Figure 4: Gene expression responses to AC treatments converge over time.
ERM
2023-09-27
Last updated: 2023-09-27
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Knit directory: Cardiotoxicity/
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|---|---|---|---|---|
| Rmd | 0477cbd | reneeisnowhere | 2023-09-27 | updates and adding code |
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library(car)
library(tidyverse)
library(BiocGenerics)
library(data.table)
library(cowplot)
library(ggsignif)
library(RColorBrewer)
library(broom)
library(limma)
library(corrplot)
library(ggVennDiagram)
library(ComplexHeatmap)
library(gridtext)
library(paletteer)toplistall <- readRDS("data/toplistall.RDS")
siglist <- readRDS("data/siglist_final.RDS")
list2env(siglist, envir = .GlobalEnv)<environment: R_GlobalEnv>cpmcounts <- readRDS("data/cpmcount.RDS")Figure 4: Gene expression responses to AC treatments converge over time.
A. Pearson correlation of drug response in pairwise analysis
efit2 <- readRDS("data/efit2_final.RDS")
FCmatrix <- subset(efit2$coefficients)
colnames(FCmatrix) <-
c(
"DNR\n3h",
"DOX\n3h",
"EPI\n3h",
"MTX\n3h",
"TRZ\n3h",
"DNR\n24h",
"DOX\n24h",
"EPI\n24h",
"MTX\n24h",
"TRZ\n24h"
)
mat_col <-
data.frame(
time = c(rep("3 hours", 5), rep("24 hours", 5)),
class = (c(
"AC", "AC", "AC", "nAC", "nAC", "AC", "AC", "AC", "nAC", "nAC"
)),
TOP2i = c(rep("yes", 4), "no", rep("yes", 4), "no")
)
rownames(mat_col) <- colnames(FCmatrix)
mat_colors <-
list(
time = c("pink", "chocolate4"),
class = c("yellow1", "lightgreen"),
TOP2i = c("darkgreen", "goldenrod")
)
names(mat_colors$time) <- unique(mat_col$time)
names(mat_colors$class) <- unique(mat_col$class)
names(mat_colors$TOP2i) <- unique(mat_col$TOP2i)
corrFC <- cor(FCmatrix)
ComplexHeatmap::pheatmap(
corrFC,
display_numbers = TRUE,
number_format = "%.2f",
main = ("Correlation of all expressed FC values, n=14084"),
annotation_col = mat_col,
annotation_colors = mat_colors,
fontsize = 10,
fontsize_row = 8,
angle_col = "0",
treeheight_row = 25,
fontsize_col = 8,
treeheight_col = 20
)
HMcorr <- ComplexHeatmap::pheatmap(
corrFC,
display_numbers = TRUE,
number_format = "%.2f",
annotation_col = mat_col,
annotation_colors = mat_colors,
fontsize = 10,
fontsize_row = 8,
angle_col = "0",
treeheight_row = 25,
fontsize_col = 8,
treeheight_col = 20
)Additional code and information can be found here
B. Venn Diagram of 3 hour DEGs
total3 <- list(sigVDA3$ENTREZID,sigVDX3$ENTREZID, sigVEP3$ENTREZID,sigVMT3$ENTREZID)
totalin_common3 <- c(sigVDA3$SYMBOL,sigVDX3$SYMBOL, sigVEP3$SYMBOL,sigVMT3$SYMBOL)
fig3hrvenn <- ggVennDiagram::ggVennDiagram(total3,
category.names = c("DNR \nn = 532\n",
"DOX\n n = 19\n",
"EPI\n n = 210\n",
" MTX\n n = 75\n"),
show_intersect = FALSE,
set_color = "black",
category_size = c(5,5,5,5),
label = "count",
label_percent_digit = 1,
label_size = 4,
label_alpha = 0,
caption_size = 4,
label_color = "black",
edge_lty = "solid", set_size = 4.5)+
scale_x_continuous(expand = expansion(mult = .3))+
scale_y_continuous(expand = expansion(mult = .2))+
scale_color_paletteer_d(palette = "fishualize::Bodianus_pulchellus")+
scale_fill_distiller(palette="Spectral", direction = -1, limits=c(0,4500))+
labs(title = "3 hour",
caption = paste("n =", length(unique(totalin_common3)),"genes\n "))+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5, vjust =1),
plot.caption=element_text(size=12))
fig3hrvenn 
C. Venn Diagram of 24 hour DEGs
total24 <-
list(sigVDA24$ENTREZID,
sigVDX24$ENTREZID,
sigVEP24$ENTREZID,
sigVMT24$ENTREZID)
in_common24 <-
c(sigVDA24$ENTREZID,
sigVDX24$ENTREZID,
sigVEP24$ENTREZID,
sigVMT24$ENTREZID)
fig24hrvenn <- ggVennDiagram::ggVennDiagram(
total24,
category.names = c(
"DNR \nn = 7017 \n ",
"DOX\n n = 6645\n",
"EPI\n n = 6328\n",
" MTX\n n = 1115\n "
),
show_intersect = FALSE,
set_color = "black",
category_size = c(5, 5, 5, 5),
label = "count",
label_percent_digit = 1,
label_size = 4,
label_alpha = 0,
label_color = "black",
edge_lty = "solid",
set_size = 4
) +
scale_x_continuous(expand = expansion(mult = .3)) +
scale_y_continuous(expand = expansion(mult = .2)) +
scale_color_paletteer_d(palette = "fishualize::Bodianus_pulchellus") +
scale_fill_distiller(palette = "Spectral", direction = -1) +
labs(title = "24 hour",
caption = paste("n =", length(unique(in_common24)), "genes\n ")) +
theme(
plot.title = element_text(
size = rel(1.6),
hjust = 0.5,
vjust = 1
),
plot.caption = element_text(size = 12)
)
fig24hrvenn
More information can be found here.
D. 3 KEGG Pathway analysis
kegglistDEG3 <- readRDS("data/kegglistDEG3.RDS")
list2env(kegglistDEG3, envir = .GlobalEnv)<environment: R_GlobalEnv>keggDEGlong3 <- list(
"DOX" = DXdeg3k,
"EPI" = EPdeg3k,
"DNR" = DAdeg3k,
"MTX" = MTdeg3k,
"Anthracyclines" = DDEdeg3k,
"TOP2Bi" = DDEMdeg3k
)
col_funkegg = circlize::colorRamp2(c(0, 31), c("white", "darkred"))
keggtable3 <-
data.table::rbindlist(keggDEGlong3,
idcol = "deg",
fill = TRUE,
use.names = TRUE) #%>% complete(deg = names(keglistDEG3))
kegg_sig_mat3 <- keggtable3 %>%
dplyr::select(deg, p_value, term_name) %>%
add_row(deg = "TOP2i",
p_value = 1,
term_name = "Herpes simplex virus 1 infection") %>%
mutate(
term_name = case_match(
term_name,
"Cell cycle" ~ "Cell\ncycle",
"p53 signaling pathway" ~ "p53\nsig.\npath.",
"Base excision repair" ~ "Base\nexcision\nrepair",
"Herpes simplex virus 1 infection" ~ "HSV1\ninfection" ,
"DNA replication" ~ "DNA\nrep.",
.default = term_name
)
) %>%
pivot_wider(
id_cols = everything(),
names_from = "term_name",
values_from = "p_value",
values_fill = list(p_value = 1)
) %>%
column_to_rownames('deg') %>%
as.matrix()#
kegg_mat3 <- keggtable3 %>%
dplyr::select(deg, log_val, term_name) %>%
add_row(deg = "TOP2i",
log_val = NA,
term_name = "Herpes simplex virus 1 infection") %>%
mutate(
term_name = case_match(
term_name,
"p53 signaling pathway" ~ "p53\nsig.\npath.",
"Base excision repair" ~ "Base\nexcision\nrepair",
"Herpes simplex virus 1 infection" ~ "HSV1\ninfection" ,
"DNA replication" ~ "DNA\nrep.",
.default = term_name
)
) %>%
pivot_wider(id_cols = everything(),
names_from = "term_name",
values_from = "log_val") %>%
column_to_rownames('deg') %>%
as.matrix()#
draw(
Heatmap(
kegg_mat3,
column_title = "KEGG Pathway -log 10(p value)",
name = "-log10 (p value)",
cluster_rows = FALSE,
cluster_columns = FALSE,
column_names_rot = 0,
column_names_centered = TRUE,
row_names_max_width = max_text_width(rownames(kegg_mat3),
gp = gpar(fontsize = 10)),
col = col_funkegg,
na_col = "lightyellow",
column_labels = gt_render(c("HSV-1 infx.",
"p53\nsig.\npath.")),
cell_fun = function(j, i, x, y, width, height, fill) {
if (kegg_sig_mat3[i, j] < 0.05)
grid.text("*", x, y, gp = gpar(fontsize = 20))
}
)
)
More information can be found here.
E. 24 KEGG Pathway analysis
library(grid)
kegglistDEG24 <- readRDS("data/kegglistDEG24.RDS")
list2env(kegglistDEG24, envir = .GlobalEnv)<environment: R_GlobalEnv>keggDEGlong <- list(
"DOX" = DXdegk,
"EPI" = EPdegk,
"DNR" = DAdegk,
"MTX" = MTdegk,
"Anthracyclines" = DDEdegk,
"TOP2i" = DDEMdegk
)
col_funkegg = circlize::colorRamp2(c(0, 31), c("white", "darkred"))
keggtable <- data.table::rbindlist(keggDEGlong, idcol = "deg")
kegg_sig_mat <- keggtable %>%
dplyr::select(deg, p_value, term_name) %>%
mutate(
term_name = case_match(
term_name,
"Cell cycle" ~ "Cell\ncycle",
"p53 signaling pathway" ~ "p53\nsig.\npath.",
"Base excision repair" ~ "Base\nexcision\nrepair",
"DNA replication" ~ "DNA\nrep.",
.default = term_name
)
) %>%
pivot_wider(
id_cols = everything(),
names_from = "term_name",
values_from = "p_value",
values_fill = list(p_value = 1)
) %>%
column_to_rownames('deg') %>%
as.matrix()#
kegg_mat <- keggtable %>%
dplyr::select(deg, log_val, term_name) %>%
mutate(
term_name = case_match(
term_name,
"Cell cycle" ~ "Cell\ncycle",
"p53 signaling pathway" ~ "p53\nsig.\npath.",
"Base excision repair" ~ "Base\nexcision\nrepair",
"DNA replication" ~ "DNA\nrep.",
.default = term_name
)
) %>%
pivot_wider(id_cols = everything(),
names_from = "term_name",
values_from = "log_val") %>%
column_to_rownames('deg') %>%
as.matrix()#
Heatmap(
kegg_mat,
column_title = "KEGG Pathway -log p values",
name = "-log10 (p value)",
cluster_rows = FALSE,
cluster_columns = FALSE,
column_names_rot = 0,
column_names_centered = TRUE,
row_names_max_width = max_text_width(rownames(kegg_mat),
gp = gpar(fontsize = 10)),
col = col_funkegg,
na_col = "lightyellow",
column_labels = gt_render(c(
"p53\nsig.\npath.",
"B.E.R.",
"Cell\ncycle",
"DNA\nrep."
)),
cell_fun = function(j, i, x, y, width, height, fill) {
if (kegg_sig_mat[i, j] < 0.05)
grid.text("*", x, y, gp = gpar(fontsize = 20))
}
)
Further KEGG analysis information and code may be found here
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] paletteer_1.5.0 gridtext_0.1.5 ComplexHeatmap_2.16.0
[4] ggVennDiagram_1.2.3 corrplot_0.92 limma_3.56.2
[7] broom_1.0.5 RColorBrewer_1.1-3 ggsignif_0.6.4
[10] cowplot_1.1.1 data.table_1.14.8 BiocGenerics_0.46.0
[13] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[16] dplyr_1.1.3 purrr_1.0.2 readr_2.1.4
[19] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.3
[22] tidyverse_2.0.0 car_3.1-2 carData_3.0-5
[25] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] DBI_1.1.3 rematch2_2.1.2 rlang_1.1.1
[4] magrittr_2.0.3 clue_0.3-64 GetoptLong_1.0.5
[7] git2r_0.32.0 matrixStats_1.0.0 e1071_1.7-13
[10] compiler_4.3.1 getPass_0.2-2 png_0.1-8
[13] callr_3.7.3 vctrs_0.6.3 pkgconfig_2.0.3
[16] shape_1.4.6 crayon_1.5.2 fastmap_1.1.1
[19] backports_1.4.1 magick_2.7.5 labeling_0.4.3
[22] utf8_1.2.3 promises_1.2.1 rmarkdown_2.24
[25] markdown_1.8 tzdb_0.4.0 ps_1.7.5
[28] xfun_0.40 cachem_1.0.8 jsonlite_1.8.7
[31] RVenn_1.1.0 later_1.3.1 parallel_4.3.1
[34] cluster_2.1.4 R6_2.5.1 bslib_0.5.1
[37] stringi_1.7.12 jquerylib_0.1.4 Rcpp_1.0.11
[40] iterators_1.0.14 knitr_1.44 IRanges_2.34.1
[43] httpuv_1.6.11 timechange_0.2.0 tidyselect_1.2.0
[46] rstudioapi_0.15.0 abind_1.4-5 yaml_2.3.7
[49] doParallel_1.0.17 codetools_0.2-19 processx_3.8.2
[52] withr_2.5.0 evaluate_0.21 sf_1.0-14
[55] units_0.8-4 proxy_0.4-27 xml2_1.3.5
[58] circlize_0.4.15 pillar_1.9.0 whisker_0.4.1
[61] KernSmooth_2.23-22 foreach_1.5.2 stats4_4.3.1
[64] generics_0.1.3 rprojroot_2.0.3 S4Vectors_0.38.1
[67] hms_1.1.3 commonmark_1.9.0 munsell_0.5.0
[70] scales_1.2.1 class_7.3-22 glue_1.6.2
[73] tools_4.3.1 fs_1.6.3 colorspace_2.1-0
[76] cli_3.6.1 fansi_1.0.4 gtable_0.3.4
[79] sass_0.4.7 digest_0.6.33 prismatic_1.1.1
[82] classInt_0.4-10 rjson_0.2.21 farver_2.1.1
[85] htmltools_0.5.6 lifecycle_1.0.3 httr_1.4.7
[88] GlobalOptions_0.1.2