Figure_2
Renee Matthews
2025-02-21
Last updated: 2025-08-07
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Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d5b0a5a | reneeisnowhere | 2025-08-07 | wflow_publish("analysis/Figure_2.Rmd") |
| html | a197856 | reneeisnowhere | 2025-05-01 | Build site. |
| 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(edgeR)
library(limma)
library(rtracklayer)
library(ggfortify)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(scales)
library(BiocParallel)
library(ggpubr)
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. Proportion of Differentially Accessible Regions (DARs)
toptable_results <- readRDS("data/Final_four_data/re_analysis/Toptable_results.RDS")
all_results <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y)) %>%
bind_rows()
all_results_list <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y))
sig_meta_and_loc <- all_results %>%
dplyr::filter(adj.P.Val<0.05) %>% ## filter by pvalue
##Create parsed dataframe from "rowname" column, "genes column will keep id"
separate(rowname, into = c("seqnames", "start", "end"), sep = "\\.", convert = TRUE)
###split into lists by DNR_3, etc..
sig_meta_and_loc_split <- split(sig_meta_and_loc, sig_meta_and_loc$source)
notsig_meta_and_loc <- all_results %>%
dplyr::filter(adj.P.Val>0.05) %>%
separate(rowname, into = c("seqnames","start","end"), sep = "\\.", convert=TRUE)
notsig_meta_and_loc_split <- split(notsig_meta_and_loc, notsig_meta_and_loc$source)
all_DAR_regions <- all_results %>%
separate(rowname, into = c("seqnames", "start", "end"), sep = "\\.", convert = TRUE)
all_DAR_regions_list <- split(all_DAR_regions, all_DAR_regions$source)
three_hour_df <- all_results %>%
dplyr::select(source, genes, logFC,adj.P.Val) %>%
mutate(sig_val=if_else(adj.P.Val<0.05,"sig","not_sig")) %>%
separate(source, into=c("trt","time"),sep="_") %>%
dplyr::filter(time=="3") %>%
mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ")))
twentyfour_hour_df <- all_results %>%
dplyr::select(source, genes, logFC,adj.P.Val) %>%
mutate(sig_val=if_else(adj.P.Val<0.05,"sig","not_sig")) %>%
separate(source, into=c("trt","time"),sep="_") %>%
dplyr::filter(time=="24") %>%
mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ")))making the figure:
# Ensure consistent trt ordering across both dataframes
ordered_trt <- c("DOX", "EPI", "DNR", "MTX", "TRZ") # adjust to match your treatment order
# 3-hour plot
plot_3h <- three_hour_df %>%
mutate(
sig_val = factor(sig_val, levels = c("not_sig", "sig")),
trt = factor(trt, levels = ordered_trt)
) %>%
ggplot(aes(x = trt, fill = sig_val)) +
geom_bar(position = "fill") +
theme_bw() +
ggtitle("3 hours") +
ylab("Proportion") +
scale_y_continuous(labels = scales::percent)
# 24-hour plot (flipped)
plot_24h <- twentyfour_hour_df %>%
mutate(
sig_val = factor(sig_val, levels = c("not_sig", "sig")),
trt = factor(trt, levels = ordered_trt)
) %>%
ggplot(aes(x = trt, fill = sig_val)) +
geom_bar(position = "fill") +
scale_y_reverse(labels = scales::percent) + # flip it
theme_bw() +
ggtitle("24 hours (flipped)") +
ylab("Proportion")
# Remove x-axis labels from the top plot and y-axis label from the bottom if needed
plot_3h <- plot_3h + theme(axis.title.x = element_blank())
plot_24h <- plot_24h + theme(axis.title.x = element_blank())
# Combine the two using cowplot
combined <- plot_grid(
plot_3h,
plot_24h,
ncol = 1,
align = "v",
axis = "lr", # align left/right axis
rel_heights = c(1, 1)
)
# Show it
print(combined)
Figure 2.B: Log2 Fold change correlation
raw_counts <- read_delim("data/Final_four_data/re_analysis/Raw_unfiltered_counts.tsv",delim="\t") %>%
column_to_rownames("Peakid") %>%
as.matrix()
lcpm <- cpm(raw_counts, log= TRUE)
### for determining the basic cutoffs
filt_raw_counts <- raw_counts[rowMeans(lcpm)> 0,]
filt_raw_counts_noY <- filt_raw_counts[!grepl("chrY",rownames(filt_raw_counts)),]
annotation_mat <- data.frame(timeset=colnames(filt_raw_counts_noY)) %>%
mutate(sample = timeset) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(time = factor(time, levels = c("3h", "24h"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH"))) %>%
mutate(indv=factor(indv, levels = c("A","B","C","D"))) %>%
mutate(trt_time=paste0(trt,"_",time))
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"))
dge <- DGEList.data.frame(counts = filt_raw_counts_noY, group = group, genes = row.names(filt_raw_counts_noY))
dge <- calcNormFactors(dge)### this code is not run here, but results from the actual run are saved as an RDS for plotting purposes
group_1 <- c(rep(c("DNR_24","DNR_3","DOX_24","DOX_3","EPI_24","EPI_3","MTX_24","MTX_3","TRZ_24","TRZ_3","VEH_24", "VEH_3"),4))
mm <- model.matrix(~0 +group_1)
colnames(mm) <- c("DNR_24", "DNR_3", "DOX_24","DOX_3","EPI_24", "EPI_3","MTX_24", "MTX_3", "TRZ_24","TRZ_3","VEH_24", "VEH_3")
y <- voom(dge, mm,plot =FALSE)
corfit <- duplicateCorrelation(y, mm, block = annotation_mat$indv)
v <- voom(dge, mm, block = annotation_mat$indv, correlation = corfit$consensus)
fit <- lmFit(v, mm, block = annotation_mat$indv, correlation = corfit$consensus)
cm <- makeContrasts(
DNR_3.VEH_3 = DNR_3-VEH_3,
DOX_3.VEH_3 = DOX_3-VEH_3,
EPI_3.VEH_3 = EPI_3-VEH_3,
MTX_3.VEH_3 = MTX_3-VEH_3,
TRZ_3.VEH_3 = TRZ_3-VEH_3,
DNR_24.VEH_24 =DNR_24-VEH_24,
DOX_24.VEH_24= DOX_24-VEH_24,
EPI_24.VEH_24= EPI_24-VEH_24,
MTX_24.VEH_24= MTX_24-VEH_24,
TRZ_24.VEH_24= TRZ_24-VEH_24,
levels = mm)
fit2<- contrasts.fit(fit, contrasts=cm)
efit2 <- eBayes(fit2)
saveRDS(efit2,"data/Final_four_data/re_analysis/Final_DAR_efit2_w_Bayes.RDS")Plotting the Pearson correlation heat map
efit2 <- readRDS("data/Final_four_data/re_analysis/Final_DAR_efit2_w_Bayes.RDS")
FCmatrix_ff <- subset(efit2$coefficients)
### note coefficients are the LFCs of each region for each contrast. this is just a quick way to pull all that information together to calculate the correlation.
colnames(FCmatrix_ff) <-
c("DNR\n3h",
"DOX\n3h",
"EPI\n3h",
"MTX\n3h",
"TRZ\n3h",
"DNR\n24h",
"DOX\n24h",
"EPI\n24h",
"MTX\n24h",
"TRZ\n24h"
)
mat_col_ff <-
data.frame(
time = c(rep("3 hours", 5), rep("24 hours", 5)),
class = (c(
"AC", "AC", "AC", "nAC","nAC", "AC", "AC", "AC", "nAC","nAC"
)))
rownames(mat_col_ff) <- colnames(FCmatrix_ff)
mat_colors_ff <-
list(
time = c("pink", "chocolate4"),
class = c("yellow1", "lightgreen"))
names(mat_colors_ff$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$TOP2i)
corrFC_ff <- cor(FCmatrix_ff)
htanno_ff <- HeatmapAnnotation(df = mat_col_ff, col = mat_colors_ff)
Heatmap(corrFC_ff, top_annotation = htanno_ff)
Figure 2.C: Venn of DARs at 3 hours
sig_venn_list <- sapply(sig_meta_and_loc_split, function(x) x$genes)
sig_venn_3hr <- sig_venn_list[c("DOX_3","EPI_3", "DNR_3","MTX_3")]
sig_venn_24hr <- sig_venn_list[c("DOX_24","EPI_24", "DNR_24","MTX_24")]
sig_3hr_df <- dplyr::bind_rows(
lapply(names(sig_venn_3hr), function(name) {
data.frame(gene = sig_venn_3hr[[name]], condition = name)
})
) %>%
distinct(gene)
sig_24hr_df <- dplyr::bind_rows(
lapply(names(sig_venn_24hr), function(name) {
data.frame(gene = sig_venn_24hr[[name]], condition = name)
})
) %>%
distinct(gene)
ggVennDiagram::ggVennDiagram(sig_venn_3hr)
### Figure 2.D: Venn of DARs at 24 hours
ggVennDiagram::ggVennDiagram(sig_venn_24hr)
Figure 2.E: Venn of DARs between 3 and 24 hours
ggVennDiagram::ggVennDiagram(list("3hr_regions"=sig_3hr_df$gene,"24hr_regions"=sig_24hr_df$gene))+
xlim(-5, 5)+
coord_flip()
### Figure 2.F: IGV example of a TOP2if DAR that belongs to 24 hours
only DARs
**see image above.
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] epitools_0.5-10.1
[2] circlize_0.4.16
[3] readxl_1.4.5
[4] smplot2_0.2.5
[5] cowplot_1.1.3
[6] ComplexHeatmap_2.22.0
[7] ggrepel_0.9.6
[8] plyranges_1.26.0
[9] ggsignif_0.6.4
[10] genomation_1.38.0
[11] ggpubr_0.6.1
[12] BiocParallel_1.40.2
[13] scales_1.4.0
[14] gridExtra_2.3
[15] ggfortify_0.4.18
[16] rtracklayer_1.66.0
[17] edgeR_4.4.2
[18] limma_3.62.2
[19] org.Hs.eg.db_3.20.0
[20] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[21] GenomicFeatures_1.58.0
[22] AnnotationDbi_1.68.0
[23] Biobase_2.66.0
[24] GenomicRanges_1.58.0
[25] GenomeInfoDb_1.42.3
[26] IRanges_2.40.1
[27] S4Vectors_0.44.0
[28] BiocGenerics_0.52.0
[29] RColorBrewer_1.1-3
[30] broom_1.0.8
[31] kableExtra_1.4.0
[32] lubridate_1.9.4
[33] forcats_1.0.0
[34] stringr_1.5.1
[35] dplyr_1.1.4
[36] purrr_1.0.4
[37] readr_2.1.5
[38] tidyr_1.3.1
[39] tibble_3.3.0
[40] ggplot2_3.5.2
[41] tidyverse_2.0.0
[42] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] later_1.4.2 BiocIO_1.16.0
[3] bitops_1.0-9 cellranger_1.1.0
[5] rpart_4.1.24 XML_3.99-0.18
[7] lifecycle_1.0.4 rstatix_0.7.2
[9] doParallel_1.0.17 rprojroot_2.0.4
[11] vroom_1.6.5 processx_3.8.6
[13] lattice_0.22-7 backports_1.5.0
[15] magrittr_2.0.3 Hmisc_5.2-3
[17] sass_0.4.10 rmarkdown_2.29
[19] jquerylib_0.1.4 yaml_2.3.10
[21] plotrix_3.8-4 httpuv_1.6.16
[23] DBI_1.2.3 abind_1.4-8
[25] zlibbioc_1.52.0 RCurl_1.98-1.17
[27] nnet_7.3-20 git2r_0.36.2
[29] GenomeInfoDbData_1.2.13 svglite_2.2.1
[31] codetools_0.2-20 DelayedArray_0.32.0
[33] xml2_1.3.8 tidyselect_1.2.1
[35] shape_1.4.6.1 UCSC.utils_1.2.0
[37] farver_2.1.2 matrixStats_1.5.0
[39] base64enc_0.1-3 GenomicAlignments_1.42.0
[41] jsonlite_2.0.0 GetoptLong_1.0.5
[43] Formula_1.2-5 iterators_1.0.14
[45] systemfonts_1.2.3 foreach_1.5.2
[47] tools_4.4.2 ggVennDiagram_1.5.4
[49] Rcpp_1.1.0 glue_1.8.0
[51] SparseArray_1.6.2 xfun_0.52
[53] MatrixGenerics_1.18.1 withr_3.0.2
[55] fastmap_1.2.0 callr_3.7.6
[57] digest_0.6.37 timechange_0.3.0
[59] R6_2.6.1 seqPattern_1.38.0
[61] textshaping_1.0.1 colorspace_2.1-1
[63] Cairo_1.6-2 dichromat_2.0-0.1
[65] RSQLite_2.4.1 generics_0.1.4
[67] data.table_1.17.6 htmlwidgets_1.6.4
[69] httr_1.4.7 S4Arrays_1.6.0
[71] whisker_0.4.1 pkgconfig_2.0.3
[73] gtable_0.3.6 blob_1.2.4
[75] impute_1.80.0 XVector_0.46.0
[77] htmltools_0.5.8.1 carData_3.0-5
[79] pwr_1.3-0 clue_0.3-66
[81] png_0.1-8 knitr_1.50
[83] rstudioapi_0.17.1 tzdb_0.5.0
[85] reshape2_1.4.4 rjson_0.2.23
[87] checkmate_2.3.2 curl_6.4.0
[89] zoo_1.8-14 cachem_1.1.0
[91] GlobalOptions_0.1.2 KernSmooth_2.23-26
[93] parallel_4.4.2 foreign_0.8-90
[95] restfulr_0.0.16 pillar_1.11.0
[97] vctrs_0.6.5 promises_1.3.3
[99] car_3.1-3 cluster_2.1.8.1
[101] htmlTable_2.4.3 evaluate_1.0.4
[103] magick_2.8.7 cli_3.6.5
[105] locfit_1.5-9.12 compiler_4.4.2
[107] Rsamtools_2.22.0 rlang_1.1.6
[109] crayon_1.5.3 labeling_0.4.3
[111] ps_1.9.1 getPass_0.2-4
[113] plyr_1.8.9 fs_1.6.6
[115] stringi_1.8.7 viridisLite_0.4.2
[117] gridBase_0.4-7 Biostrings_2.74.1
[119] Matrix_1.7-3 BSgenome_1.74.0
[121] patchwork_1.3.1 hms_1.1.3
[123] bit64_4.6.0-1 KEGGREST_1.46.0
[125] statmod_1.5.0 SummarizedExperiment_1.36.0
[127] memoise_2.0.1 bslib_0.9.0
[129] bit_4.6.0