Seoane_chromatin
ERM
2023-06-16
Last updated: 2023-06-16
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Knit directory: Cardiotoxicity/
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## R Markdown
library(ComplexHeatmap)
library(tidyverse)
library(ggsignif)
library(biomaRt)
library(RColorBrewer)
library(cowplot)
library(ggpubr)
library(scales)
library(sjmisc)
library(kableExtra)
library(broom)
library(ggstats)##DEG list sig and not sig
toplistall <- read.csv("output/toplistall.csv", row.names = 1)
col_fun = circlize::colorRamp2(c(0, 2), c("white", "purple"))
col_fun1 = circlize::colorRamp2(c(-1, 3), c("white", "purple"))
col_fun5 = circlize::colorRamp2(c(0, 5), c("white", "purple"))
col_fun4= circlize::colorRamp2(c(-1, 4), c("white", "purple"))
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")
siglist <- readRDS("data/siglist.RDS")
list2env(siglist,envir=.GlobalEnv)<environment: R_GlobalEnv>DEG_cormotif <- readRDS("data/DEG_cormotif.RDS")
list2env(DEG_cormotif,envir=.GlobalEnv)<environment: R_GlobalEnv>backGL <- read_csv("data/backGL.txt",
col_types = cols(...1 = col_skip()))
cpmcounts <- read.csv("data/filtered_cpm_counts.csv", row.names = 1)
##sup1()
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))
## sup4
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")Seoane 2019
Seoane, Jose Chromatin gene comparison: comes from supp data NAT. MED 2019 #### 24 hours in Pairwise with supplemental data 1
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")| 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 0.000003263 hours data Pairwise
| 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")| 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
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")| 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$entrez, "y", "no")) %>%
group_by(chrom,ER,TI,LR,NR) %>%
dplyr::summarize(n=n()) %>%
as.tibble %>%
pivot_wider(id_cols = c(chrom), names_from = c('ER', 'TI', 'LR', 'NR'), values_from= n) %>%
rename(.,c("chrom"=chrom,"none"= 2 , "ER" = 3 , "TI" = 4 , "LR" = 5 ,"NR" = 6))
motifSeoanesummary4 %>% kable(., caption= "Summary of genes from Cormotif that are also in Seoane Supp4" )%>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE, position = "left",bootstrap_options = c("striped"),font_size = 18) %>%
scroll_box(width = "60%", height = "400px")| chrom | none | ER | TI | LR | NR |
|---|---|---|---|---|---|
| no | 63 | 7482 | 5525 | 525 | 439 |
| y | NA | 22 | 20 | 3 | 5 |
# chisqS4 <-
##making the matrix
chi_list4 <- toplist24hr %>%
distinct(ENTREZID) %>%
mutate(ER=if_else(ENTREZID %in%motif_ER,"y","no")) %>%
mutate(LR=if_else(ENTREZID %in%motif_LR,"y","no")) %>%
mutate(TI=if_else(ENTREZID %in%motif_TI,"y","no")) %>%
mutate(NR=if_else(ENTREZID %in%motif_NR,"y","no")) %>%
mutate(chrom=if_else(ENTREZID %in%Sup4genes$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 = 0.5467chisup4ER <- 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 = 0.01203chisup4TI <- 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 = 0.04291chisq.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 = 0.6054chisq.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 = 0.4887chisq.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 = 0.7107Seoane 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")| 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 = 0.0005824chisup1ER <- 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-15chisup1TI <- 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-09chisq.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 = 0.01707chisq.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 = 0.2786chisq.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 = 0.9541chi 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 value)",
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
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] ggstats_0.3.0 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 ComplexHeatmap_2.12.1
[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_0.6.3 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 systemfonts_1.0.4
[73] rlang_1.1.1 pkgconfig_2.0.3 matrixStats_1.0.0
[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 webshot_0.5.4 digest_0.6.31
[106] httpuv_1.6.11 stats4_4.2.2 munsell_0.5.0
[109] viridisLite_0.4.2 bslib_0.5.0