Figure 5
ERM
2023-07-26
Last updated: 2023-07-26
Checks: 7 0
Knit directory: Cardiotoxicity/
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library(car)
library(tidyverse)
library(tinytex)
library(BiocGenerics)
library(data.table)
library(Cormotif)
library(cowplot)
library(ggsignif)
library(RColorBrewer)
library(broom)toplistall <- readRDS("data/toplistall.RDS")
siglist <- readRDS("data/siglist_final.RDS")
list2env(siglist, envir = .GlobalEnv)<environment: R_GlobalEnv>cpmcounts <- readRDS("data/cpmcount.RDS")
cormotif_initial <- readRDS("data/cormotif_initialall.RDS")
DEG_cormotif <- readRDS("data/DEG_cormotif.RDS")
list2env(DEG_cormotif,envir=.GlobalEnv)<environment: R_GlobalEnv>drug_pal_fact <- c("#8B006D" ,"#DF707E", "#F1B72B" ,"#3386DD", "#707031","#41B333")Figure 6
TOP2i induce a time-dependent gene expression response in cardiomyocytes
A. Cormotif plot unedited
myColors <- rev(c("#FFFFFF", "#E6E6E6" ,"#CCCCCC", "#B3B3B3", "#999999", "#808080", "#666666","#4C4C4C", "#333333", "#191919","#000000"))
plotMotif(cormotif_initial)-1.png)
plot.new()
legend('bottomleft',fill=myColors, legend =rev(c("0", "0.1", "0.2", "0.3", "0.4", "0.5", "0.6", "0.7", "0.8","0.9", "1")), box.col="white",title = "Probability\nlegend", horiz=FALSE,title.cex=.8)-2.png)
## B. Pie chart of motifs
Set <- c("Early Acute Response", "Early Sustained Response","Late Response", "No Response")
gene_num <- c(487,589,5596,7409)
fills <- c("#F8766D", "#00BFC4","#7CAE00", "#C77CFF")
pie_chartdata <- data.frame(Set, gene_num, fills)
pie_chartdata <- pie_chartdata %>%
mutate(prop = gene_num / sum(pie_chartdata$gene_num) *100) %>%
mutate(ypos = (prop)+ 0.5*prop )
pie_chartdata %>%
ggplot(.,aes(x="",y=gene_num, fill=Set))+
geom_col(width =1) +
coord_polar("y", pi/2)+
theme_void()+
ggtitle("Distribution of genes in each motif")+
geom_text(aes(label = paste0(Set," (",gene_num,")")),
position = position_stack(vjust =.55)) +
theme(legend.position="none") +
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5))
ggsave("output/Figures/piechart.eps",width = 5, height =5, units = "in")C. Average absolute value of LFC by motif
lfc_nums <- readRDS("data/toplistall.RDS")
mean_lfc <- lfc_nums %>%
mutate(ER=if_else(ENTREZID %in%motif_ER,"y","no")) %>%
mutate(LR=if_else(ENTREZID %in%motif_LR,"y","no")) %>%
mutate(TI=if_else(ENTREZID %in%motif_TI,"y","no")) %>%
mutate(NR=if_else(ENTREZID %in%motif_NR,"y","no")) %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"),labels=c("3","24"))) %>%
group_by(id,time) %>%
mutate(absFC=abs(logFC)) %>%
dplyr::select(id,time,absFC,ER,TI,LR,NR) %>%
dplyr::summarize(ERm=mean(absFC[ER=="y"]),TIm=mean(absFC[TI=="y"]),LRm=mean(absFC[LR=="y"]),NRm=mean(absFC[NR=="y"])) %>%
as.data.frame()
mean_lfc %>%
ungroup() %>%
mutate(id=factor(id,
levels =c('DOX', 'EPI', 'DNR', 'MTX', 'TRZ', 'VEH'))) %>%
pivot_longer(!c(id,time), names_to = "Motif",
values_to="meanLFC") %>%
mutate(Set=case_match(Motif,"ERm"~"Early Acute Response",
"TIm"~"Early Sustained Response",
"LRm"~"Late Response",
"NRm"~"No Response",
.default = Motif))%>%
ggplot(., aes(x=time,y= meanLFC,col=id,group=id))+
geom_point(size=2)+
geom_line(size=2)+
ggpubr::fill_palette(drug_pal_fact)+
# guides(fill=guide_legend(title = "Treatment"))+
facet_wrap(~Set, nrow = 1)+
theme_bw()+
xlab("Time (hours)")+
scale_color_manual(values=drug_pal_fact)+
ylab(" Avg. |Log Fold Change|")+
theme_bw()+
# ggtitle(" average |Log Fold| for genes across treatment each motif")+
theme(plot.title = element_text(size = rel(1.0), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.line = element_line(linewidth = 1.5),
strip.background = element_rect(fill = "transparent"),
axis.text = element_text(size = 10, color = "black", angle = 0),
strip.text.x = element_text(size = 11, color = "black", face = "bold"))
ggsave("output/Figures/mean_lfc_motif.eps", units = "in")
sessionInfo()R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] broom_1.0.5 RColorBrewer_1.1-3 ggsignif_0.6.4
[4] cowplot_1.1.1 Cormotif_1.46.0 limma_3.56.2
[7] affy_1.78.2 Biobase_2.60.0 data.table_1.14.8
[10] BiocGenerics_0.46.0 tinytex_0.45 lubridate_1.9.2
[13] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
[16] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[19] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
[22] car_3.1-2 carData_3.0-5 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] gtable_0.3.3 xfun_0.39 bslib_0.5.0
[4] rstatix_0.7.2 processx_3.8.2 callr_3.7.3
[7] tzdb_0.4.0 vctrs_0.6.3 tools_4.3.1
[10] ps_1.7.5 generics_0.1.3 fansi_1.0.4
[13] highr_0.10 pkgconfig_2.0.3 lifecycle_1.0.3
[16] farver_2.1.1 compiler_4.3.1 git2r_0.32.0
[19] textshaping_0.3.6 munsell_0.5.0 getPass_0.2-2
[22] httpuv_1.6.11 htmltools_0.5.5 sass_0.4.7
[25] yaml_2.3.7 preprocessCore_1.62.1 ggpubr_0.6.0
[28] later_1.3.1 pillar_1.9.0 jquerylib_0.1.4
[31] whisker_0.4.1 cachem_1.0.8 abind_1.4-5
[34] tidyselect_1.2.0 digest_0.6.33 stringi_1.7.12
[37] labeling_0.4.2 rprojroot_2.0.3 fastmap_1.1.1
[40] grid_4.3.1 colorspace_2.1-0 cli_3.6.1
[43] magrittr_2.0.3 utf8_1.2.3 withr_2.5.0
[46] backports_1.4.1 scales_1.2.1 promises_1.2.0.1
[49] timechange_0.2.0 rmarkdown_2.23 httr_1.4.6
[52] affyio_1.70.0 ragg_1.2.5 hms_1.1.3
[55] evaluate_0.21 knitr_1.43 rlang_1.1.1
[58] Rcpp_1.0.11 glue_1.6.2 BiocManager_1.30.21.1
[61] rstudioapi_0.15.0 jsonlite_1.8.7 R6_2.5.1
[64] systemfonts_1.0.4 fs_1.6.3 zlibbioc_1.46.0