Knowles 2019 data
ERM
2023-06-30
Last updated: 2023-06-30
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Knit directory: Cardiotoxicity/
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| Rmd | 7fc7ec7 | reneeisnowhere | 2023-06-14 | updating code |
package loading
function load
Knowles data
The code below is how I wrangled the knowles supplemental lists into entrezgene_ids to overlap with my expressed gene lists
Enrichment of Pairwise
backGL <- read.csv("data/backGL.txt")
drug_palNoVeh <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031")
drug_palc <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
time <- rep((rep(c("3h", "24h"), c(6,6))), 6)
time <- ordered(time, levels =c("3h", "24h"))
drug <- rep(c("Daunorubicin","Doxorubicin","Epirubicin","Mitoxantrone","Trastuzumab", "Vehicle"),12)
mat_drug <- c("Daunorubicin","Doxorubicin","Epirubicin","Mitoxantrone")
indv <- as.factor(rep(c(1,2,3,4,5,6), c(12,12,12,12,12,12)))
labeltop <- (interaction(substring(drug, 0, 2), indv, time))
level_order2 <- c('75','87','77','79','78','71')
toplistall <- readRDS("data/toplistall.RDS")
knowles4 <-readRDS("output/knowles4.RDS")
knowles5 <-readRDS("output/knowles5.RDS")
DOXmeSNPs <- readRDS("output/DOXmeSNPs.RDS")
DOXreQTLs <- readRDS("output/DOXreQTLs.RDS")
DNRmeSNPs <- readRDS("output/DNRmeSNPs.RDS")
DNRreQTLs <- readRDS("output/DNRreQTLs.RDS")
EPImeSNPs <- readRDS("output/EPImeSNPs.RDS")
EPIreQTLs <- readRDS("output/EPIreQTLs.RDS")
MTXmeSNPs <- readRDS("output/MTXmeSNPs.RDS")
MTXreQTLs <- readRDS("output/MTXreQTLs.RDS")
toplist24hr <- toplistall %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
filter(time=="24_hours")
toplist3hr <- toplistall %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
filter(time=="3_hours")Determining the genetic basis of anthracycline-cardiotoxicity by
molecular response QTL mapping in induced cardiomyocytes
David A Knowles, Courtney K Burrows†, John D Blischak, Kristen M
Patterson, Daniel J Serie, Nadine Norton, Carole Ober, Jonathan K
Pritchard, Yoav Gilad
Knowles \(~~et~ al.~\) eLife 2018;7:e33480. DOI: https://doi.org/10.7554/eLife.33480
My first question was about transcriptional response at the 24 hour mark with my treatments. 3 hour RNA-seq had low numbers of DEGs,so my initial focus is at 24 hours. This time also happens to be when the Knowles paper collected their RNA-seq data.
Supplementary 4 contains a list of 518 SNPs within 1 Mb of TSS, which had a detectable marginal effect on expression (5% FDR). When converted from ensembl gene id to entrez gene id, my list of genes for this supplement = 521. I will call these meSNPS for marginal effect QTLs.
Supplementary 5 contains a list of 376 response eQTLs (reQTLs). These are variants that were associated with modulation of transcriptomic response to doxorubicin treatment. After database name conversion, I have 377 genes.
meSNPdf <- toplistall %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
mutate(meSNP = if_else( ENTREZID %in% knowles4$entrezgene_id, "y" , "no")) %>%
dplyr::select(ENTREZID,id,time,meSNP,sigcount) %>%
group_by(id,time,meSNP,sigcount) %>%
tally() %>%
pivot_wider(id_cols = c(id,time,sigcount), names_from = meSNP,names_glue = "meSNP_{meSNP}", values_from = n)
meSNPdf %>%
kable(., caption= "Number of genes from minimally expressed SNPs found in DEGs (sig and nonsig) broken down by drug,time, and significance") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>%
scroll_box(width = "80%", height = "400px")| id | time | sigcount | meSNP_no | meSNP_y |
|---|---|---|---|---|
| Daunorubicin | 3_hours | notsig | 13055 | 497 |
| Daunorubicin | 3_hours | sig | 526 | 6 |
| Daunorubicin | 24_hours | notsig | 6772 | 295 |
| Daunorubicin | 24_hours | sig | 6809 | 208 |
| Doxorubicin | 3_hours | notsig | 13562 | 503 |
| Doxorubicin | 3_hours | sig | 19 | NA |
| Doxorubicin | 24_hours | notsig | 7122 | 317 |
| Doxorubicin | 24_hours | sig | 6459 | 186 |
| Epirubicin | 3_hours | notsig | 13375 | 499 |
| Epirubicin | 3_hours | sig | 206 | 4 |
| Epirubicin | 24_hours | notsig | 7426 | 330 |
| Epirubicin | 24_hours | sig | 6155 | 173 |
| Mitoxantrone | 3_hours | notsig | 13508 | 501 |
| Mitoxantrone | 3_hours | sig | 73 | 2 |
| Mitoxantrone | 24_hours | notsig | 12491 | 478 |
| Mitoxantrone | 24_hours | sig | 1090 | 25 |
| Trastuzumab | 3_hours | notsig | 13581 | 503 |
| Trastuzumab | 24_hours | notsig | 13581 | 503 |
reQTLdf <- toplistall %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
mutate(reQTL = if_else( ENTREZID %in% knowles5$entrezgene_id, "y" , "no")) %>%
dplyr::select(ENTREZID,id,time,reQTL,sigcount) %>%
group_by(id,time,reQTL,sigcount) %>%
tally() %>%
pivot_wider(id_cols = c(id,time,sigcount), names_from = reQTL,names_glue = "reQTL_{reQTL}", values_from = n)
reQTLdf %>%
kable(., caption= "Number of genes from response eQTLS found in DEGs (sig and nonsig) broken down by drug,time, and significance") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>%
scroll_box(width = "80%", height = "400px")| id | time | sigcount | reQTL_no | reQTL_y |
|---|---|---|---|---|
| Daunorubicin | 3_hours | notsig | 13192 | 360 |
| Daunorubicin | 3_hours | sig | 518 | 14 |
| Daunorubicin | 24_hours | notsig | 6888 | 179 |
| Daunorubicin | 24_hours | sig | 6822 | 195 |
| Doxorubicin | 3_hours | notsig | 13691 | 374 |
| Doxorubicin | 3_hours | sig | 19 | NA |
| Doxorubicin | 24_hours | notsig | 7251 | 188 |
| Doxorubicin | 24_hours | sig | 6459 | 186 |
| Epirubicin | 3_hours | notsig | 13504 | 370 |
| Epirubicin | 3_hours | sig | 206 | 4 |
| Epirubicin | 24_hours | notsig | 7558 | 198 |
| Epirubicin | 24_hours | sig | 6152 | 176 |
| Mitoxantrone | 3_hours | notsig | 13636 | 373 |
| Mitoxantrone | 3_hours | sig | 74 | 1 |
| Mitoxantrone | 24_hours | notsig | 12631 | 338 |
| Mitoxantrone | 24_hours | sig | 1079 | 36 |
| Trastuzumab | 3_hours | notsig | 13710 | 374 |
| Trastuzumab | 24_hours | notsig | 13710 | 374 |
dataframK_45 <- toplistall %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
mutate(K4 = if_else(ENTREZID %in% knowles4$entrezgene_id,'y','no'))%>%
mutate(K5 = if_else(ENTREZID %in% knowles5$entrezgene_id,'y','no'))%>%
filter(adj.P.Val<0.05) %>%
group_by(time, id) %>%
dplyr::summarize(n=n(), meSNP=sum(if_else(K4=="y",1,0)), reQTL=sum(if_else(K5=="y",1,0))) %>%
as.data.frame()
dataframK_45 %>%
kable(., caption= "Summary of meSNPs and reQTLs found in my DEGs") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(font_size = 18) %>%
scroll_box(width = "80%", height = "400px")| time | id | n | meSNP | reQTL |
|---|---|---|---|---|
| 3_hours | Daunorubicin | 532 | 6 | 14 |
| 3_hours | Doxorubicin | 19 | 0 | 0 |
| 3_hours | Epirubicin | 210 | 4 | 4 |
| 3_hours | Mitoxantrone | 75 | 2 | 1 |
| 24_hours | Daunorubicin | 7017 | 208 | 195 |
| 24_hours | Doxorubicin | 6645 | 186 | 186 |
| 24_hours | Epirubicin | 6328 | 173 | 176 |
| 24_hours | Mitoxantrone | 1115 | 25 | 36 |
Visualization of proportions
toplistall %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
mutate(meSNP=if_else(ENTREZID %in%knowles4$entrezgene_id,"y","no")) %>%
mutate(reQTL=if_else(ENTREZID %in%knowles5$entrezgene_id,"a","b")) %>%
filter(time =="24_hours") %>%
group_by(id,sigcount,meSNP) %>%
summarize(K4count=n())%>%
pivot_wider(id_cols = c(id,sigcount), names_from=c(meSNP), values_from=K4count) %>%
mutate(SNPprop=(y/(y+no)*100)) %>%
ggplot(., aes(x=id, y=SNPprop)) +
geom_col()+
geom_text(aes(x=id, label = sprintf("%.2f",SNPprop), vjust=-.2))+
#geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
facet_wrap(~sigcount)+
ggtitle("non-significant and significant enrichment proportions of Knowles meSNPs ")
toplistall %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
mutate(meSNP=if_else(ENTREZID %in%knowles4$entrezgene_id,"y","no")) %>%
mutate(reQTL=if_else(ENTREZID %in%knowles5$entrezgene_id,"a","b")) %>%
filter(time =="24_hours") %>%
group_by(id,sigcount,reQTL) %>%
summarize(K5count=n())%>%
pivot_wider(id_cols = c(id,sigcount), names_from=c(reQTL), values_from=K5count) %>%
mutate(QTLprop=(a/(a+b)*100)) %>%
ggplot(., aes(x=id, y=QTLprop)) +
geom_col()+
geom_text(aes(x=id, label = sprintf("%.2f",QTLprop), vjust=-.2))+
#geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
facet_wrap(~sigcount)+
ggtitle("non-significant and significant enrichment proportions of Knowles reQTLs ")
chi_squarek4 <- toplistall %>%
mutate(id = as.factor(id)) %>%
dplyr::filter(id!="Trastuzumab") %>%
mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
mutate(meSNP=if_else(ENTREZID %in%knowles4$entrezgene_id,"y","no")) %>%
group_by(id,time) %>%
summarise(pvalue= chisq.test(meSNP, sigcount)$p.value)
chi_squarek5 <- toplistall %>%
mutate(id = as.factor(id)) %>%
dplyr::filter(id!="Trastuzumab") %>%
mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
mutate(reQTL=if_else(ENTREZID %in%knowles5$entrezgene_id,"a","b")) %>%
group_by(id,time) %>%
summarise(pvalue= chisq.test(reQTL, sigcount)$p.value)
chi_squarek4 %>%
kable(., caption= "meSNPs (mininmally expressed QTLS) chi-squared pvalues") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE, position = "right",bootstrap_options = c("striped"),font_size = 18) %>% scroll_box(width = "50%", height = "400px")| id | time | pvalue |
|---|---|---|
| Daunorubicin | 24_hours | 0.0001314 |
| Daunorubicin | 3_hours | 0.0029100 |
| Doxorubicin | 24_hours | 0.0000038 |
| Doxorubicin | 3_hours | 0.8251668 |
| Epirubicin | 24_hours | 0.0000016 |
| Epirubicin | 3_hours | 0.2610310 |
| Mitoxantrone | 24_hours | 0.0160211 |
| Mitoxantrone | 3_hours | 0.9112927 |
chi_squarek5 %>%
kable(., caption= "reQTLS (response eQTLS) chi-squared pvalues") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE, position = "right",bootstrap_options = c("striped"),font_size = 18) %>% scroll_box(width = "50%", height = "400px")| id | time | pvalue |
|---|---|---|
| Daunorubicin | 24_hours | 0.3921463 |
| Daunorubicin | 3_hours | 1.0000000 |
| Doxorubicin | 24_hours | 0.3424614 |
| Doxorubicin | 3_hours | 0.9948230 |
| Epirubicin | 24_hours | 0.4318542 |
| Epirubicin | 3_hours | 0.6415467 |
| Mitoxantrone | 24_hours | 0.2528166 |
| Mitoxantrone | 3_hours | 0.7233217 |
We can “see” the proportions of SNPs are not enriched significantly
in the DE genes compared to the non-DE genes (chi square test).
We do not see significant enrichment of reQTLS in our DE gene sets over
the non-DE gene sets.
So what about enrichment of meSNPs in DE compared to reQTLs in significantly DE gene sets?
Knowles_count <-
toplistall %>%
filter(id!='Trastuzumab') %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
group_by(time, id) %>%
mutate(K4 = if_else(ENTREZID %in% knowles4$entrezgene_id,1,0))%>%
mutate(K5 = if_else(ENTREZID %in% knowles5$entrezgene_id,1,0))%>%
filter(adj.P.Val<0.05) %>%
dplyr::summarize(n=n(), K4=sum(K4), K5=sum(K5)) %>%
as.tibble() %>%
dplyr::select(time,id,K4,K5) %>% rename("K4_y"='K4',"K5_y"='K5') %>%
mutate(time = case_match(time, '3_hours'~'3 hrs',
'24_hours'~'24 hrs',.default = time)) %>%
mutate(K4_n= 503-K4_y, K5_n=371-K5_y) %>%
pivot_longer(!c(time,id), names_to='QTL',values_to="gene_count") %>%
separate(QTL,into=c("QTL_type",'group'),sep = '_') %>%
mutate(QTL_type =case_match(QTL_type, 'K4'~'meQTLs',
'K5'~'reQTLs',.default = QTL_type)) %>%
mutate(time=factor(time, levels=c("3 hrs","24 hrs"))) %>%
group_by(id,time,QTL_type) %>%
mutate(percent=gene_count/sum(gene_count)*100)
ggplot(Knowles_count, aes(x=QTL_type,y=gene_count, group=group, fill=group))+
geom_col(position='fill')+
# guides(fill="none")+
facet_wrap(time~id,nrow=2,ncol=4)+
theme_classic()+
scale_color_manual(values=drug_palNoVeh)+
scale_fill_manual(values=drug_palNoVeh)+
scale_y_continuous(label = scales::percent)
###This works
chi_frame <- Knowles_count %>%
pivot_wider(id_cols = c(time, id,QTL_type), names_from = group, values_from = gene_count)
testDNR3chi <- matrix(c(6,14,497, 357), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
DNR_3chi <- chisq.test(testDNR3chi,correct=FALSE)$p.value
testDNR24chi <- matrix(c(208,195,295, 176), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
DNR_24chi <- chisq.test(testDNR24chi,correct=FALSE)$p.value
testDOX3chi <- matrix(c(0,0,503, 371), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
DOX_3chi <- chisq.test(testDOX3chi,correct=FALSE)$p.value
testDOX24chi <- matrix(c(186,186,317, 185), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
DOX_24chi <- chisq.test(testDOX24chi,correct=FALSE)$p.value
testEPI3chi <- matrix(c(4,4,499, 367), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
EPI_3chi <- chisq.test(testEPI3chi,correct=FALSE)$p.value
testEPI24chi <- matrix(c(173,176,330, 195), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
EPI_24chi <- chisq.test(testEPI24chi,correct=FALSE)$p.value
testMTX3chi <- matrix(c(2,1,501, 370), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
MTX_3chi <- chisq.test(testMTX3chi,correct=FALSE)$p.value
testMTX24chi <- matrix(c(25,36,478, 335), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
MTX_24chi <- chisq.test(testMTX24chi,correct=FALSE)$p.value
K4nK5chi <- data.frame(treatment=c('DNR_3','DNR_24','DOX_3','DOX_24','EPI_3','EPI_24','MTX_3','MTX_24'), chi_p.value=c(DNR_3chi,DNR_24chi,DOX_3chi,DOX_24chi,EPI_3chi,EPI_24chi,MTX_3chi,MTX_24chi))
K4nK5chi %>%
separate(treatment, into= c('Drug','time')) %>%
pivot_wider(id_cols = Drug, names_from = time, values_from = chi_p.value) %>%
kable(., caption= "Chi Square p. values from chi-square test between proportions of sig-DE meQTLs and reQTLS by time and treatment") %>%
kable_paper("striped", full_width = TRUE) %>%
kable_styling(full_width = FALSE, font_size = 16) %>%
scroll_box( height = "500px")| Drug | 3 | 24 |
|---|---|---|
| DNR | 0.0116724 | 0.0010175 |
| DOX | NaN | 0.0001010 |
| EPI | 0.6641977 | 0.0000993 |
| MTX | 0.7489890 | 0.0066385 |
Here we found that 24 hour reQTLs are significantly enriched in sig-DEgens compared to meQTLS, with Daunorubicin 3 hour treatment also showing significant enrichment.
toplistall %>%
filter(time=="24_hours") %>%
group_by(time, id) %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
ggplot(., aes(x=id, y=abs(logFC)))+
geom_boxplot(aes(fill=id))+
ggpubr::fill_palette(palette =drug_palNoVeh)+
guides(fill=guide_legend(title = "Treatment"))+
facet_wrap(sigcount~time)+
theme_bw()+
xlab("")+
ylab("abs |Log Fold Change|")+
theme_bw()+
facet_wrap(~time)+
#ggtitle("All QTLs from all expressed genes (n=507)")+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
# axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
strip.background = element_rect(fill = "transparent"),
axis.text = element_text(size = 8, color = "black", angle = 0),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))
Dox-reQTLs
When I look ot the reQTLs across treatments at 24 hours, I see that dox has a total of 180 reQTLS. Because this gene set was specifically about doxorubicin response eQTLs, I wanted to see if these reQTLs were only Dox specific, AC specific, Top2i specific, or show any type of pattern.
siglist <- readRDS("data/siglist_final.RDS")
list2env(siglist,.GlobalEnv)<environment: R_GlobalEnv>reQTLcombine=list(DNRreQTLs$ENTREZID,DOXreQTLs$ENTREZID,EPIreQTLs$ENTREZID,MTXreQTLs$ENTREZID)
length(unique(c(DNRreQTLs$ENTREZID,DOXreQTLs$ENTREZID,EPIreQTLs$ENTREZID,MTXreQTLs$ENTREZID)))[1] 219print("number of unique genes expressed in pairwise DE set")[1] "number of unique genes expressed in pairwise DE set"QTLoverlaps <- VennDiagram::get.venn.partitions(reQTLcombine)
DoxonlyQTL <- QTLoverlaps$..values..[[14]]
ggVennDiagram::ggVennDiagram(reQTLcombine, category.names = c("DNR-195\nsigDEG","DOX-186 \nsigDEG\n","EPI-176\nsigDEG\n","MTX-36\nsigDEG"),label = "count") +scale_x_continuous(expand = expansion(mult = .2))+scale_y_continuous(expand = expansion(mult = .1))
Here, I found 121 reQTLs that were related to all anthracyclines, with
31 reQTLS related to all Top2i drugs at 24 hours.
DOXeQTL_table <- toplistall %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
mutate(DOXreQTLs=if_else(ENTREZID %in%DOXreQTLs$ENTREZID,"y","no")) %>%
dplyr::filter(time =="24_hours") %>%
dplyr::select(ENTREZID,id,DOXreQTLs,sigcount) %>%
group_by(id,DOXreQTLs,sigcount) %>%
tally() %>% as.data.frame() %>%
pivot_wider(.,id_cols = c(id,DOXreQTLs),names_from = sigcount,values_from = n) %>%
dplyr::select(id,DOXreQTLs,sig)
DOXeQTL_table%>%
kable(., caption= "All 24 hour sigDEGs broken down by drug and DOXreQTL status") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>%
scroll_box(width = "80%", height = "400px")| id | DOXreQTLs | sig |
|---|---|---|
| Daunorubicin | no | 6843 |
| Daunorubicin | y | 174 |
| Doxorubicin | no | 6459 |
| Doxorubicin | y | 186 |
| Epirubicin | no | 6170 |
| Epirubicin | y | 158 |
| Mitoxantrone | no | 1081 |
| Mitoxantrone | y | 34 |
| Trastuzumab | no | NA |
| Trastuzumab | y | NA |
DOXeQTL_table %>%
add_row(id=c("Dox-specific DEGs","Dox-specific DEGs"),DOXreQTLs=c("no","y"), sig = c(62,1)) %>%
dplyr::filter(DOXreQTLs=="y") %>%
mutate(opp= 180-sig) %>%
filter(id !=c('Trastuzumab','Doxorubicin')) %>%
rename("yes"=sig, "no"=opp) %>%
mutate(y_percent= paste0(sprintf("%2.1f", yes/(yes+no)*100), "%"),n_percent = paste0(sprintf("%2.1f", no/(yes+no)*100),"%")) %>%
pivot_longer(!c(id,DOXreQTLs,y_percent,n_percent), names_to="group", values_to = "count") %>%
mutate(id=factor(id, levels=c("Daunorubicin","Epirubicin", "Mitoxantrone","Dox-specific DEGs"))) %>%
ggplot(., aes(y=id,x=count,fill=group))+
geom_col(position='fill')+
theme_classic()+
scale_color_manual(values=c("red4","navy"))+
scale_fill_manual(values=c("#2297E6","red2"))+
ggtitle("DOXreQTLs overlaps")+
scale_y_discrete(limits=rev)+
geom_text(aes(y=id,x=1, label = n_percent,hjust=.8))+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.line = element_line(linewidth = 1.0),
axis.text = element_text(size = 10, color = "black", angle = 0),
strip.text.x = element_text(size = 15, color = "black", face = "bold"))
DOX specific-Dox dE-reqtl
# DoxonlyQTL <- as.numeric(QTLoverlaps$..values..[[14]])
# geneDoxonlyQTL <- getBM(attributes=my_attributes,filters ='entrezgene_id',
# values =DoxonlyQTL, mart = ensembl)
# write.csv(geneDoxonlyQTL,"data/geneDoxonlyQTL.csv")
geneDoxonlyQTL <- read.csv("data/geneDoxonlyQTL.csv",row.names = 1)
cpmcounts <- readRDS("data/cpmcount.RDS")
for (g in seq(from=1, to=length(geneDoxonlyQTL$entrezgene_id))){
a <- geneDoxonlyQTL$hgnc_symbol[g]
cpm_boxplot(cpmcounts,GOI=geneDoxonlyQTL[g,1],"Dark2",drug_palc,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
### Dox specific DEG examination
#pull list
total24 <-list(sigVDA24$ENTREZID,sigVDX24$ENTREZID,sigVEP24$ENTREZID,sigVMT24$ENTREZID)
## do venn partition and pull doxspe genes
# total24 <- list(sigVDA24$SYMBOL,sigVDX24$SYMBOL,sigVEP24$SYMBOL,sigVMT24$SYMBOL)
venn_24h <- VennDiagram::get.venn.partitions(total24)
DoxonlyDEG <- venn_24h$..values..[[14]]
EpionlyDEG <- venn_24h$..values..[[12]]
DnronlyDEG <- venn_24h$..values..[[15]]
MtxonlyDEG <- venn_24h$..values..[[8]]
intersect(DoxonlyDEG,DOXreQTLs$ENTREZID)[1] "57338" "29097" "7027" "10179" "9852" Dox24_lfc <- toplist24hr %>%
filter(ENTREZID %in% DoxonlyDEG) %>%
# group_by(id) %>%
# dplyr::filter(adj.P.Val<0.05) #%>%
mutate(logFC=logFC*(-1)) %>%
ggplot(., aes(x= id, y=logFC))+
geom_boxplot(aes(fill=id))+
theme_classic()+
fill_palette(palette = drug_palNoVeh)+
ggtitle("LogFC of Dox specific DEGs")+
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 20))
# strip.text.x = element_text(size = 12, color = "black", face = "italic"))
toplist24hr %>%
# filter(ENTREZID %in% DOXdeg_sp$ENTREZID) %>%
filter(adj.P.Val<0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_histogram(aes(fill=id,position="dodge"))+
# geom_density(aes(fill=id))+
facet_wrap(~id)+
fill_palette(palette = drug_palNoVeh)
toplist24hr %>%
dplyr::filter(ENTREZID %in% DoxonlyDEG) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)
print(Dox24_lfc)
Plots of some genes:
### making list
#
# Doxonly_deg <- getBM(attributes=my_attributes,filters ='entrezgene_id',
# values =DoxonlyDEG, mart = ensembl)
# write.csv(Doxonly_deg,"output/Doxonly_deg.csv")
Doxonly_deg <- read.csv("output/Doxonly_deg.csv", row.names = 1)
set.seed(12345)
sampset <- Doxonly_deg %>%
distinct(entrezgene_id,.keep_all = TRUE) %>%
sample_n(.,12)
for (g in seq(from=1, to=length(sampset$entrezgene_id))){
a <- sampset$hgnc_symbol[g]
cpm_boxplot(cpmcounts,GOI=sampset[g,1],"Dark2",drug_palc,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
More investigation of 24 hour specific genes
Doxorubicin
DOXdeg_sp <- toplist24hr %>%
dplyr::filter(ENTREZID %in% DoxonlyDEG) %>%
dplyr::filter(adj.P.Val<0.01) %>%
filter(id=="Doxorubicin") %>%
dplyr::select(ENTREZID, SYMBOL)
DOXdeg_sp %>%
kable(., caption= "68 DOX specific genes") %>%
kable_paper("striped", full_width = TRUE) %>%
kable_styling(full_width = FALSE, font_size = 16) %>%
scroll_box( height = "500px")| ENTREZID | SYMBOL | |
|---|---|---|
| 169200 | 169200 | TMEM64 |
| 1452 | 1452 | CSNK1A1 |
| 653082 | 653082 | ZDHHC11B |
| 202181 | 202181 | LOC202181 |
| 8501 | 8501 | SLC43A1 |
| 114882 | 114882 | OSBPL8 |
| 23108 | 23108 | RAP1GAP2 |
| 135154 | 135154 | SDHAF4 |
| 5000 | 5000 | ORC4 |
| 2130 | 2130 | EWSR1 |
| 122553 | 122553 | TRAPPC6B |
| 54165 | 54165 | DCUN1D1 |
| 57862 | 57862 | ZNF410 |
| 57181 | 57181 | SLC39A10 |
| 112858 | 112858 | TP53RK |
| 79065 | 79065 | ATG9A |
| 8515 | 8515 | ITGA10 |
| 25842 | 25842 | ASF1A |
| 284900 | 284900 | TTC28-AS1 |
| 2230 | 2230 | FDX1 |
| 9524 | 9524 | TECR |
| 256586 | 256586 | LYSMD2 |
| 57338 | 57338 | JPH3 |
| 440944 | 440944 | THUMPD3-AS1 |
| 10190 | 10190 | TXNDC9 |
| 805 | 805 | CALM2 |
| 100131211 | 100131211 | NEMP2 |
| 4987 | 4987 | OPRL1 |
| 79624 | 79624 | ARMT1 |
| 23548 | 23548 | TTC33 |
| 27430 | 27430 | MAT2B |
| 81532 | 81532 | MOB2 |
| 158234 | 158234 | TRMT10B |
| 147 | 147 | ADRA1B |
| 26001 | 26001 | RNF167 |
| 57186 | 57186 | RALGAPA2 |
| 5431 | 5431 | POLR2B |
| 8763 | 8763 | CD164 |
| 55234 | 55234 | SMU1 |
| 84752 | 84752 | B3GNT9 |
| 375056 | 375056 | MIA3 |
| 7917 | 7917 | BAG6 |
| 54629 | 54629 | MINDY2 |
| 102723508 | 102723508 | KANTR |
| 26994 | 26994 | RNF11 |
| 29944 | 29944 | PNMA3 |
| 9446 | 9446 | GSTO1 |
| 7881 | 7881 | KCNAB1 |
| 6139 | 6139 | RPL17 |
| 100133331 | 100133331 | NA |
| 101927720 | 101927720 | ZNF793-AS1 |
| 6653 | 6653 | SORL1 |
| 5966 | 5966 | REL |
| 5411 | 5411 | PNN |
| 6498 | 6498 | SKIL |
| 8218 | 8218 | CLTCL1 |
| 83695 | 83695 | RHNO1 |
| 1838 | 1838 | DTNB |
| 347918 | 347918 | EP400P1 |
| 9894 | 9894 | TELO2 |
| 10973 | 10973 | ASCC3 |
| 5598 | 5598 | MAPK7 |
| 8241 | 8241 | RBM10 |
| 27229 | 27229 | TUBGCP4 |
| 2281 | 2281 | FKBP1B |
| 116068 | 116068 | LYSMD3 |
| 56998 | 56998 | CTNNBIP1 |
| 2551 | 2551 | GABPA |
intersect(DOXreQTLs$ENTREZID, DOXdeg_sp$ENTREZID)[1] "57338"##lfc
toplist24hr %>%
group_by(time,id) %>%
dplyr::filter(ENTREZID %in% DOXdeg_sp$ENTREZID) %>%
mutate(logFC=logFC*(-1)) %>%
ggplot(., aes(x= id, y=logFC))+
geom_boxplot(aes(fill=id))+
theme_classic()+
fill_palette(palette = drug_palNoVeh)+
ggtitle("LogFC of n = 68 Dox specific DEGs")+
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 20))
##histo check
toplist24hr %>%
dplyr::filter(ENTREZID %in% DOXdeg_sp$ENTREZID) %>%
dplyr::filter(adj.P.Val <0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_histogram(aes(fill=id))+
geom_vline(xintercept=0.01,linetype=2)+
# geom_density(aes(fill=id))+
facet_wrap(~id)+
ggtitle("all Dox specific DEG adj p. value <0.01")+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
toplist24hr %>%
filter(ENTREZID %in% DoxonlyDEG) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
densityDOXsp <- toplist24hr %>%
filter(ENTREZID %in% DoxonlyDEG) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
densityDOXsp
set.seed(12345)
sampset <- DOXdeg_sp %>%
sample_n(.,12)
for (g in seq(from=1, to=length(sampset$ENTREZID))){
a <- sampset$SYMBOL[g]
cpm_boxplot(cpmcounts,GOI=sampset[g,1],"Dark2",drug_palc,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
##most convincing of these is ZNF793-AS1 101927720Mitoxantrone
MTXdeg_sp <- toplist24hr %>%
dplyr::filter(ENTREZID %in% MtxonlyDEG) %>%
dplyr::filter(adj.P.Val<0.01) %>%
filter(id=="Mitoxantrone") %>%
dplyr::select(ENTREZID, SYMBOL)
MTXdeg_sp %>%
kable(., caption= "MTX specific genes") %>%
kable_paper("striped", full_width = TRUE) %>%
kable_styling(full_width = FALSE, font_size = 16) %>%
scroll_box( height = "500px")| ENTREZID | SYMBOL | |
|---|---|---|
| 25894 | 25894 | PLEKHG4 |
| 253714 | 253714 | MMS22L |
| 126432 | 126432 | RINL |
| 4001 | 4001 | LMNB1 |
| 6240 | 6240 | RRM1 |
| 83879 | 83879 | CDCA7 |
| 25886 | 25886 | POC1A |
| 100128191 | 100128191 | TMPO-AS1 |
| 84892 | 84892 | POMGNT2 |
| 27346 | 27346 | TMEM97 |
| 63827 | 63827 | BCAN |
| 55723 | 55723 | ASF1B |
| 4173 | 4173 | MCM4 |
| 54853 | 54853 | WDR55 |
| 8317 | 8317 | CDC7 |
| 57699 | 57699 | CPNE5 |
| 655 | 655 | BMP7 |
| 9401 | 9401 | RECQL4 |
| 126382 | 126382 | NR2C2AP |
| 79109 | 79109 | MAPKAP1 |
| 441478 | 441478 | NRARP |
| 22950 | 22950 | SLC4A1AP |
| 100996573 | 100996573 | NA |
| 55780 | 55780 | ERMARD |
| 3148 | 3148 | HMGB2 |
| 3983 | 3983 | ABLIM1 |
| 9134 | 9134 | CCNE2 |
| 25981 | 25981 | DNAH1 |
| 9811 | 9811 | CTIF |
| 56970 | 56970 | ATXN7L3 |
| 79758 | 79758 | DHRS12 |
| 55147 | 55147 | RBM23 |
| 65057 | 65057 | ACD |
| 645954 | 645954 | SVIL2P |
| 64116 | 64116 | SLC39A8 |
| 147645 | 147645 | VSIG10L |
| 23582 | 23582 | CCNDBP1 |
| 8321 | 8321 | FZD1 |
| 10432 | 10432 | RBM14 |
| 9820 | 9820 | CUL7 |
| 55706 | 55706 | NDC1 |
#intersect(DOXreQTLs$ENTREZID, MTXdeg_sp$ENTREZID)#none
##lfc
toplist24hr %>%
group_by(time,id) %>%
filter(ENTREZID %in% MtxonlyDEG) %>%
mutate(logFC=logFC*(-1)) %>%
mutate(treatment =case_match( id,
'Daunorubicin'~'DNR',
'Doxorubicin'~'DOX' ,
'Epirubicin'~'EPI' ,
'Mitoxantrone' ~ 'MTX',
'Trastuzumab'~ 'TRX', .default = id)) %>%
ggplot(., aes(x= treatment, y=logFC))+
geom_boxplot(aes(fill=id))+
xlab(" ")+
theme_classic()+
fill_palette(palette = drug_palNoVeh)+
ggtitle("LogFC of MTX specific DEGs (n = 48)")+
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 0))
##histo check
toplist24hr %>%
dplyr::filter(ENTREZID %in% MTXdeg_sp$ENTREZID) %>%
dplyr::filter(adj.P.Val <0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_histogram(aes(fill=id))+
geom_vline(xintercept=0.01,linetype=2)+
# geom_density(aes(fill=id))+
facet_wrap(~id)+
ggtitle("all MTX specific DEG adj p. value <0.01")+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
toplist24hr %>%
filter(ENTREZID %in% MtxonlyDEG) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
densityMTXsp <- toplist24hr %>%
filter(ENTREZID %in% MtxonlyDEG) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
densityMTXsp
set.seed(12345)
sampset <- MTXdeg_sp %>%
sample_n(.,12)
for (g in seq(from=1, to=length(sampset$ENTREZID))){
a <- sampset$SYMBOL[g]
cpm_boxplot(cpmcounts,GOI=sampset[g,1],"Dark2",drug_palc,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
##most convincing of these is 54853 WDR55 Daunorubicin
DNRdeg_sp <- toplist24hr %>%
filter(adj.P.Val<0.01) %>%
filter(ENTREZID %in% DnronlyDEG) %>%
filter(id=="Daunorubicin") %>%
dplyr::select(ENTREZID, SYMBOL)
##lfc
toplist24hr %>%
filter(ENTREZID %in% DNRdeg_sp$ENTREZID) %>%
group_by(time,id) %>%
mutate(logFC=logFC*(-1)) %>%
mutate(treatment =case_match( id,
'Daunorubicin'~'DNR',
'Doxorubicin'~'DOX' ,
'Epirubicin'~'EPI' ,
'Mitoxantrone' ~ 'MTX',
'Trastuzumab'~ 'TRX', .default = id)) %>%
ggplot(., aes(x= treatment, y=logFC))+
geom_boxplot(aes(fill=id))+
xlab(" ")+
theme_classic()+
fill_palette(palette = drug_palNoVeh)+
ggtitle("LogFC of DNR specific DEGs (n = 112)")+
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 0))
##histo check
toplist24hr %>%
dplyr::filter(ENTREZID %in% DNRdeg_sp$ENTREZID) %>%
dplyr::filter(adj.P.Val <0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_histogram(aes(fill=id))+
geom_vline(xintercept=0.01,linetype=2)+
# geom_density(aes(fill=id))+
facet_wrap(~id)+
ggtitle("all DNR specific DEG adj p. value <0.01")+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
toplist24hr %>%
filter(ENTREZID %in% DnronlyDEG) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
set.seed(12345)
sampset <- DNRdeg_sp %>%
sample_n(.,12)
for (g in seq(from=1, to=length(sampset$ENTREZID))){
a <- sampset$SYMBOL[g]
cpm_boxplot(cpmcounts,GOI=sampset[g,1],"Dark2",drug_palc,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
##most convincing of these is 114826 SMYD4Epirubicin
EPIdeg_sp <- toplist24hr %>%
filter(adj.P.Val<0.01) %>%
filter(ENTREZID %in% EpionlyDEG) %>%
filter(id=="Epirubicin") %>%
dplyr::select(ENTREZID, SYMBOL)
EPIdeg_sp %>%
kable(., caption= "EPI specific genes") %>%
kable_paper("striped", full_width = TRUE) %>%
kable_styling(full_width = FALSE, font_size = 16) %>%
scroll_box( height = "500px")| ENTREZID | SYMBOL | |
|---|---|---|
| 9685 | 9685 | CLINT1 |
| 9373 | 9373 | PLAA |
| 55006 | 55006 | TRMT61B |
| 79798 | 79798 | ARMC5 |
| 79759 | 79759 | ZNF668 |
| 51434 | 51434 | ANAPC7 |
| 54508 | 54508 | EPB41L4A-DT |
| 92140 | 92140 | MTDH |
| 11097 | 11097 | NUP42 |
| 64781 | 64781 | CERK |
| 57587 | 57587 | CFAP97 |
| 57325 | 57325 | KAT14 |
| 201627 | 201627 | DENND6A |
| 55702 | 55702 | YJU2 |
| 51132 | 51132 | RLIM |
| 55105 | 55105 | GPATCH2 |
| 64863 | 64863 | METTL4 |
| 90864 | 90864 | SPSB3 |
| 8624 | 8624 | PSMG1 |
| 60561 | 60561 | RINT1 |
| 56252 | 56252 | YLPM1 |
| 55339 | 55339 | WDR33 |
| 23086 | 23086 | EXPH5 |
| 1938 | 1938 | EEF2 |
| 493812 | 493812 | HCG11 |
| 7110 | 7110 | TMF1 |
| 83852 | 83852 | SETDB2 |
| 5884 | 5884 | RAD17 |
| 64860 | 64860 | ARMCX5 |
| 220963 | 220963 | SLC16A9 |
| 22796 | 22796 | COG2 |
| 11232 | 11232 | POLG2 |
| 1385 | 1385 | CREB1 |
| 10111 | 10111 | RAD50 |
| 10342 | 10342 | TFG |
| 57609 | 57609 | DIP2B |
| 55727 | 55727 | BTBD7 |
| 2043 | 2043 | EPHA4 |
| 339210 | 339210 | C17orf67 |
| 9972 | 9972 | NUP153 |
| 285636 | 285636 | RIMOC1 |
| 54942 | 54942 | ABITRAM |
| 143282 | 143282 | FGFBP3 |
| 340359 | 340359 | KLHL38 |
| 339122 | 339122 | RAB43 |
| 79038 | 79038 | ZFYVE21 |
| 5533 | 5533 | PPP3CC |
| 2966 | 2966 | GTF2H2 |
| 105371932 | 105371932 | LOC105371932 |
| 55223 | 55223 | TRIM62 |
| 84967 | 84967 | LSM10 |
| 7592 | 7592 | ZNF41 |
| 11282 | 11282 | MGAT4B |
| 7869 | 7869 | SEMA3B |
| 65260 | 65260 | COA7 |
| 9967 | 9967 | THRAP3 |
| 10240 | 10240 | MRPS31 |
| 130507 | 130507 | UBR3 |
| 9829 | 9829 | DNAJC6 |
| 349136 | 349136 | WDR86 |
| 55676 | 55676 | SLC30A6 |
| 64844 | 64844 | MARCHF7 |
| 23122 | 23122 | CLASP2 |
| 26046 | 26046 | LTN1 |
| 138241 | 138241 | C9orf85 |
| 10021 | 10021 | HCN4 |
| 84859 | 84859 | LRCH3 |
| 85457 | 85457 | CIPC |
| 79713 | 79713 | IGFLR1 |
| 79657 | 79657 | RPAP3 |
| 5887 | 5887 | RAD23B |
| 8899 | 8899 | PRPF4B |
| 54904 | 54904 | NSD3 |
| 27246 | 27246 | RNF115 |
| 23064 | 23064 | SETX |
| 6897 | 6897 | TARS1 |
| 55937 | 55937 | APOM |
| 79230 | 79230 | ZNF557 |
| 11054 | 11054 | OGFR |
| 1032 | 1032 | CDKN2D |
| 55958 | 55958 | KLHL9 |
| 5048 | 5048 | PAFAH1B1 |
| 728229 | 728229 | TMEM191B |
| 80176 | 80176 | SPSB1 |
##lfc
toplist24hr %>%
filter(ENTREZID %in% EPIdeg_sp$ENTREZID) %>%
group_by(time,id) %>%
mutate(logFC=logFC*(-1)) %>%
mutate(treatment =case_match( id,
'Daunorubicin'~'DNR',
'Doxorubicin'~'DOX' ,
'Epirubicin'~'EPI' ,
'Mitoxantrone' ~ 'MTX',
'Trastuzumab'~ 'TRX', .default = id)) %>%
ggplot(., aes(x= treatment, y=logFC))+
geom_boxplot(aes(fill=id))+
xlab(" ")+
theme_classic()+
fill_palette(palette = drug_palNoVeh)+
ggtitle("LogFC of EPI specific DEGs (n = 84) ")+
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 0))
##histo check
toplist24hr %>%
dplyr::filter(ENTREZID %in% EPIdeg_sp$ENTREZID) %>%
dplyr::filter(adj.P.Val <0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_histogram(aes(fill=id))+
geom_vline(xintercept=0.01,linetype=2)+
# geom_density(aes(fill=id))+
facet_wrap(~id)+
ggtitle("all DNR specific DEG adj p. value <0.01")+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
densityEPIsp <- toplist24hr %>%
filter(ENTREZID %in% EpionlyDEG) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
densityEPIsp
set.seed(12345)
sampset <- EPIdeg_sp %>%
sample_n(.,12)
for (g in seq(from=1, to=length(sampset$ENTREZID))){
a <- sampset$SYMBOL[g]
cpm_boxplot(cpmcounts,GOI=sampset[g,1],"Dark2",drug_palc,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
##most convincing of these is 220963 SLC16A9GO analysis
library(gprofiler2)
#
# DNRdeg_sp had NO enrichment
# EPIdeg_sp had NO enrichment
# MTXdeg_sep had 2, "DNA-templated DNA replication" "nuclear pore localization" "DNA replication"
# gostresDOXdeg_sp <- gost(query = c(DOXdeg_sp),
# organism = "hsapiens",
# ordered_query = FALSE,
# domain_scope = "custom",
# measure_underrepresentation = FALSE,
# evcodes = FALSE,
# user_threshold = 0.05,
# correction_method = c("fdr"),
# custom_bg = backGL$ENTREZID,
# sources=c("GO:BP", "KEGG"))
#
# saveRDS(gostresDOXdeg_sp,"data/DEG-GO/gostresDOXdeg_sp.RDS")
DX_sp_DEGgostres <- readRDS("data/DEG-GO/gostresDOXdeg_sp.RDS")
DX_spgenes <- gostplot(DX_sp_DEGgostres, capped = FALSE, interactive = TRUE)
DX_spgenesDX_sp_DEGtable <- DX_sp_DEGgostres$result %>%
dplyr::select(c(source, term_id,term_name,intersection_size,
term_size, p_value))
DX_sp_DEGtable %>%
dplyr::filter(source=="GO:BP") %>%
dplyr::select(p_value,term_name,intersection_size) %>%
slice_min(., n=10 ,order_by=p_value) %>%
mutate(log_val = -log10(p_value)) %>%
# slice_max(., n=10,order_by = p_value) %>%
ggplot(., aes(x = log_val, y =reorder(term_name,p_value), col= intersection_size)) +
geom_point(aes(size = intersection_size)) +
scale_y_discrete(labels = wrap_format(30))+
guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
ggtitle('DOX specific gene set GO:BP terms') +
xlab(expression(" -"~log[10]~("adj. p-value")))+
ylab("GO: BP term")+
theme_bw()+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(size = 10, color = "black", angle = 0),
strip.text.x = element_text(size = 15, color = "black", face = "bold"))
DX_sp_DEGtable %>%
mutate_at(.vars = 6, .funs= scientific_format()) %>%
kable(., caption= "Significant (adj. P value of <0.01) Doxorubicin specific genes (n = 68) and top 10 enriched GO terms") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped","hover")) %>%
scroll_box(width = "100%", height = "400px")| source | term_id | term_name | intersection_size | term_size | p_value |
|---|---|---|---|---|---|
| GO:BP | GO:2001258 | negative regulation of cation channel activity | 5 | 30 | 4.78e-04 |
| GO:BP | GO:0032413 | negative regulation of ion transmembrane transporter activity | 5 | 51 | 1.98e-03 |
| GO:BP | GO:1901020 | negative regulation of calcium ion transmembrane transporter activity | 4 | 27 | 1.98e-03 |
| GO:BP | GO:0060314 | regulation of ryanodine-sensitive calcium-release channel activity | 4 | 23 | 1.98e-03 |
| GO:BP | GO:0032410 | negative regulation of transporter activity | 5 | 58 | 1.98e-03 |
| GO:BP | GO:2001257 | regulation of cation channel activity | 6 | 100 | 1.98e-03 |
| GO:BP | GO:0034763 | negative regulation of transmembrane transport | 6 | 96 | 1.98e-03 |
| GO:BP | GO:0060315 | negative regulation of ryanodine-sensitive calcium-release channel activity | 3 | 10 | 2.51e-03 |
| GO:BP | GO:1904063 | negative regulation of cation transmembrane transport | 5 | 64 | 2.51e-03 |
| GO:BP | GO:0051280 | negative regulation of release of sequestered calcium ion into cytosol | 3 | 11 | 2.88e-03 |
| GO:BP | GO:1901019 | regulation of calcium ion transmembrane transporter activity | 5 | 71 | 2.95e-03 |
| GO:BP | GO:1903170 | negative regulation of calcium ion transmembrane transport | 4 | 35 | 2.95e-03 |
| GO:BP | GO:0034766 | negative regulation of monoatomic ion transmembrane transport | 5 | 70 | 2.95e-03 |
| GO:BP | GO:0051284 | positive regulation of sequestering of calcium ion | 3 | 13 | 3.54e-03 |
| GO:BP | GO:0051926 | negative regulation of calcium ion transport | 4 | 45 | 7.02e-03 |
| GO:BP | GO:0043271 | negative regulation of monoatomic ion transport | 5 | 90 | 7.39e-03 |
| GO:BP | GO:0034762 | regulation of transmembrane transport | 9 | 391 | 9.58e-03 |
| GO:BP | GO:0010881 | regulation of cardiac muscle contraction by regulation of the release of sequestered calcium ion | 3 | 20 | 1.07e-02 |
| GO:BP | GO:0051279 | regulation of release of sequestered calcium ion into cytosol | 4 | 60 | 1.72e-02 |
| GO:BP | GO:0010882 | regulation of cardiac muscle contraction by calcium ion signaling | 3 | 25 | 1.91e-02 |
| GO:BP | GO:1903169 | regulation of calcium ion transmembrane transport | 5 | 122 | 2.16e-02 |
| GO:BP | GO:0032412 | regulation of monoatomic ion transmembrane transporter activity | 6 | 190 | 2.16e-02 |
| GO:BP | GO:0010880 | regulation of release of sequestered calcium ion into cytosol by sarcoplasmic reticulum | 3 | 27 | 2.16e-02 |
| GO:BP | GO:1903514 | release of sequestered calcium ion into cytosol by endoplasmic reticulum | 3 | 28 | 2.16e-02 |
| GO:BP | GO:0014808 | release of sequestered calcium ion into cytosol by sarcoplasmic reticulum | 3 | 28 | 2.16e-02 |
| GO:BP | GO:0055117 | regulation of cardiac muscle contraction | 4 | 70 | 2.28e-02 |
| GO:BP | GO:0022898 | regulation of transmembrane transporter activity | 6 | 199 | 2.34e-02 |
| GO:BP | GO:0070296 | sarcoplasmic reticulum calcium ion transport | 3 | 31 | 2.62e-02 |
| GO:BP | GO:0032409 | regulation of transporter activity | 6 | 211 | 2.88e-02 |
| GO:BP | GO:0065009 | regulation of molecular function | 20 | 1939 | 2.88e-02 |
| GO:BP | GO:0051209 | release of sequestered calcium ion into cytosol | 4 | 82 | 3.49e-02 |
| GO:BP | GO:0051283 | negative regulation of sequestering of calcium ion | 4 | 83 | 3.54e-02 |
| GO:BP | GO:0006942 | regulation of striated muscle contraction | 4 | 85 | 3.65e-02 |
| GO:BP | GO:0051282 | regulation of sequestering of calcium ion | 4 | 85 | 3.65e-02 |
| GO:BP | GO:1904062 | regulation of monoatomic cation transmembrane transport | 6 | 230 | 3.78e-02 |
| GO:BP | GO:0060316 | positive regulation of ryanodine-sensitive calcium-release channel activity | 2 | 9 | 3.78e-02 |
| GO:BP | GO:0051208 | sequestering of calcium ion | 4 | 89 | 3.98e-02 |
| GO:BP | GO:0007204 | positive regulation of cytosolic calcium ion concentration | 4 | 90 | 4.04e-02 |
| GO:BP | GO:0051342 | regulation of cyclic-nucleotide phosphodiesterase activity | 2 | 10 | 4.35e-02 |
| GO:BP | GO:2001258 | negative regulation of cation channel activity | 4 | 30 | 1.88e-02 |
| GO:BP | GO:2001257 | regulation of cation channel activity | 5 | 100 | 4.45e-02 |
| GO:BP | GO:0032413 | negative regulation of ion transmembrane transporter activity | 4 | 51 | 4.45e-02 |
| GO:BP | GO:0034763 | negative regulation of transmembrane transport | 5 | 96 | 4.45e-02 |
| GO:BP | GO:0032410 | negative regulation of transporter activity | 4 | 58 | 4.63e-02 |
| GO:BP | GO:0060314 | regulation of ryanodine-sensitive calcium-release channel activity | 3 | 23 | 4.63e-02 |
Grid for supplement
sessionInfo()R version 4.2.2 (2022-10-31 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
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gprofiler2_0.2.2 ComplexHeatmap_2.12.1 broom_1.0.5
[4] kableExtra_1.3.4 sjmisc_2.8.9 scales_1.2.1
[7] ggpubr_0.6.0 cowplot_1.1.1 ggsignif_0.6.4
[10] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[13] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4
[16] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[19] tidyverse_2.0.0 RColorBrewer_1.1-3 limma_3.52.4
[22] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] ggVennDiagram_1.2.2 colorspace_2.1-0 rjson_0.2.21
[4] ellipsis_0.3.2 class_7.3-22 sjlabelled_1.2.0
[7] rprojroot_2.0.3 circlize_0.4.15 futile.logger_1.4.3
[10] GlobalOptions_0.1.2 fs_1.6.2 proxy_0.4-27
[13] clue_0.3-64 rstudioapi_0.14 farver_2.1.1
[16] fansi_1.0.4 xml2_1.3.4 codetools_0.2-19
[19] doParallel_1.0.17 cachem_1.0.8 knitr_1.43
[22] jsonlite_1.8.5 cluster_2.1.4 png_0.1-8
[25] shiny_1.7.4 compiler_4.2.2 httr_1.4.6
[28] backports_1.4.1 lazyeval_0.2.2 fastmap_1.1.1
[31] cli_3.6.1 later_1.3.1 formatR_1.14
[34] htmltools_0.5.5 tools_4.2.2 gtable_0.3.3
[37] glue_1.6.2 Rcpp_1.0.10 carData_3.0-5
[40] jquerylib_0.1.4 vctrs_0.6.3 svglite_2.1.1
[43] crosstalk_1.2.0 iterators_1.0.14 insight_0.19.2
[46] xfun_0.39 ps_1.7.5 rvest_1.0.3
[49] mime_0.12 timechange_0.2.0 lifecycle_1.0.3
[52] rstatix_0.7.2 getPass_0.2-2 hms_1.1.3
[55] promises_1.2.0.1 parallel_4.2.2 lambda.r_1.2.4
[58] yaml_2.3.7 sass_0.4.6 stringi_1.7.12
[61] highr_0.10 S4Vectors_0.34.0 foreach_1.5.2
[64] e1071_1.7-13 BiocGenerics_0.42.0 shape_1.4.6
[67] rlang_1.1.1 pkgconfig_2.0.3 systemfonts_1.0.4
[70] matrixStats_1.0.0 evaluate_0.21 sf_1.0-13
[73] htmlwidgets_1.6.2 labeling_0.4.2 processx_3.8.1
[76] tidyselect_1.2.0 magrittr_2.0.3 R6_2.5.1
[79] IRanges_2.30.1 generics_0.1.3 DBI_1.1.3
[82] pillar_1.9.0 whisker_0.4.1 withr_2.5.0
[85] units_0.8-2 abind_1.4-5 crayon_1.5.2
[88] car_3.1-2 futile.options_1.0.1 KernSmooth_2.23-21
[91] utf8_1.2.3 plotly_4.10.2 RVenn_1.1.0
[94] tzdb_0.4.0 rmarkdown_2.22 GetoptLong_1.0.5
[97] data.table_1.14.8 callr_3.7.3 git2r_0.32.0
[100] classInt_0.4-9 digest_0.6.31 webshot_0.5.4
[103] xtable_1.8-4 VennDiagram_1.7.3 httpuv_1.6.11
[106] stats4_4.2.2 munsell_0.5.0 viridisLite_0.4.2
[109] bslib_0.5.0