Last updated: 2023-11-09

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Knit directory: Cardiotoxicity/

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File Version Author Date Message
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
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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)

Version Author Date
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"))+
     ggpubr:: stat_cor(method="spearman",
                       # cor.coef.name="rho",
               aes(label = paste(..r.label.., ..p.label.., sep = "~`,\n`~")),
               color = "red",
               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)
   
  }

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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)

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))})

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") %>% 
  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))})

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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))})

Version Author Date
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))})

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))})

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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))})

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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))})

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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))})

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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)

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bd0342c reneeisnowhere 2023-11-07

corrlation 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))})

  #  all3hrdata <- lcpm_main %>%
  # full_join(., lcpm_trial_full, by= "ENTREZID") %>% 
  #   dplyr::select(ENTREZID,'3hr_0.5',start) %>% 
  #   column_to_rownames("ENTREZID") %>% 
  # cor(.,) 

barplots

GOI_genelist <- read.csv("output/GOI_genelist.txt")

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 "))
  
}  

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d12232a reneeisnowhere 2023-11-07

expression of trial RNA seq data


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] RColorBrewer_1.1-3    edgeR_3.42.4          limma_3.56.2         
 [4] biomaRt_2.56.1        ggVennDiagram_1.2.3   ComplexHeatmap_2.16.0
 [7] broom_1.0.5           kableExtra_1.3.4      sjmisc_2.8.9         
[10] scales_1.2.1          ggpubr_0.6.0          cowplot_1.1.1        
[13] ggsignif_0.6.4        lubridate_1.9.3       forcats_1.0.0        
[16] stringr_1.5.0         dplyr_1.1.3           purrr_1.0.2          
[19] readr_2.1.4           tidyr_1.3.0           tibble_3.2.1         
[22] ggplot2_3.4.4         tidyverse_2.0.0       workflowr_1.7.1      

loaded via a namespace (and not attached):
  [1] rstudioapi_0.15.0       jsonlite_1.8.7          shape_1.4.6            
  [4] magrittr_2.0.3          magick_2.8.1            farver_2.1.1           
  [7] rmarkdown_2.25          GlobalOptions_0.1.2     fs_1.6.3               
 [10] zlibbioc_1.46.0         vctrs_0.6.4             memoise_2.0.1          
 [13] RCurl_1.98-1.13         rstatix_0.7.2           webshot_0.5.5          
 [16] htmltools_0.5.7         progress_1.2.2          curl_5.1.0             
 [19] sass_0.4.7              KernSmooth_2.23-22      bslib_0.5.1            
 [22] htmlwidgets_1.6.2       plotly_4.10.3           cachem_1.0.8           
 [25] whisker_0.4.1           lifecycle_1.0.3         iterators_1.0.14       
 [28] pkgconfig_2.0.3         Matrix_1.6-1.1          sjlabelled_1.2.0       
 [31] R6_2.5.1                fastmap_1.1.1           GenomeInfoDbData_1.2.10
 [34] clue_0.3-65             digest_0.6.33           colorspace_2.1-0       
 [37] AnnotationDbi_1.62.2    S4Vectors_0.38.2        ps_1.7.5               
 [40] rprojroot_2.0.4         crosstalk_1.2.0         RSQLite_2.3.3          
 [43] labeling_0.4.3          filelock_1.0.2          fansi_1.0.5            
 [46] timechange_0.2.0        mgcv_1.9-0              httr_1.4.7             
 [49] abind_1.4-5             compiler_4.3.1          proxy_0.4-27           
 [52] bit64_4.0.5             withr_2.5.2             doParallel_1.0.17      
 [55] backports_1.4.1         carData_3.0-5           DBI_1.1.3              
 [58] highr_0.10              rappdirs_0.3.3          classInt_0.4-10        
 [61] rjson_0.2.21            units_0.8-4             tools_4.3.1            
 [64] httpuv_1.6.12           glue_1.6.2              callr_3.7.3            
 [67] nlme_3.1-163            promises_1.2.1          sf_1.0-14              
 [70] getPass_0.2-2           cluster_2.1.4           generics_0.1.3         
 [73] gtable_0.3.4            tzdb_0.4.0              class_7.3-22           
 [76] data.table_1.14.8       hms_1.1.3               xml2_1.3.5             
 [79] car_3.1-2               utf8_1.2.4              XVector_0.40.0         
 [82] BiocGenerics_0.46.0     foreach_1.5.2           pillar_1.9.0           
 [85] yulab.utils_0.1.0       later_1.3.1             splines_4.3.1          
 [88] circlize_0.4.15         lattice_0.22-5          BiocFileCache_2.8.0    
 [91] bit_4.0.5               tidyselect_1.2.0        locfit_1.5-9.8         
 [94] Biostrings_2.68.1       knitr_1.45              git2r_0.32.0           
 [97] IRanges_2.34.1          svglite_2.1.2           stats4_4.3.1           
[100] xfun_0.41               Biobase_2.60.0          matrixStats_1.0.0      
[103] stringi_1.7.12          lazyeval_0.2.2          yaml_2.3.7             
[106] evaluate_0.23           codetools_0.2-19        RVenn_1.1.0            
[109] cli_3.6.1               systemfonts_1.0.5       munsell_0.5.0          
[112] processx_3.8.2          jquerylib_0.1.4         Rcpp_1.0.11            
[115] GenomeInfoDb_1.36.4     dbplyr_2.4.0            png_0.1-8              
[118] XML_3.99-0.15           parallel_4.3.1          ellipsis_0.3.2         
[121] blob_1.2.4              prettyunits_1.2.0       bitops_1.0-7           
[124] viridisLite_0.4.2       e1071_1.7-13            insight_0.19.6         
[127] crayon_1.5.2            GetoptLong_1.0.5        rlang_1.1.2            
[130] KEGGREST_1.40.1         rvest_1.0.3