plot <-
function(df, col_var = NULL, shape_var = NULL, title = "") {
ggplot(df) + geom_point(aes(
x = PC1,
y = PC2,
color = col_var,
shape = shape_var
),
size = 5) +
labs(title = title, x = "PC 1", y = "PC 2") +
scale_color_manual(values = c(
"#8B006D",
"#DF707E",
"#F1B72B",
"#3386DD",
"#707031",
"#41B333"
))
}
get_regr_pval <- function(mod) {
# Returns the p-value for the Fstatistic of a linear model
# mod: class lm
stopifnot(class(mod) == "lm")
fstat <- summary(mod)$fstatistic
pval <- 1 - pf(fstat[1], fstat[2], fstat[3])
return(pval)
}
plot_versus_pc <- function(df, pc_num, fac) {
# df: data.frame
# pc_num: numeric, specific PC for plotting
# fac: column name of df for plotting against PC
pc_char <- paste0("PC", pc_num)
# Calculate F-statistic p-value for linear model
pval <- get_regr_pval(lm(df[[ pc_char]] ~ df[[ fac]]))
if (is.numeric(df[, f])) {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_point() +
geom_smooth(method = "lm") + labs(title = sprintf("p-val: %.2f", pval))
} else {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_boxplot() +
labs(title = sprintf("p-val: %.3f", pval))
}
}
facs <- c("indv", "trt", "time")
names(facs) <- c("Individual", "Treatment", "Time")
drug1 <- c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH")##for changing shapes and colors
time <- rep(c("24h", "3h"),24) %>% factor(., levels = c("3h","24h"))
##gglistmaking
for (f in facs) {
# PC1 v PC2
pca_plot(pca_final_four_anno, col_var = f, shape_var = time,
title = names(facs)[which(facs == f)])
# print(last_plot())
# Plot f versus PC1 and PC2
f_v_pc1 <- arrangeGrob(plot_versus_pc(pca_final_four_anno, 1, f))
f_v_pc2 <- arrangeGrob(plot_versus_pc(pca_final_four_anno, 2, f))
grid.arrange(f_v_pc1, f_v_pc2, ncol = 2, top = names(facs)[which(facs == f)])
# summary(plot_versus_pc(PCA_info_anno_all, 1, f))
# summary(plot_versus_pc(PCA_info_anno_all, 2, f))
}
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Figure S9: Thousands of chromatin regions show changes in accessibility in response to TOP2i treatment.
### results from diff analysis check out the final_four_analysis.html file for detailed steps
efit4 <- readRDS("data/Final_four_data/efit4_filt_bl.RDS")
V.DNR_3.top= topTable(efit4, coef=1, adjust.method="BH", number=Inf, sort.by="p")
V.DOX_3.top= topTable(efit4, coef=2, adjust.method="BH", number=Inf, sort.by="p")
V.EPI_3.top= topTable(efit4, coef=3, adjust.method="BH", number=Inf, sort.by="p")
V.MTX_3.top= topTable(efit4, coef=4, adjust.method="BH", number=Inf, sort.by="p")
V.TRZ_3.top= topTable(efit4, coef=5, adjust.method="BH", number=Inf, sort.by="p")
V.DNR_24.top= topTable(efit4, coef=6, adjust.method="BH", number=Inf, sort.by="p")
V.DOX_24.top= topTable(efit4, coef=7, adjust.method="BH", number=Inf, sort.by="p")
V.EPI_24.top= topTable(efit4, coef=8, adjust.method="BH", number=Inf, sort.by="p")
V.MTX_24.top= topTable(efit4, coef=9, adjust.method="BH", number=Inf, sort.by="p")
V.TRZ_24.top= topTable(efit4, coef=10, adjust.method="BH", number=Inf, sort.by="p")
plot_filenames <- c("V.DNR_3.top","V.DOX_3.top","V.EPI_3.top","V.MTX_3.top",
"V.TRZ_.top","V.DNR_24.top","V.DOX_24.top","V.EPI_24.top",
"V.MTX_24.top","V.TRZ_24.top")
plot_files <- c( V.DNR_3.top,V.DOX_3.top,V.EPI_3.top,V.MTX_3.top,
V.TRZ_3.top,V.DNR_24.top,V.DOX_24.top,V.EPI_24.top,
V.MTX_24.top,V.TRZ_24.top)
volcanosig <- function(df, psig.lvl) {
df <- df %>%
mutate(threshold = ifelse(adj.P.Val > psig.lvl, "A", ifelse(adj.P.Val <= psig.lvl & logFC<=0,"B","C")))
ggplot(df, aes(x=logFC, y=-log10(P.Value))) +
geom_point(aes(color=threshold))+
xlab(expression("Log"[2]*" FC"))+
ylab(expression("-log"[10]*"P Value"))+
scale_color_manual(values = c("black", "red","blue"))+
theme_cowplot()+
ylim(0,20)+
xlim(-6,6)+
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(0.8)))
}
v1 <- volcanosig(V.DNR_3.top, 0.05)+ ggtitle("DNR 3 hour")
v2 <- volcanosig(V.DNR_24.top, 0.05)+ ggtitle("DNR 24 hour")+ylab("")
v3 <- volcanosig(V.DOX_3.top, 0.05)+ ggtitle("DOX 3 hour")
v4 <- volcanosig(V.DOX_24.top, 0.05)+ ggtitle("DOX 24 hour")+ylab("")
v5 <- volcanosig(V.EPI_3.top, 0.05)+ ggtitle("EPI 3 hour")
v6 <- volcanosig(V.EPI_24.top, 0.05)+ ggtitle("EPI 24 hour")+ylab("")
v7 <- volcanosig(V.MTX_3.top, 0.05)+ ggtitle("MTX 3 hour")
v8 <- volcanosig(V.MTX_24.top, 0.05)+ ggtitle("MTX 24 hour")+ylab("")
v9 <- volcanosig(V.TRZ_3.top, 0.05)+ ggtitle("TRZ 3 hour")
v10 <- volcanosig(V.TRZ_24.top, 0.05)+ ggtitle("TRZ 24 hour")+ylab("")
plot_grid(v1,v2, rel_widths =c(.8,1))
| Version | Author | Date |
|---|---|---|
| 6b0cfc3 | E. Renee Matthews | 2025-02-27 |
plot_grid(v3,v4, rel_widths =c(.8,1))
| Version | Author | Date |
|---|---|---|
| 6b0cfc3 | E. Renee Matthews | 2025-02-27 |
plot_grid(v5,v6, rel_widths =c(.8,1))
| Version | Author | Date |
|---|---|---|
| 6b0cfc3 | E. Renee Matthews | 2025-02-27 |
plot_grid(v7,v8, rel_widths =c(.8,1))
| Version | Author | Date |
|---|---|---|
| 6b0cfc3 | E. Renee Matthews | 2025-02-27 |
plot_grid(v9,v10, rel_widths =c(.8,1))
| Version | Author | Date |
|---|---|---|
| 6b0cfc3 | E. Renee Matthews | 2025-02-27 |
Figure S10: Top differentiall accesible regions are shared across anthracyclines
Figure S10A: examples at 3 and 24 hours
DNR_3_top3_ff <- row.names(V.DNR_3.top[1:3,])
log_filt_ff <- ATAC_counts%>%
as.data.frame()
row.names(log_filt_ff) <- row.names(ATAC_counts)
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DNR_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 3 hour DNR")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
DOX_3_top3_ff <- row.names(V.DOX_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DOX_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 3 hour DOX")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
EPI_3_top3_ff <- row.names(V.EPI_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% EPI_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 3 hour EPI")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
MTX_3_top3_ff <- row.names(V.MTX_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% MTX_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 3 hour MTX")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
TRZ_3_top3_ff <- row.names(V.TRZ_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% TRZ_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 3 hour TRZ")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
DNR_24_top3_ff <- row.names(V.DNR_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DNR_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 24 hour DNR")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
DOX_24_top3_ff <- row.names(V.DOX_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DOX_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 24 hour DOX")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
EPI_24_top3_ff <- row.names(V.EPI_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% EPI_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 24 hour EPI")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
MTX_24_top3_ff <- row.names(V.MTX_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% MTX_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 24 hour MTX")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
TRZ_24_top3_ff <- row.names(V.TRZ_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% TRZ_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","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(Peak~.)+
ggtitle("top 3 DAR in 24 hour TRZ")+
scale_fill_manual(values = drug_pal)+
theme_bw()
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Figure S10B: LFC correlation
FCmatrix_ff <- subset(efit4$coefficients)
colnames(FCmatrix_ff) <-
c("DNR\n3h",
"DOX\n3h",
"EPI\n3h",
"MTX\n3h",
"TRZ\n3h",
"DNR\n24h",
"DOX\n24h",
"EPI\n24h",
"MTX\n24h",
"TRZ\n24h"
)
mat_col_ff <-
data.frame(
time = c(rep("3 hours", 5), rep("24 hours", 5)),
class = (c(
"AC", "AC", "AC", "nAC","nAC", "AC", "AC", "AC", "nAC","nAC"
)))
rownames(mat_col_ff) <- colnames(FCmatrix_ff)
mat_colors_ff <-
list(
time = c("pink", "chocolate4"),
class = c("yellow1", "lightgreen"))
names(mat_colors_ff$time) <- unique(mat_col_ff$time)
names(mat_colors_ff$class) <- unique(mat_col_ff$class)
# names(mat_colors_FC$TOP2i) <- unique(mat_col_FC$TOP2i)
corrFC_ff <- cor(FCmatrix_ff)
htanno_ff <- HeatmapAnnotation(df = mat_col_ff, col = mat_colors_ff)
Heatmap(corrFC_ff, top_annotation = htanno_ff)
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Figure S11: Four differentially accessible signatures capture the response to treatment over time.
Figure S11A: Bayesian information criterion
## Fit limma model using code as it is found in the original cormotif code. It has
## only been modified to add names to the matrix of t values, as well as the
## limma fits
limmafit.default <- function(exprs,groupid,compid) {
limmafits <- list()
compnum <- nrow(compid)
genenum <- nrow(exprs)
limmat <- matrix(0,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- rownames(exprs)
colnames(limmat) <- rownames(compid)
names(limmas2) <- rownames(compid)
names(limmadf) <- rownames(compid)
names(limmav0) <- rownames(compid)
names(limmag1num) <- rownames(compid)
names(limmag2num) <- rownames(compid)
for(i in 1:compnum) {
selid1 <- which(groupid == compid[i,1])
selid2 <- which(groupid == compid[i,2])
eset <- new("ExpressionSet", exprs=cbind(exprs[,selid1],exprs[,selid2]))
g1num <- length(selid1)
g2num <- length(selid2)
designmat <- cbind(base=rep(1,(g1num+g2num)), delta=c(rep(0,g1num),rep(1,g2num)))
fit <- lmFit(eset,designmat)
fit <- eBayes(fit)
limmat[,i] <- fit$t[,2]
limmas2[i] <- fit$s2.prior
limmadf[i] <- fit$df.prior
limmav0[i] <- fit$var.prior[2]
limmag1num[i] <- g1num
limmag2num[i] <- g2num
limmafits[[i]] <- fit
# log odds
# w<-sqrt(1+fit$var.prior[2]/(1/g1num+1/g2num))
# log(0.99)+dt(fit$t[1,2],g1num+g2num-2+fit$df.prior,log=TRUE)-log(0.01)-dt(fit$t[1,2]/w, g1num+g2num-2+fit$df.prior, log=TRUE)+log(w)
}
names(limmafits) <- rownames(compid)
limmacompnum<-nrow(compid)
result<-list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
limmafit.counts <-
function (exprs, groupid, compid, norm.factor.method = "TMM", voom.normalize.method = "none")
{
limmafits <- list()
compnum <- nrow(compid)
genenum <- nrow(exprs)
limmat <- matrix(NA,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- rownames(exprs)
colnames(limmat) <- rownames(compid)
names(limmas2) <- rownames(compid)
names(limmadf) <- rownames(compid)
names(limmav0) <- rownames(compid)
names(limmag1num) <- rownames(compid)
names(limmag2num) <- rownames(compid)
for (i in 1:compnum) {
message(paste("Running limma for comparision",i,"/",compnum))
selid1 <- which(groupid == compid[i, 1])
selid2 <- which(groupid == compid[i, 2])
# make a new count data frame
counts <- cbind(exprs[, selid1], exprs[, selid2])
# remove NAs
not.nas <- which(apply(counts, 1, function(x) !any(is.na(x))) == TRUE)
# runn voom/limma
d <- DGEList(counts[not.nas,])
d <- calcNormFactors(d, method = norm.factor.method)
g1num <- length(selid1)
g2num <- length(selid2)
designmat <- cbind(base = rep(1, (g1num + g2num)), delta = c(rep(0,
g1num), rep(1, g2num)))
y <- voom(d, designmat, normalize.method = voom.normalize.method)
fit <- lmFit(y, designmat)
fit <- eBayes(fit)
limmafits[[i]] <- fit
limmat[not.nas, i] <- fit$t[, 2]
limmas2[i] <- fit$s2.prior
limmadf[i] <- fit$df.prior
limmav0[i] <- fit$var.prior[2]
limmag1num[i] <- g1num
limmag2num[i] <- g2num
}
limmacompnum <- nrow(compid)
names(limmafits) <- rownames(compid)
result <- list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
limmafit.list <-
function (fitlist, cmp.idx=2)
{
compnum <- length(fitlist)
genes <- c()
for (i in 1:compnum) genes <- unique(c(genes, rownames(fitlist[[i]])))
genenum <- length(genes)
limmat <- matrix(NA,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- genes
colnames(limmat) <- names(fitlist)
names(limmas2) <- names(fitlist)
names(limmadf) <- names(fitlist)
names(limmav0) <- names(fitlist)
names(limmag1num) <- names(fitlist)
names(limmag2num) <- names(fitlist)
for (i in 1:compnum) {
this.t <- fitlist[[i]]$t[,cmp.idx]
limmat[names(this.t),i] <- this.t
limmas2[i] <- fitlist[[i]]$s2.prior
limmadf[i] <- fitlist[[i]]$df.prior
limmav0[i] <- fitlist[[i]]$var.prior[cmp.idx]
limmag1num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==0)
limmag2num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==1)
}
limmacompnum <- compnum
result <- list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
## Rank genes based on statistics
generank<-function(x) {
xcol<-ncol(x)
xrow<-nrow(x)
result<-matrix(0,xrow,xcol)
z<-(1:1:xrow)
for(i in 1:xcol) {
y<-sort(x[,i],decreasing=TRUE,na.last=TRUE)
result[,i]<-match(x[,i],y)
result[,i]<-order(result[,i])
}
result
}
## Log-likelihood for moderated t under H0
modt.f0.loglike<-function(x,df) {
a<-dt(x, df, log=TRUE)
result<-as.vector(a)
flag<-which(is.na(result)==TRUE)
result[flag]<-0
result
}
## Log-likelihood for moderated t under H1
## param=c(df,g1num,g2num,v0)
modt.f1.loglike<-function(x,param) {
df<-param[1]
g1num<-param[2]
g2num<-param[3]
v0<-param[4]
w<-sqrt(1+v0/(1/g1num+1/g2num))
dt(x/w, df, log=TRUE)-log(w)
a<-dt(x/w, df, log=TRUE)-log(w)
result<-as.vector(a)
flag<-which(is.na(result)==TRUE)
result[flag]<-0
result
}
## Correlation Motif Fit
cmfit.X<-function(x, type, K=1, tol=1e-3, max.iter=100) {
## initialize
xrow <- nrow(x)
xcol <- ncol(x)
loglike0 <- list()
loglike1 <- list()
p <- rep(1, K)/K
q <- matrix(runif(K * xcol), K, xcol)
q[1, ] <- rep(0.01, xcol)
for (i in 1:xcol) {
f0 <- type[[i]][[1]]
f0param <- type[[i]][[2]]
f1 <- type[[i]][[3]]
f1param <- type[[i]][[4]]
loglike0[[i]] <- f0(x[, i], f0param)
loglike1[[i]] <- f1(x[, i], f1param)
}
condlike <- list()
for (i in 1:xcol) {
condlike[[i]] <- matrix(0, xrow, K)
}
loglike.old <- -1e+10
for (i.iter in 1:max.iter) {
if ((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations for K=",
K, sep = ""))
}
err <- tol + 1
clustlike <- matrix(0, xrow, K)
#templike <- matrix(0, xrow, 2)
templike1 <- rep(0, xrow)
templike2 <- rep(0, xrow)
for (j in 1:K) {
for (i in 1:xcol) {
templike1 <- log(q[j, i]) + loglike1[[i]]
templike2 <- log(1 - q[j, i]) + loglike0[[i]]
tempmax <- Rfast::Pmax(templike1, templike2)
templike1 <- exp(templike1 - tempmax)
templike2 <- exp(templike2 - tempmax)
tempsum <- templike1 + templike2
clustlike[, j] <- clustlike[, j] + tempmax +
log(tempsum)
condlike[[i]][, j] <- templike1/tempsum
}
clustlike[, j] <- clustlike[, j] + log(p[j])
}
#tempmax <- apply(clustlike, 1, max)
tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
for (j in 1:K) {
clustlike[, j] <- exp(clustlike[, j] - tempmax)
}
#tempsum <- apply(clustlike, 1, sum)
tempsum <- Rfast::rowsums(clustlike)
for (j in 1:K) {
clustlike[, j] <- clustlike[, j]/tempsum
}
#p.new <- (apply(clustlike, 2, sum) + 1)/(xrow + K)
p.new <- (Rfast::colsums(clustlike) + 1)/(xrow + K)
q.new <- matrix(0, K, xcol)
for (j in 1:K) {
clustpsum <- sum(clustlike[, j])
for (i in 1:xcol) {
q.new[j, i] <- (sum(clustlike[, j] * condlike[[i]][,
j]) + 1)/(clustpsum + 2)
}
}
err.p <- max(abs(p.new - p)/p)
err.q <- max(abs(q.new - q)/q)
err <- max(err.p, err.q)
loglike.new <- (sum(tempmax + log(tempsum)) + sum(log(p.new)) +
sum(log(q.new) + log(1 - q.new)))/xrow
p <- p.new
q <- q.new
loglike.old <- loglike.new
if (err < tol) {
break
}
}
clustlike <- matrix(0, xrow, K)
for (j in 1:K) {
for (i in 1:xcol) {
templike1 <- log(q[j, i]) + loglike1[[i]]
templike2 <- log(1 - q[j, i]) + loglike0[[i]]
tempmax <- Rfast::Pmax(templike1, templike2)
templike1 <- exp(templike1 - tempmax)
templike2 <- exp(templike2 - tempmax)
tempsum <- templike1 + templike2
clustlike[, j] <- clustlike[, j] + tempmax + log(tempsum)
condlike[[i]][, j] <- templike1/tempsum
}
clustlike[, j] <- clustlike[, j] + log(p[j])
}
#tempmax <- apply(clustlike, 1, max)
tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
for (j in 1:K) {
clustlike[, j] <- exp(clustlike[, j] - tempmax)
}
#tempsum <- apply(clustlike, 1, sum)
tempsum <- Rfast::rowsums(clustlike)
for (j in 1:K) {
clustlike[, j] <- clustlike[, j]/tempsum
}
p.post <- matrix(0, xrow, xcol)
for (j in 1:K) {
for (i in 1:xcol) {
p.post[, i] <- p.post[, i] + clustlike[, j] * condlike[[i]][,
j]
}
}
loglike.old <- loglike.old - (sum(log(p)) + sum(log(q) +
log(1 - q)))/xrow
loglike.old <- loglike.old * xrow
result <- list(p.post = p.post, motif.prior = p, motif.q = q,
loglike = loglike.old, clustlike=clustlike, condlike=condlike)
}
## Fit using (0,0,...,0) and (1,1,...,1)
cmfitall<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
p<-0.01
## compute loglikelihood
L0<-matrix(0,xrow,1)
L1<-matrix(0,xrow,1)
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
L0<-L0+loglike0[[i]]
L1<-L1+loglike1[[i]]
}
## EM algorithm to get MLE of p and q
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p)+L0
clustlike[,2]<-log(p)+L1
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## update motif occurrence rate
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(sum(clustlike[,2])+1)/(xrow+2)
## evaluate convergence
err<-abs(p.new-p)/p
## evaluate whether the log.likelihood increases
loglike.new<-(sum(tempmax+log(tempsum))+log(p.new)+log(1-p.new))/xrow
loglike.old<-loglike.new
p<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p)+L0
clustlike[,2]<-log(p)+L1
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post<-matrix(0,xrow,xcol)
for(i in 1:xcol) {
p.post[,i]<-clustlike[,2]
}
## return
#calculate back loglikelihood
loglike.old<-loglike.old-(log(p)+log(1-p))/xrow
loglike.old<-loglike.old*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}
## Fit each dataset separately
cmfitsep<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
p<-0.01*rep(1,xcol)
loglike.final<-rep(0,xcol)
## compute loglikelihood
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
}
p.post<-matrix(0,xrow,xcol)
## EM algorithm to get MLE of p
for(coli in 1:xcol) {
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
clustlike[,2]<-log(p[coli])+loglike1[[coli]]
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## evaluate whether the log.likelihood increases
loglike.new<-sum(tempmax+log(tempsum))/xrow
## update motif occurrence rate
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(sum(clustlike[,2]))/(xrow)
## evaluate convergence
err<-abs(p.new-p[coli])/p[coli]
loglike.old<-loglike.new
p[coli]<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
clustlike[,2]<-log(p[coli])+loglike1[[coli]]
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post[,coli]<-clustlike[,2]
loglike.final[coli]<-loglike.old
}
## return
loglike.final<-loglike.final*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.final)
}
## Fit the full model
cmfitfull<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
K<-2^xcol
p<-rep(1,K)/K
pattern<-rep(0,xcol)
patid<-matrix(0,K,xcol)
## compute loglikelihood
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
}
L<-matrix(0,xrow,K)
for(i in 1:K)
{
patid[i,]<-pattern
for(j in 1:xcol) {
if(pattern[j] < 0.5) {
L[,i]<-L[,i]+loglike0[[j]]
} else {
L[,i]<-L[,i]+loglike1[[j]]
}
}
if(i < K) {
pattern[xcol]<-pattern[xcol]+1
j<-xcol
while(pattern[j] > 1) {
pattern[j]<-0
j<-j-1
pattern[j]<-pattern[j]+1
}
}
}
## EM algorithm to get MLE of p and q
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,K)
for(j in 1:K) {
clustlike[,j]<-log(p[j])+L[,j]
}
tempmax<-apply(clustlike,1,max)
for(j in 1:K) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## update motif occurrence rate
for(j in 1:K) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(apply(clustlike,2,sum)+1)/(xrow+K)
## evaluate convergence
err<-max(abs(p.new-p)/p)
## evaluate whether the log.likelihood increases
loglike.new<-(sum(tempmax+log(tempsum))+sum(log(p.new)))/xrow
loglike.old<-loglike.new
p<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,K)
for(j in 1:K) {
clustlike[,j]<-log(p[j])+L[,j]
}
tempmax<-apply(clustlike,1,max)
for(j in 1:K) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:K) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post<-matrix(0,xrow,xcol)
for(j in 1:K) {
for(i in 1:xcol) {
if(patid[j,i] > 0.5) {
p.post[,i]<-p.post[,i]+clustlike[,j]
}
}
}
## return
#calculate back loglikelihood
loglike.old<-loglike.old-sum(log(p))/xrow
loglike.old<-loglike.old*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}
generatetype<-function(limfitted)
{
jtype<-list()
df<-limfitted$g1num+limfitted$g2num-2+limfitted$df0
for(j in 1:limfitted$compnum)
{
jtype[[j]]<-list(f0=modt.f0.loglike, f0.param=df[j], f1=modt.f1.loglike, f1.param=c(df[j],limfitted$g1num[j],limfitted$g2num[j],limfitted$v0[j]))
}
jtype
}
cormotiffit <- function(exprs, groupid=NULL, compid=NULL, K=1, tol=1e-3,
max.iter=100, BIC=TRUE, norm.factor.method="TMM",
voom.normalize.method = "none", runtype=c("logCPM","counts","limmafits"), each=3)
{
# first I want to do some typechecking. Input can be either a normalized
# matrix, a count matrix, or a list of limma fits. Dispatch the correct
# limmafit accordingly.
# todo: add some typechecking here
limfitted <- list()
if (runtype=="counts") {
limfitted <- limmafit.counts(exprs,groupid,compid, norm.factor.method, voom.normalize.method)
} else if (runtype=="logCPM") {
limfitted <- limmafit.default(exprs,groupid,compid)
} else if (runtype=="limmafits") {
limfitted <- limmafit.list(exprs)
} else {
stop("runtype must be one of 'logCPM', 'counts', or 'limmafits'")
}
jtype<-generatetype(limfitted)
fitresult<-list()
ks <- rep(K, each = each)
fitresult <- bplapply(1:length(ks), function(i, x, type, ks, tol, max.iter) {
cmfit.X(x, type, K = ks[i], tol = tol, max.iter = max.iter)
}, x=limfitted$t, type=jtype, ks=ks, tol=tol, max.iter=max.iter)
best.fitresults <- list()
for (i in 1:length(K)) {
w.k <- which(ks==K[i])
this.bic <- c()
for (j in w.k) this.bic[j] <- -2 * fitresult[[j]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
w.min <- which(this.bic == min(this.bic, na.rm = TRUE))[1]
best.fitresults[[i]] <- fitresult[[w.min]]
}
fitresult <- best.fitresults
bic <- rep(0, length(K))
aic <- rep(0, length(K))
loglike <- rep(0, length(K))
for (i in 1:length(K)) loglike[i] <- fitresult[[i]]$loglike
for (i in 1:length(K)) bic[i] <- -2 * fitresult[[i]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
for (i in 1:length(K)) aic[i] <- -2 * fitresult[[i]]$loglike + 2 * (K[i] - 1 + K[i] * limfitted$compnum)
if(BIC==TRUE) {
bestflag=which(bic==min(bic))
}
else {
bestflag=which(aic==min(aic))
}
result<-list(bestmotif=fitresult[[bestflag]],bic=cbind(K,bic),
aic=cbind(K,aic),loglike=cbind(K,loglike), allmotifs=fitresult)
}
cormotiffitall<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitall(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
cormotiffitsep<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitsep(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
cormotiffitfull<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitfull(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
plotIC<-function(fitted_cormotif)
{
oldpar<-par(mfrow=c(1,2))
plot(fitted_cormotif$bic[,1], fitted_cormotif$bic[,2], type="b",xlab="Motif Number", ylab="BIC", main="BIC")
plot(fitted_cormotif$aic[,1], fitted_cormotif$aic[,2], type="b",xlab="Motif Number", ylab="AIC", main="AIC")
}
plotMotif<-function(fitted_cormotif,title="")
{
layout(matrix(1:2,ncol=2))
u<-1:dim(fitted_cormotif$bestmotif$motif.q)[2]
v<-1:dim(fitted_cormotif$bestmotif$motif.q)[1]
image(u,v,t(fitted_cormotif$bestmotif$motif.q),
col=gray(seq(from=1,to=0,by=-0.1)),xlab="Study",yaxt = "n",
ylab="Corr. Motifs",main=paste(title,"pattern",sep=" "))
axis(2,at=1:length(v))
for(i in 1:(length(u)+1))
{
abline(v=(i-0.5))
}
for(i in 1:(length(v)+1))
{
abline(h=(i-0.5))
}
Ng=10000
if(is.null(fitted_cormotif$bestmotif$p.post)!=TRUE)
Ng=nrow(fitted_cormotif$bestmotif$p.post)
genecount=floor(fitted_cormotif$bestmotif$motif.p*Ng)
NK=nrow(fitted_cormotif$bestmotif$motif.q)
plot(0,0.7,pch=".",xlim=c(0,1.2),ylim=c(0.75,NK+0.25),
frame.plot=FALSE,axes=FALSE,xlab="No. of genes",ylab="", main=paste(title,"frequency",sep=" "))
segments(0,0.7,fitted_cormotif$bestmotif$motif.p[1],0.7)
rect(0,1:NK-0.3,fitted_cormotif$bestmotif$motif.p,1:NK+0.3,
col="dark grey")
mtext(1:NK,at=1:NK,side=2,cex=0.8)
text(fitted_cormotif$bestmotif$motif.p+0.15,1:NK,
labels=floor(fitted_cormotif$bestmotif$motif.p*Ng))
}
plotMotifnew<-function(fitted_cormotif,title="")
{
layout(matrix(1:2,ncol=2))
u<-1:dim(fitted_cormotif$motif.q)[2]
v<-1:dim(fitted_cormotif$motif.q)[1]
image(u,v,t(fitted_cormotif$motif.q),
col=gray(seq(from=1,to=0,by=-0.1)),xlab="Experiment",yaxt = "n",
ylab="Corr. Motifs",main=paste(title,"pattern",sep=" "))
axis(2,at=1:length(v))
for(i in 1:(length(u)+1))
{
abline(v=(i-0.5))
}
for(i in 1:(length(v)+1))
{
abline(h=(i-0.5))
}
Ng=10000
if(is.null(fitted_cormotif$p.post)!=TRUE)
Ng=nrow(fitted_cormotif$p.post)
genecount=floor(fitted_cormotif$motif.p*Ng)
NK=nrow(fitted_cormotif$motif.q)
plot(0,0.7,pch=".",xlim=c(0,1.2),ylim=c(0.75,NK+0.25),
frame.plot=FALSE,axes=FALSE,xlab="No. of regions",ylab="", main=paste(title,"frequency",sep=" "))
segments(0,0.7,fitted_cormotif$motif.p[1],0.7)
rect(0,1:NK-0.3,fitted_cormotif$motif.p,1:NK+0.3,
col="dark grey")
mtext(1:NK,at=1:NK,side=2,cex=0.8)
text(fitted_cormotif$motif.p+0.15,1:NK,
labels=floor(fitted_cormotif$motif.p*Ng))
}group <- c( rep(c(1,2,3,4,5,6,7,8,9,10,11,12),4))
group <- factor(group, levels =c("1","2","3","4","5","6","7","8","9","10","11","12"))
group_fac_ff <- group
groupid_ff <- as.numeric(group_fac_ff)
compid_ff <- data.frame(c1= c(2,4,6,8,10,1,3,5,7,9), c2 = c( 12,12,12,12,12,11,11,11,11,11))
y_TMM_cpm_ff <- ATAC_counts
# set.seed(31415)
# cormotif_initial_ff <- cormotiffit(exprs = y_TMM_cpm_ff, groupid = groupid_ff, compid = compid_ff, K=1:8, max.iter = 500, runtype = "logCPM")
#
# saveRDS(cormotif_initial_ff,"data/Final_four_data/cormotif_ff_4_run.RDS")
# cormotif_four_ff <- cormotiffit(exprs = y_TMM_cpm_ff, groupid = groupid_ff, compid = compid_ff, K=4, max.iter = 500, runtype = "logCPM")
# saveRDS(cormotif_four_ff,"data/Final_four_data/cormotif_only4_run.RDS")
cormotif_initial_ff <- readRDS("data/Final_four_data/cormotif_ff_4_run.RDS")
Cormotif::plotIC(cormotif_initial_ff)
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
Figure S11B: Akaike information criterion
Cormotif::plotIC(cormotif_initial_ff)
| Version | Author | Date |
|---|---|---|
| e446dec | E. Renee Matthews | 2025-02-26 |
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] eulerr_7.0.2
[2] vargen_0.2.3
[3] devtools_2.4.5
[4] usethis_3.1.0
[5] readxl_1.4.5
[6] smplot2_0.2.5
[7] cowplot_1.1.3
[8] ComplexHeatmap_2.22.0
[9] ggrepel_0.9.6
[10] plyranges_1.26.0
[11] ggsignif_0.6.4
[12] genomation_1.38.0
[13] edgeR_4.4.2
[14] limma_3.62.2
[15] ggpubr_0.6.0
[16] BiocParallel_1.40.0
[17] ggVennDiagram_1.5.2
[18] scales_1.3.0
[19] VennDiagram_1.7.3
[20] futile.logger_1.4.3
[21] gridExtra_2.3
[22] ggfortify_0.4.17
[23] rtracklayer_1.66.0
[24] org.Hs.eg.db_3.20.0
[25] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[26] GenomicFeatures_1.58.0
[27] AnnotationDbi_1.68.0
[28] Biobase_2.66.0
[29] GenomicRanges_1.58.0
[30] GenomeInfoDb_1.42.3
[31] IRanges_2.40.1
[32] S4Vectors_0.44.0
[33] BiocGenerics_0.52.0
[34] ChIPseeker_1.42.1
[35] RColorBrewer_1.1-3
[36] broom_1.0.7
[37] kableExtra_1.4.0
[38] lubridate_1.9.4
[39] forcats_1.0.0
[40] stringr_1.5.1
[41] dplyr_1.1.4
[42] purrr_1.0.4
[43] readr_2.1.5
[44] tidyr_1.3.1
[45] tibble_3.2.1
[46] ggplot2_3.5.1
[47] tidyverse_2.0.0
[48] 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] 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_3.6.4
[17] formatR_1.14
[18] Cormotif_1.52.0
[19] labeling_0.4.3
[20] sass_0.4.9
[21] Rsamtools_2.22.0
[22] systemfonts_1.2.1
[23] yulab.utils_0.2.0
[24] foreign_0.8-88
[25] DOSE_4.0.0
[26] svglite_2.1.3
[27] R.utils_2.13.0
[28] sessioninfo_1.2.3
[29] plotrix_3.8-4
[30] BSgenome_1.74.0
[31] pwr_1.3-0
[32] rstudioapi_0.17.1
[33] impute_1.80.0
[34] RSQLite_2.3.9
[35] generics_0.1.3
[36] gridGraphics_0.5-1
[37] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[38] shape_1.4.6.1
[39] BiocIO_1.16.0
[40] vroom_1.6.5
[41] gtools_3.9.5
[42] car_3.1-3
[43] GO.db_3.20.0
[44] Matrix_1.7-3
[45] abind_1.4-8
[46] R.methodsS3_1.8.2
[47] lifecycle_1.0.4
[48] whisker_0.4.1
[49] yaml_2.3.10
[50] carData_3.0-5
[51] SummarizedExperiment_1.36.0
[52] gplots_3.2.0
[53] qvalue_2.38.0
[54] SparseArray_1.6.2
[55] blob_1.2.4
[56] promises_1.3.2
[57] crayon_1.5.3
[58] miniUI_0.1.1.1
[59] ggtangle_0.0.6
[60] lattice_0.22-6
[61] KEGGREST_1.46.0
[62] magick_2.8.5
[63] pillar_1.10.1
[64] knitr_1.49
[65] fgsea_1.32.2
[66] rjson_0.2.23
[67] boot_1.3-31
[68] codetools_0.2-20
[69] fastmatch_1.1-6
[70] glue_1.8.0
[71] getPass_0.2-4
[72] ggfun_0.1.8
[73] remotes_2.5.0
[74] data.table_1.17.0
[75] vctrs_0.6.5
[76] png_0.1-8
[77] treeio_1.30.0
[78] cellranger_1.1.0
[79] gtable_0.3.6
[80] cachem_1.1.0
[81] xfun_0.51
[82] mime_0.12
[83] S4Arrays_1.6.0
[84] iterators_1.0.14
[85] statmod_1.5.0
[86] ellipsis_0.3.2
[87] nlme_3.1-167
[88] ggtree_3.14.0
[89] bit64_4.6.0-1
[90] rprojroot_2.0.4
[91] bslib_0.9.0
[92] affyio_1.76.0
[93] rpart_4.1.24
[94] KernSmooth_2.23-26
[95] colorspace_2.1-1
[96] DBI_1.2.3
[97] Hmisc_5.2-2
[98] nnet_7.3-20
[99] seqPattern_1.38.0
[100] tidyselect_1.2.1
[101] processx_3.8.6
[102] bit_4.6.0
[103] compiler_4.4.2
[104] curl_6.2.1
[105] git2r_0.35.0
[106] htmlTable_2.4.3
[107] xml2_1.3.7
[108] DelayedArray_0.32.0
[109] checkmate_2.3.2
[110] caTools_1.18.3
[111] affy_1.84.0
[112] callr_3.7.6
[113] digest_0.6.37
[114] rmarkdown_2.29
[115] XVector_0.46.0
[116] htmltools_0.5.8.1
[117] pkgconfig_2.0.3
[118] base64enc_0.1-3
[119] MatrixGenerics_1.18.1
[120] fastmap_1.2.0
[121] htmlwidgets_1.6.4
[122] rlang_1.1.5
[123] GlobalOptions_0.1.2
[124] UCSC.utils_1.2.0
[125] shiny_1.10.0
[126] farver_2.1.2
[127] jquerylib_0.1.4
[128] zoo_1.8-13
[129] jsonlite_1.9.1
[130] GOSemSim_2.32.0
[131] R.oo_1.27.0
[132] RCurl_1.98-1.16
[133] magrittr_2.0.3
[134] Formula_1.2-5
[135] GenomeInfoDbData_1.2.13
[136] ggplotify_0.1.2
[137] patchwork_1.3.0
[138] munsell_0.5.1
[139] Rcpp_1.0.14
[140] ape_5.8-1
[141] stringi_1.8.4
[142] zlibbioc_1.52.0
[143] pkgbuild_1.4.6
[144] plyr_1.8.9
[145] parallel_4.4.2
[146] Biostrings_2.74.1
[147] splines_4.4.2
[148] hms_1.1.3
[149] circlize_0.4.16
[150] polylabelr_0.3.0
[151] locfit_1.5-9.12
[152] ps_1.9.0
[153] igraph_2.1.4
[154] pkgload_1.4.0
[155] reshape2_1.4.4
[156] futile.options_1.0.1
[157] XML_3.99-0.18
[158] evaluate_1.0.3
[159] BiocManager_1.30.25
[160] lambda.r_1.2.4
[161] tzdb_0.4.0
[162] foreach_1.5.2
[163] httpuv_1.6.15
[164] polyclip_1.10-7
[165] clue_0.3-66
[166] gridBase_0.4-7
[167] xtable_1.8-4
[168] restfulr_0.0.15
[169] tidytree_0.4.6
[170] rstatix_0.7.2
[171] later_1.4.1
[172] viridisLite_0.4.2
[173] aplot_0.2.5
[174] memoise_2.0.1
[175] GenomicAlignments_1.42.0
[176] cluster_2.1.8.1
[177] timechange_0.3.0