Variance

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Last updated: 2023-07-20

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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/variance_scrip.Rmd) and HTML (docs/variance_scrip.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	3ea3f28	reneeisnowhere	2023-07-20	adding new analysis
html	e48b9f5	reneeisnowhere	2023-07-14	Build site.
Rmd	fcc6805	reneeisnowhere	2023-07-14	adding link to variance scrip for Michelle

---

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggpubr)
library(ggsignif)
library(gprofiler2)

initial calculations and data loading

by individual

by drug

plot by drug

mean_vardrug1 <- read.csv("data/mean_vardrug1.csv", row.names = 1)


drug_frame<- mean_vardrug1 %>% 
  rownames_to_column(var = "entrezid") %>% 
  pivot_longer(cols = mean.Da.3:var.Ve.24, names_to = "short", values_to = "values") %>% 
  separate(short, into=c("calc","treatment","time")) %>% 
  mutate(treatment= factor(treatment, levels = c("Do","Ep","Da","Mi","Tr","Ve"),labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>% 
  mutate(time= factor(time, levels = c("3","24"), labels= c("3 hours","24 hours"))) %>% 
  group_by(treatment, time, calc) %>% 
  as.data.frame



drug_frame %>% 
  filter(calc!="mean") %>% 
  ggplot(., aes(x= treatment, y=values))+
  geom_boxplot()+
  geom_signif(comparisons =list(c("DOX","VEH"),
                                c("EPI","VEH"),
                                c("DNR","VEH"),
                                c("MTX","VEH"),
                                c("TRZ","VEH")),
              test= "t.test",
              map_signif_level=FALSE,
              textsize =4,
              step_increase = 0.1)+
  facet_wrap(~time)+
  ggtitle("var")+
  ylim(0,0.5)

Warning: Removed 23214 rows containing non-finite values (`stat_boxplot()`).

Warning: Removed 23214 rows containing non-finite values (`stat_signif()`).

Warning: Removed 30 rows containing missing values (`geom_signif()`).

drug_frame %>% 
  filter(calc!="mean") %>% 
  ggplot(., aes(x= treatment, y=values))+
  geom_boxplot()+
  geom_signif(comparisons =list(c("DOX","VEH"),
                                c("EPI","VEH"),
                                c("DNR","VEH"),
                                c("MTX","VEH"),
                                c("TRZ","VEH")),
              test= "t.test",
              map_signif_level=FALSE,
              textsize =4,
              step_increase = 0.1)+
  facet_wrap(~time)+
  ggtitle("var")#+

  # ylim(0,0.5)

drug_frame %>% 
  filter(calc!="var") %>% 
  ggplot(., aes(x= treatment, y=values))+
  geom_boxplot()+
  geom_signif(comparisons =list(c("TRZ","VEH"),
                                c("MTX","VEH"),
                                c("DNR","VEH"),
                                c("EPI","VEH"),
                                c("DOX","VEH")),
              test= "t.test",
              map_signif_level=FALSE,
              textsize =4,
              step_increase = 0.05)+
  facet_wrap(.~time)+
  ggtitle("Means")+
  ylim(0,18)

Warning: Removed 3297 rows containing non-finite values (`stat_boxplot()`).

Warning: Removed 3297 rows containing non-finite values (`stat_signif()`).

mean_vardrug1 <- read.csv("data/mean_vardrug1.csv",row.names = 1)
my_exp_genes <- read.csv("data/backGL.txt")
GTEx_genes <- read.csv("data/GTEx_gene_list.csv",row.names = 1)
GTEx <- intersect(GTEx_genes$entrezgene_id,my_exp_genes$ENTREZID)
knowles5 <-readRDS("output/knowles5.RDS")


drug_frame2<- mean_vardrug1 %>% 
  rownames_to_column(var = "entrezid") %>% 
  filter(entrezid %in%knowles5$entrezgene_id) %>% 
  pivot_longer(cols = mean.Da.3:var.Ve.24, names_to = "short", values_to = "values") %>% 
  separate(short, into=c("calc","treatment","time")) %>% 
  mutate(treatment= factor(treatment, levels = c("Do","Ep","Da","Mi","Tr","Ve"),labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>% 
  mutate(time= factor(time, levels = c("3","24"), labels= c("3 hours","24 hours"))) %>% 
  group_by(treatment, time, calc) %>% 
  as.data.frame

ggplot(drug_frame2, aes(x= treatment, y=values))+
  geom_boxplot()+
  geom_signif(comparisons =list(c("DOX","VEH"),
                                c("EPI","VEH"),
                                c("DNR","VEH"),
                                c("MTX","VEH"),
                                c("TRZ","VEH")),
              test= "t.test",
              map_signif_level=TRUE,
              textsize =4,
              step_increase = 0.1)+
  # stat_compare_means(method= "anova", label.y=15, label.x =2)+
  facet_wrap(time~calc, scales = "free_y")

  drug_frame2 %>% 
  filter(calc!="mean") %>% 
  ggplot(., aes(x= treatment, y=values))+
  geom_boxplot()+
  geom_signif(comparisons =list(c("DOX","VEH"),
                                c("EPI","VEH"),
                                c("DNR","VEH"),
                                c("MTX","VEH"),
                                c("TRZ","VEH")),
              test= "t.test",
              map_signif_level=TRUE,
              textsize =4,
              step_increase = 0.1)+
  facet_wrap(~time)+
  ggtitle("var using response eGenes")+
  ylim(0,1.0)

Warning: Removed 146 rows containing non-finite values (`stat_boxplot()`).

Warning: Removed 146 rows containing non-finite values (`stat_signif()`).

Warning: Removed 30 rows containing missing values (`geom_signif()`).

drug_frame2 %>% 
  filter(calc!="var") %>% 
  ggplot(., aes(x= treatment, y=values))+
  geom_boxplot()+
  geom_signif(comparisons =list(c("TRZ","VEH"),
                                c("MTX","VEH"),
                                c("DNR","VEH"),
                                c("EPI","VEH"),
                                c("DOX","VEH")),
              test= "t.test",
              map_signif_level=TRUE,
              textsize =4,
              step_increase = 0.05)+
  facet_wrap(.~time)+
  ggtitle("Means using response eGenes")+
  ylim(0,18)

Warning: Removed 9 rows containing non-finite values (`stat_boxplot()`).

Warning: Removed 9 rows containing non-finite values (`stat_signif()`).

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

DNR.VEH.24 <- organized_drugframe %>% 
  select(c(starts_with("Da")&ends_with("24h")),
         c(starts_with("Ve")&ends_with("24h"))) %>%
  rowwise() %>% 
  mutate(data=list(var.test(c_across(starts_with("Da")),c_across(starts_with("Ve"))))) 
DOX.VEH.24 <- organized_drugframe %>% 
  select(c(starts_with("Do")&ends_with("24h")),
         c(starts_with("Ve")&ends_with("24h"))) %>%
 rowwise() %>% 
  mutate(data=list(var.test(c_across(starts_with("Do")),c_across(starts_with("Ve")))))

EPI.VEH.24 <- organized_drugframe %>% 
  select(c(starts_with("Ep")&ends_with("24h")),
         c(starts_with("Ve")&ends_with("24h"))) %>%
 rowwise() %>% 
  mutate(data=list(var.test(c_across(starts_with("Ep")),c_across(starts_with("Ve")))))

MTX.VEH.24 <- organized_drugframe %>% 
  select(c(starts_with("Mi")&ends_with("24h")),
         c(starts_with("Ve")&ends_with("24h"))) %>%
  rowwise() %>% 
  mutate(data=list(var.test(c_across(starts_with("Mi")),c_across(starts_with("Ve")))))
TRZ.VEH.24 <- organized_drugframe %>% 
  select(c(starts_with("Tr")&ends_with("24h")),
         c(starts_with("Ve")&ends_with("24h"))) %>%
  rowwise() %>% 
  mutate(data=list(var.test(c_across(starts_with("Tr")),c_across(starts_with("Ve")))))

DNR.VEH.3 <- organized_drugframe %>% 
  select(c(starts_with("Da")&ends_with("3h")),
         c(starts_with("Ve")&ends_with("3h"))) %>%
  rowwise() %>% 
  mutate(data=list(var.test(c_across(starts_with("Da")),c_across(starts_with("Ve")))))
DOX.VEH.3 <- organized_drugframe %>% 
  select(c(starts_with("Do")&ends_with("3h")),
         c(starts_with("Ve")&ends_with("3h"))) %>%
  rowwise() %>% 
  mutate(data=list(var.test(c_across(starts_with("Do")),c_across(starts_with("Ve"))))) 
EPI.VEH.3 <- organized_drugframe %>% 
  select(c(starts_with("Ep")&ends_with("3h")),
         c(starts_with("Ve")&ends_with("3h"))) %>%
  rowwise() %>% 
  mutate(data=list(var.test(c_across(starts_with("Ep")),c_across(starts_with("Ve")))))
MTX.VEH.3 <- organized_drugframe %>% 
  select(c(starts_with("Mi")&ends_with("3h")),
         c(starts_with("Ve")&ends_with("3h"))) %>%
  rowwise() %>% 
  mutate(data=list(var.test(c_across(starts_with("Mi")),c_across(starts_with("Ve")))))


TRZ.VEH.3 <- organized_drugframe %>% 
  rownames_to_column(.id = "gene") %>% 
  rowwise() %>% 
  select(c(starts_with("Tr")&ends_with("3h")),
         c(starts_with("Ve")&ends_with("3h"))) %>%
  mutate(data=list(var.test(c_across(starts_with("Tr")),c_across(starts_with("Ve")))))

Var_test_list24 <- list(DNR.VEH.24,DOX.VEH.24,EPI.VEH.24,MTX.VEH.24,TRZ.VEH.24)
                      
names(Var_test_list24) <- c('DNR.VEH.24','DOX.VEH.24','EPI.VEH.24','MTX.VEH.24','TRZ.VEH.24')
                    
  
 saveRDS(Var_test_list24,"data/Var_test_list24.RDS")   
 
 
 Var_test_list3 <- list(DNR.VEH.3,DOX.VEH.3,EPI.VEH.3,MTX.VEH.3,TRZ.VEH.3)
                      
names(Var_test_list3) <- c('DNR.VEH.3','DOX.VEH.3','EPI.VEH.3','MTX.VEH.3','TRZ.VEH.3')
                     saveRDS(Var_test_list3,"data/Var_test_list3.RDS") 

organized_drugframe <- read.csv("data/organized_drugframe.csv",
                                row.names = 1)
Var_test_list3 <- readRDS("data/Var_test_list3.RDS")
set3 <- names(Var_test_list3)
shorterlist <- Var_test_list3[[1]][[13]]
names(shorterlist) <- rownames(organized_drugframe)

framefun <- data.frame(ENTREZID=rownames(organized_drugframe))
for (i in 1:length(set3)){
  a <- set3[i]
    s_list<- Var_test_list3[[i]][[13]]
  names(s_list) <- rownames(organized_drugframe)
  hold<- map_df(s_list, ~as.data.frame(.x$p.value), .id="ENTREZID")
   framefun[paste0(a)] <- hold$`.x$p.value`
 
}
Var_test_list24 <- readRDS("data/Var_test_list24.RDS")
set24 <- names(Var_test_list24) 

framefun24 <- data.frame(ENTREZID=rownames(organized_drugframe))
for (i in 1:length(set24)){
  a <- set24[i]
    s_list<- Var_test_list24[[i]][[13]]
  names(s_list) <- rownames(organized_drugframe)
  hold<- map_df(s_list, ~as.data.frame(.x$p.value), .id="ENTREZID")
   framefun24[paste0(a)] <- hold$`.x$p.value`
 
}

framefun %>% 
  pivot_longer(col=!ENTREZID,names_to= "combo", values_to = "p.value") %>% 
  separate(combo, into= c("drug", NA,"time")) %>%
  mutate(drug = factor(drug, levels = c("DOX", "EPI", "DNR","MTX", "TRZ"))) %>% 
  ggplot(.,aes(x=p.value,col=drug,fill=drug))+
  geom_histogram()+
  facet_wrap(~drug)+
  ggtitle("3 hour var.test p.value")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

framefun24 %>% 
  pivot_longer(col=!ENTREZID,names_to= "combo", values_to = "p.value") %>% 
  separate(combo, into= c("drug", NA,"time")) %>%
  mutate(drug = factor(drug, levels = c("DOX", "EPI", "DNR","MTX", "TRZ"))) %>% 
  ggplot(.,aes(x=p.value,col=drug,fill=drug))+
  geom_histogram()+
  facet_wrap(~drug)+
  ggtitle("24 hour var.test p.value")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

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

Warning: The following named parsers don't match the column names: ...1

var3 <- list <- framefun %>% 
  pivot_longer(col=!ENTREZID,names_to= "combo", values_to = "p.value") %>% 
  separate(combo, into= c("drug", NA,"time")) %>%
  mutate(drug = factor(drug, levels = c("DOX", "EPI", "DNR","MTX", "TRZ"))) %>% 
  filter(p.value<0.05)

var24 <- list <- framefun24 %>% 
  pivot_longer(col=!ENTREZID,names_to= "combo", values_to = "p.value") %>% 
  separate(combo, into= c("drug", NA,"time")) %>%
  mutate(drug = factor(drug, levels = c("DOX", "EPI", "DNR","MTX", "TRZ"))) %>% 
  filter(p.value<0.05)

DOX_24_var <- var24 %>% filter(drug=="DOX") %>% select(ENTREZID)#1089 
EPI_24_var <- var24 %>% filter(drug=="EPI")%>% select(ENTREZID)#2211
DNR_24_var <- var24 %>% filter(drug=="DNR")%>% select(ENTREZID)#928
MTX_24_var <- var24 %>% filter(drug=="MTX")%>% select(ENTREZID)#269
TRZ_24_var <- var24 %>% filter(drug=="TRZ")%>% select(ENTREZID)#157

geneset_24 <- list(DOX_24_var$ENTREZID, EPI_24_var$ENTREZID, DNR_24_var$ENTREZID, MTX_24_var$ENTREZID, TRZ_24_var$ENTREZID)
names(geneset_24) <- c("DOX_24_var","EPI_24_var","DNR_24_var" ,"MTX_24_var","TRZ_24_var")

DOX_3_var <- var3 %>% filter(drug=="DOX")%>% select(ENTREZID)#346
EPI_3_var <- var3 %>% filter(drug=="EPI")%>% select(ENTREZID)#332
DNR_3_var <- var3 %>% filter(drug=="DNR")%>% select(ENTREZID)#385
MTX_3_var <- var3 %>% filter(drug=="MTX")%>% select(ENTREZID)#277
TRZ_3_var <- var3 %>% filter(drug=="TRZ")%>% select(ENTREZID)#262

geneset_3 <- list(DOX_3_var$ENTREZID,EPI_3_var$ENTREZID,DNR_3_var$ENTREZID ,MTX_3_var$ENTREZID,TRZ_3_var$ENTREZID)
names(geneset_3) <- c("DOX_3_var","EPI_3_var","DNR_3_var" ,"MTX_3_var","TRZ_3_var")

Venn

library(paletteer)
library(ggVennDiagram)


ggVennDiagram::ggVennDiagram(geneset_24,
              category.names = c("DOX\nn = 1089 ",
                                 "EPI\nn = 2211\n",
                                 "DNR\nn = 928",
                                 "MTX\nn = 269", 
                                "TRZ\nn = 157"),
              show_intersect = FALSE,
              set_color = "black",
              category_size = c(6,6,6,6),
              label = "count",
              label_percent_digit = 1,
              label_size = 4,
              label_alpha = 0,
              label_color = "black",
              edge_lty = "solid", set_size = 4.5)+
  scale_x_continuous(expand = expansion(mult = .3))+
  scale_y_continuous(expand = expansion(mult = .2))+
  scale_color_paletteer_d(palette = "fishualize::Bodianus_pulchellus")+
  scale_fill_distiller(palette="Spectral", direction = -1)+
  labs(title = "24 hour var genes p. value <0.05",
       caption = paste("n =", length(unique(var24$ENTREZID)),"genes"))+
  
  theme(plot.title = element_text(size = rel(1.6), hjust = 0.5, vjust =1))

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggVennDiagram_1.2.2 paletteer_1.5.0     gprofiler2_0.2.2   
 [4] ggsignif_0.6.4      ggpubr_0.6.0        lubridate_1.9.2    
 [7] forcats_1.0.0       stringr_1.5.0       dplyr_1.1.2        
[10] purrr_1.0.1         readr_2.1.4         tidyr_1.3.0        
[13] tibble_3.2.1        ggplot2_3.4.2       tidyverse_2.0.0    
[16] workflowr_1.7.0    

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0   viridisLite_0.4.2  farver_2.1.1       fastmap_1.1.1     
 [5] lazyeval_0.2.2     promises_1.2.0.1   digest_0.6.33      timechange_0.2.0  
 [9] lifecycle_1.0.3    sf_1.0-14          processx_3.8.1     magrittr_2.0.3    
[13] compiler_4.3.1     rlang_1.1.1        sass_0.4.6         tools_4.3.1       
[17] utf8_1.2.3         yaml_2.3.7         data.table_1.14.8  knitr_1.43        
[21] labeling_0.4.2     htmlwidgets_1.6.2  bit_4.0.5          classInt_0.4-9    
[25] RColorBrewer_1.1-3 KernSmooth_2.23-22 abind_1.4-5        withr_2.5.0       
[29] grid_4.3.1         fansi_1.0.4        git2r_0.32.0       e1071_1.7-13      
[33] colorspace_2.1-0   scales_1.2.1       cli_3.6.1          rmarkdown_2.23    
[37] crayon_1.5.2       generics_0.1.3     rstudioapi_0.15.0  httr_1.4.6        
[41] tzdb_0.4.0         DBI_1.1.3          cachem_1.0.8       proxy_0.4-27      
[45] parallel_4.3.1     vctrs_0.6.3        jsonlite_1.8.7     carData_3.0-5     
[49] car_3.1-2          callr_3.7.3        hms_1.1.3          bit64_4.0.5       
[53] rstatix_0.7.2      plotly_4.10.2      jquerylib_0.1.4    units_0.8-2       
[57] glue_1.6.2         rematch2_2.1.2     ps_1.7.5           stringi_1.7.12    
[61] gtable_0.3.3       RVenn_1.1.0        later_1.3.1        prismatic_1.1.1   
[65] munsell_0.5.0      pillar_1.9.0       htmltools_0.5.5    R6_2.5.1          
[69] rprojroot_2.0.3    vroom_1.6.3        evaluate_0.21      highr_0.10        
[73] backports_1.4.1    broom_1.0.5        httpuv_1.6.11      bslib_0.5.0       
[77] class_7.3-22       Rcpp_1.0.11        whisker_0.4.1      xfun_0.39         
[81] fs_1.6.2           getPass_0.2-2      pkgconfig_2.0.3