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Last updated: 2023-11-27

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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.

---

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	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

library(ggsignif)
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

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=list(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")
saveRDS(store_var, "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
  )

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)

library(qvalue)


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

 plot(estDOXk)

summary(estDOXk) 

Call:
qvalue(p = p_list)

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("pvalues"=estDOXk$pvalues,"qvalues"=estDOXk$qvalues,"lfdr"= estDOXk$lfdr)  
 colnames(knowlesvar) <- c("pvalues", "qvalues","lfdr")
intersecting_K <- knowlesvar %>% 
  filter(lfdr<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 = rownames(intersecting_K), mart = ensembl)

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

[1] 299

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

intersect_genes %>% 
kable(.,caption = "EPI Highly variable 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

ENTREZID	SYMBOL
49856	WRAP73
23463	ICMT
5195	PEX14
23207	PLEKHM2
79363	CPLANE2
55160	ARHGEF10L
55616	ASAP3
57095	PITHD1
84065	TMEM222
23673	STX12
56063	TMEM234
127544	RNF19B
113444	SMIM12
27095	TRAPPC3
112950	MED8
9670	IPO13
387338	NSUN4
8543	LMO4
64858	DCLRE1B
128077	LIX1L
6944	VPS72
65005	MRPL9
11266	DUSP12
5279	PIGC
83479	DDX59
134	ADORA1
163859	SDE2
5664	PSEN2
65094	JMJD4
126731	CCSAP
22796	COG2
79723	SUV39H2
253430	IPMK
26091	HERC4
219738	FAM241B
11319	ECD
5532	PPP3CB
79933	SYNPO2L
118924	FRA10AC1
10360	NPM3
9937	DCLRE1A
9184	BUB3
161	AP2A2
10612	TRIM3
65975	STK33
113174	SAAL1
627	BDNF
79797	ZNF408
10978	CLP1
55048	VPS37C
144097	SPINDOC
57410	SCYL1
10432	RBM14
338692	ANKRD13D
5883	RAD9A
116985	ARAP1
282679	AQP11
51585	PCF11
60492	CCDC90B
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
1407	CRY1
51228	GLTP
400073	C12orf76
5564	PRKAB1
51499	TRIAP1
387893	KMT5A
11066	SNRNP35
5901	RAN
283537	SLC46A3
2963	GTF2F2
337867	UBAC2
3621	ING1
253959	RALGAPA1
123016	TTC8
51527	GSKIP
2972	BRF1
22893	BAHD1
9325	TRIP4
5371	PML
60490	PPCDC
84219	WDR24
23059	CLUAP1
2072	ERCC4
91949	COG7
29070	CCDC113
8824	CES2
1874	E2F4
6560	SLC12A4
146198	ZFP90
5119	CHMP1A
64359	NXN
5048	PAFAH1B1
9135	RABEP1
57336	ZNF287
79736	TEFM
55813	UTP6
54475	NLE1
5193	PEX12
22794	CASC3
3292	HSD17B1
10614	HEXIM1
114881	OSBPL7
8405	SPOP
10237	SLC35B1
81558	FAM117A
55316	RSAD1
8161	COIL
6426	SRSF1
54903	MKS1
284161	GDPD1
57508	INTS2
84923	FAM104A
55028	C17orf80
6730	SRP68
9489	PGS1
9775	EIF4A3
57521	RPTOR
79643	CHMP6
5881	RAC3
55364	IMPACT
54531	MIER2
126308	MOB3A
29985	SLC39A3
51343	FZR1
6455	SH3GL1
5609	MAP2K7
79603	CERS4
93134	ZNF561
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
9668	ZNF432
147657	ZNF480
112724	RDH13
163033	ZNF579
147694	ZNF548
100293516	ZNF587B
25799	ZNF324
55006	TRMT61B
253635	GPATCH11
92906	HNRNPLL
8491	MAP4K3
57504	MTA3
53335	BCL11A
5861	RAB1A
27332	ZNF638
129303	TMEM150A
5903	RANBP2
10254	STAM2
79828	METTL8
80067	DCAF17
129831	RBM45
3628	INPP1
6775	STAT4
9330	GTF3C3
57404	CYP20A1
377007	KLHL30
4735	SEPTIN2
80023	NRSN2
55317	AP5S1
64412	GZF1
51230	PHF20
25980	AAR2
10904	BLCAP
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
131601	TPRA1
7879	RAB7A
51122	COMMD2
86	ACTL6A
90407	TMEM41A
1487	CTBP1
7469	NELFA
10606	PAICS
92597	MOB1B
266812	NAP1L5
56916	SMARCAD1
55212	BBS7
90826	PRMT9
4750	NEK1
55100	WDR70
55814	BDP1
167153	TENT2
9765	ZFYVE16
55781	RIOK2
90355	MACIR
153443	SRFBP1
8572	PDLIM4
202052	DNAJC18
23438	HARS2
10826	FAXDC2
9443	MED7
5917	RARS1
8899	PRPF4B
10473	HMGN4
7746	ZSCAN9
8449	DHX16
57827	C6orf47
578	BAK1
5467	PPARD
6428	SRSF3
9477	MED20
25821	MTO1
57226	LYRM2
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
23087	TRIM35
90362	FAM110B
55824	PAG1
55656	INTS8
51123	ZNF706
51105	PHF20L1
203062	TSNARE1
55958	KLHL9
54840	APTX
55035	NOL8
5998	RGS3
399665	FAM102A
84885	ZDHHC12
5900	RALGDS
6837	MED22
57109	REXO4
92715	DPH7
23708	GSPT2
29934	SNX12
139596	UPRT
64860	ARMCX5

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

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

other attached packages:
 [1] qvalue_2.32.0         RColorBrewer_1.1-3    edgeR_3.42.4         
 [4] limma_3.56.2          biomaRt_2.56.1        ggVennDiagram_1.2.3  
 [7] ComplexHeatmap_2.16.0 broom_1.0.5           kableExtra_1.3.4     
[10] sjmisc_2.8.9          scales_1.2.1          ggpubr_0.6.0         
[13] cowplot_1.1.1         ggsignif_0.6.4        lubridate_1.9.3      
[16] forcats_1.0.0         stringr_1.5.0         dplyr_1.1.3          
[19] purrr_1.0.2           readr_2.1.4           tidyr_1.3.0          
[22] tibble_3.2.1          ggplot2_3.4.4         tidyverse_2.0.0      
[25] 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] BiocGenerics_0.46.0     RCurl_1.98-1.13         yulab.utils_0.1.0      
 [31] rappdirs_0.3.3          git2r_0.32.0            circlize_0.4.15        
 [34] GenomeInfoDbData_1.2.10 IRanges_2.34.1          S4Vectors_0.38.2       
 [37] units_0.8-4             svglite_2.1.2           codetools_0.2-19       
 [40] xml2_1.3.5              tidyselect_1.2.0        shape_1.4.6            
 [43] farver_2.1.1            matrixStats_1.1.0       stats4_4.3.1           
 [46] BiocFileCache_2.8.0     webshot_0.5.5           jsonlite_1.8.7         
 [49] GetoptLong_1.0.5        e1071_1.7-13            ellipsis_0.3.2         
 [52] iterators_1.0.14        systemfonts_1.0.5       foreach_1.5.2          
 [55] tools_4.3.1             progress_1.2.2          Rcpp_1.0.11            
 [58] glue_1.6.2              xfun_0.41               mgcv_1.9-0             
 [61] GenomeInfoDb_1.36.4     withr_2.5.2             fastmap_1.1.1          
 [64] fansi_1.0.5             callr_3.7.3             digest_0.6.33          
 [67] timechange_0.2.0        R6_2.5.1                colorspace_2.1-0       
 [70] RSQLite_2.3.3           utf8_1.2.4              generics_0.1.3         
 [73] data.table_1.14.8       class_7.3-22            prettyunits_1.2.0      
 [76] httr_1.4.7              htmlwidgets_1.6.2       whisker_0.4.1          
 [79] pkgconfig_2.0.3         gtable_0.3.4            blob_1.2.4             
 [82] XVector_0.40.0          htmltools_0.5.7         carData_3.0-5          
 [85] clue_0.3-65             Biobase_2.60.0          png_0.1-8              
 [88] knitr_1.45              rstudioapi_0.15.0       tzdb_0.4.0             
 [91] reshape2_1.4.4          rjson_0.2.21            nlme_3.1-163           
 [94] curl_5.1.0              proxy_0.4-27            cachem_1.0.8           
 [97] GlobalOptions_0.1.2     sjlabelled_1.2.0        RVenn_1.1.0            
[100] KernSmooth_2.23-22      parallel_4.3.1          AnnotationDbi_1.62.2   
[103] pillar_1.9.0            vctrs_0.6.4             promises_1.2.1         
[106] car_3.1-2               dbplyr_2.3.4            cluster_2.1.4          
[109] evaluate_0.23           magick_2.8.1            cli_3.6.1              
[112] locfit_1.5-9.8          compiler_4.3.1          rlang_1.1.2            
[115] crayon_1.5.2            labeling_0.4.3          classInt_0.4-10        
[118] ps_1.7.5                plyr_1.8.9              getPass_0.2-2          
[121] fs_1.6.3                stringi_1.7.12          viridisLite_0.4.2      
[124] munsell_0.5.0           Biostrings_2.68.1       lazyeval_0.2.2         
[127] Matrix_1.6-2            hms_1.1.3               bit64_4.0.5            
[130] KEGGREST_1.40.1         highr_0.10              memoise_2.0.1          
[133] bslib_0.5.1             bit_4.0.5