Knowles 2019 data
ERM
2023-06-15
Last updated: 2023-06-15
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Knit directory: Cardiotoxicity/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | af93421 | reneeisnowhere | 2023-06-15 | adding in seperate files |
| Rmd | 1a1a034 | reneeisnowhere | 2023-06-15 | updates galore |
| Rmd | f8f511a | reneeisnowhere | 2023-06-15 | updates and simplifications of code |
| Rmd | 7fc7ec7 | reneeisnowhere | 2023-06-14 | updating code |
package loading
Knowles data
The code below is how I wrangled the knowles supplemental lists into entrezgene_ids to overlap with my expressed gene lists
Enrichiment and Pairwise
backGL <- read.csv("data/backGL.txt", row.names =1)
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 |
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 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"))
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.
reQTLcombine=list(DNRreQTLs$ENTREZID,DOXreQTLs$ENTREZID,EPIreQTLs$ENTREZID,MTXreQTLs$ENTREZID)
length(unique(c(DNRreQTLs$ENTREZID,DOXreQTLs$ENTREZID,EPIreQTLs$ENTREZID,MTXreQTLs$ENTREZID)))[1] 218print("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 sidDEGs 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 %>%
dplyr::filter(DOXreQTLs=="y") %>%
mutate(opp= 180-sig) %>%
filter(id !=c('Trastuzumab','Doxorubicin')) %>%
rename("yes"=sig, "no"=opp) %>%
pivot_longer(!c(id,DOXreQTLs), names_to="group", values_to = "count") %>%
mutate(id=factor(id, levels=c("Daunorubicin","Epirubicin", "Mitoxantrone"))) %>%
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")
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 RColorBrewer_1.1-3 biomaRt_2.52.0
[10] ggsignif_0.6.4 lubridate_1.9.2 forcats_1.0.0
[13] stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1
[16] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[19] ggplot2_3.4.2 tidyverse_2.0.0 limma_3.52.4
[22] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] backports_1.4.1 circlize_0.4.15 BiocFileCache_2.4.0
[4] systemfonts_1.0.4 GenomeInfoDb_1.32.4 digest_0.6.31
[7] foreach_1.5.2 htmltools_0.5.5 fansi_1.0.4
[10] magrittr_2.0.3 memoise_2.0.1 cluster_2.1.4
[13] doParallel_1.0.17 tzdb_0.4.0 Biostrings_2.64.1
[16] matrixStats_1.0.0 svglite_2.1.1 timechange_0.2.0
[19] prettyunits_1.1.1 RVenn_1.1.0 colorspace_2.1-0
[22] blob_1.2.4 rvest_1.0.3 rappdirs_0.3.3
[25] xfun_0.39 callr_3.7.3 crayon_1.5.2
[28] RCurl_1.98-1.12 jsonlite_1.8.5 iterators_1.0.14
[31] glue_1.6.2 gtable_0.3.3 zlibbioc_1.42.0
[34] XVector_0.36.0 webshot_0.5.4 GetoptLong_1.0.5
[37] car_3.1-2 shape_1.4.6 BiocGenerics_0.42.0
[40] abind_1.4-5 futile.options_1.0.1 DBI_1.1.3
[43] rstatix_0.7.2 Rcpp_1.0.10 viridisLite_0.4.2
[46] progress_1.2.2 units_0.8-2 clue_0.3-64
[49] proxy_0.4-27 bit_4.0.5 stats4_4.2.2
[52] httr_1.4.6 pkgconfig_2.0.3 XML_3.99-0.14
[55] farver_2.1.1 sass_0.4.6 dbplyr_2.3.2
[58] utf8_1.2.3 tidyselect_1.2.0 labeling_0.4.2
[61] rlang_1.1.1 later_1.3.1 AnnotationDbi_1.58.0
[64] munsell_0.5.0 tools_4.2.2 cachem_1.0.8
[67] cli_3.6.1 generics_0.1.3 RSQLite_2.3.1
[70] ggVennDiagram_1.2.2 sjlabelled_1.2.0 evaluate_0.21
[73] fastmap_1.1.1 yaml_2.3.7 processx_3.8.1
[76] knitr_1.43 bit64_4.0.5 fs_1.6.2
[79] KEGGREST_1.36.3 whisker_0.4.1 formatR_1.14
[82] xml2_1.3.4 compiler_4.2.2 rstudioapi_0.14
[85] filelock_1.0.2 curl_5.0.1 png_0.1-8
[88] e1071_1.7-13 bslib_0.5.0 stringi_1.7.12
[91] highr_0.10 ps_1.7.5 futile.logger_1.4.3
[94] classInt_0.4-9 vctrs_0.6.2 pillar_1.9.0
[97] lifecycle_1.0.3 jquerylib_0.1.4 GlobalOptions_0.1.2
[100] bitops_1.0-7 insight_0.19.2 httpuv_1.6.11
[103] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-21
[106] IRanges_2.30.1 codetools_0.2-19 lambda.r_1.2.4
[109] rprojroot_2.0.3 rjson_0.2.21 withr_2.5.0
[112] S4Vectors_0.34.0 GenomeInfoDbData_1.2.8 parallel_4.2.2
[115] hms_1.1.3 VennDiagram_1.7.3 class_7.3-22
[118] rmarkdown_2.22 carData_3.0-5 git2r_0.32.0
[121] sf_1.0-13 getPass_0.2-2 Biobase_2.56.0