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Last updated: 2023-11-28

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Knit directory: Cardiotoxicity/

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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Knowels_trop_analysis.Rmd) and HTML (docs/Knowels_trop_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	8606776	reneeisnowhere	2023-11-28	updated with GO on Highly variable genes
html	b651aa9	reneeisnowhere	2023-11-27	Build site.
Rmd	522ca8c	reneeisnowhere	2023-11-27	adding more analysis
html	b474072	reneeisnowhere	2023-11-21	Build site.
Rmd	5c45b92	reneeisnowhere	2023-11-21	adding knowles data again
html	b4d55c4	reneeisnowhere	2023-11-21	Build site.
Rmd	b1b9563	reneeisnowhere	2023-11-21	adding logcpm with knowles data
html	b442590	reneeisnowhere	2023-11-21	Build site.
Rmd	b464f89	reneeisnowhere	2023-11-21	updates to links again
html	19a3502	reneeisnowhere	2023-11-21	Build site.
Rmd	d17d270	reneeisnowhere	2023-11-21	adding links
html	c72467c	reneeisnowhere	2023-11-09	Build site.
Rmd	8a6ebc1	reneeisnowhere	2023-11-09	updates on plots
html	9af9df6	reneeisnowhere	2023-11-09	Build site.
Rmd	60c64d1	reneeisnowhere	2023-11-09	adding boxplots
html	d12232a	reneeisnowhere	2023-11-07	Build site.
Rmd	441f82b	reneeisnowhere	2023-11-07	adding more code
html	cae46a9	reneeisnowhere	2023-11-07	Build site.
html	bd0342c	reneeisnowhere	2023-11-07	Build site.
Rmd	453ebe6	reneeisnowhere	2023-11-07	adding code
Rmd	d6ecce9	reneeisnowhere	2023-11-07	adding code
html	ae9124e	reneeisnowhere	2023-10-30	Build site.
Rmd	74c2dc1	reneeisnowhere	2023-10-30	updated
Rmd	d970e84	reneeisnowhere	2023-10-30	adding more analysis

---

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

palette_colors_mine <- colorRampPalette(colors = c("green","white","purple","red" ))(60)
scales::show_col(palette_colors_mine)

Past versions of colorpalette-1.png

Version	Author	Date
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Here I will attempt to recreate my correlation analysis on the knowles data using their troponin and RNAseq log2cpm.

### genes I want to know about
interest_genes <- read.csv("output/GOI_genelist.txt", row.names = 1)
trop_knowles <- read.csv("output/trop_knowles_fun.csv", row.names = 1)
Knowles_log2cpm <- readRDS("data/Knowles_log2cpm_real.RDS")
trop0.625 <- trop_knowles %>% 
  filter(dosage <1) 
store <- Knowles_log2cpm %>% 
  dplyr::select( 'ESGN',ends_with(c('0.625', '0'))) %>% 
  dplyr::filter(ESGN %in% interest_genes$ensembl_gene_id) %>% 
  pivot_longer(cols=!ESGN, names_to = "ind", values_to = "counts") %>% 
  separate(ind,into=c("cell_line","dosage"), sep = ":") %>%
  mutate(dosage = as.numeric(dosage)) %>% 
  full_join(., trop0.625, by=c("cell_line", "dosage")) %>% 
  group_by(cell_line) %>% 
  full_join(., interest_genes, by = c("ESGN" = "ensembl_gene_id"))
  
  
  ###new graph stuff
  for (gene in interest_genes$ensembl_gene_id){
    gene_plot <- store %>% 
      dplyr::filter(ESGN == gene) %>%
      ggplot(., aes(x=troponin, y=counts))+
      geom_point(aes(col=cell_line))+
      geom_smooth(method="lm")+
      
      facet_wrap(hgnc_symbol~dosage, scales="free")+
      theme_classic()+
      xlab("troponin I expression") +
      ylab("Gene counts in log2 cpm") +
      ggtitle(expression(paste("Correlation between counts and troponin I Knowles")))+
      scale_color_manual(values = palette_colors_mine, aesthetics = c("color", "fill"), guide=FALSE)+
      # guides(fill="none")+
     ggpubr:: stat_cor(method="spearman",
                       # cor.coef.name="rho",
               aes(label = paste(..r.label.., ..p.label.., sep = "~`,\n`~")),
               color = "black",
               label.x.npc = 0.01,
               label.y.npc=0.01, 
               size = 3)+
      theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
            axis.title = element_text(size = 15, color = "black"),
            axis.ticks = element_line(size = 1.5),
            axis.text = element_text(size = 8, color = "black", angle = 20),
            strip.text.x = element_text(size = 12, color = "black", face = "italic"))
   print(gene_plot)
   
  }

Past versions of "troponin knowles-1.png"

Version	Author	Date
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-2.png"

Version	Author	Date
b442590	reneeisnowhere	2023-11-21
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-3.png"

Version	Author	Date
b442590	reneeisnowhere	2023-11-21
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-4.png"

Version	Author	Date
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-5.png"

Version	Author	Date
b442590	reneeisnowhere	2023-11-21
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-6.png"

Version	Author	Date
b442590	reneeisnowhere	2023-11-21
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-7.png"

Version	Author	Date
b442590	reneeisnowhere	2023-11-21
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-8.png"

Version	Author	Date
b442590	reneeisnowhere	2023-11-21
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-9.png"

Version	Author	Date
b442590	reneeisnowhere	2023-11-21
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-10.png"

Version	Author	Date
b442590	reneeisnowhere	2023-11-21
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Past versions of "troponin knowles-11.png"

Version	Author	Date
b442590	reneeisnowhere	2023-11-21
9af9df6	reneeisnowhere	2023-11-09
bd0342c	reneeisnowhere	2023-11-07

Knowles Boxplots of Fig9 genes

Knowles_log2cpm_box <- readRDS("data/Knowles_log2cpm_real.RDS")

store_box <- Knowles_log2cpm_box %>% 
  # dplyr::select( 'ESGN',ends_with(c('0.625', '0'))) %>% 
  dplyr::filter(ESGN %in% interest_genes$ensembl_gene_id) %>% 
  pivot_longer(cols=!ESGN, names_to = "ind", values_to = "counts") %>% 
  separate(ind,into=c("cell_line","dosage"), sep = ":") %>%
  mutate(dosage = as.numeric(dosage)) %>% 
  # full_join(., trop0.625, by=c("cell_line", "dosage")) %>% 
  group_by(cell_line) %>% 
  full_join(., interest_genes, by = c("ESGN" = "ensembl_gene_id"))
store_box %>% 
  mutate(dosage=factor(dosage, levels=c('0','0.000', '0.625','1.25', '2.5','5'))) %>% 
  ggplot(., aes(x=dosage,y=counts), group=dosage)+
  geom_boxplot()+
  facet_wrap(~hgnc_symbol)

Past versions of KnowBoxfig9-1.png

Version	Author	Date
9af9df6	reneeisnowhere	2023-11-09

RNA-seq trial analysis

Analysis of expressed genes

RNA_seq_trial<- readRDS("data/RNA_seq_trial.RDS")

all_cpmcount <-  read_table("data/Counts_RNA_ERMatthews.txt")
cpm_count_main <- readRDS("data/cpmcount.RDS") %>% rownames_to_column(var = "ENTREZID")
colnames(cpm_count_main) <- colnames(all_cpmcount)


test_run_sample_list <- read.csv("data/test_run_sample_list.txt", row.names = 1)

colnames(RNA_seq_trial) <- c("ENTREZID",test_run_sample_list$Sample_ID)

lcpm_trial <- RNA_seq_trial %>% 
  column_to_rownames("ENTREZID") %>% 
  cpm(., log=TRUE) %>% 
  as.data.frame() #%>% 
 

row_means <- rowMeans(lcpm_trial)
x_trial <- lcpm_trial[row_means > 0,]
dim(x_trial)

[1] 13277     4

list_genes_trial <- rownames(x_trial)
ggVennDiagram::ggVennDiagram(list(list_genes_trial, cpm_count_main$ENTREZID),
                             category.names = c("Trialgenes","Maingenes"),
              show_intersect = TRUE,
              set_color = "black",
              label = "count",
              label_percent_digit = 1,
              label_size = 4,
              label_alpha = 0,
              label_color = "black",
              edge_lty = "solid", set_size = 4.5)#+

Correlation of counts files

lcpm_trial_full <- RNA_seq_trial %>% 
  column_to_rownames("ENTREZID") %>% 
  cpm(., log=TRUE) %>% 
  as.data.frame() %>% 
  rownames_to_column(var = "ENTREZID")

lcpm_trial_full %>%
  column_to_rownames(var="ENTREZID") %>%
  cor(.) %>% 
  Heatmap(.,layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(., i, j)), x, y, 
            gp = gpar(fontsize = 10))})

Past versions of correlations-1.png

Version	Author	Date
ae9124e	reneeisnowhere	2023-10-30

lcpm_main <- all_cpmcount %>% 
  column_to_rownames("ENTREZID") %>% 
  cpm(., log=TRUE) %>% 
  as.data.frame() %>% 
  rownames_to_column(var = "ENTREZID") %>% 
  dplyr::select(ENTREZID, all_of(starts_with("DOX"))) %>% 
  dplyr::select(ENTREZID, all_of(ends_with("3h")))  
  
combined_data <- lcpm_main %>%
  full_join(., lcpm_trial_full, by= "ENTREZID") %>%
  column_to_rownames("ENTREZID") %>% 
  dplyr::select(starts_with("DOX"),`3hr_0.5`)%>% 
  cor(.,) 


  
  Heatmap(combined_data,column_title = "Full gene list",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(combined_data, i, j)), x, y, 
            gp = gpar(fontsize = 10))})

Past versions of correlations-2.png

Version	Author	Date
b4d55c4	reneeisnowhere	2023-11-21
ae9124e	reneeisnowhere	2023-10-30

  only79_ind <- lcpm_main %>%
  full_join(., lcpm_trial_full, by= "ENTREZID") %>% 
    dplyr::select(ENTREZID,'3hr_0.5',"DOX.4.3h") %>% 
    column_to_rownames("ENTREZID") %>% 
  cor(.,) 

  
  Heatmap(only79_ind,column_title = "Full gene list_79",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(only79_ind, i, j)), x, y, 
            gp = gpar(fontsize = 10))})

Past versions of correlations-3.png

Version	Author	Date
9af9df6	reneeisnowhere	2023-11-09
ae9124e	reneeisnowhere	2023-10-30

lcpm_main_veh <- all_cpmcount %>% 
  column_to_rownames("ENTREZID") %>% 
  cpm(., log=TRUE) %>% 
  as.data.frame() %>% 
  rownames_to_column(var = "ENTREZID") %>% 
  dplyr::select(ENTREZID, all_of(c(starts_with("DOX"),starts_with("VEH")))) %>% 
   dplyr::select(ENTREZID, all_of(ends_with("3h")))  
  

combined_data_veh<- lcpm_main_veh %>%
  full_join(., lcpm_trial_full, by= "ENTREZID") %>% 
  column_to_rownames("ENTREZID") %>% 
  cor(.,) 
  
  
  
  Heatmap(combined_data_veh, column_title = "all genes in list, no filtering",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(combined_data_veh, i, j)), x, y, 
            gp = gpar(fontsize = 8))})

Past versions of correlations-4.png

Version	Author	Date
ae9124e	reneeisnowhere	2023-10-30

lcpm_trial_filter_main <- lcpm_trial_full %>% 
  filter(ENTREZID %in% cpm_count_main$ENTREZID)
 


lcpm_trial_filter_main %>% 
column_to_rownames(var="ENTREZID") %>%
  cor(.) %>% 
  Heatmap(.,column_title = "Using 14,084 expressed genes from Main data",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(., i, j)), x, y, 
            gp = gpar(fontsize = 8))})

Past versions of "correlation RNAseqtrial filter-1.png"

Version	Author	Date
ae9124e	reneeisnowhere	2023-10-30

lcpm_trial_filter <- lcpm_trial_full %>% 
  filter(ENTREZID %in% list_genes_trial)
 

lcpm_trial_filter %>% 
column_to_rownames(var="ENTREZID") %>%
  cor(.) %>% 
  Heatmap(.,column_title = "Using 13277 expressed genes",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(., i, j)), x, y, 
            gp = gpar(fontsize = 8))})

Past versions of "correlation RNAseqtrial filter-2.png"

Version	Author	Date
ae9124e	reneeisnowhere	2023-10-30

lcpm_main_filter_trial <- lcpm_main_veh %>% 
  filter(ENTREZID %in% list_genes_trial)

lcpm_trial_filter %>% 
  full_join(., lcpm_main_filter_trial, by = "ENTREZID") %>% 
  column_to_rownames(var="ENTREZID") %>%
  cor(.) %>% 
  Heatmap(.,column_title = "Using 13277 expressed genes",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(., i, j)), x, y, 
            gp = gpar(fontsize = 8))})

Past versions of "correlation RNAseqtrial filter-3.png"

Version	Author	Date
ae9124e	reneeisnowhere	2023-10-30

lcpm_trial_filter_main %>% 
  left_join(., lcpm_main, by = "ENTREZID") %>% 
  column_to_rownames(var="ENTREZID") %>%
  dplyr::select(DOX.4.3h,starts_with(("3hr")))%>% 
  cor(.) %>% 
  Heatmap(.,column_title = "Using 14084 expressed genes, just 79-1",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(., i, j)), x, y, 
            gp = gpar(fontsize = 8))})

Past versions of "correlation RNAseqtrial filter-4.png"

Version	Author	Date
bd0342c	reneeisnowhere	2023-11-07

hr3_indv4 <- lcpm_trial_filter_main %>% 
  left_join(., lcpm_main, by = "ENTREZID") %>% 
  column_to_rownames(var="ENTREZID") %>%
  dplyr::select(DOX.4.3h,`3hr_0.5`,`3hr_0.0`)%>% 
  cor(.) %>% 
  Heatmap(.,column_title = "Using 14084 expressed genes, just 79-1",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(., i, j)), x, y, 
            gp = gpar(fontsize = 8))})
  
plot(hr3_indv4)

Past versions of "correlation RNAseqtrial filter-5.png"

Version	Author	Date
bd0342c	reneeisnowhere	2023-11-07

correlation heatmap of 3hr Dox 1-6 individuals and trial data

lcpm_trial_filter_main %>% 
  left_join(., lcpm_main, by = "ENTREZID") %>% 
  column_to_rownames(var="ENTREZID") %>%
  dplyr::select(starts_with("DOX"),`3hr_0.5`)%>% 
  cor(.) %>% 
  Heatmap(.,column_title = "Using 14084 expressed genes, just 79-1 with all 3 hour samples",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(., i, j)), x, y, 
            gp = gpar(fontsize = 8))})

Past versions of ind1-6trial-1.png

Version	Author	Date
9af9df6	reneeisnowhere	2023-11-09

lcpm_main %>% 
  left_join(., lcpm_trial_full, by = "ENTREZID") %>% 
  column_to_rownames(var="ENTREZID") %>%
  dplyr::select(starts_with("DOX"),`3hr_0.5`)%>% 
  cor(.) %>% 
  Heatmap(.,column_title = "all 29395 genes expriment with trial 0.5 uM",
          layer_fun = function(j, i, x, y, width, height, fill) {
              grid.text(sprintf("%.3f", pindex(., i, j)), x, y, 
            gp = gpar(fontsize = 8))})

Past versions of ind1-6trial-2.png

Version	Author	Date
b4d55c4	reneeisnowhere	2023-11-21

barplots

backGL <-read_csv("data/backGL.txt", 
    col_types = cols(...1 = col_skip()))

GOI_genelist <- read.csv("output/GOI_genelist.txt", row.names = 1)

cpm_boxplot_trial <-function(lcpm_trial, GOI, ylab) {
    ##GOI needs to be ENTREZID
  df_plot <- lcpm_trial %>% 
    dplyr::filter(rownames(.)== GOI) %>%
    pivot_longer(everything(),
                 names_to = "treatment",
                 values_to = "counts") %>%
    separate(treatment, c("time","conc"), sep= "_") %>%
    mutate(conc = factor(conc,levels=c('0.0','0.1','0.5','1.0'), labels = c ("NT", "0.1 uM", "0.5 uM", "1.0 uM")))
  
 plota <-  ggplot2::ggplot(df_plot, aes(x=conc, y= counts))+
    geom_col(position="identity")+
    theme_bw()+
    ylab(ylab)+
    xlab("")+
     ggtitle(paste(GOI))+
      theme(
        # strip.background = element_rect(fill = "white",linetype=1, linewidth = 0.5),
          plot.title = element_text(size=12,hjust = 0.5,face="bold"),
          axis.title = element_text(size = 10, color = "black"),
          axis.ticks = element_line(linewidth = 1.0),
          panel.background = element_rect(colour = "black", size=1),
          # axis.text.x = element_blank(),
          strip.text.x = element_text(margin = margin(2,0,2,0, "pt"),face = "bold"))
    print(plota)
}
  



for (g in seq(1:11)){
  datafilter <- GOI_genelist
  a <- GOI_genelist[g,3]
  # b <- datafilter[g,1]
  cpm_boxplot_trial(lcpm_trial,GOI=datafilter[g,1],
                           ylab =bquote(~italic(.(a))~log[2]~"cpm "))
  
}  

Past versions of "bar_plots of GOI-1.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-2.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-3.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-4.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-5.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-6.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-7.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-8.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-9.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-10.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

Past versions of "bar_plots of GOI-11.png"

Version	Author	Date
c72467c	reneeisnowhere	2023-11-09
d12232a	reneeisnowhere	2023-11-07

expression of trial RNA seq data ### Knowles log2cpm 24hr and my log2cpm 24hr

kcpm <- store_box %>%  
  mutate(dosage=factor(dosage, levels=c('0','0.000', '0.625','1.25', '2.5','5'))) %>% 
  dplyr::filter(dosage==("0")|dosage == "0.625") %>% 
  mutate(expr="K")
  
lcpm_24h <- all_cpmcount %>% 
  column_to_rownames("ENTREZID") %>% 
  cpm(., log=TRUE) %>% 
  as.data.frame() %>% 
  rownames_to_column(var = "ENTREZID") %>% 
  dplyr::select(ENTREZID, all_of(starts_with(c("DOX","VEH")))) %>% 
  dplyr::select(ENTREZID, all_of(ends_with("24h")))  %>% 
  dplyr::filter(ENTREZID %in% interest_genes$entrezgene_id) %>% 
  pivot_longer(cols=!ENTREZID, names_to = "ind", values_to = "counts") %>%   mutate(ENTREZID = as.numeric(ENTREZID)) %>% 
  full_join(., interest_genes, by = c("ENTREZID"="entrezgene_id")) %>% 
  mutate(expr="ME") %>% 
  rename("ESGN"="ensembl_gene_id","entrezgene_id"="ENTREZID") %>% 
  separate(ind, into = c("dosage","cell_line",NA)) %>% 
  mutate(dosage=case_match(dosage,"DOX"~"0.5", .default = dosage)) 
  
lcpm_24h %>% 
  rbind(.,kcpm) %>% 
  mutate(dosage=factor(dosage, levels=c('0','0.625',"VEH","0.5"))) %>% 
  ggplot(., aes(x=dosage,y=counts), group=expr)+
  geom_boxplot()+
  facet_wrap(~hgnc_symbol, scales="free_y" )#+

Past versions of "K and Me-1.png"

Version	Author	Date
b474072	reneeisnowhere	2023-11-21
b4d55c4	reneeisnowhere	2023-11-21

  # geom_signif(
  #   comparisons = list(
  #     c('0', '0.5'),
  #     c('0', '0.625'),
  #     c('0.5','0.625')
  #   ),
  #   test = "t.test",
  #   tip_length = 0.01,
  #   map_signif_level = FALSE,
  #   textsize = 4,
  #   step_increase = 0.3
  # ) 

Replication of variance figures using Knowels data

# saveRDS(store_var, "data/Knowles_variation_data.RDS")
# store_var <- Knowles_log2cpm %>% 
#   dplyr::select( 'ESGN',ends_with(c('0.625', '0'))) %>% 
#   rowwise() %>% 
#   mutate(mean_DOX=mean(c_across(ends_with('0.625'))),
#          var_DOX=var(c_across(ends_with('0.625'))),
#         mean_NT=mean(c_across(ends_with('0'))),
#          var_NT=var(c_across(ends_with('0')))) %>% 
#   mutate(data=tidy(var.test(c_across(ends_with("0.625")),c_across(ends_with("0")))))# %>% 
  # dplyr::select("ESGN","mean_DOX","var_DOX","mean_NT", "var_NT","data")


store_var <- readRDS("data/Knowles_variation_data.RDS")

knowlesdrug<- store_var %>% 
  dplyr::select("ESGN","mean_DOX","var_DOX","mean_NT", "var_NT") %>% 
  pivot_longer(cols = !"ESGN", names_to = "short", values_to = "values") %>% 
  separate(short, into=c("calc","treatment")) #%>% 
knowlesdrug %>% 
  as.data.frame() %>% 
  dplyr::filter(calc == "mean") %>% 
  ggplot(., aes(x= treatment, y=values))+
  geom_boxplot()+
  ggtitle("Knowles Means across all genes")+
  geom_signif(
    comparisons = list(
      c("DOX", "NT")),
    test = "t.test",
    tip_length = 0.01,
    map_signif_level = FALSE
    # textsize = 4,
    # # y_position = 11,
    # step_increase = 0.05
  )

Past versions of varianceKnowles-1.png

Version	Author	Date
b651aa9	reneeisnowhere	2023-11-27

knowlesdrug %>% 
  as.data.frame() %>% 
  dplyr::filter(calc == "var") %>% 
  ggplot(., aes(x= treatment, y=values))+
  geom_boxplot(outlier.shape= NA)+
  ggtitle(" Knowles Variance across all genes")+
  
  geom_signif(
    comparisons = list(
      c("DOX", "NT")),
    test = "t.test",
    tip_length = 0.01,
    y_position = 0.5,
    # vjust=1,
    map_signif_level = FALSE)+
 ylim(NA,1.25)

Past versions of varianceKnowles-2.png

Version	Author	Date
b651aa9	reneeisnowhere	2023-11-27

qvalue data

library(qvalue)


# p_list <- map_df(store_var$data,~as.data.frame(.x$p.value))
#  rownames(p_list) <- store_var$ESGN

p_list <- store_var %>% 
  unnest(data) %>% 
   dplyr::select(ESGN,statistic,p.value)
estDOXk <- qvalue(p_list$p.value) 
 hist(estDOXk)

 plot(estDOXk)

summary(estDOXk) 

Call:
qvalue(p = p_list$p.value)

pi0:    0.3993136   

Cumulative number of significant calls:

          <1e-04 <0.001 <0.01 <0.025 <0.05 <0.1    <1
p-value     1919   2762  4065   4832  5533 6453 12317
q-value     1616   2447  3895   4810  5723 6956 12317
local FDR   1143   1759  2724   3313  3800 4507 12317

knowlesvar <- data.frame("ESGN"=p_list$ESGN,"pvalues"=estDOXk$pvalues,"qvalues"=estDOXk$qvalues,"lfdr"= estDOXk$lfdr)  

intersecting_K <- knowlesvar %>% 
  filter(qvalues<0.1)
my_qval_list24 <- readRDS("data/qval24hr.RDS") 

EPI508_list <- my_qval_list24 %>% 
  dplyr::select(ENTREZID,EPIqvalues) %>% 
  filter(EPIqvalues<0.1) %>% 
  dplyr::select(ENTREZID) %>%
  mutate(ENTREZID=as.numeric(ENTREZID)) %>% 
  left_join(.,backGL, by="ENTREZID")
Knowlesvarlist <- readRDS("data/Knowlesvarlist.RDS")
  
# Knowlesvarlist<- getBM(attributes=my_attributes,filters ='ensembl_gene_id',values = intersecting_K$ESGN, mart = ensembl)

length(intersect(EPI508_list$ENTREZID,Knowlesvarlist$entrezgene_id))

[1] 367

intersect_genes <- EPI508_list %>% 
  dplyr::filter(ENTREZID %in% Knowlesvarlist$entrezgene_id)

intersect_genes %>% 
kable(.,caption = "EPI Highly variable genes found in Knowles higly  variable DOX genes") %>% 
  kable_paper("striped", full_width = FALSE) %>%  
  kable_styling(full_width = TRUE, bootstrap_options = c("striped","hover")) %>% 
  scroll_box(width = "100%", height = "400px")

EPI Highly variable genes found in Knowles higly variable DOX genes

ENTREZID	SYMBOL
49856	WRAP73
23463	ICMT
5195	PEX14
57085	AGTRAP
23207	PLEKHM2
79363	CPLANE2
55160	ARHGEF10L
55616	ASAP3
57095	PITHD1
11313	LYPLA2
84065	TMEM222
23673	STX12
56063	TMEM234
3065	HDAC1
127544	RNF19B
113444	SMIM12
27095	TRAPPC3
112950	MED8
9670	IPO13
149483	CCDC17
387338	NSUN4
8543	LMO4
64858	DCLRE1B
128077	LIX1L
388695	LYSMD1
6944	VPS72
65005	MRPL9
4000	LMNA
11266	DUSP12
261726	TIPRL
5279	PIGC
83479	DDX59
134	ADORA1
64853	AIDA
163859	SDE2
5664	PSEN2
65094	JMJD4
126731	CCSAP
22796	COG2
79723	SUV39H2
253430	IPMK
26091	HERC4
219738	FAM241B
11319	ECD
5532	PPP3CB
79933	SYNPO2L
27063	ANKRD1
118924	FRA10AC1
282991	BLOC1S2
10360	NPM3
9937	DCLRE1A
9184	BUB3
161	AP2A2
7748	ZNF195
10612	TRIM3
23647	ARFIP2
65975	STK33
113174	SAAL1
627	BDNF
79797	ZNF408
10978	CLP1
55048	VPS37C
5436	POLR2G
144097	SPINDOC
57410	SCYL1
10524	KAT5
25855	BRMS1
10432	RBM14
338692	ANKRD13D
5883	RAD9A
5499	PPP1CA
2950	GSTP1
116985	ARAP1
5612	THAP12
56946	EMSY
282679	AQP11
51585	PCF11
60492	CCDC90B
9440	MED17
54851	ANKRD49
10929	SRSF8
5049	PAFAH1B2
219902	TLCD5
219899	TBCEL
9538	EI24
219833	KCNJ5-AS1
57102	C12orf4
8079	MLF2
25977	NECAP1
79657	RPAP3
55652	SLC48A1
1019	CDK4
23041	MON2
29110	TBK1
253827	MSRB3
117177	RAB3IP
22822	PHLDA1
89795	NAV3
121053	NOPCHAP1
1407	CRY1
51228	GLTP
400073	C12orf76
5564	PRKAB1
51499	TRIAP1
56616	DIABLO
387893	KMT5A
11066	SNRNP35
5901	RAN
100289635	ZNF605
221178	SPATA13
283537	SLC46A3
2963	GTF2F2
337867	UBAC2
3981	LIG4
84945	ABHD13
3621	ING1
253959	RALGAPA1
54813	KLHL28
6617	SNAPC1
123016	TTC8
51527	GSKIP
2972	BRF1
22893	BAHD1
9325	TRIP4
5371	PML
60490	PPCDC
79631	EFL1
84219	WDR24
23059	CLUAP1
2072	ERCC4
91949	COG7
23568	ARL2BP
29070	CCDC113
8824	CES2
1874	E2F4
6560	SLC12A4
146198	ZFP90
54386	TERF2IP
5119	CHMP1A
64359	NXN
5048	PAFAH1B1
388324	INCA1
9135	RABEP1
57336	ZNF287
79736	TEFM
55813	UTP6
54475	NLE1
5193	PEX12
22794	CASC3
3292	HSD17B1
10614	HEXIM1
114881	OSBPL7
29916	SNX11
8405	SPOP
10237	SLC35B1
81558	FAM117A
55316	RSAD1
8161	COIL
6426	SRSF1
54903	MKS1
55771	PRR11
284161	GDPD1
6198	RPS6KB1
57508	INTS2
84923	FAM104A
55028	C17orf80
6730	SRP68
9489	PGS1
9775	EIF4A3
57521	RPTOR
79643	CHMP6
5881	RAC3
8780	RIOK3
55364	IMPACT
54531	MIER2
126308	MOB3A
29985	SLC39A3
51343	FZR1
51588	PIAS4
6455	SH3GL1
5609	MAP2K7
79603	CERS4
93134	ZNF561
126070	ZNF440
2193	FARSA
85360	SYDE1
8907	AP1M1
54858	PGPEP1
93436	ARMC6
79414	LRFN3
163087	ZNF383
84503	ZNF527
22835	ZFP30
284323	ZNF780A
29950	SERTAD1
90324	CCDC97
56006	SMG9
7773	ZNF230
7769	ZNF226
9668	ZNF432
147657	ZNF480
112724	RDH13
163033	ZNF579
147694	ZNF548
100293516	ZNF587B
25799	ZNF324
7260	EIPR1
55006	TRMT61B
253635	GPATCH11
92906	HNRNPLL
8491	MAP4K3
57504	MTA3
53335	BCL11A
5861	RAB1A
27332	ZNF638
8446	DUSP11
1716	DGUOK
84173	ELMOD3
129303	TMEM150A
5903	RANBP2
10254	STAM2
10213	PSMD14
115677	NOSTRIN
79828	METTL8
80067	DCAF17
129831	RBM45
3628	INPP1
6775	STAT4
9330	GTF3C3
57404	CYP20A1
54891	INO80D
377007	KLHL30
4735	SEPTIN2
80023	NRSN2
55968	NSFL1C
55317	AP5S1
64412	GZF1
51230	PHF20
25980	AAR2
63905	MANBAL
10904	BLCAP
60625	DHX35
51098	IFT52
51006	SLC35C2
10564	ARFGEF2
11054	OGFR
80331	DNAJC5
29104	N6AMT1
84221	SPATC1L
51586	MED15
6598	SMARCB1
84700	MYO18B
402055	SRRD
84164	ASCC2
23780	APOL2
129138	ANKRD54
9463	PICK1
84247	RTL6
132001	TAMM41
23609	MKRN2
22908	SACM1L
51385	ZNF589
64925	CCDC71
11070	TMEM115
28972	SPCS1
25871	NEPRO
55254	TMEM39A
131601	TPRA1
7879	RAB7A
51122	COMMD2
64393	ZMAT3
86	ACTL6A
55486	PARL
90407	TMEM41A
1487	CTBP1
7469	NELFA
152	ADRA2C
132789	GNPDA2
10606	PAICS
92597	MOB1B
266812	NAP1L5
56916	SMARCAD1
55212	BBS7
90826	PRMT9
4750	NEK1
55100	WDR70
55814	BDP1
79810	PTCD2
167153	TENT2
9765	ZFYVE16
55781	RIOK2
90355	MACIR
153733	CCDC112
153443	SRFBP1
8572	PDLIM4
202052	DNAJC18
23438	HARS2
10826	FAXDC2
9443	MED7
5917	RARS1
80315	CPEB4
8899	PRPF4B
10473	HMGN4
7746	ZSCAN9
8449	DHX16
57827	C6orf47
578	BAK1
5467	PPARD
6428	SRSF3
9477	MED20
9533	POLR1C
26036	ZNF451
51715	RAB23
25821	MTO1
57226	LYRM2
23376	UFL1
26235	FBXL4
91749	MFSD4B
10758	TRAF3IP2
5689	PSMB1
5575	PRKAR1B
90639	COX19
84262	PSMG3
8379	MAD1L1
54476	RNF216
221830	POLR1F
3364	HUS1
55695	NSUN5
9569	GTF2IRD1
113878	DTX2
6717	SRI
9069	CLDN12
10898	CPSF4
3268	AGFG2
5001	ORC5
60561	RINT1
64418	TMEM168
27153	ZNF777
80346	REEP4
5533	PPP3CC
157313	CDCA2
23087	TRIM35
157574	FBXO16
7336	UBE2V2
90362	FAM110B
55824	PAG1
55656	INTS8
51123	ZNF706
51105	PHF20L1
203062	TSNARE1
55958	KLHL9
54840	APTX
55035	NOL8
10592	SMC2
5998	RGS3
51552	RAB14
399665	FAM102A
84885	ZDHHC12
5900	RALGDS
6837	MED22
57109	REXO4
92715	DPH7
23708	GSPT2
29934	SNX12
139596	UPRT
64860	ARMCX5
644596	SMIM10L2B

# saveRDS(Knowlesvarlist,"data/Knowlesvarlist.RDS")

GO analysis of Highly Variable genes

I am using two backgrounds:
(1) the expressed background of the expressed genes in my data in the A set (n = 14,084)
(2) the full EPI highly variable genes from my data in the B set (n = 508) set 2 or B did not yield anything!

A set

library(gprofiler2)
library(org.Hs.eg.db)
### This is for retrieving all genes annotated in the Herpes simplex virus list
# https://support.bioconductor.org/p/109871/
library(limma)
tab <- getGeneKEGGLinks(species="hsa")
tab$Symbol <- mapIds(org.Hs.eg.db, tab$GeneID,
                       column="SYMBOL", keytype="ENTREZID")
KEGG_05168 <- tab %>% dplyr::filter(PathwayID=="hsa05168")

# backGL <-read_csv("data/backGL.txt", 
#     col_types = cols(...1 = col_skip()))

# SETA_resgenes <- gost(query = intersect_genes$SYMBOL,
#                     organism = "hsapiens",
#                     significant = FALSE,
#                     ordered_query = TRUE,
#                     domain_scope = "custom",
#                     measure_underrepresentation = FALSE,
#                     evcodes = FALSE,
#                     user_threshold = 0.05,
#                     correction_method = c("fdr"),
#                     custom_bg = backGL$SYMBOL,
#                     sources=c("KEGG"))
# saveRDS(SETA_resgenes,"data/DEG-GO/SETA_resgenes.RDS")
SETA_resgenes <- readRDS("data/DEG-GO/SETA_resgenes.RDS")

Set_A_genes <- gostplot(SETA_resgenes, capped = FALSE, interactive = TRUE)
Set_A_genes

setA_table <- SETA_resgenes$result %>% 
  dplyr::select(c(source, term_id,
                  term_name,intersection_size, 
                   term_size, p_value))# %>% 

list_intersect_path <- KEGG_05168 %>% 
  filter(Symbol%in% intersect_genes$SYMBOL) 

list_intersect_path%>% 
  kable(., caption = "List of intersecting genes from the Herpes simplex KEGG pathway") %>%
  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 intersecting genes from the Herpes simplex KEGG pathway

GeneID	PathwayID	Symbol
100289635	hsa05168	ZNF605
10929	hsa05168	SRSF8
126070	hsa05168	ZNF440
146198	hsa05168	ZFP90
147657	hsa05168	ZNF480
147694	hsa05168	ZNF548
163087	hsa05168	ZNF383
22835	hsa05168	ZFP30
25799	hsa05168	ZNF324
27153	hsa05168	ZNF777
284323	hsa05168	ZNF780A
29110	hsa05168	TBK1
51385	hsa05168	ZNF589
5371	hsa05168	PML
5499	hsa05168	PPP1CA
578	hsa05168	BAK1
6426	hsa05168	SRSF1
6428	hsa05168	SRSF3
7748	hsa05168	ZNF195
7769	hsa05168	ZNF226
7773	hsa05168	ZNF230
84503	hsa05168	ZNF527
93134	hsa05168	ZNF561
9668	hsa05168	ZNF432

setA_table %>% 
  kable(., caption = "List of KEGG pathways enriched") %>%
  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 KEGG pathways enriched

source	term_id	term_name	intersection_size	term_size	p_value
KEGG	KEGG:05168	Herpes simplex virus 1 infection	19	415	0.0462437
KEGG	KEGG:00900	Terpenoid backbone biosynthesis	1	21	0.2135685
KEGG	KEGG:04144	Endocytosis	13	232	0.2135685
KEGG	KEGG:04213	Longevity regulating pathway - multiple species	4	55	0.2343037
KEGG	KEGG:03250	Viral life cycle - HIV-1	5	56	0.2343037
KEGG	KEGG:05031	Amphetamine addiction	3	49	0.2343037
KEGG	KEGG:03015	mRNA surveillance pathway	5	86	0.2343037
KEGG	KEGG:00565	Ether lipid metabolism	3	28	0.3935654
KEGG	KEGG:04146	Peroxisome	1	68	0.3935654
KEGG	KEGG:04218	Cellular senescence	5	145	0.3935654
KEGG	KEGG:04924	Renin secretion	2	50	0.3935654
KEGG	KEGG:05166	Human T-cell leukemia virus 1 infection	5	180	0.4307887
KEGG	KEGG:04710	Circadian rhythm	2	30	0.4307887
KEGG	KEGG:03013	Nucleocytoplasmic transport	5	99	0.4307887
KEGG	KEGG:03020	RNA polymerase	3	34	0.5006801
KEGG	KEGG:04330	Notch signaling pathway	1	51	0.5006801
KEGG	KEGG:05016	Huntington disease	4	256	0.5006801
KEGG	KEGG:05212	Pancreatic cancer	5	75	0.5006801
KEGG	KEGG:04110	Cell cycle	5	151	0.5006801
KEGG	KEGG:04022	cGMP-PKG signaling pathway	3	133	0.5006801
KEGG	KEGG:04666	Fc gamma R-mediated phagocytosis	1	75	0.5006801
KEGG	KEGG:00563	Glycosylphosphatidylinositol (GPI)-anchor biosynthesis	1	25	0.5006801
KEGG	KEGG:04720	Long-term potentiation	2	55	0.5006801
KEGG	KEGG:05034	Alcoholism	3	112	0.5150544
KEGG	KEGG:00970	Aminoacyl-tRNA biosynthesis	3	44	0.5150544
KEGG	KEGG:05170	Human immunodeficiency virus 1 infection	6	165	0.5150544
KEGG	KEGG:03060	Protein export	2	23	0.5150544
KEGG	KEGG:00564	Glycerophospholipid metabolism	1	76	0.5150544
KEGG	KEGG:05220	Chronic myeloid leukemia	1	75	0.5158959
KEGG	KEGG:05132	Salmonella infection	1	214	0.5466451
KEGG	KEGG:00310	Lysine degradation	2	59	0.5466451
KEGG	KEGG:04115	p53 signaling pathway	2	65	0.5466451
KEGG	KEGG:04910	Insulin signaling pathway	4	121	0.5494968
KEGG	KEGG:04152	AMPK signaling pathway	3	103	0.5520660
KEGG	KEGG:04140	Autophagy - animal	5	133	0.5520660
KEGG	KEGG:04145	Phagosome	1	99	0.5520660
KEGG	KEGG:04120	Ubiquitin mediated proteolysis	4	134	0.5520660
KEGG	KEGG:05418	Fluid shear stress and atherosclerosis	4	114	0.5520660
KEGG	KEGG:04215	Apoptosis - multiple species	2	30	0.5520660
KEGG	KEGG:05032	Morphine addiction	1	56	0.5520660
KEGG	KEGG:04114	Oocyte meiosis	2	108	0.5520660
KEGG	KEGG:04961	Endocrine and other factor-regulated calcium reabsorption	1	41	0.5520660
KEGG	KEGG:04370	VEGF signaling pathway	2	49	0.5520660
KEGG	KEGG:04931	Insulin resistance	3	95	0.5520660
KEGG	KEGG:04923	Regulation of lipolysis in adipocytes	1	41	0.5520660
KEGG	KEGG:04922	Glucagon signaling pathway	2	84	0.5520660
KEGG	KEGG:04921	Oxytocin signaling pathway	3	115	0.5520660
KEGG	KEGG:04919	Thyroid hormone signaling pathway	1	112	0.5520660
KEGG	KEGG:04728	Dopaminergic synapse	2	103	0.5520660
KEGG	KEGG:04613	Neutrophil extracellular trap formation	1	100	0.5520660
KEGG	KEGG:04211	Longevity regulating pathway	3	78	0.5520660
KEGG	KEGG:05030	Cocaine addiction	1	35	0.5520660
KEGG	KEGG:04660	T cell receptor signaling pathway	2	70	0.5520660
KEGG	KEGG:03008	Ribosome biogenesis in eukaryotes	3	75	0.5520660
KEGG	KEGG:05202	Transcriptional misregulation in cancer	1	128	0.5520660
KEGG	KEGG:05210	Colorectal cancer	4	84	0.5520660
KEGG	KEGG:05163	Human cytomegalovirus infection	5	176	0.5520660
KEGG	KEGG:05221	Acute myeloid leukemia	2	56	0.5520660
KEGG	KEGG:05169	Epstein-Barr virus infection	3	153	0.5520660
KEGG	KEGG:04024	cAMP signaling pathway	3	151	0.5520660
KEGG	KEGG:03450	Non-homologous end-joining	1	12	0.5520660
KEGG	KEGG:00983	Drug metabolism - other enzymes	2	44	0.5520660
KEGG	KEGG:05167	Kaposi sarcoma-associated herpesvirus infection	3	145	0.5520660
KEGG	KEGG:00770	Pantothenate and CoA biosynthesis	1	15	0.5520660
KEGG	KEGG:00562	Inositol phosphate metabolism	3	66	0.5520660
KEGG	KEGG:05410	Hypertrophic cardiomyopathy	2	73	0.5520660
KEGG	KEGG:05412	Arrhythmogenic right ventricular cardiomyopathy	1	63	0.5520660
KEGG	KEGG:05414	Dilated cardiomyopathy	1	78	0.5520660
KEGG	KEGG:05225	Hepatocellular carcinoma	6	145	0.5520660
KEGG	KEGG:05203	Viral carcinogenesis	1	162	0.5520660
KEGG	KEGG:05164	Influenza A	3	103	0.5528215
KEGG	KEGG:04071	Sphingolipid signaling pathway	3	107	0.6010714
KEGG	KEGG:04664	Fc epsilon RI signaling pathway	2	46	0.6037440
KEGG	KEGG:04658	Th1 and Th2 cell differentiation	1	53	0.6289663
KEGG	KEGG:04662	B cell receptor signaling pathway	1	56	0.6289663
KEGG	KEGG:03010	Ribosome	1	127	0.6289663
KEGG	KEGG:04070	Phosphatidylinositol signaling system	1	91	0.6289663
KEGG	KEGG:00480	Glutathione metabolism	1	44	0.6289663
KEGG	KEGG:04721	Synaptic vesicle cycle	1	51	0.6289663
KEGG	KEGG:05219	Bladder cancer	1	37	0.6289663
KEGG	KEGG:05235	PD-L1 expression and PD-1 checkpoint pathway in cancer	1	69	0.6289663
KEGG	KEGG:05162	Measles	2	98	0.6289663
KEGG	KEGG:04210	Apoptosis	1	118	0.6289663
KEGG	KEGG:05204	Chemical carcinogenesis - DNA adducts	1	23	0.6312264
KEGG	KEGG:04625	C-type lectin receptor signaling pathway	1	76	0.6312264
KEGG	KEGG:05165	Human papillomavirus infection	1	272	0.6312264
KEGG	KEGG:05160	Hepatitis C	2	115	0.6312264
KEGG	KEGG:04659	Th17 cell differentiation	1	64	0.6312264
KEGG	KEGG:05135	Yersinia infection	3	112	0.6312264
KEGG	KEGG:04936	Alcoholic liver disease	3	97	0.6312264
KEGG	KEGG:04150	mTOR signaling pathway	3	135	0.6312264
KEGG	KEGG:03022	Basal transcription factors	2	40	0.6312264
KEGG	KEGG:04012	ErbB signaling pathway	2	78	0.6312264
KEGG	KEGG:03050	Proteasome	2	42	0.6312264
KEGG	KEGG:05206	MicroRNAs in cancer	1	149	0.6312264
KEGG	KEGG:00982	Drug metabolism - cytochrome P450	1	21	0.6312264
KEGG	KEGG:04014	Ras signaling pathway	3	171	0.6324571
KEGG	KEGG:04724	Glutamatergic synapse	1	74	0.6324571
KEGG	KEGG:01522	Endocrine resistance	2	85	0.6324571
KEGG	KEGG:04350	TGF-beta signaling pathway	2	84	0.6324571
KEGG	KEGG:01524	Platinum drug resistance	1	65	0.6324571
KEGG	KEGG:00980	Metabolism of xenobiotics by cytochrome P450	1	26	0.6324571
KEGG	KEGG:04136	Autophagy - other	1	31	0.6350212
KEGG	KEGG:04650	Natural killer cell mediated cytotoxicity	1	63	0.6424831
KEGG	KEGG:04380	Osteoclast differentiation	1	87	0.6424831
KEGG	KEGG:05231	Choline metabolism in cancer	2	84	0.6480728
KEGG	KEGG:03420	Nucleotide excision repair	1	43	0.6480728
KEGG	KEGG:05223	Non-small cell lung cancer	1	67	0.6663121
KEGG	KEGG:04010	MAPK signaling pathway	2	240	0.6663121
KEGG	KEGG:05218	Melanoma	1	58	0.6663121
KEGG	KEGG:04750	Inflammatory mediator regulation of TRP channels	1	72	0.6663121
KEGG	KEGG:05215	Prostate cancer	1	86	0.6663121
KEGG	KEGG:04620	Toll-like receptor signaling pathway	2	60	0.6663121
KEGG	KEGG:04722	Neurotrophin signaling pathway	1	110	0.6663121
KEGG	KEGG:05214	Glioma	1	67	0.6663121
KEGG	KEGG:04622	RIG-I-like receptor signaling pathway	1	49	0.6663121
KEGG	KEGG:04137	Mitophagy - animal	1	70	0.6663121
KEGG	KEGG:04623	Cytosolic DNA-sensing pathway	1	42	0.6663121
KEGG	KEGG:03460	Fanconi anemia pathway	1	48	0.6663121
KEGG	KEGG:04530	Tight junction	2	137	0.6663121
KEGG	KEGG:04920	Adipocytokine signaling pathway	1	51	0.6697082
KEGG	KEGG:04310	Wnt signaling pathway	5	138	0.6801724
KEGG	KEGG:05200	Pathways in cancer	1	409	0.6812521
KEGG	KEGG:04914	Progesterone-mediated oocyte maturation	3	86	0.6812521
KEGG	KEGG:04913	Ovarian steroidogenesis	1	28	0.6822912
KEGG	KEGG:05417	Lipid and atherosclerosis	2	153	0.6855345
KEGG	KEGG:03040	Spliceosome	4	132	0.6855345
KEGG	KEGG:05152	Tuberculosis	1	106	0.6855345
KEGG	KEGG:04392	Hippo signaling pathway - multiple species	1	25	0.6909349
KEGG	KEGG:04360	Axon guidance	1	162	0.6909349
KEGG	KEGG:04714	Thermogenesis	6	195	0.6949445
KEGG	KEGG:04657	IL-17 signaling pathway	1	56	0.6949445
KEGG	KEGG:05222	Small cell lung cancer	1	87	0.6949445
KEGG	KEGG:04340	Hedgehog signaling pathway	1	49	0.6949445
KEGG	KEGG:05416	Viral myocarditis	1	40	0.7004481
KEGG	KEGG:05161	Hepatitis B	3	136	0.7004481
KEGG	KEGG:04611	Platelet activation	1	89	0.7004481
KEGG	KEGG:00140	Steroid hormone biosynthesis	1	19	0.7004481
KEGG	KEGG:04270	Vascular smooth muscle contraction	1	94	0.7004481
KEGG	KEGG:00600	Sphingolipid metabolism	1	44	0.7004481
KEGG	KEGG:04371	Apelin signaling pathway	2	106	0.7004481
KEGG	KEGG:04933	AGE-RAGE signaling pathway in diabetic complications	1	91	0.7004481
KEGG	KEGG:05017	Spinocerebellar ataxia	1	124	0.7004481
KEGG	KEGG:04217	Necroptosis	3	106	0.7004481
KEGG	KEGG:05224	Breast cancer	2	117	0.7096628
KEGG	KEGG:04630	JAK-STAT signaling pathway	3	88	0.7096628
KEGG	KEGG:04141	Protein processing in endoplasmic reticulum	4	161	0.7096628
KEGG	KEGG:04020	Calcium signaling pathway	1	157	0.7366591
KEGG	KEGG:04261	Adrenergic signaling in cardiomyocytes	1	128	0.7385556
KEGG	KEGG:04390	Hippo signaling pathway	1	134	0.7428813
KEGG	KEGG:05216	Thyroid cancer	1	35	0.7474086
KEGG	KEGG:04520	Adherens junction	1	67	0.7590965
KEGG	KEGG:05020	Prion disease	1	220	0.7590965
KEGG	KEGG:04080	Neuroactive ligand-receptor interaction	1	108	0.7590965
KEGG	KEGG:05131	Shigellosis	3	210	0.7590965
KEGG	KEGG:05134	Legionellosis	1	39	0.7590965
KEGG	KEGG:05022	Pathways of neurodegeneration - multiple diseases	2	385	0.7707889
KEGG	KEGG:01521	EGFR tyrosine kinase inhibitor resistance	1	74	0.7707889
KEGG	KEGG:05415	Diabetic cardiomyopathy	1	169	0.7785395
KEGG	KEGG:05321	Inflammatory bowel disease	1	22	0.7785395
KEGG	KEGG:01240	Biosynthesis of cofactors	2	115	0.7785395
KEGG	KEGG:00830	Retinol metabolism	1	16	0.7785395
KEGG	KEGG:05205	Proteoglycans in cancer	1	170	0.7785395
KEGG	KEGG:00520	Amino sugar and nucleotide sugar metabolism	1	46	0.7785395
KEGG	KEGG:00000	KEGG root term	2	5548	0.7785395
KEGG	KEGG:04151	PI3K-Akt signaling pathway	4	254	0.7785395
KEGG	KEGG:04934	Cushing syndrome	1	121	0.7785395
KEGG	KEGG:04068	FoxO signaling pathway	1	109	0.7785395
KEGG	KEGG:04510	Focal adhesion	1	177	0.7785395
KEGG	KEGG:05014	Amyotrophic lateral sclerosis	8	303	0.7990055
KEGG	KEGG:04932	Non-alcoholic fatty liver disease	1	131	0.8015859
KEGG	KEGG:04810	Regulation of actin cytoskeleton	1	179	0.8042126
KEGG	KEGG:04066	HIF-1 signaling pathway	1	89	0.8135646
KEGG	KEGG:04621	NOD-like receptor signaling pathway	1	117	0.8135646
KEGG	KEGG:00230	Purine metabolism	2	97	0.8135646
KEGG	KEGG:05213	Endometrial cancer	1	57	0.8135646
KEGG	KEGG:05010	Alzheimer disease	1	315	0.8135646
KEGG	KEGG:04912	GnRH signaling pathway	1	76	0.8135646
KEGG	KEGG:05100	Bacterial invasion of epithelial cells	1	74	0.8135646
KEGG	KEGG:04064	NF-kappa B signaling pathway	1	71	0.8148829
KEGG	KEGG:01232	Nucleotide metabolism	1	69	0.8149497
KEGG	KEGG:05217	Basal cell carcinoma	1	49	0.8297427
KEGG	KEGG:04668	TNF signaling pathway	1	87	0.8437401
KEGG	KEGG:05171	Coronavirus disease - COVID-19	1	162	0.8625927
KEGG	KEGG:03320	PPAR signaling pathway	1	50	0.8755330
KEGG	KEGG:04926	Relaxin signaling pathway	1	104	0.8755330
KEGG	KEGG:05226	Gastric cancer	1	117	0.8811497
KEGG	KEGG:04142	Lysosome	1	118	0.8839738
KEGG	KEGG:01100	Metabolic pathways	3	1146	0.8970300
KEGG	KEGG:05146	Amoebiasis	1	64	0.9038124
KEGG	KEGG:04062	Chemokine signaling pathway	1	113	0.9447847
KEGG	KEGG:05207	Chemical carcinogenesis - receptor activation	1	142	0.9507918
KEGG	KEGG:05130	Pathogenic Escherichia coli infection	2	150	0.9522431
KEGG	KEGG:05012	Parkinson disease	2	226	0.9522431
KEGG	KEGG:04015	Rap1 signaling pathway	1	164	0.9522431
KEGG	KEGG:04550	Signaling pathways regulating pluripotency of stem cells	1	104	0.9552542
KEGG	KEGG:05208	Chemical carcinogenesis - reactive oxygen species	1	186	0.9552542
KEGG	KEGG:04072	Phospholipase D signaling pathway	1	110	0.9806823

B set

# SETB_resgenes <- gost(query = intersect_genes$ENTREZID,
#                     organism = "hsapiens",
#                     significant = FALSE,
#                     ordered_query = TRUE,
#                     domain_scope = "custom",
#                     measure_underrepresentation = FALSE,
#                     evcodes = FALSE,
#                     user_threshold = 0.05,
#                     correction_method = c("fdr"),
#                     custom_bg = EPI508_list$ENTREZID,
#                     sources=c("KEGG"))
# saveRDS(SETB_resgenes,"data/DEG-GO/SETB_resgenes.RDS")
# SETB_resgenes <- readRDS("data/DEG-GO/SETB_resgenes.RDS")
# 
# Set_B_genes <- gostplot(SETB_resgenes, capped = FALSE, interactive = TRUE)
# Set_B_genes
# 
# setB_table <- SETB_resgenes$result %>% 
#   dplyr::select(c(source, term_id,
#                   term_name,intersection_size, 
#                    term_size, p_value))# %>% 
# 
# # list_intersect_path <- KEGG_05168 %>% 
# #   filter(Symbol%in% intersect_genes$SYMBOL) 
# 
# setB_table%>% 
#   kable(., caption = "No enrichment with this small background of KEGG pathway") %>%
#   kable_paper("striped", full_width = FALSE) %>%
#   kable_styling(
#     full_width = FALSE,
#     position = "left",
#     bootstrap_options = c("striped", "hover")
#   ) %>%
#   scroll_box(width = "100%", height = "400px")

Link to other pages here:

after_comments

GO analysis on after_comments data here

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] stats4    grid      stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] org.Hs.eg.db_3.17.0   AnnotationDbi_1.62.2  IRanges_2.34.1       
 [4] S4Vectors_0.38.2      Biobase_2.60.0        BiocGenerics_0.46.0  
 [7] gprofiler2_0.2.2      qvalue_2.32.0         RColorBrewer_1.1-3   
[10] edgeR_3.42.4          limma_3.56.2          biomaRt_2.56.1       
[13] ggVennDiagram_1.2.3   ComplexHeatmap_2.16.0 broom_1.0.5          
[16] kableExtra_1.3.4      sjmisc_2.8.9          scales_1.2.1         
[19] ggpubr_0.6.0          cowplot_1.1.1         ggsignif_0.6.4       
[22] lubridate_1.9.3       forcats_1.0.0         stringr_1.5.0        
[25] dplyr_1.1.3           purrr_1.0.2           readr_2.1.4          
[28] tidyr_1.3.0           tibble_3.2.1          ggplot2_3.4.4        
[31] tidyverse_2.0.0       workflowr_1.7.1      

loaded via a namespace (and not attached):
  [1] splines_4.3.1           later_1.3.1             bitops_1.0-7           
  [4] filelock_1.0.2          XML_3.99-0.15           lifecycle_1.0.4        
  [7] sf_1.0-14               rstatix_0.7.2           doParallel_1.0.17      
 [10] rprojroot_2.0.4         processx_3.8.2          lattice_0.22-5         
 [13] vroom_1.6.4             insight_0.19.6          crosstalk_1.2.0        
 [16] backports_1.4.1         magrittr_2.0.3          plotly_4.10.3          
 [19] sass_0.4.7              rmarkdown_2.25          jquerylib_0.1.4        
 [22] yaml_2.3.7              httpuv_1.6.12           DBI_1.1.3              
 [25] abind_1.4-5             zlibbioc_1.46.0         rvest_1.0.3            
 [28] RCurl_1.98-1.13         yulab.utils_0.1.0       rappdirs_0.3.3         
 [31] git2r_0.32.0            circlize_0.4.15         GenomeInfoDbData_1.2.10
 [34] units_0.8-4             svglite_2.1.2           codetools_0.2-19       
 [37] xml2_1.3.5              tidyselect_1.2.0        shape_1.4.6            
 [40] farver_2.1.1            matrixStats_1.1.0       BiocFileCache_2.8.0    
 [43] webshot_0.5.5           jsonlite_1.8.7          GetoptLong_1.0.5       
 [46] e1071_1.7-13            ellipsis_0.3.2          iterators_1.0.14       
 [49] systemfonts_1.0.5       foreach_1.5.2           tools_4.3.1            
 [52] progress_1.2.2          Rcpp_1.0.11             glue_1.6.2             
 [55] xfun_0.41               mgcv_1.9-0              GenomeInfoDb_1.36.4    
 [58] withr_2.5.2             fastmap_1.1.1           fansi_1.0.5            
 [61] callr_3.7.3             digest_0.6.33           mime_0.12              
 [64] timechange_0.2.0        R6_2.5.1                colorspace_2.1-0       
 [67] RSQLite_2.3.3           utf8_1.2.4              generics_0.1.3         
 [70] data.table_1.14.8       class_7.3-22            prettyunits_1.2.0      
 [73] httr_1.4.7              htmlwidgets_1.6.2       whisker_0.4.1          
 [76] pkgconfig_2.0.3         gtable_0.3.4            blob_1.2.4             
 [79] XVector_0.40.0          htmltools_0.5.7         carData_3.0-5          
 [82] clue_0.3-65             png_0.1-8               knitr_1.45             
 [85] rstudioapi_0.15.0       tzdb_0.4.0              reshape2_1.4.4         
 [88] rjson_0.2.21            nlme_3.1-163            curl_5.1.0             
 [91] proxy_0.4-27            cachem_1.0.8            GlobalOptions_0.1.2    
 [94] sjlabelled_1.2.0        RVenn_1.1.0             KernSmooth_2.23-22     
 [97] parallel_4.3.1          pillar_1.9.0            vctrs_0.6.4            
[100] promises_1.2.1          car_3.1-2               dbplyr_2.3.4           
[103] xtable_1.8-4            cluster_2.1.4           evaluate_0.23          
[106] magick_2.8.1            cli_3.6.1               locfit_1.5-9.8         
[109] compiler_4.3.1          rlang_1.1.2             crayon_1.5.2           
[112] labeling_0.4.3          classInt_0.4-10         ps_1.7.5               
[115] plyr_1.8.9              getPass_0.2-2           fs_1.6.3               
[118] stringi_1.7.12          viridisLite_0.4.2       munsell_0.5.0          
[121] Biostrings_2.68.1       lazyeval_0.2.2          Matrix_1.6-2           
[124] hms_1.1.3               bit64_4.0.5             shiny_1.7.5.1          
[127] KEGGREST_1.40.1         highr_0.10              memoise_2.0.1          
[130] bslib_0.5.1             bit_4.0.5