Last updated: 2023-06-26
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Knit directory: Cardiotoxicity/
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The code below is how I wrangled the knowles supplemental lists into entrezgene_ids to overlap with my expressed gene lists
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$entrezgene_id, "y" , "no")) %>%
dplyr::select(ENTREZID,id,time,meSNP,sigcount) %>%
group_by(id,time,meSNP,sigcount) %>%
tally() %>%
pivot_wider(id_cols = c(id,time,sigcount), names_from = meSNP,names_glue = "meSNP_{meSNP}", values_from = n)
meSNPdf %>%
kable(., caption= "Number of genes from minimally expressed SNPs found in DEGs (sig and nonsig) broken down by drug,time, and significance") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>%
scroll_box(width = "80%", height = "400px")
id | time | sigcount | meSNP_no | meSNP_y |
---|---|---|---|---|
Daunorubicin | 3_hours | notsig | 13032 | 497 |
Daunorubicin | 3_hours | sig | 549 | 6 |
Daunorubicin | 24_hours | notsig | 6916 | 304 |
Daunorubicin | 24_hours | sig | 6665 | 199 |
Doxorubicin | 3_hours | notsig | 13565 | 503 |
Doxorubicin | 3_hours | sig | 16 | NA |
Doxorubicin | 24_hours | notsig | 7249 | 319 |
Doxorubicin | 24_hours | sig | 6332 | 184 |
Epirubicin | 3_hours | notsig | 13365 | 499 |
Epirubicin | 3_hours | sig | 216 | 4 |
Epirubicin | 24_hours | notsig | 7551 | 331 |
Epirubicin | 24_hours | sig | 6030 | 172 |
Mitoxantrone | 3_hours | notsig | 13524 | 502 |
Mitoxantrone | 3_hours | sig | 57 | 1 |
Mitoxantrone | 24_hours | notsig | 12284 | 473 |
Mitoxantrone | 24_hours | sig | 1297 | 30 |
Trastuzumab | 3_hours | notsig | 13581 | 503 |
Trastuzumab | 24_hours | notsig | 13581 | 503 |
reQTLdf <- toplistall %>%
mutate(id = as.factor(id)) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
mutate(reQTL = if_else( ENTREZID %in% knowles5$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 | 13168 | 361 |
Daunorubicin | 3_hours | sig | 542 | 13 |
Daunorubicin | 24_hours | notsig | 7033 | 187 |
Daunorubicin | 24_hours | sig | 6677 | 187 |
Doxorubicin | 3_hours | notsig | 13694 | 374 |
Doxorubicin | 3_hours | sig | 16 | NA |
Doxorubicin | 24_hours | notsig | 7374 | 194 |
Doxorubicin | 24_hours | sig | 6336 | 180 |
Epirubicin | 3_hours | notsig | 13494 | 370 |
Epirubicin | 3_hours | sig | 216 | 4 |
Epirubicin | 24_hours | notsig | 7684 | 198 |
Epirubicin | 24_hours | sig | 6026 | 176 |
Mitoxantrone | 3_hours | notsig | 13652 | 374 |
Mitoxantrone | 3_hours | sig | 58 | NA |
Mitoxantrone | 24_hours | notsig | 12423 | 334 |
Mitoxantrone | 24_hours | sig | 1287 | 40 |
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 | 555 | 6 | 13 |
3_hours | Doxorubicin | 16 | 0 | 0 |
3_hours | Epirubicin | 220 | 4 | 4 |
3_hours | Mitoxantrone | 58 | 1 | 0 |
24_hours | Daunorubicin | 6864 | 199 | 187 |
24_hours | Doxorubicin | 6516 | 184 | 180 |
24_hours | Epirubicin | 6202 | 172 | 176 |
24_hours | Mitoxantrone | 1327 | 30 | 40 |
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 proporitions 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 proporitions 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 | 0.0000338 |
Daunorubicin | 3_hours | 0.0018777 |
Doxorubicin | 24_hours | 0.0000113 |
Doxorubicin | 3_hours | 0.9232984 |
Epirubicin | 24_hours | 0.0000074 |
Epirubicin | 3_hours | 0.2189641 |
Mitoxantrone | 24_hours | 0.0086490 |
Mitoxantrone | 3_hours | 0.6853643 |
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 | 0.6576304 |
Daunorubicin | 3_hours | 0.7387684 |
Doxorubicin | 24_hours | 0.4965954 |
Doxorubicin | 3_hours | 1.0000000 |
Epirubicin | 24_hours | 0.2539396 |
Epirubicin | 3_hours | 0.5705567 |
Mitoxantrone | 24_hours | 0.4445513 |
Mitoxantrone | 3_hours | 0.3946217 |
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(6,13,497, 358), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
DNR_3chi <- chisq.test(testDNR3chi,correct=FALSE)$p.value
testDNR24chi <- matrix(c(199,187,304, 184), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
DNR_24chi <- chisq.test(testDNR24chi,correct=FALSE)$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)$p.value
testDOX24chi <- matrix(c(184,180,318, 191), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
DOX_24chi <- chisq.test(testDOX24chi,correct=FALSE)$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)$p.value
testEPI24chi <- matrix(c(172,176,331, 195), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
EPI_24chi <- chisq.test(testEPI24chi,correct=FALSE)$p.value
testMTX3chi <- matrix(c(1,0,502, 370), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
MTX_3chi <- chisq.test(testMTX3chi,correct=FALSE)$p.value
testMTX24chi <- matrix(c(30,40,473, 331), nrow=2, ncol=2, byrow=FALSE,
dimnames=list(c("k4", "k5"),c( "y", "n")))
MTX_24chi <- chisq.test(testMTX24chi,correct=FALSE)$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 | 0.0205682 | 0.0014217 |
DOX | NaN | 0.0004405 |
EPI | 0.6641977 | 0.0000770 |
MTX | 0.3908069 | 0.0095036 |
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"))
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.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] 218
print("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$..values..[[14]]
ggVennDiagram::ggVennDiagram(reQTLcombine, category.names = c("DNR-187\nsigDEG","DOX-180 \nsigDEG\n","EPI-176\nsigDEG\n","MTX-40\nsigDEG"),label = "count") +scale_x_continuous(expand = expansion(mult = .2))+scale_y_continuous(expand = expansion(mult = .1))
Here, I found 119 reQTLs that were related to all anthracyclines, with
31 reQTLS related to all Top2i drugs at 24 hours.
DOXeQTL_table <- toplistall %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
mutate(DOXreQTLs=if_else(ENTREZID %in%DOXreQTLs$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 | 6695 |
Daunorubicin | y | 169 |
Doxorubicin | no | 6336 |
Doxorubicin | y | 180 |
Epirubicin | no | 6048 |
Epirubicin | y | 154 |
Mitoxantrone | no | 1291 |
Mitoxantrone | y | 36 |
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"))
DoxonlyQTL <- as.numeric(QTLoverlaps$..values..[[14]])
# geneDoxonlyQTL <- getBM(attributes=my_attributes,filters ='entrezgene_id',
# values =DoxonlyQTL, mart = ensembl)
# write.csv(geneDoxonlyQTL,"data/geneDoxonlyQTL.csv")
# saveRDS(cpmcounts,"data/cpmcount.RDS")
geneDoxonlyQTL <- read.csv("data/geneDoxonlyQTL.csv",row.names = 1)
cpmcounts <- readRDS("data/cpmcount.RDS")
for (g in seq(from=1, to=length(geneDoxonlyQTL$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$ENTREZID)
## do venn partition and pull doxspe genes
venn_24h <- VennDiagram::get.venn.partitions(total24)
DoxonlyDEG <- venn_24h$..values..[[14]]
EpionlyDEG <- venn_24h$..values..[[12]]
DnronlyDEG <- venn_24h$..values..[[15]]
MtxonlyDEG <- venn_24h$..values..[[8]]
Dox24_lfc <- toplist24hr %>%
filter(ENTREZID %in% DoxonlyDEG) %>%
# group_by(id) %>%
# dplyr::filter(adj.P.Val<0.05) #%>%
mutate(logFC=logFC*(-1)) %>%
ggplot(., aes(x= id, y=logFC))+
geom_boxplot(aes(fill=id))+
theme_classic()+
fill_palette(palette = drug_palNoVeh)+
ggtitle("LogFC of Dox specific DEGs")+
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 20))
# strip.text.x = element_text(size = 12, color = "black", face = "italic"))
toplist24hr %>%
# filter(ENTREZID %in% DOXdeg_sp$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)
### 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 "))
}
DOXdeg_sp <- toplist24hr %>%
dplyr::filter(if_else(id=="Doxorubicin",adj.P.Val<0.01,adj.P.Val>0.01 )) %>%
dplyr::filter(ENTREZID %in% DoxonlyDEG) %>%
filter(id=="Doxorubicin") %>%
dplyr::select(ENTREZID, SYMBOL)
DOXdeg_sp %>%
kable(., caption= "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 |
653082 | 653082 | ZDHHC11B |
1452 | 1452 | CSNK1A1 |
23108 | 23108 | RAP1GAP2 |
114882 | 114882 | OSBPL8 |
8501 | 8501 | SLC43A1 |
5000 | 5000 | ORC4 |
202181 | 202181 | LOC202181 |
135154 | 135154 | SDHAF4 |
122553 | 122553 | TRAPPC6B |
79065 | 79065 | ATG9A |
8763 | 8763 | CD164 |
284900 | 284900 | TTC28-AS1 |
26994 | 26994 | RNF11 |
256586 | 256586 | LYSMD2 |
54165 | 54165 | DCUN1D1 |
8515 | 8515 | ITGA10 |
440944 | 440944 | THUMPD3-AS1 |
57181 | 57181 | SLC39A10 |
57338 | 57338 | JPH3 |
2230 | 2230 | FDX1 |
23548 | 23548 | TTC33 |
81532 | 81532 | MOB2 |
27430 | 27430 | MAT2B |
9524 | 9524 | TECR |
805 | 805 | CALM2 |
147 | 147 | ADRA1B |
2551 | 2551 | GABPA |
26001 | 26001 | RNF167 |
100131211 | 100131211 | NEMP2 |
79624 | 79624 | ARMT1 |
10190 | 10190 | TXNDC9 |
7917 | 7917 | BAG6 |
6653 | 6653 | SORL1 |
9039 | 9039 | UBA3 |
57862 | 57862 | ZNF410 |
6498 | 6498 | SKIL |
112858 | 112858 | TP53RK |
101927720 | 101927720 | ZNF793-AS1 |
56998 | 56998 | CTNNBIP1 |
5431 | 5431 | POLR2B |
25842 | 25842 | ASF1A |
55664 | 55664 | CDC37L1 |
57186 | 57186 | RALGAPA2 |
5598 | 5598 | MAPK7 |
5594 | 5594 | MAPK1 |
58490 | 58490 | RPRD1B |
8848 | 8848 | TSC22D1 |
29944 | 29944 | PNMA3 |
2130 | 2130 | EWSR1 |
6139 | 6139 | RPL17 |
9446 | 9446 | GSTO1 |
102723508 | 102723508 | KANTR |
4987 | 4987 | OPRL1 |
84458 | 84458 | LCOR |
84752 | 84752 | B3GNT9 |
54629 | 54629 | MINDY2 |
54469 | 54469 | ZFAND6 |
144363 | 144363 | ETFRF1 |
9821 | 9821 | RB1CC1 |
116068 | 116068 | LYSMD3 |
66036 | 66036 | MTMR9 |
intersect(DOXreQTLs$ENTREZID, DOXdeg_sp$ENTREZID)
[1] "57338"
toplist24hr %>%
group_by(time,id) %>%
dplyr::filter(ENTREZID %in% DOXdeg_sp$ENTREZID) %>%
mutate(logFC=logFC*(-1)) %>%
ggplot(., aes(x= id, y=logFC))+
geom_boxplot(aes(fill=id))+
theme_classic()+
fill_palette(palette = drug_palNoVeh)+
ggtitle("LogFC of n=62 Dox specific DEGs")+
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 20))
# strip.text.x = element_text(size = 12, color = "black", face = "italic"))
toplist24hr %>% group_by(time,id) %>%
dplyr::filter(ENTREZID %in% DoxonlyDEG) %>%
dplyr::filter(adj.P.Val <0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_histogram(aes(fill=id))+
geom_vline(xintercept=0.01,linetype=2)+
# geom_density(aes(fill=id))+
facet_wrap(~id)+
ggtitle("all DE-DOX adj p. value <0.05")+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
toplist24hr %>%
dplyr::filter(ENTREZID %in% DOXdeg_sp$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()
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
toplist24hr %>%
filter(ENTREZID %in% DOXdeg_sp$ENTREZID) %>%
# filter(adj.P.Val<0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()
set.seed(12345)
sampset <- DOXdeg_sp %>%
sample_n(.,12)
for (g in seq(from=1, to=length(sampset$ENTREZID))){
a <- sampset$SYMBOL[g]
cpm_boxplot(cpmcounts,GOI=sampset[g,1],"Dark2",drug_palc,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
### Mitoxantrone
MTXdeg_sp <- toplist24hr %>%
filter(if_else(id=="Mitoxantrone",adj.P.Val<0.01,adj.P.Val>0.01 )) %>%
filter(ENTREZID %in% MtxonlyDEG) %>%
filter(id=="Mitoxantrone") %>%
dplyr::select(ENTREZID, SYMBOL)
MTXdeg_sp %>%
kable(., caption= "MTX specific genes") %>%
kable_paper("striped", full_width = TRUE) %>%
kable_styling(full_width = FALSE, font_size = 16) %>%
scroll_box( height = "500px")
ENTREZID | SYMBOL | |
---|---|---|
25894 | 25894 | PLEKHG4 |
126432 | 126432 | RINL |
100128191 | 100128191 | TMPO-AS1 |
63827 | 63827 | BCAN |
55147 | 55147 | RBM23 |
253714 | 253714 | MMS22L |
23582 | 23582 | CCNDBP1 |
4077 | 4077 | NBR1 |
7014 | 7014 | TERF2 |
25886 | 25886 | POC1A |
6240 | 6240 | RRM1 |
83879 | 83879 | CDCA7 |
79758 | 79758 | DHRS12 |
23786 | 23786 | BCL2L13 |
55723 | 55723 | ASF1B |
22950 | 22950 | SLC4A1AP |
54853 | 54853 | WDR55 |
4001 | 4001 | LMNB1 |
100996573 | 100996573 | NA |
55780 | 55780 | ERMARD |
25981 | 25981 | DNAH1 |
56970 | 56970 | ATXN7L3 |
11152 | 11152 | WDR45 |
9820 | 9820 | CUL7 |
23558 | 23558 | WBP2 |
6293 | 6293 | VPS52 |
150962 | 150962 | PUS10 |
26164 | 26164 | MTG2 |
64799 | 64799 | IQCH |
27346 | 27346 | TMEM97 |
55717 | 55717 | WDR11 |
8723 | 8723 | SNX4 |
4173 | 4173 | MCM4 |
10432 | 10432 | RBM14 |
655 | 655 | BMP7 |
9391 | 9391 | CIAO1 |
3148 | 3148 | HMGB2 |
8317 | 8317 | CDC7 |
441478 | 441478 | NRARP |
90120 | 90120 | TMEM250 |
57699 | 57699 | CPNE5 |
6273 | 6273 | S100A2 |
55670 | 55670 | PEX26 |
79027 | 79027 | ZNF655 |
11176 | 11176 | BAZ2A |
100131067 | 100131067 | CKMT2-AS1 |
84892 | 84892 | POMGNT2 |
9134 | 9134 | CCNE2 |
toplist24hr %>%
filter(ENTREZID %in% MTXdeg_sp$ENTREZID) %>%
filter(adj.P.Val <0.05) %>%
# filter(id=="Mitoxantrone")
mutate(logFC=logFC*(-1)) %>%
add_row(time="24_hours",id="Epirubicin") %>%
add_row(time="24_hours",id="Daunorubicin") %>%
mutate(id= factor(id,levels= c("Daunorubicin", "Doxorubicin", "Epirubicin", "Mitoxantrone"))) %>%
ggplot(., aes(x= id, y=logFC))+
geom_boxplot(aes(fill=id))+
theme_classic()+
fill_palette(palette = drug_palNoVeh,drop = FALSE)+
ggtitle("LogFC of n=48 MTX 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(if_else(id=="Mitoxantrone",adj.P.Val<0.01,adj.P.Val>0.01 )) %>%
filter(ENTREZID %in% MtxonlyDEG) %>%
filter(adj.P.Val <0.05) %>%
add_row(time="24_hours",id="Epirubicin") %>%
mutate(id= factor(id, levels = c("Daunorubicin", "Doxorubicin", "Epirubicin", "Mitoxantrone"))) %>%
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("MTX adj p. value <0.05")+
fill_palette(palette = drug_palNoVeh, drop = FALSE)+
theme_bw()
toplist24hr %>%
# filter(if_else(id=="Mitoxantrone",adj.P.Val<0.01,adj.P.Val>0.01 )) %>%
filter(ENTREZID %in% MtxonlyDEG) %>%
filter(adj.P.Val <0.05) %>%
add_row(time="24_hours",id="Epirubicin") %>%
mutate(id= factor(id, levels = c("Daunorubicin", "Doxorubicin", "Epirubicin", "Mitoxantrone"))) %>%
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("MTX adj p. value <0.05")+
fill_palette(palette = drug_palNoVeh, drop = FALSE)+
theme_bw()
toplist24hr %>%
filter(ENTREZID %in% MtxonlyDEG) %>%
# filter(adj.P.Val<0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()+
ggtitle("All sigDE (adj.P.val <0.05) MTX genes")
toplist24hr %>%
filter(ENTREZID %in% MTXdeg_sp$ENTREZID) %>%
# filter(adj.P.Val<0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()+
ggtitle("Only MTX specific (n=48) genes across all treatments")
set.seed(12345)
sampset <- MTXdeg_sp %>%
sample_n(.,12)
for (g in seq(from=1, to=length(sampset$ENTREZID))){
a <- sampset$SYMBOL[g]
cpm_boxplot(cpmcounts,GOI=sampset[g,1],"Dark2",drug_palc,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
DNRdeg_sp <- toplist24hr %>%
filter(if_else(id=="Daunorubicin",adj.P.Val<0.01,adj.P.Val>0.01 )) %>%
filter(ENTREZID %in% DnronlyDEG) %>%
filter(id=="Daunorubicin") %>%
dplyr::select(ENTREZID, SYMBOL)
DNRdeg_sp %>%
kable(., caption= "DNR specific genes") %>%
kable_paper("striped", full_width = TRUE) %>%
kable_styling(full_width = FALSE, font_size = 16) %>%
scroll_box( height = "500px")
ENTREZID | SYMBOL | |
---|---|---|
5742 | 5742 | PTGS1 |
114826 | 114826 | SMYD4 |
6117 | 6117 | RPA1 |
54550 | 54550 | NECAB2 |
55000 | 55000 | TUG1 |
23150 | 23150 | FRMD4B |
128637 | 128637 | TBC1D20 |
8864 | 8864 | PER2 |
132430 | 132430 | PABPC4L |
54973 | 54973 | INTS11 |
140465 | 140465 | MYL6B |
81628 | 81628 | TSC22D4 |
53838 | 53838 | C11orf24 |
387640 | 387640 | SKIDA1 |
57654 | 57654 | UVSSA |
11273 | 11273 | ATXN2L |
56624 | 56624 | ASAH2 |
255043 | 255043 | TMEM86B |
23428 | 23428 | SLC7A8 |
4007 | 4007 | PRICKLE3 |
5915 | 5915 | RARB |
79137 | 79137 | RETREG2 |
57605 | 57605 | PITPNM2 |
4241 | 4241 | MELTF |
10043 | 10043 | TOM1 |
8766 | 8766 | RAB11A |
10233 | 10233 | LRRC23 |
23140 | 23140 | ZZEF1 |
7675 | 7675 | ZNF121 |
83606 | 83606 | GUCD1 |
6726 | 6726 | SRP9 |
5026 | 5026 | P2RX5 |
6550 | 6550 | SLC9A3 |
10457 | 10457 | GPNMB |
22982 | 22982 | DIP2C |
1200 | 1200 | TPP1 |
84922 | 84922 | FIZ1 |
200014 | 200014 | CC2D1B |
4149 | 4149 | MAX |
55818 | 55818 | KDM3A |
3710 | 3710 | ITPR3 |
5350 | 5350 | PLN |
22938 | 22938 | SNW1 |
284612 | 284612 | SYPL2 |
10992 | 10992 | SF3B2 |
5463 | 5463 | POU6F1 |
9757 | 9757 | KMT2B |
146880 | 146880 | ARHGAP27P1 |
1827 | 1827 | RCAN1 |
29763 | 29763 | PACSIN3 |
10081 | 10081 | PDCD7 |
56132 | 56132 | PCDHB3 |
257144 | 257144 | GCSAM |
9929 | 9929 | JOSD1 |
7052 | 7052 | TGM2 |
11190 | 11190 | CEP250 |
388722 | 388722 | C1orf53 |
2188 | 2188 | FANCF |
375690 | 375690 | WASH5P |
28957 | 28957 | MRPS28 |
644150 | 644150 | WIPF3 |
4861 | 4861 | NPAS1 |
55876 | 55876 | GSDMB |
6832 | 6832 | SUPV3L1 |
90993 | 90993 | CREB3L1 |
64761 | 64761 | PARP12 |
7249 | 7249 | TSC2 |
158431 | 158431 | ZNF782 |
10580 | 10580 | SORBS1 |
220869 | 220869 | CBWD5 |
55140 | 55140 | ELP3 |
5587 | 5587 | PRKD1 |
80347 | 80347 | COASY |
6302 | 6302 | TSPAN31 |
9564 | 9564 | BCAR1 |
7760 | 7760 | ZNF213 |
26267 | 26267 | FBXO10 |
5570 | 5570 | PKIB |
6938 | 6938 | TCF12 |
55132 | 55132 | LARP1B |
143689 | 143689 | PIWIL4 |
6455 | 6455 | SH3GL1 |
57610 | 57610 | RANBP10 |
337876 | 337876 | CHSY3 |
54520 | 54520 | CCDC93 |
23476 | 23476 | BRD4 |
84897 | 84897 | TBRG1 |
81575 | 81575 | APOLD1 |
728492 | 728492 | SERF1B |
285464 | 285464 | NA |
960 | 960 | CD44 |
25850 | 25850 | ZNF345 |
6158 | 6158 | RPL28 |
157773 | 157773 | C8orf48 |
26156 | 26156 | RSL1D1 |
1487 | 1487 | CTBP1 |
23467 | 23467 | NPTXR |
91355 | 91355 | LRP5L |
26005 | 26005 | C2CD3 |
11108 | 11108 | PRDM4 |
25936 | 25936 | NSL1 |
128826 | 128826 | MIR1-1HG |
254048 | 254048 | UBN2 |
145165 | 145165 | ST13P4 |
105371592 | 105371592 | LOC105371592 |
6143 | 6143 | RPL19 |
57139 | 57139 | RGL3 |
6767 | 6767 | ST13 |
6992 | 6992 | PPP1R11 |
989 | 989 | SEPTIN7 |
2115 | 2115 | ETV1 |
643180 | 643180 | CCT6P3 |
4152 | 4152 | MBD1 |
57404 | 57404 | CYP20A1 |
171391 | 171391 | GATD1-DT |
6612 | 6612 | SUMO3 |
642799 | 642799 | NPIPA2 |
8934 | 8934 | RAB29 |
6722 | 6722 | SRF |
10628 | 10628 | TXNIP |
79894 | 79894 | ZNF672 |
118987 | 118987 | PDZD8 |
157697 | 157697 | ERICH1 |
81502 | 81502 | HM13 |
55787 | 55787 | TXLNG |
11135 | 11135 | CDC42EP1 |
5493 | 5493 | PPL |
55102 | 55102 | ATG2B |
3925 | 3925 | STMN1 |
147700 | 147700 | KLC3 |
10475 | 10475 | TRIM38 |
toplist24hr %>%
group_by(time,id) %>%
filter(ENTREZID %in% DNRdeg_sp$ENTREZID) %>%
filter(adj.P.Val <0.05) %>%
# filter(id=="Daunorubicin")
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=131 DNR 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(if_else(id=="Daunorubicin",adj.P.Val<0.01,adj.P.Val>0.01 )) %>%
group_by(time,id) %>%
filter(ENTREZID %in% DnronlyDEG) %>%
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("DNR adj p. value <0.05")+
fill_palette(palette = drug_palNoVeh)+
theme_bw()+
ggtitle("All sigDE (adj.P.val <0.05) DNR genes")
toplist24hr %>%
filter(ENTREZID %in% DnronlyDEG) %>%
# filter(adj.P.Val<0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()+
ggtitle("All sigDE (adj.P.val <0.05) DNR genes")
toplist24hr %>%
filter(ENTREZID %in% DNRdeg_sp$ENTREZID) %>%
# filter(adj.P.Val<0.05) %>%
ggplot(., aes(x=adj.P.Val))+
geom_density(aes(fill=id, alpha= 0.8))+
fill_palette(palette = drug_palNoVeh)+
theme_bw()+
ggtitle("Only DNR specific n=131 genes")
set.seed(12345)
sampset <- DNRdeg_sp %>%
sample_n(.,12)
for (g in seq(from=1, to=length(sampset$ENTREZID))){
a <- sampset$SYMBOL[g]
cpm_boxplot(cpmcounts,GOI=sampset[g,1],"Dark2",drug_palc,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
#### Epirubicin
EPIdeg_sp <- toplist24hr %>%
filter(if_else(id=="Epirubicin",adj.P.Val<0.01,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 | |
---|---|---|
79798 | 79798 | ARMC5 |
9685 | 9685 | CLINT1 |
55006 | 55006 | TRMT61B |
9373 | 9373 | PLAA |
54508 | 54508 | EPB41L4A-DT |
55702 | 55702 | YJU2 |
79759 | 79759 | ZNF668 |
64781 | 64781 | CERK |
57587 | 57587 | CFAP97 |
23086 | 23086 | EXPH5 |
90864 | 90864 | SPSB3 |
11097 | 11097 | NUP42 |
64863 | 64863 | METTL4 |
10342 | 10342 | TFG |
51132 | 51132 | RLIM |
57325 | 57325 | KAT14 |
285636 | 285636 | RIMOC1 |
220963 | 220963 | SLC16A9 |
7110 | 7110 | TMF1 |
51434 | 51434 | ANAPC7 |
55105 | 55105 | GPATCH2 |
92140 | 92140 | MTDH |
1938 | 1938 | EEF2 |
56252 | 56252 | YLPM1 |
7592 | 7592 | ZNF41 |
79038 | 79038 | ZFYVE21 |
11232 | 11232 | POLG2 |
339210 | 339210 | C17orf67 |
5884 | 5884 | RAD17 |
64860 | 64860 | ARMCX5 |
493812 | 493812 | HCG11 |
105371932 | 105371932 | LOC105371932 |
130507 | 130507 | UBR3 |
10111 | 10111 | RAD50 |
26135 | 26135 | SERBP1 |
27246 | 27246 | RNF115 |
57609 | 57609 | DIP2B |
85457 | 85457 | CIPC |
60561 | 60561 | RINT1 |
339122 | 339122 | RAB43 |
54942 | 54942 | ABITRAM |
5533 | 5533 | PPP3CC |
10021 | 10021 | HCN4 |
23122 | 23122 | CLASP2 |
84967 | 84967 | LSM10 |
340359 | 340359 | KLHL38 |
79713 | 79713 | IGFLR1 |
83852 | 83852 | SETDB2 |
55937 | 55937 | APOM |
2043 | 2043 | EPHA4 |
22796 | 22796 | COG2 |
1032 | 1032 | CDKN2D |
8624 | 8624 | PSMG1 |
26046 | 26046 | LTN1 |
64844 | 64844 | MARCHF7 |
1385 | 1385 | CREB1 |
84541 | 84541 | KBTBD8 |
55676 | 55676 | SLC30A6 |
79797 | 79797 | ZNF408 |
143282 | 143282 | FGFBP3 |
55339 | 55339 | WDR33 |
79230 | 79230 | ZNF557 |
55223 | 55223 | TRIM62 |
23466 | 23466 | CBX6 |
201627 | 201627 | DENND6A |
284232 | 284232 | ANKRD20A9P |
7869 | 7869 | SEMA3B |
9972 | 9972 | NUP153 |
10240 | 10240 | MRPS31 |
63943 | 63943 | FKBPL |
55727 | 55727 | BTBD7 |
79657 | 79657 | RPAP3 |
23258 | 23258 | DENND5A |
84859 | 84859 | LRCH3 |
51507 | 51507 | RTF2 |
3665 | 3665 | IRF7 |
138241 | 138241 | C9orf85 |
727851 | 727851 | RGPD8 |
140456 | 140456 | ASB11 |
349136 | 349136 | WDR86 |
2966 | 2966 | GTF2H2 |
toplist24hr %>%
group_by(time,id) %>%
filter(ENTREZID %in% EPIdeg_sp$ENTREZID) %>%
filter(adj.P.Val <0.05) %>%
# filter(id=="Daunorubicin")
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=81 EPI 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 %>%
group_by(time,id) %>%
filter(adj.P.Val <0.05) %>%
filter(ENTREZID %in% EpionlyDEG) %>%
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("EPI adj p. value <0.05")+
fill_palette(palette = drug_palNoVeh)+
theme_bw() +
ggtitle("only showing EPI n=81 specific genes ")
toplist24hr %>%
filter(ENTREZID %in% EpionlyDEG) %>%
# filter(adj.P.Val<0.05) %>%
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 <- 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 "))
}
library(gprofiler2)
# DOXdeg_sp had NO enrichment, even at 0.1 user_threshold
# DNRdeg_sp had NO enrichment
# EPIdeg_sp had NO enrichment
# MTXdeg_sep had 2, both were DNA-templated DNA replication
gostresMTXdeg_sp <- gost(query = c(MTXdeg_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"))
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] ComplexHeatmap_2.12.1 broom_1.0.5 kableExtra_1.3.4
[4] sjmisc_2.8.9 scales_1.2.1 ggpubr_0.6.0
[7] cowplot_1.1.1 ggsignif_0.6.4 lubridate_1.9.2
[10] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
[13] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[16] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
[19] RColorBrewer_1.1-3 limma_3.52.4 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] class_7.3-22 sjlabelled_1.2.0 rprojroot_2.0.3
[7] circlize_0.4.15 futile.logger_1.4.3 GlobalOptions_0.1.2
[10] fs_1.6.2 proxy_0.4-27 clue_0.3-64
[13] rstudioapi_0.14 farver_2.1.1 fansi_1.0.4
[16] xml2_1.3.4 codetools_0.2-19 doParallel_1.0.17
[19] cachem_1.0.8 knitr_1.43 jsonlite_1.8.5
[22] cluster_2.1.4 png_0.1-8 compiler_4.2.2
[25] httr_1.4.6 backports_1.4.1 fastmap_1.1.1
[28] cli_3.6.1 later_1.3.1 formatR_1.14
[31] htmltools_0.5.5 tools_4.2.2 gtable_0.3.3
[34] glue_1.6.2 Rcpp_1.0.10 carData_3.0-5
[37] jquerylib_0.1.4 vctrs_0.6.3 svglite_2.1.1
[40] iterators_1.0.14 insight_0.19.2 xfun_0.39
[43] ps_1.7.5 rvest_1.0.3 timechange_0.2.0
[46] lifecycle_1.0.3 rstatix_0.7.2 getPass_0.2-2
[49] hms_1.1.3 promises_1.2.0.1 parallel_4.2.2
[52] lambda.r_1.2.4 yaml_2.3.7 sass_0.4.6
[55] stringi_1.7.12 highr_0.10 S4Vectors_0.34.0
[58] foreach_1.5.2 e1071_1.7-13 BiocGenerics_0.42.0
[61] shape_1.4.6 rlang_1.1.1 pkgconfig_2.0.3
[64] systemfonts_1.0.4 matrixStats_1.0.0 evaluate_0.21
[67] sf_1.0-13 labeling_0.4.2 processx_3.8.1
[70] tidyselect_1.2.0 magrittr_2.0.3 R6_2.5.1
[73] IRanges_2.30.1 generics_0.1.3 DBI_1.1.3
[76] pillar_1.9.0 whisker_0.4.1 withr_2.5.0
[79] units_0.8-2 abind_1.4-5 crayon_1.5.2
[82] car_3.1-2 futile.options_1.0.1 KernSmooth_2.23-21
[85] utf8_1.2.3 RVenn_1.1.0 tzdb_0.4.0
[88] rmarkdown_2.22 GetoptLong_1.0.5 callr_3.7.3
[91] git2r_0.32.0 classInt_0.4-9 digest_0.6.31
[94] webshot_0.5.4 VennDiagram_1.7.3 httpuv_1.6.11
[97] stats4_4.2.2 munsell_0.5.0 viridisLite_0.4.2
[100] bslib_0.5.0