Last updated: 2023-06-15

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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

Seaone 2019
Supplemental 1 (408 genes)
Supplemental 4 (54 genes)

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(id = as.factor(id)) %>%
  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) %>% 
  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 ")

##make table of numbers:


dataframARR <- toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  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) %>% 
  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
Daunorubicin notsig no 7169
Daunorubicin notsig y 51
Daunorubicin sig no 6795
Daunorubicin sig y 69
Doxorubicin notsig no 7512
Doxorubicin notsig y 56
Doxorubicin sig no 6452
Doxorubicin sig y 64
Epirubicin notsig no 7827
Epirubicin notsig y 55
Epirubicin sig no 6137
Epirubicin sig y 65
Mitoxantrone notsig no 12650
Mitoxantrone notsig y 107
Mitoxantrone sig no 1314
Mitoxantrone sig y 13
Trastuzumab notsig no 13964
Trastuzumab notsig y 120

3 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 3 hour GWAS
id sigcount ARR ARRcount
Daunorubicin notsig no 13419
Daunorubicin notsig y 110
Daunorubicin sig no 545
Daunorubicin sig y 10
Doxorubicin notsig no 13948
Doxorubicin notsig y 120
Doxorubicin sig no 16
Epirubicin notsig no 13747
Epirubicin notsig y 117
Epirubicin sig no 217
Epirubicin sig y 3
Mitoxantrone notsig no 13909
Mitoxantrone notsig y 117
Mitoxantrone sig no 55
Mitoxantrone sig y 3
Trastuzumab notsig no 13964
Trastuzumab notsig y 120

chi square test ARR

chi_funarr <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(ARR=if_else(ENTREZID %in%Arr_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(ARR, sigcount)$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
Daunorubicin 24_hours 0.0661799
Daunorubicin 3_hours 0.0245603
Doxorubicin 24_hours 0.1422138
Doxorubicin 3_hours 1.0000000
Epirubicin 24_hours 0.0313365
Epirubicin 3_hours 0.6437411
Mitoxantrone 24_hours 0.7079851
Mitoxantrone 3_hours 0.0040856

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) %>% 
  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 ")

##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) %>% 
  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
Daunorubicin notsig no 7209
Daunorubicin notsig y 11
Daunorubicin sig no 6842
Daunorubicin sig y 22
Doxorubicin notsig no 7556
Doxorubicin notsig y 12
Doxorubicin sig no 6495
Doxorubicin sig y 21
Epirubicin notsig no 7868
Epirubicin notsig y 14
Epirubicin sig no 6183
Epirubicin sig y 19
Mitoxantrone notsig no 12728
Mitoxantrone notsig y 29
Mitoxantrone sig no 1323
Mitoxantrone sig y 4
Trastuzumab notsig no 14051
Trastuzumab notsig y 33

3 hours HF

Significant (adj. P value of <0.05) and non-sig gene counts in Three hour HFhythmia GWAS
id sigcount HF HFcount
Daunorubicin notsig no 13498
Daunorubicin notsig y 31
Daunorubicin sig no 553
Daunorubicin sig y 2
Doxorubicin notsig no 14035
Doxorubicin notsig y 33
Doxorubicin sig no 16
Epirubicin notsig no 13831
Epirubicin notsig y 33
Epirubicin sig no 220
Mitoxantrone notsig no 13993
Mitoxantrone notsig y 33
Mitoxantrone sig no 58
Trastuzumab notsig no 14051
Trastuzumab notsig y 33

chi square test HF

chi_funhf <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(HF, sigcount)$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
Daunorubicin 24_hours 0.0589189
Daunorubicin 3_hours 0.8581056
Doxorubicin 24_hours 0.0674061
Doxorubicin 3_hours 1.0000000
Epirubicin 24_hours 0.1635851
Epirubicin 3_hours 0.9826436
Mitoxantrone 24_hours 0.8156838
Mitoxantrone 3_hours 1.0000000

## 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) %>% 
  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 ")

##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) %>% 
  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
Daunorubicin notsig no 7107
Daunorubicin notsig y 113
Daunorubicin sig no 6748
Daunorubicin sig y 116
Doxorubicin notsig no 7447
Doxorubicin notsig y 121
Doxorubicin sig no 6408
Doxorubicin sig y 108
Epirubicin notsig no 7762
Epirubicin notsig y 120
Epirubicin sig no 6093
Epirubicin sig y 109
Mitoxantrone notsig no 12547
Mitoxantrone notsig y 210
Mitoxantrone sig no 1308
Mitoxantrone sig y 19
Trastuzumab notsig no 13855
Trastuzumab notsig y 229

3 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in 3 hour CAD GWAS
id sigcount CAD CADcount
Daunorubicin notsig no 13317
Daunorubicin notsig y 212
Daunorubicin sig no 538
Daunorubicin sig y 17
Doxorubicin notsig no 13839
Doxorubicin notsig y 229
Doxorubicin sig no 16
Epirubicin notsig no 13643
Epirubicin notsig y 221
Epirubicin sig no 212
Epirubicin sig y 8
Mitoxantrone notsig no 13798
Mitoxantrone notsig y 228
Mitoxantrone sig no 57
Mitoxantrone sig y 1
Trastuzumab notsig no 13855
Trastuzumab notsig y 229

chi square test CAD

chi_funCAD <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD=if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(CAD, sigcount)$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
Daunorubicin 24_hours 0.6037088
Daunorubicin 3_hours 0.0104647
Doxorubicin 24_hours 0.8356522
Doxorubicin 3_hours 1.0000000
Epirubicin 24_hours 0.3040510
Epirubicin 3_hours 0.0350528
Mitoxantrone 24_hours 0.6358068
Mitoxantrone 3_hours 1.0000000 

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

The star represents chi square p.value < 0.05.

# 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) %>% 
  # filter(adj.P.Val <0.05) %>% 
  mutate(id =case_match(id,'Daunorubicin'~'DNR','Doxorubicin'~'DOX','Epirubicin'~'EPI','Mitoxantrone'~'MTX', .default = id)) %>% 
  # mutate(time =case_match(time,"3_hours"~'3_hr',"24_hours"~'24_hr',.default = time)) %>% 
 # unite("trt_time",id:time,sep = '_') %>% 
  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(time=factor(time, levels=c("3_hours","24_hours"))) %>%
  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)) %>% 
  # mutate(time =case_match(time,"3_hours"~'3_hr',"24_hours"~'24_hr',.default = time)) %>%
  # unite("trt_time",id:time,sep = '_') %>% 
  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))
})

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

Seoane 2019

Seoane, Jose Chromatin gene comparison: comes from supp data NAT. MED 2019 #### 24 hours in Pairwise with supplemental data 1

# Seoane_chromatinregs <- read_excel("C:/Users/renee/Downloads/Supplements folde manuscriptr/NIHMS1539805-supplement-SuppTables.xlsx", 
#     range = "A13:H469")

#write.csv(Seoane_chromatinregs, "data/Seonane2019supp1.txt")

chrom_reg_Seoane <- read_csv(file = "data/Seonane2019supp1.txt",col_types = cols(...1 = col_skip()))
                            
Seoane_2019 <- chrom_reg_Seoane[,2]
names(Seoane_2019) <- "ENTREZID"
chrom_genes <- (unique(Seoane_2019$ENTREZID))

toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(chrom=if_else(ENTREZID %in%chrom_genes,"y","no")) %>% 
  group_by(id,sigcount,chrom) %>% 
  summarize(chromcount=n()) %>% 
    pivot_wider(id_cols = c(id,sigcount), names_from=c(chrom), values_from=chromcount) %>% 
   mutate(chromprop=(y/(y+no)*100)) %>% 
       ggplot(., aes(x=id, y=chromprop)) +
       geom_col()+
       geom_text(aes(x=id, label = sprintf("%.2f",chromprop), vjust=-.2))+
       #geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
       facet_wrap(~sigcount)+
       ggtitle("non-significant and significant enrichment proportions of chromatin gene set ")

dataframchrom <- toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(chrom=if_else(ENTREZID %in%chrom_genes,"y","no")) %>% 
  group_by(id,sigcount,chrom) %>% 
  summarize(chromcount=n()) %>% 
  as.data.frame()

dataframchrom %>%
  pivot_wider(., names_from=c('sigcount','chrom'), values_from = 'chromcount') %>% 
  kable(., caption= "Significant (adj. P value of <0.05) and non-sig gene counts in Seoane geneset") %>% 
  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 Seoane geneset
id notsig_no notsig_y sig_no sig_y
Daunorubicin 7065 155 6689 175
Doxorubicin 7407 161 6347 169
Epirubicin 7717 165 6037 165
Mitoxantrone 12483 274 1271 56
Trastuzumab 13754 330 NA NA 
toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
   dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(chrom=if_else(ENTREZID %in%chrom_genes,"y","no")) %>% 
  group_by(id) %>% 
 summarise(pvalue= chisq.test(chrom, sigcount)$p.value) 
# A tibble: 4 × 2
  id               pvalue
  <fct>             <dbl>
1 Daunorubicin 0.128     
2 Doxorubicin  0.0771    
3 Epirubicin   0.0314    
4 Mitoxantrone 0.00000326

3 hours data Pairwise

Significant (adj. P value of <0.05) and non-sig gene counts in 3 hours Seoane geneset
id notsig_no notsig_y sig_no sig_y
Daunorubicin 13227 302 527 28
Doxorubicin 13738 330 16 NA
Epirubicin 13551 313 203 17
Mitoxantrone 13698 328 56 2
Trastuzumab 13754 330 NA NA

chi square test Seaone

##remove Trastuzumab in order to perform chi square tests by time and drug between  DE and non DE enrichment
chi_fun <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(chrom=if_else(ENTREZID %in%chrom_genes,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(chrom, sigcount)$p.value) 

chi_fun%>% 
  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
Daunorubicin 24_hours 0.1276264
Daunorubicin 3_hours 0.0000332
Doxorubicin 24_hours 0.0770694
Doxorubicin 3_hours 1.0000000
Epirubicin 24_hours 0.0313650
Epirubicin 3_hours 0.0000003
Mitoxantrone 24_hours 0.0000033
Mitoxantrone 3_hours 0.9023793

Supplemental 4 Seoane 3 hours followed by 24 hours

# library(readxl)
# Sup4seoane <- read_excel("~/Ward Lab/Cardiotoxicity/Manuscript/seoane201941591_2019_638_MOESM3_ESM.xlsx", 
#     sheet = "SupTable4", range = "A16:J567")

#     sheet = "SupTable4", range = "A16:J567")
#write.csv(Sup4seoane, "output/Sup4seoane.csv")
Sup4seoane <- read.csv("output/Sup4seoane.csv", row.names = 1)
Sup4genes <- Sup4seoane  %>% 
  filter(pval.expAnth<0.05) %>% 
  distinct(entrez, .keep_all = TRUE) %>% 
  dplyr::select(entrez)

Sup4seoane  %>% 
  filter(pval.expAnth<0.05) %>% 
  distinct(entrez, .keep_all = TRUE) %>% 
  dplyr::select(entrez,gene,pval.exp,pval.anthr,pval.expAnth,adjpval) %>% 
  kable(., caption = "List of Seoane Supplemental 4 genes") %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped","hover")) %>% 
  scroll_box(width = "100%", height = "400px")
List of Seoane Supplemental 4 genes
entrez gene pval.exp pval.anthr pval.expAnth adjpval
11176 BAZ2A 0.0020064 0.0000768 0.0004553 0.1299006
10284 SAP18 0.0013141 0.0000648 0.0006081 0.1299006
8819 SAP30 0.0023742 0.0000576 0.0007455 0.1299006
23522 KAT6B 0.0050327 0.0001601 0.0012776 0.1343691
7786 MAP3K12 0.0062822 0.0001296 0.0014816 0.1343691
2146 EZH2 0.0075650 0.0001626 0.0020478 0.1343691
4297 KMT2A 0.0096126 0.0001301 0.0023292 0.1343691
79913 ACTR5 0.0087568 0.0001883 0.0035210 0.1373866
8242 KDM5C 0.0139853 0.0001783 0.0036176 0.1373866
51780 KDM3B 0.0155602 0.0001675 0.0039239 0.1373866
6872 TAF1 0.0105527 0.0001952 0.0043619 0.1447734
23135 KDM6B 0.0074796 0.0001950 0.0047811 0.1514738
6877 TAF5 0.0233826 0.0002047 0.0067329 0.1624738
23030 KDM4B 0.0239951 0.0004270 0.0069023 0.1624738
64324 NSD1 0.0164702 0.0003839 0.0069286 0.1624738
79885 HDAC11 0.0256039 0.0002383 0.0071964 0.1624738
10847 SRCAP 0.0174738 0.0003660 0.0077132 0.1624738
7404 UTY 0.0114041 0.0002112 0.0078450 0.1624738
51773 RSF1 0.0283587 0.0001948 0.0080182 0.1624738
5253 PHF2 0.0119978 0.0002989 0.0093089 0.1624738
9126 SMC3 0.0347884 0.0002127 0.0095907 0.1624738
3054 HCFC1 0.0317868 0.0003159 0.0097354 0.1624738
9734 HDAC9 0.0353794 0.0001985 0.0103307 0.1649465
53335 BCL11A 0.0063102 0.0004723 0.0105391 0.1649465
83444 INO80B 0.0255912 0.0003477 0.0112276 0.1701220
27350 APOBEC3C 0.0051330 0.0004220 0.0122160 0.1745980
6601 SMARCC2 0.0336512 0.0003435 0.0122745 0.1745980
1108 CHD4 0.0238388 0.0003994 0.0127656 0.1778779
8289 ARID1A 0.0492112 0.0004149 0.0146053 0.1870798
890 CCNA2 0.0444477 0.0004539 0.0147624 0.1870798
64151 NCAPG 0.0003946 0.0003956 0.0154184 0.1919043
10445 MCRS1 0.0185317 0.0003143 0.0162352 0.1977683
7150 TOP1 0.0468031 0.0003256 0.0175446 0.2072644
8110 DPF3 0.0612773 0.0004235 0.0182917 0.2124890
54531 MIER2 0.0244962 0.0004771 0.0198964 0.2273412
51409 HEMK1 0.0718548 0.0004890 0.0223436 0.2395917
27097 TAF5L 0.0450661 0.0003586 0.0237889 0.2512251
9739 SETD1A 0.0590016 0.0005136 0.0245980 0.2558930
6595 SMARCA2 0.0491644 0.0005485 0.0267793 0.2645703
9555 H2AFY 0.0852250 0.0004323 0.0277200 0.2645703
22823 MTF2 0.0823105 0.0005160 0.0278843 0.2645703
54556 ING3 0.0701823 0.0004542 0.0280892 0.2645703
10592 SMC2 0.0788583 0.0006366 0.0286097 0.2658792
8360 HIST1H4D 0.0801302 0.0004891 0.0300157 0.2715200
7528 YY1 0.1017709 0.0005254 0.0342873 0.2836505
9031 BAZ1B 0.1069563 0.0005045 0.0354054 0.2836505
51377 UCHL5 0.1048249 0.0005627 0.0372967 0.2954064
7799 PRDM2 0.0130131 0.0006154 0.0382200 0.2993182
6602 SMARCD1 0.1110653 0.0006993 0.0446426 0.3241241
8202 NCOA3 0.1179716 0.0006899 0.0454845 0.3251323
51564 HDAC7 0.1331938 0.0007507 0.0463305 0.3251323
26038 CHD5 0.0624026 0.0005717 0.0477023 0.3265622
79858 NEK11 0.1358428 0.0006363 0.0490482 0.3265622
10856 RUVBL2 0.1277997 0.0007652 0.0498579 0.3278390 
toplist3hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(chrom=if_else(ENTREZID %in%Sup4genes$entrez,"y","no")) %>% 
  group_by(id,sigcount,chrom) %>% 
  summarize(chromcount=n()) %>% 
    pivot_wider(id_cols = c(id,sigcount), names_from=c(chrom), values_from=chromcount) %>% 
   mutate(chromprop=(y/(y+no)*100)) %>% 
       ggplot(., aes(x=id, y=chromprop)) +
       geom_col()+
       geom_text(aes(x=id, label = sprintf("%.2f",chromprop), vjust=-.2))+
       #geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
       facet_wrap(~sigcount)+
       ggtitle("Seoane supp 4 enrichment proportions found in my pairwise 3 hour data")

toplist24hr %>% 
  mutate(id = as.factor(id)) %>%
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(chrom=if_else(ENTREZID %in%Sup4genes$entrez,"y","no")) %>% 
  group_by(id,sigcount,chrom) %>% 
  summarize(chromcount=n()) %>% 
    pivot_wider(id_cols = c(id,sigcount), names_from=c(chrom), values_from=chromcount) %>% 
   mutate(chromprop=(y/(y+no)*100)) %>% 
       ggplot(., aes(x=id, y=chromprop)) +
       geom_col()+
       geom_text(aes(x=id, label = sprintf("%.2f",chromprop), vjust=-.2))+
       #geom_text(aes(label = expression(paste0("number"~a,"out of",~b))))+
       facet_wrap(~sigcount)+
       ggtitle("Seoane supp 4 enrichment proportions found in my pairwise 24 hour data")

 chi_fun2 <-  
  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
 mutate(chrom=if_else(ENTREZID %in%Sup4genes$entrez,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(chrom, sigcount)$p.value) 
print("These are the chisquare values from the 54 genes")
[1] "These are the chisquare values from the 54 genes"
chi_fun2
# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours 0.546 
2 Daunorubicin 3_hours  0.732 
3 Doxorubicin  24_hours 0.501 
4 Doxorubicin  3_hours  1.00  
5 Epirubicin   24_hours 0.320 
6 Epirubicin   3_hours  0.748 
7 Mitoxantrone 24_hours 0.0202
8 Mitoxantrone 3_hours  1.00  
pairS14_mat <- chi_fun2 %>% 
  full_join(chi_fun,by=c("id","time")) %>% 
  mutate("n = 54"=-log(pvalue.x), "n = 408"=-log(pvalue.y)) %>% 
  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 ) %>% 
  dplyr::select(!c(pvalue.x,pvalue.y)) %>% 
  column_to_rownames('pairset') %>% 
  as.matrix()
 

Heatmap( pairS14_mat,
         column_title="Pairwise version of Chromatin gene sets \nchi square -log p values", row_order = c(2,4,6,8,1,3,5,7),
         name = "-log p values", 
         cluster_rows = FALSE, 
         cluster_columns = FALSE, 
         column_names_rot = 0,
         col = col_fun5, 
         cell_fun = function(j, i, x, y, width, height, fill) {
        if(pairS14_mat[i, j] > -log(0.05))
            grid.text("*", x, y, gp = gpar(fontsize = 20))
})

Stars indicate a chi square pvalue < 0.05

Using Baysian gene sets to look at enrichment of Seoane data

Seoane Supplemental 4

DEG_cormotif <- readRDS("data/DEG_cormotif.RDS")
list2env(DEG_cormotif,envir=.GlobalEnv)
<environment: R_GlobalEnv>
backGL <- read.csv("data/backGL.txt")
##data sets to compare   Sup4genes$entrez,"y","no"

motifSeoanesummary4 <- toplist24hr %>% 
  distinct(ENTREZID) %>% 
  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(chrom = if_else(ENTREZID %in% Sup4genes$entrez, "y", "no")) %>% 
  group_by(chrom,ER,TI,LR,NR) %>% 
  dplyr::summarize(n=n()) %>% 
  as.tibble  %>% 
  pivot_wider(id_cols = c(chrom), names_from = c('ER', 'TI', 'LR', 'NR'), values_from= n) %>% 
  rename(.,c("chrom"=chrom,"none"= 2 , "ER" = 3 , "TI" = 4 , "LR" = 5 ,"NR" = 6))
motifSeoanesummary4 %>% kable(., caption= "Summary of genes from Cormotif that are also in Seoane Supp4" )%>% 
  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 from Cormotif that are also in Seoane Supp4
chrom none ER TI LR NR
no 63 7482 5525 525 439
y NA 22 20 3 5 
    # chisqS4 <- 
##making the matrix

chi_list4 <- toplist24hr %>% 
  distinct(ENTREZID) %>% 
  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(chrom=if_else(ENTREZID %in%Sup4genes$entrez,"y","no")) %>% 
  group_by(chrom,ER,TI,LR,NR) %>% 
  dplyr::summarize(n=n()) %>% 
  as.tibble  %>% 
  pivot_wider(id_cols = c(chrom), names_from = c('ER', 'TI', 'LR', 'NR'), values_from= n) %>% 
  rename(.,c("chrom"=chrom,"none"= 2 , "ER" = 3 , "TI" = 4 , "LR" = 5 ,"NR" = 6))

chisup4LR <- chisq.test(chi_list4[,c('NR','LR')])
chisup4LR

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("NR", "LR")]
X-squared = 0.36327, df = 1, p-value = 0.5467
chisup4ER <- chisq.test(chi_list4[,c('NR','ER')])
chisup4ER

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("NR", "ER")]
X-squared = 6.3065, df = 1, p-value = 0.01203
chisup4TI <- chisq.test(chi_list4[,c('NR','TI')])
chisup4TI

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("NR", "TI")]
X-squared = 4.099, df = 1, p-value = 0.04291
chisq.test(chi_list4[,c('ER','TI')])

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("ER", "TI")]
X-squared = 0.26698, df = 1, p-value = 0.6054
chisq.test(chi_list4[,c('ER','LR')])

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("ER", "LR")]
X-squared = 0.47936, df = 1, p-value = 0.4887
chisq.test(chi_list4[,c('TI','LR')])

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("TI", "LR")]
X-squared = 0.13763, df = 1, p-value = 0.7107

Seoane Supplemental 1

 motifSeoanesummary1 <- toplist24hr %>% 
  distinct(ENTREZID) %>% 
  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(chrom = if_else(ENTREZID %in% chrom_genes, "y", "no")) %>% 
  group_by(chrom,ER,TI,LR,NR) %>% 
  dplyr::summarize(n=n()) %>% 
  as.tibble  %>% 
  pivot_wider(id_cols = c(chrom), names_from = c('ER', 'TI', 'LR', 'NR'), values_from= n) %>% 
  rename(.,c("chrom"=chrom,"none"= 2 , "ER" = 3 , "TI" = 4 , "LR" = 5 ,"NR" = 6))
motifSeoanesummary1 %>% kable(., caption= "Summary of genes from Cormotif that are also in Seoane Supp1" )%>% 
  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 from Cormotif that are also in Seoane Supp1
chrom none ER TI LR NR
no 61 7363 5406 514 410
y 2 141 139 14 34 
    # chisqS4 <- 
##making the matrix

chi_list1 <- toplist24hr %>% 
  distinct(ENTREZID) %>% 
  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(chrom=if_else(ENTREZID %in% chrom_genes,"y","no")) %>% 
  group_by(chrom,ER,TI,LR,NR) %>% 
  dplyr::summarize(n=n()) %>% 
  as.tibble  %>% 
  pivot_wider(id_cols = c(chrom), names_from = c('ER', 'TI', 'LR', 'NR'), values_from= n) %>% 
  rename(.,c("chrom"=chrom,"none"= 2 , "ER" = 3 , "TI" = 4 , "LR" = 5 ,"NR" = 6))

chisup1LR <- chisq.test(chi_list1[,c('NR','LR')])
chisup1LR

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("NR", "LR")]
X-squared = 11.832, df = 1, p-value = 0.0005824
chisup1ER <- chisq.test(chi_list1[,c('NR','ER')])
chisup1ER

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("NR", "ER")]
X-squared = 62.351, df = 1, p-value = 2.873e-15
chisup1TI <- chisq.test(chi_list1[,c('NR','TI')])
chisup1TI

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("NR", "TI")]
X-squared = 37.066, df = 1, p-value = 1.142e-09
chisq.test(chi_list1[,c('ER','TI')])

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("ER", "TI")]
X-squared = 5.6896, df = 1, p-value = 0.01707
chisq.test(chi_list1[,c('ER','LR')])

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("ER", "LR")]
X-squared = 1.1741, df = 1, p-value = 0.2786
chisq.test(chi_list1[,c('TI','LR')])

    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("TI", "LR")]
X-squared = 0.003306, df = 1, p-value = 0.9541

chi square heatmap

supp45X <- NULL
motif <- c('ER','TI','LR')
pval1=c(chisup1ER$p.value,chisup1TI$p.value,chisup1LR$p.value)
pval4=c(chisup4ER$p.value ,chisup4TI$p.value ,chisup4LR$p.value)
supp45X <- tibble("motif"=motif,"pval1"=pval1,"pval4"=pval4)
supp45Xmat <- supp45X %>% 
  mutate(pval1=(-1*log(pval1))) %>% 
  mutate(pval4=(-1*log(pval4))) %>% 
  column_to_rownames('motif') %>% 
  rename(c("n = 408"= pval1, "n = 54"=pval4)) %>% 
  as.matrix()

Heatmap( supp45Xmat,
         column_title="Chromatin gene sets \nchi square -log p values", 
         name = "-log p values", 
         cluster_rows = FALSE, 
         cluster_columns = FALSE, 
         column_names_rot = 0,
         col = col_fun5,
         cell_fun = function(j, i, x, y, width, height, fill) {
        if(supp45Xmat[i, j] > -log(0.05))
            grid.text("*", x, y, gp = gpar(fontsize = 20))
})

Stars represent chi square p values of < 0.05

Crispr list

# 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))
})

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


pairwisecrispr <- 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$entrezgene_id, "y", "no")) %>% 
  group_by(time, id) %>%
  summarise(pvalue= chisq.test(crisp, sigcount, correct=FALSE)$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 Daunorubicin notsig no 13414
3_hours Daunorubicin notsig y 115
3_hours Daunorubicin sig no 553
3_hours Daunorubicin sig y 2
3_hours Doxorubicin notsig no 13951
3_hours Doxorubicin notsig y 117
3_hours Doxorubicin sig no 16
3_hours Epirubicin notsig no 13749
3_hours Epirubicin notsig y 115
3_hours Epirubicin sig no 218
3_hours Epirubicin sig y 2
3_hours Mitoxantrone notsig no 13909
3_hours Mitoxantrone notsig y 117
3_hours Mitoxantrone sig no 58
24_hours Daunorubicin notsig no 7165
24_hours Daunorubicin notsig y 55
24_hours Daunorubicin sig no 6802
24_hours Daunorubicin sig y 62
24_hours Doxorubicin notsig no 7512
24_hours Doxorubicin notsig y 56
24_hours Doxorubicin sig no 6455
24_hours Doxorubicin sig y 61
24_hours Epirubicin notsig no 7825
24_hours Epirubicin notsig y 57
24_hours Epirubicin sig no 6142
24_hours Epirubicin sig y 60
24_hours Mitoxantrone notsig no 12652
24_hours Mitoxantrone notsig y 105
24_hours Mitoxantrone sig no 1315
24_hours Mitoxantrone sig y 12 
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 Daunorubicin 0.2128915
3_hours Doxorubicin 0.7141341
3_hours Epirubicin 0.8973055
3_hours Mitoxantrone 0.4848796
24_hours Daunorubicin 0.3551194
24_hours Doxorubicin 0.2008692
24_hours Epirubicin 0.1128575
24_hours Mitoxantrone 0.7563882 
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))
})


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] colorspace_2.1-0       rjson_0.2.21           sjlabelled_1.2.0      
  [4] rprojroot_2.0.3        circlize_0.4.15        XVector_0.36.0        
  [7] GlobalOptions_0.1.2    fs_1.6.2               clue_0.3-64           
 [10] rstudioapi_0.14        farver_2.1.1           bit64_4.0.5           
 [13] AnnotationDbi_1.58.0   fansi_1.0.4            xml2_1.3.4            
 [16] codetools_0.2-19       doParallel_1.0.17      cachem_1.0.8          
 [19] knitr_1.43             jsonlite_1.8.5         cluster_2.1.4         
 [22] dbplyr_2.3.2           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            htmltools_0.5.5       
 [31] prettyunits_1.1.1      tools_4.2.2            gtable_0.3.3          
 [34] glue_1.6.2             GenomeInfoDbData_1.2.8 rappdirs_0.3.3        
 [37] Rcpp_1.0.10            carData_3.0-5          Biobase_2.56.0        
 [40] jquerylib_0.1.4        vctrs_0.6.2            Biostrings_2.64.1     
 [43] svglite_2.1.1          iterators_1.0.14       insight_0.19.2        
 [46] xfun_0.39              ps_1.7.5               rvest_1.0.3           
 [49] timechange_0.2.0       lifecycle_1.0.3        rstatix_0.7.2         
 [52] XML_3.99-0.14          getPass_0.2-2          zlibbioc_1.42.0       
 [55] vroom_1.6.3            hms_1.1.3              promises_1.2.0.1      
 [58] parallel_4.2.2         yaml_2.3.7             curl_5.0.1            
 [61] memoise_2.0.1          sass_0.4.6             stringi_1.7.12        
 [64] RSQLite_2.3.1          highr_0.10             S4Vectors_0.34.0      
 [67] foreach_1.5.2          BiocGenerics_0.42.0    filelock_1.0.2        
 [70] shape_1.4.6            GenomeInfoDb_1.32.4    matrixStats_1.0.0     
 [73] rlang_1.1.1            pkgconfig_2.0.3        systemfonts_1.0.4     
 [76] bitops_1.0-7           evaluate_0.21          labeling_0.4.2        
 [79] bit_4.0.5              processx_3.8.1         tidyselect_1.2.0      
 [82] magrittr_2.0.3         R6_2.5.1               IRanges_2.30.1        
 [85] generics_0.1.3         DBI_1.1.3              pillar_1.9.0          
 [88] whisker_0.4.1          withr_2.5.0            KEGGREST_1.36.3       
 [91] abind_1.4-5            RCurl_1.98-1.12        crayon_1.5.2          
 [94] car_3.1-2              utf8_1.2.3             BiocFileCache_2.4.0   
 [97] tzdb_0.4.0             rmarkdown_2.22         GetoptLong_1.0.5      
[100] progress_1.2.2         blob_1.2.4             callr_3.7.3           
[103] git2r_0.32.0           digest_0.6.31          webshot_0.5.4         
[106] httpuv_1.6.11          stats4_4.2.2           munsell_0.5.0         
[109] viridisLite_0.4.2      bslib_0.5.0