Comparisons with other data sets

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Last updated: 2024-02-05

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Knit directory: Cardiotoxicity/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/other_analysis.Rmd) and HTML (docs/other_analysis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	df08393	reneeisnowhere	2024-02-05	updates to scripts
Rmd	06800c9	reneeisnowhere	2023-07-26	Commits to small changes and edits
html	7758920	reneeisnowhere	2023-07-03	Build site.
Rmd	4126f95	reneeisnowhere	2023-07-03	updated with final data
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Rmd	028f18d	reneeisnowhere	2023-06-21	update on Fold Change plot
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Rmd	751239e	reneeisnowhere	2023-06-16	updating and moving code
Rmd	3d4ca64	reneeisnowhere	2023-06-16	updates on Friday
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Rmd	9afd6a0	reneeisnowhere	2023-06-15	fixing wflow error
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Rmd	9ad6b91	reneeisnowhere	2023-06-15	showing code adding pvalue text
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Rmd	637531c	reneeisnowhere	2023-06-15	moving out the knowles data
Rmd	f8f511a	reneeisnowhere	2023-06-15	updates and simplifications of code
Rmd	7fc7ec7	reneeisnowhere	2023-06-14	updating code
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Rmd	4b62a1e	reneeisnowhere	2023-06-07	updated numbers for grant
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Rmd	81f100c	reneeisnowhere	2023-06-07	add Dox reQTL grouping and AC shared numbers
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Rmd	0ecede3	reneeisnowhere	2023-06-07	data with CRispr set added and heatmap changes
html	9a62d7c	reneeisnowhere	2023-06-06	Build site.
Rmd	232d3b0	reneeisnowhere	2023-06-06	Finally tested chisquare between knowles data
Rmd	10bcf05	reneeisnowhere	2023-06-06	updating the k4/k5 analysis of DEG
html	b4dd015	reneeisnowhere	2023-06-02	Build site.
Rmd	652d7e8	reneeisnowhere	2023-06-02	updated heatmap Seoane Chisqure for cormotif
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Rmd	6524ecd	reneeisnowhere	2023-06-02	Adding in heatmaps of chi values
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Rmd	8eaea47	reneeisnowhere	2023-06-02	chi square updates
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Rmd	573a477	reneeisnowhere	2023-06-01	Updateing supplement 1 seoan chi results
html	cc3dfc3	reneeisnowhere	2023-06-01	Build site.
Rmd	522cce8	reneeisnowhere	2023-06-01	Adding chisquare and other analysis
html	4723cdd	reneeisnowhere	2023-05-31	Build site.
Rmd	07a6e06	reneeisnowhere	2023-05-31	adding in more data including Cormotif enrichment numbers
html	6fd877b	reneeisnowhere	2023-05-31	Build site.
Rmd	b2ba055	reneeisnowhere	2023-05-31	adding Seoane data with cormotif things
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Rmd	c7e0fcc	reneeisnowhere	2023-05-26	adding in Gtex and chisquare values
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Rmd	0f512c3	reneeisnowhere	2023-05-26	adding in Gtex and chisquare values
Rmd	1f8c483	reneeisnowhere	2023-05-26	updating code with gtex and chisq
Rmd	25d32da	reneeisnowhere	2023-05-26	Adding 3 hour and chisq test to populations
html	5610749	reneeisnowhere	2023-05-22	Build site.
Rmd	889832a	reneeisnowhere	2023-05-22	add Seoane data again
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Rmd	de54fd5	reneeisnowhere	2023-05-22	add Seoane data
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Rmd	e2b3215	reneeisnowhere	2023-05-22	add Seoane data
html	c3481d8	reneeisnowhere	2023-05-22	Build site.
Rmd	acbd0a8	reneeisnowhere	2023-05-22	updates on GWAS enrichment
Rmd	e8c82ec	reneeisnowhere	2023-05-18	adding other_analysis and genes of interest log2cpm

---

library(limma)
library(tidyverse)
library(ggsignif)
library(biomaRt)
library(RColorBrewer)
library(cowplot)
library(ggpubr)
library(scales)
library(sjmisc)
library(kableExtra)
library(broom)
library(ComplexHeatmap)

Data set comparison

order:

ArrGWAS
HFGWAS
CADGWAS

Crispr sets

GWAS

ArrGWAS to 24 hour DEG genes p < 0.05

24 hour data set

# How I did the string split
# Arr_GWAS <- ArrGWAS[,13]
# names(Arr_GWAS) <- "genesplit"
# Arr_GWAS <- Arr_GWAS %>% 
#   separate_longer_delim(genesplit, delim = ",")
#write.csv(Arr_GWAS,"data/Arr_GWAS.txt")
arr_GWAS <- read.csv("data/Arr_GWAS.txt", row.names = 1)
Arr_geneset <- readRDS("data/Arr_geneset.RDS")
# Arr_geneset <- getBM(attributes=my_attributes,filters ='hgnc_symbol',
#                   values = arr_GWAS, mart = ensembl)
# #remove duplicates
# Arr_geneset <- Arr_geneset %>% distinct(entrezgene_id, .keep_all =TRUE)
# saveRDS(Arr_geneset,"data/Arr_geneset.RDS")
#Apply sorting
toplist24hr %>% 
   mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(ARR=if_else(ENTREZID %in%Arr_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,ARR) %>% 
  dplyr::summarize(ARRcount=n())%>% 
    pivot_wider(id_cols = c(id,sigcount), names_from=c(ARR), values_from=ARRcount) %>% 
   mutate(ARRprop=(y/(y+no)*100)) %>% 
       ggplot(., aes(x=id, y=ARRprop)) +
       geom_col()+
       geom_text(aes(x=id, label = sprintf("%.2f",ARRprop), vjust=-.2))+
       #geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
       facet_wrap(~sigcount)+
       ggtitle("24 hour non-significant and significant enrichment proporitions of Arrhythmia GWAS ")

Past versions of "24 hour ArrGWAS-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
750ee45	reneeisnowhere	2023-06-15

##make table of numbers:


dataframARR <- toplist24hr %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(ARR=if_else(ENTREZID %in%Arr_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,ARR) %>% 
  dplyr::summarize(ARRcount=n()) %>% 
  as.data.frame()

dataframARR %>% 
  kable(., caption= "Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS") %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS

id	sigcount	ARR	ARRcount
DNR	notsig	no	7016
DNR	notsig	y	51
DNR	sig	no	6948
DNR	sig	y	69
DOX	notsig	no	7382
DOX	notsig	y	57
DOX	sig	no	6582
DOX	sig	y	63
EPI	notsig	no	7699
EPI	notsig	y	57
EPI	sig	no	6265
EPI	sig	y	63
MTX	notsig	no	12863
MTX	notsig	y	106
MTX	sig	no	1101
MTX	sig	y	14
TRZ	notsig	no	13964
TRZ	notsig	y	120

3 hour data set

Past versions of "three hour ArrGWAS-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
e1bcef0	reneeisnowhere	2023-05-26

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 3 hour GWAS

id	sigcount	ARR	ARRcount
DNR	notsig	no	13440
DNR	notsig	y	112
DNR	sig	no	524
DNR	sig	y	8
DOX	notsig	no	13945
DOX	notsig	y	120
DOX	sig	no	19
EPI	notsig	no	13757
EPI	notsig	y	117
EPI	sig	no	207
EPI	sig	y	3
MTX	notsig	no	13894
MTX	notsig	y	115
MTX	sig	no	70
MTX	sig	y	5
TRZ	notsig	no	13964
TRZ	notsig	y	120

chi square test ARR

chi_funarr <-  toplistall %>% 
  dplyr::filter(id!="TRZ") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(ARR=if_else(ENTREZID %in%Arr_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  dplyr::summarise(pvalue= chisq.test(ARR, sigcount)$p.value) 


chi_funarr %>% 
  kable(., caption= "after performing chi square test between DEgenes, and non DE genes") %>% 
  kable_paper("striped") %>%  
  kable_styling(full_width = FALSE,font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

after performing chi square test between DEgenes, and non DE genes

id	time	pvalue
DNR	24_hours	0.1101318
DNR	3_hours	0.1536193
DOX	24_hours	0.2799966
DOX	3_hours	1.0000000
EPI	24_hours	0.1136502
EPI	3_hours	0.5908261
MTX	24_hours	0.1744165
MTX	3_hours	0.0000012

Past versions of "ARR heatmap-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
e02ca18	reneeisnowhere	2023-06-15
750ee45	reneeisnowhere	2023-06-15
47f85a2	reneeisnowhere	2023-06-07
5aeda27	reneeisnowhere	2023-06-02

HFGWAS

24 hours HF

##just like ARrGWAS- imported the total csv, the took the "nearest" column and separated out the gene info
# test <- HFGWAS %>% 
#   select(nearest) %>% 
#   separate_wider_delim(nearest, delim = "[", names_sep = "", too_few = "align_start")
# test2 <- str_sub(test$nearest2,0,nchar(test$nearest2)-1)
# Hf_GWAS <- test2
#write.csv(Hf_GWAS, "data/Hf_GWAS.txt")
# HF_GWAS <- read.csv("data/Hf_GWAS.txt", row.names =1)
# 
# HF_geneset <- getBM(attributes=my_attributes,filters ='hgnc_symbol',
#                   values = HF_GWAS, mart = ensembl)
# #remove duplicates
# HF_geneset <- HF_geneset %>% distinct(entrezgene_id, .keep_all =TRUE)
# saveRDS(HF_geneset,"data/HF_geneset.RDS")
HF_geneset <- readRDS("data/HF_geneset.RDS")
#Apply sorting
toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,HF) %>% 
  dplyr::summarize(HFcount=n())%>% 
    pivot_wider(id_cols = c(id,sigcount), names_from=c(HF), values_from=HFcount) %>% 
   mutate(HFprop=(y/(y+no)*100)) %>% 
       ggplot(., aes(x=id, y=HFprop)) +
       geom_col()+
       geom_text(aes(x=id, label = sprintf("%.2f",HFprop), vjust=-.2))+
       #geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
       facet_wrap(~sigcount)+
       ggtitle("non-significant and significant enrichment proporitions of Heart Failure GWAS ")

Past versions of "24 hours HFGWAS-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
e1bcef0	reneeisnowhere	2023-05-26

##make table of numbers:


dataframHF <- toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,HF) %>% 
  dplyr::summarize(HFcount=n()) %>% 
  as.data.frame()

dataframHF %>% #mutate_at(.vars = 6, .funs= scientific_format()) %>% 
  kable(., caption= "Significant (adj. P value of <0.05) and non-sig gene counts in HFhythmia GWAS") %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

Significant (adj. P value of <0.05) and non-sig gene counts in HFhythmia GWAS

id	sigcount	HF	HFcount
DNR	notsig	no	7056
DNR	notsig	y	11
DNR	sig	no	6995
DNR	sig	y	22
DOX	notsig	no	7427
DOX	notsig	y	12
DOX	sig	no	6624
DOX	sig	y	21
EPI	notsig	no	7742
EPI	notsig	y	14
EPI	sig	no	6309
EPI	sig	y	19
MTX	notsig	no	12939
MTX	notsig	y	30
MTX	sig	no	1112
MTX	sig	y	3
TRZ	notsig	no	14051
TRZ	notsig	y	33

3 hours HF

#Apply sorting
toplist3hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,HF) %>% 
  dplyr::summarize(HFcount=n())%>% 
    pivot_wider(id_cols = c(id,sigcount), names_from=c(HF), values_from=HFcount) %>% 
   mutate(HFprop=(y/(y+no)*100)) %>% 
       ggplot(., aes(x=id, y=HFprop)) +
       geom_col()+
       geom_text(aes(x=id, label = sprintf("%.2f",HFprop), vjust=-.2))+
       #geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
       facet_wrap(~sigcount)+
       ggtitle("non-significant and significant enrichment proportions of Heart Failure GWAS ")

Past versions of "3 hours HFGWAS-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
750ee45	reneeisnowhere	2023-06-15
e1bcef0	reneeisnowhere	2023-05-26

##make table of numbers:


dataframHF3 <- toplist3hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,HF) %>% 
  dplyr::summarize(HFcount=n()) %>% 
  as.data.frame()

dataframHF3 %>%
  kable(., caption= "Significant (adj. P value of <0.05) and non-sig gene counts in Three hour HFhythmia GWAS") %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

Significant (adj. P value of <0.05) and non-sig gene counts in Three hour HFhythmia GWAS

id	sigcount	HF	HFcount
DNR	notsig	no	13521
DNR	notsig	y	31
DNR	sig	no	530
DNR	sig	y	2
DOX	notsig	no	14032
DOX	notsig	y	33
DOX	sig	no	19
EPI	notsig	no	13841
EPI	notsig	y	33
EPI	sig	no	210
MTX	notsig	no	13976
MTX	notsig	y	33
MTX	sig	no	75
TRZ	notsig	no	14051
TRZ	notsig	y	33

chi square test HF

chi_funhf <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="TRZ") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  dplyr::summarise(pvalue= chisq.test(HF, sigcount)$p.value) 

chi_funhf %>% 
  kable(., caption= "after performing chi square test between DEgenes, and non DE genes") %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE,font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

after performing chi square test between DEgenes, and non DE genes

id	time	pvalue
DNR	24_hours	0.0778586
DNR	3_hours	0.8167557
DOX	24_hours	0.0852093
DOX	3_hours	1.0000000
EPI	24_hours	0.1981312
EPI	3_hours	1.0000000
MTX	24_hours	1.0000000
MTX	3_hours	1.0000000

HFmat <- chi_funhf %>% ungroup() %>% 
  mutate(HF= log(pvalue)*(-1)) %>% 
  filter(time=="24_hours") %>% 
  # mutate(id =case_match( id, 'Daunorubicin'~'DNR',
  #                        'Doxorubicin'~'DOX',
  #                        'Epirubicin'~'EPI',
  #                        'Mitoxantrone'~'MTX', 
  #                        .default = id)) %>% 
  mutate(time=case_match(time,"24_hours"~"24_hrs",.default = time)) %>% 
  dplyr::select(id,time,HF) %>% 
  unite("term_name",id,time,sep="_") %>% 
  column_to_rownames('term_name') 
  
col_fun5 = circlize::colorRamp2(c(0, 5), c("white", "purple"))

Heatmap( as.matrix(HFmat), name = "Heart Failure GWAS\n chi square -log p values", 
         cluster_rows = FALSE, cluster_columns = FALSE,
         col=col_fun1,
         column_names_rot = 0,
         cell_fun = function(j, i, x, y, width, height, fill) {
        if(HFmat[i, j] > -log(0.05))
            grid.text("*", x, y, gp = gpar(fontsize = 20))
})

Past versions of "HF heatmap-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
750ee45	reneeisnowhere	2023-06-15
47f85a2	reneeisnowhere	2023-06-07
b4dd015	reneeisnowhere	2023-06-02

CAD GWAS

24 hour data set

# test <- CADGWAS %>% 
#    select(nearest) %>% 
#    separate_wider_delim(nearest, delim = "[", names_sep = "", too_few = "align_start")
#  test2 <- str_sub(test$nearest2,0,nchar(test$nearest2)-1)
# 
# test2[c(32,38,44,74,112,126,191,212)] <- c("TPCN1","C2orf43","FAM222A", "TDRD15"  ,"AGPAT4","SVOP","SVOP","PLG")
#    
#  test2 [c(218,226,228,233,239,245,256,270,281,322,324,332,335,338,347,351,352,358)] <-  c("HPCAL1", "KLHL29"  , "COL4A3BP"  , "ARAP1" ,
#  "VEGFA", "TBPL1","SLC22A3" ,"C19orf38","LPA","VPS29","ATP2A2" ,"ATP2A2","KLHL29","GUCY1A3","KCNE2",  "HOXB9","P2RY2" ,"CTC-236F12.4")
#  
#  CAD_GWAS <- test2
#write.csv(CAD_GWAS, "data/cvd_GWAS.txt")
CAD_GWAS <- read.csv("data/cvd_GWAS.txt", row.names =1)

# CAD_geneset <- getBM(attributes=my_attributes,filters ='hgnc_symbol',
#                   values = CAD_GWAS, mart = ensembl)
# #remove duplicates
# CAD_geneset <- CAD_geneset %>% distinct(entrezgene_id, .keep_all =TRUE)
# 
# saveRDS(CAD_geneset,"data/CAD_geneset.RDS")
CAD_geneset <- readRDS("data/CAD_geneset.RDS")
#Apply sorting



toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD=if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,CAD) %>% 
  dplyr::summarize(CADcount=n())%>% 
    pivot_wider(id_cols = c(id,sigcount), names_from=c(CAD), values_from=CADcount) %>% 
   mutate(CADprop=(y/(y+no)*100)) %>% 
       ggplot(., aes(x=id, y=CADprop)) +
       geom_col()+
       geom_text(aes(x=id, label = sprintf("%.2f",CADprop), vjust=-.2))+
       #geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
       facet_wrap(~sigcount)+
       ggtitle("non-significant and significant enrichment proporitions of CAD GWAS ")

Past versions of "CAD 24 GWAS-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
e1bcef0	reneeisnowhere	2023-05-26

##make table of numbers:


dataframCAD <- toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD=if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,CAD) %>% 
  dplyr::summarize(CADcount=n()) %>% 
  as.data.frame()

dataframCAD %>% #mutate_at(.vars = 6, .funs= scientific_format()) %>% 
  kable(., caption= "Significant (adj. P value of <0.05) and non-sig gene counts in CAD GWAS") %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

Significant (adj. P value of <0.05) and non-sig gene counts in CAD GWAS

id	sigcount	CAD	CADcount
DNR	notsig	no	6956
DNR	notsig	y	111
DNR	sig	no	6899
DNR	sig	y	118
DOX	notsig	no	7318
DOX	notsig	y	121
DOX	sig	no	6537
DOX	sig	y	108
EPI	notsig	no	7636
EPI	notsig	y	120
EPI	sig	no	6219
EPI	sig	y	109
MTX	notsig	no	12756
MTX	notsig	y	213
MTX	sig	no	1099
MTX	sig	y	16
TRZ	notsig	no	13855
TRZ	notsig	y	229

3 hour data set

#Apply sorting
toplist3hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD=if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,CAD) %>% 
  dplyr::summarize(CADcount=n())%>% 
    pivot_wider(id_cols = c(id,sigcount), names_from=c(CAD), values_from=CADcount) %>% 
   mutate(CADprop=(y/(y+no)*100)) %>% 
       ggplot(., aes(x=id, y=CADprop)) +
       geom_col()+
       geom_text(aes(x=id, label = sprintf("%.2f",CADprop), vjust=-.2))+
       #geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
       facet_wrap(~sigcount)+
       ggtitle("3 hour non-significant and significant enrichment proporitions of CAD GWAS ")

Past versions of "CAD 3 GWAS-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
e1bcef0	reneeisnowhere	2023-05-26

##make table of numbers:


dataframCAD3 <- toplist3hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD=if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,sigcount,CAD) %>% 
  dplyr::summarize(CADcount=n()) %>% 
  as.data.frame()

dataframCAD3 %>% #mutate_at(.vars = 6, .funs= scientific_format()) %>% 
  kable(., caption= "Significant (adj. P value of <0.05) and non-sig gene counts in 3 hour CAD GWAS") %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

Significant (adj. P value of <0.05) and non-sig gene counts in 3 hour CAD GWAS

id	sigcount	CAD	CADcount
DNR	notsig	no	13338
DNR	notsig	y	214
DNR	sig	no	517
DNR	sig	y	15
DOX	notsig	no	13836
DOX	notsig	y	229
DOX	sig	no	19
EPI	notsig	no	13651
EPI	notsig	y	223
EPI	sig	no	204
EPI	sig	y	6
MTX	notsig	no	13782
MTX	notsig	y	227
MTX	sig	no	73
MTX	sig	y	2
TRZ	notsig	no	13855
TRZ	notsig	y	229

chi square test CAD

chi_funCAD <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="TRZ") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD = if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  dplyr::summarise(pvalue = chisq.test(sigcount, CAD)$p.value) 

chi_funCAD %>% 
  kable(., caption= "after performing chi square test between DEgenes, and non DE genes") %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE,font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

after performing chi square test between DEgenes, and non DE genes

id	time	pvalue
DNR	24_hours	0.6498845
DNR	3_hours	0.0409172
DOX	24_hours	1.0000000
DOX	3_hours	1.0000000
EPI	24_hours	0.4524570
EPI	3_hours	0.2515982
MTX	24_hours	0.6876234
MTX	3_hours	0.7973241

Past versions of "CAD heatmap-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
e02ca18	reneeisnowhere	2023-06-15
750ee45	reneeisnowhere	2023-06-15
47f85a2	reneeisnowhere	2023-06-07
5aeda27	reneeisnowhere	2023-06-02

Past versions of "CAD heatmap-2.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
e02ca18	reneeisnowhere	2023-06-15
750ee45	reneeisnowhere	2023-06-15
47f85a2	reneeisnowhere	2023-06-07
5aeda27	reneeisnowhere	2023-06-02

[1] "This is for  GWAS 24 hours -log(chi square pvalue)"

The star represents chi square p.value < 0.05.

GWAS heatmap

Genes in CVD and AC toxicity respond to Top2i

A. GWAS chi square results

toplist24hr <- toplistall %>% 
  filter(time=="24_hours")
chi_funarr <-  toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="TRZ") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(ARR=if_else(ENTREZID %in%Arr_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id) %>% 
  summarise(pvalue= chisq.test(ARR, sigcount, correct =FALSE)$p.value) 
  

chi_funhf <-  toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
   dplyr::filter(id!="TRZ") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id) %>% 
  summarise(pvalue= chisq.test(HF, sigcount, correct =FALSE)$p.value) 
chi_funCAD <-  toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="TRZ") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD=if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id) %>% 
  summarise(pvalue= chisq.test(CAD, sigcount, correct =FALSE)$p.value) 

HFmat <- chi_funhf %>% ungroup() %>% 
  mutate(HF= log(pvalue)*(-1)) %>% 
  dplyr::select(id,HF) %>% 
  column_to_rownames("id")

ARRmat <- chi_funarr %>% ungroup() %>% 
  mutate(ARR= log(pvalue)*(-1)) %>% 
  dplyr::select(id,ARR) %>% 
  column_to_rownames("id")


CADmat <- chi_funCAD %>% ungroup() %>% 
  mutate(CAD= -log(pvalue)) %>% 
  dplyr::select(id,CAD) %>% 
  column_to_rownames("id")

GWASmat <- bind_cols(CADmat$CAD,HFmat$HF,ARRmat$ARR) 
colnames(GWASmat) <- c('CAD','HF','ARR')
rownames(GWASmat) <- c('DNR','DOX','EPI','MTX')

 Heatmap(as.matrix(GWASmat), name = "GWAS chi square\n -log p values", 
         cluster_rows = FALSE, 
         column_title = 'This is for GWAS 24 hours -log(chi square pvalue)', 
         cluster_columns = FALSE,
         row_names_side = 'left',
         column_names_rot = 0, col_fun1,
         cell_fun = function(j, i, x, y, width, height, fill) {
        if(GWASmat[i, j] > -log(0.05))
            grid.text("*", x, y, gp = gpar(fontsize = 20))
})

# GWAS_goi <- c('RARG', 'ITGB7', 'TNS2','ZNF740','SLC28A3','RMI1',
# 'FEDORA' ,'GDF5','FRS2','HDDC2','EEF1B2')
# 
# library(biomaRt)
# ensembl <- useMart("ensembl", dataset="hsapiens_gene_ensembl")
# my_chr <- c(1:22, 'M', 'X', 'Y')  
# my_attributes <- c('entrezgene_id', 'ensembl_gene_id', 'hgnc_symbol')
# 
# 
# GWAS_goi<- getBM(attributes=my_attributes,filters ='hgnc_symbol',
#          values = GWAS_goi, mart = ensembl)
# GWAS_goi<-GWAS_goi %>% distinct(entrezgene_id,.keep_all = TRUE) %>% add_row(entrezgene_id='124903732',ensembl_gene_id='ENSG00000260788', hgnc_symbol="RP11-298D21.1
# ")

  
# write.csv(GWAS_goi,"output/GWAS_goi.csv")
GWAS_goi <- read.csv("output/GWAS_goi.csv")
##get the abs FC of all GOI
GWASabsFCsig <- 
  toplistall %>% 
  # mutate(absFC=abs(logFC)) %>% 
  mutate(id = as.factor(id)) %>%
  filter(id !="Trastuzumab") %>%
  mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
  filter(ENTREZID %in% GWAS_goi$entrezgene_id) %>% 
   filter(time =="24_hours") %>% 
  dplyr::select(ENTREZID ,time, id,logFC, adj.P.Val, SYMBOL) %>%
  # mutate(id =case_match(id,
  #                       'Daunorubicin'~'DNR',
  #                       'Doxorubicin'~'DOX',
  #                       'Epirubicin'~'EPI',
  #                       'Mitoxantrone'~'MTX',
  #                       .default = id)) %>% 
  pivot_wider(id_cols=id, 
              names_from = SYMBOL, 
              values_from =adj.P.Val)
  
gwas_sig_mat <- GWASabsFCsig %>% 
   column_to_rownames(var="id") %>%
  as.matrix()
 

GWASabsFC <- toplistall %>% 
  # mutate(absFC=abs(logFC)) %>% 
  mutate(id = as.factor(id)) %>%
  filter(id !="Trastuzumab") %>% 
  filter(time=="24_hours") %>% 
  mutate(logFC= logFC*(-1)) %>%
  filter(ENTREZID %in% GWAS_goi$entrezgene_id) %>% 
  dplyr::select(SYMBOL ,time, id, logFC) %>% 
  # mutate(id =case_match(id,'Daunorubicin'~'DNR', 
  #                       'Doxorubicin'~'DOX',
  #                       'Epirubicin'~'EPI',
  #                       'Mitoxantrone'~'MTX',
  #                       .default = id)) %>% 
  pivot_wider(id_cols=id, 
              names_from = SYMBOL, 
              values_from = logFC) %>% 
  column_to_rownames(var="id") %>%
  as.matrix()

 

Heatmap(GWASabsFC, name = "Fold change\nvalues", 
         cluster_rows = FALSE,
        cluster_columns = FALSE, 
        row_names_side = "left",
        column_title = "Fold change values of GWAS and TWAS genes", 
        column_title_side = "top",
        column_title_gp = gpar(fontsize = 16, fontface = "bold"),
        column_order= c('RARG',
                        'TNS2', 
                        'ZNF740',
                        'SLC28A3',
                        'RMI1',
                        'EEF1B2',
                        'FRS2', 
                        'HDDC2'),
        column_names_rot = 0, 
        column_names_gp = gpar(fontsize = 12),
        column_names_centered = TRUE,
         cell_fun = function(j, i, x, y, width, height, fill) {
        if(gwas_sig_mat[i, j] <0.05)
            grid.text("*", x, y, gp = gpar(fontsize = 20))
})

Past versions of "GWAS genes of interest-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
6e4c867	reneeisnowhere	2023-06-21
750ee45	reneeisnowhere	2023-06-15

The stars represent all genes that have an adj. P. value of < 0.05 (significantly differentially expressed)

Crispr list

DEG_cormotif <- readRDS("data/DEG_cormotif.RDS")
list2env(DEG_cormotif,envir=.GlobalEnv)

<environment: R_GlobalEnv>

# Crispr_list <- read_excel("C:/Users/renee/Downloads/41598_2021_92988_MOESM2_ESM.xlsx")
#  View(Crispr_list)
# crispr_genes <- Crispr_list %>% 
#   dplyr::filter(p.value <0.05) %>% 
#   select(GeneName)
  

# crispr_genes <- getBM(attributes=my_attributes,filters ='hgnc_symbol',
                  # values =crispr_genes$GeneName, mart = ensembl)
# write.csv(crispr_genes,'data/crispr_genes.csv')

crispr_genes <- read.csv("data/crispr_genes.csv", row.names = 1)
print(" number of unique crispr_genes after conversion from hgnc symbol to entrezid")

[1] " number of unique crispr_genes after conversion from hgnc symbol to entrezid"

length(unique(crispr_genes$entrezgene_id))

[1] 154

crisprunique <- crispr_genes %>% distinct(entrezgene_id,.keep_all = TRUE)

Doxcrispall <- toplistall %>%
  distinct(ENTREZID,.keep_all = TRUE) %>% 
  dplyr::select(ENTREZID,id,time)
  

crispmotifsummary <- Doxcrispall %>% 
  mutate(ER=if_else(ENTREZID %in% motif_ER,"y","no")) %>% 
  mutate(LR=if_else(ENTREZID %in% motif_LR,"y","no")) %>%
  mutate(TI=if_else(ENTREZID %in% motif_TI,"y","no")) %>%
  mutate(NR=if_else(ENTREZID %in% motif_NR,"y","no")) %>%
  mutate(crisp = if_else(ENTREZID %in% crisprunique$entrezgene_id, "y", "no")) %>% 
  group_by(crisp,ER,TI,LR,NR) %>% 
  dplyr::summarize(n=n()) %>% 
  as.tibble  %>% 
  pivot_wider(id_cols = c(crisp), names_from = c('ER', 'TI', 'LR', 'NR'), values_from= n) %>% 
  rename(.,c("crisp"=crisp,"none"= 2 , "ER" = 3 , "TI" = 4 , "LR" = 5 ,"NR" = 6)) 

cris_mat <- crispmotifsummary %>% dplyr::select(ER:NR) %>% as.matrix()
chicheck <- data.frame(one= c("LR","ER","TI"),two=rep("NR",3),p.value=c("","",""))
  
 chicheck$p.value[1] <- chisq.test(cris_mat[,c('LR','NR')],correct = FALSE)$p.value

chicheck$p.value[2] <- chisq.test(cris_mat[,c('ER','NR')],correct = FALSE)$p.value
chicheck$p.value[3] <- chisq.test(cris_mat[,c('TI','NR')],correct = FALSE)$p.value

chicheck%>% kable(., caption= "chi square test p.values for encrichment of  Doxcrispr gene sets in motif sets" )%>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

chi square test p.values for encrichment of Doxcrispr gene sets in motif sets

one	two	p.value
LR	NR	0.861985991471947
ER	NR	0.402681880154749
TI	NR	0.309642007916355

chicheck_1 <- chicheck %>% mutate(p.value=as.numeric(p.value)) %>% 
  mutate(neg.logvalue=(-1*log(p.value))) %>% column_to_rownames('one') %>% dplyr::select(neg.logvalue) %>% as.matrix
col_fun = circlize::colorRamp2(c(0, 2), c("white", "purple"))

Heatmap( chicheck_1, name = "Doxcrispr enrichment \nchi square -log p values", cluster_rows = FALSE, cluster_columns = FALSE, col=col_fun,
         cell_fun = function(j, i, x, y, width, height, fill) {
        if(chicheck_1[i, j] > -log(0.05))
            grid.text("*", x, y, gp = gpar(fontsize = 20))
})

Past versions of "Crispr data analysis-1.png"

Version	Author	Date
47f85a2	reneeisnowhere	2023-06-07

col_fun4 = circlize::colorRamp2(c(0, 5), c("white", "purple"))


pairwisecrispr <- toplistall %>%
  filter(id!='TRZ') %>% 
  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(crisp = if_else(ENTREZID %in% crisprunique$entrezgene_id, "y", "no")) %>% 
  group_by(time, id) %>%
  dplyr::summarise(pvalue= chisq.test(crisp, sigcount, correct=FALSE)$p.value)
 
  
  crisprnumbers <- toplistall %>%
  filter(id!='Trastuzumab') %>% 
  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(crisp = if_else(ENTREZID %in% crisprunique$entrezgene_id, "y", "no")) %>% 
  group_by(time, id,sigcount,crisp) %>%
  dplyr::summarize(n=n()) %>% 
  as.tibble() #%>% 
  
crisprnumbers %>% kable(., caption= "Summary of genes found in both sigDE and non sigDE by treatment" )%>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

Summary of genes found in both sigDE and non sigDE by treatment

time	id	sigcount	crisp	n
3_hours	DNR	notsig	no	13437
3_hours	DNR	notsig	y	115
3_hours	DNR	sig	no	530
3_hours	DNR	sig	y	2
3_hours	DOX	notsig	no	13948
3_hours	DOX	notsig	y	117
3_hours	DOX	sig	no	19
3_hours	EPI	notsig	no	13758
3_hours	EPI	notsig	y	116
3_hours	EPI	sig	no	209
3_hours	EPI	sig	y	1
3_hours	MTX	notsig	no	13892
3_hours	MTX	notsig	y	117
3_hours	MTX	sig	no	75
3_hours	TRZ	notsig	no	13967
3_hours	TRZ	notsig	y	117
24_hours	DNR	notsig	no	7016
24_hours	DNR	notsig	y	51
24_hours	DNR	sig	no	6951
24_hours	DNR	sig	y	66
24_hours	DOX	notsig	no	7385
24_hours	DOX	notsig	y	54
24_hours	DOX	sig	no	6582
24_hours	DOX	sig	y	63
24_hours	EPI	notsig	no	7700
24_hours	EPI	notsig	y	56
24_hours	EPI	sig	no	6267
24_hours	EPI	sig	y	61
24_hours	MTX	notsig	no	12861
24_hours	MTX	notsig	y	108
24_hours	MTX	sig	no	1106
24_hours	MTX	sig	y	9
24_hours	TRZ	notsig	no	13967
24_hours	TRZ	notsig	y	117

pairwisecrispr%>% kable(., caption= "Summary of chisqure values between numbers of sigDE and non sigDE by treatment" )%>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>% 
  scroll_box(width = "60%", height = "400px")

Summary of chisqure values between numbers of sigDE and non sigDE by treatment

time	id	pvalue
3_hours	DNR	0.2387271
3_hours	DOX	0.6897318
3_hours	EPI	0.5684613
3_hours	MTX	0.4267580
24_hours	DNR	0.1523968
24_hours	DOX	0.1470074
24_hours	EPI	0.1155814
24_hours	MTX	0.9280448

crisp_pair_mat <- pairwisecrispr %>%
  mutate(neg.log.pvalue= (-1*log(pvalue))) %>% 
  # mutate(time= case_match(time, '3_hours'~'3_hrs', '24_hours'~'24_hrs',.default = id)) %>% 
  # mutate(id =case_match( id, 'Daunorubicin'~'DNR',   'Doxorubicin'~'DOX' ,'Epirubicin'~'EPI' , 'Mitoxantrone' ~ 'MTX',.default = id)) %>% 
  unite('pairset',time,id ) %>%
  column_to_rownames('pairset') %>% dplyr::select(neg.log.pvalue) %>% as.matrix()
    
Heatmap( crisp_pair_mat, name = "Doxcrispr pairwise enrichment \nchi square -log p values", 
         cluster_rows = FALSE, 
         cluster_columns = FALSE, 
         col=col_fun5, column_names_rot = 0,
         cell_fun = function(j, i, x, y, width, height, fill) {
        if(crisp_pair_mat[i, j] > -log(0.05))
            grid.text("*", x, y, gp = gpar(fontsize = 20))
})

Past versions of "crisper DEG-1.png"

Version	Author	Date
771a192	reneeisnowhere	2023-06-29
e02ca18	reneeisnowhere	2023-06-15
750ee45	reneeisnowhere	2023-06-15
47f85a2	reneeisnowhere	2023-06-07

Session information

sessionInfo()

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ComplexHeatmap_2.16.0 broom_1.0.5           kableExtra_1.3.4     
 [4] sjmisc_2.8.9          scales_1.3.0          ggpubr_0.6.0         
 [7] cowplot_1.1.1         RColorBrewer_1.1-3    biomaRt_2.56.1       
[10] ggsignif_0.6.4        lubridate_1.9.3       forcats_1.0.0        
[13] stringr_1.5.0         dplyr_1.1.3           purrr_1.0.2          
[16] readr_2.1.4           tidyr_1.3.0           tibble_3.2.1         
[19] ggplot2_3.4.4         tidyverse_2.0.0       limma_3.56.2         
[22] workflowr_1.7.1      

loaded via a namespace (and not attached):
  [1] rstudioapi_0.15.0       jsonlite_1.8.7          shape_1.4.6            
  [4] magrittr_2.0.3          magick_2.8.1            farver_2.1.1           
  [7] rmarkdown_2.25          GlobalOptions_0.1.2     fs_1.6.3               
 [10] zlibbioc_1.46.0         vctrs_0.6.4             memoise_2.0.1          
 [13] RCurl_1.98-1.14         rstatix_0.7.2           webshot_0.5.5          
 [16] htmltools_0.5.7         progress_1.2.3          curl_5.2.0             
 [19] sass_0.4.7              bslib_0.6.1             cachem_1.0.8           
 [22] whisker_0.4.1           lifecycle_1.0.4         iterators_1.0.14       
 [25] pkgconfig_2.0.3         sjlabelled_1.2.0        R6_2.5.1               
 [28] fastmap_1.1.1           GenomeInfoDbData_1.2.10 clue_0.3-65            
 [31] digest_0.6.33           colorspace_2.1-0        AnnotationDbi_1.62.2   
 [34] S4Vectors_0.38.2        ps_1.7.5                rprojroot_2.0.4        
 [37] RSQLite_2.3.5           labeling_0.4.3          filelock_1.0.3         
 [40] fansi_1.0.5             timechange_0.2.0        httr_1.4.7             
 [43] abind_1.4-5             compiler_4.3.1          bit64_4.0.5            
 [46] withr_3.0.0             doParallel_1.0.17       backports_1.4.1        
 [49] carData_3.0-5           DBI_1.2.1               highr_0.10             
 [52] rappdirs_0.3.3          rjson_0.2.21            tools_4.3.1            
 [55] httpuv_1.6.12           glue_1.6.2              callr_3.7.3            
 [58] promises_1.2.1          getPass_0.2-2           cluster_2.1.4          
 [61] generics_0.1.3          gtable_0.3.4            tzdb_0.4.0             
 [64] hms_1.1.3               xml2_1.3.5              car_3.1-2              
 [67] utf8_1.2.4              XVector_0.40.0          BiocGenerics_0.46.0    
 [70] foreach_1.5.2           pillar_1.9.0            later_1.3.1            
 [73] circlize_0.4.15         BiocFileCache_2.8.0     bit_4.0.5              
 [76] tidyselect_1.2.0        Biostrings_2.68.1       knitr_1.45             
 [79] git2r_0.32.0            IRanges_2.34.1          svglite_2.1.2          
 [82] stats4_4.3.1            xfun_0.41               Biobase_2.60.0         
 [85] matrixStats_1.1.0       stringi_1.7.12          yaml_2.3.7             
 [88] evaluate_0.23           codetools_0.2-19        cli_3.6.1              
 [91] systemfonts_1.0.5       munsell_0.5.0           processx_3.8.2         
 [94] jquerylib_0.1.4         Rcpp_1.0.11             GenomeInfoDb_1.36.4    
 [97] dbplyr_2.4.0            png_0.1-8               XML_3.99-0.16.1        
[100] parallel_4.3.1          blob_1.2.4              prettyunits_1.2.0      
[103] bitops_1.0-7            viridisLite_0.4.2       insight_0.19.8         
[106] crayon_1.5.2            GetoptLong_1.0.5        rlang_1.1.2            
[109] KEGGREST_1.40.1         rvest_1.0.3