255 lines
8.3 KiB
R
255 lines
8.3 KiB
R
#!/usr/bin/Rscript
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getwd()
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setwd('~/git/mosaic_2020/')
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getwd()
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############################################################
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# TASK: summary stats of mediators by time and outcome
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############################################################
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# load libraries and packages
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source("../Header_TT.R")
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library(tidyverse)
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library(ggpubr)
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library(rstatix)
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library(Hmisc)
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library(qwraps2)
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#==========================================================
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#datadir = "~/git/covid19/Data"
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source("mosaic_bmi_data_extraction_v2.R")
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#=============
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# Input
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#=============
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#infile_icu_wf = paste0(datadir,"/icu_covid_wf.csv")
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#infile_icu_lf = paste0(datadir,"/icu_covid_lf.csv")
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# version 2
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#infile_icu_wf = paste0(datadir,"/icu_covid_wf_v3.csv")
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#infile_icu_lf = paste0(datadir,"/icu_covid_lf_v3.csv")
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#npa_adults_lf
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#=============
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# Output
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#=============
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outdir = paste0("~/git/mosaic_2020/version1")
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# unpaired analysis
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stats_time_unpaired = paste0(outdir, "stats_unpaired_npa.csv")
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#%%========================================================
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# read file
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#wf_data = read.csv(infile_icu_wf , stringsAsFactors = F)
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#dim(wf_data)
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#lf_data = read.csv(infile_icu_lf , stringsAsFactors = F)
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#dim(lf_data)
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#%%========================================================
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# data assignment for stats
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#wf = wf_data
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#lf = lf_data
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wf = npa_df_adults_clean
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lf = npa_adults_lf
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table(lf$timepoint)
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lf$timepoint = paste0("t", lf$timepoint)
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########################################################################
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# Unpaired stats at each timepoint b/w groups: wilcoxon UNpaired analysis with correction
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#######################################################################
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# with adjustment: fdr and BH are identical
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my_adjust_method = "BH"
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#==============
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# unpaired: t1
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#==============
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lf_t1 = lf[lf$timepoint == "t1",]
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sum(is.na(lf_t1$value))
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foo = lf_t1[which(is.na(lf_t1$value)),]
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ci = which(is.na(lf_t1$value))
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#lf_t1_comp = lf_t1[-ci,]
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lf_t1_comp = lf_t1[-which(is.na(lf_t1$value)),]
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stats_un_t1 = compare_means(value~obese2
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, group.by = "mediator"
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#, data = lf_t1
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, data = lf_t1_comp
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, paired = FALSE
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, p.adjust.method = my_adjust_method)
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foo$mosaic[!unique(foo$mosaic)%in%unique(lf_t1_comp$mosaic)]
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stats_un_t1$timepoint = "t1"
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stats_un_t1$n_obs = length(unique(lf_t1_comp$mosaic)) # CHECK
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stats_un_t1 = as.data.frame(stats_un_t1)
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class(stats_un_t1)
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# check: satisfied!!!!
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wilcox.test()
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#==============
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# unpaired: t2
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#==============
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lf_t2 = lf[lf$timepoint == "t2",]
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lf_t2_comp = lf_t2[-which(is.na(lf_t2$value)),]
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stats_un_t2 = compare_means(value~obese2
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, group.by = "mediator"
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#, data = lf_t2
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, data = lf_t2_comp
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, paired = FALSE
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, p.adjust.method = my_adjust_method)
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stats_un_t2$timepoint = "t2"
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stats_un_t2$n_obs = length(unique(lf_t2_comp$mosaic)) # CHECK
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stats_un_t2 = as.data.frame(stats_un_t2)
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class(stats_un_t2)
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# check: satisfied!!!!
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wilcox.test()
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#==============
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# unpaired: t3
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#==============
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lf_t3 = lf[lf$timepoint == "t3",]
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lf_t3_comp = lf_t3[-which(is.na(lf_t3$value)),]
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stats_un_t3 = compare_means(value~obese2
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, group.by = "mediator"
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#, data = lf_t3
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, data = lf_t3_comp
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, paired = FALSE
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, p.adjust.method = my_adjust_method)
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stats_un_t3$timepoint = "t3"
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stats_un_t3$n_obs = length(unique(lf_t3_comp$mosaic)) # CHECK
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stats_un_t3 = as.data.frame(stats_un_t3)
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class(stats_un_t3)
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# check: satisfied!!!!
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wilcox.test()
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#==============
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# Rbind these dfs
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#==============
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str(stats_un_t1);str(stats_un_t2); str(stats_un_t3)
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n_dfs = 3
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if ( all.equal(nrow(stats_un_t1), nrow(stats_un_t2), nrow(stats_un_t3)) &&
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all.equal(ncol(stats_un_t1), ncol(stats_un_t2), ncol(stats_un_t3)) ) {
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expected_rows = nrow(stats_un_t1) * n_dfs
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expected_cols = ncol(stats_un_t1)
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print("PASS: expected_rows and cols variables generated for downstream sanity checks")
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}else{
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cat("FAIL: dfs have different no. of rows and cols"
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, "\nCheck harcoded value of n_dfs"
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, "\nexpected_rows and cols could not be generated")
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quit()
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}
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if ( all.equal(colnames(stats_un_t1), colnames(stats_un_t2), colnames(stats_un_t3)) ){
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print("PASS: colnames match. Rbind the 3 dfs...")
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combined_unpaired_stats = rbind(stats_un_t1, stats_un_t2, stats_un_t3)
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} else{
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cat("FAIL: cannot combined dfs. Colnames don't match!")
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quit()
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}
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if ( nrow(combined_unpaired_stats) == expected_rows && ncol(combined_unpaired_stats) == expected_cols ){
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cat("PASS: combined_df has expected dimension"
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, "\nNo. of rows in combined_df:", nrow(combined_unpaired_stats)
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, "\nNo. of cols in combined_df:", ncol(combined_unpaired_stats) )
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}else{
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cat("FAIL: combined_df dimension mismatch")
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quit()
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}
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#===============================================================
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# formatting df
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# delete unnecessary column
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combined_unpaired_stats = subset(combined_unpaired_stats, select = -c(.y.))
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# reflect stats method correctly
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combined_unpaired_stats$method
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combined_unpaired_stats$method = gsub("Wilcoxon", "Wilcoxon_unpaired", combined_unpaired_stats$method)
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combined_unpaired_stats$method
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# replace "." in colnames with "_"
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colnames(combined_unpaired_stats)
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#names(combined_unpaired_stats) = gsub("\.", "_", names(combined_unpaired_stats)) # weird!!!!
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colnames(combined_unpaired_stats) = c("mediator"
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, "group1"
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, "group2"
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, "p"
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, "p_adj"
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, "p_format"
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, "p_signif"
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, "method"
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, "timepoint"
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, "n_obs")
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colnames(combined_unpaired_stats)
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combined_unpaired_stats$sample_type = "npa"
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# add an extra column for padjust_signif
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combined_unpaired_stats$padjust_signif = round(combined_unpaired_stats$p_adj, digits = 2)
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# add appropriate symbols for padjust_signif
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#combined_unpaired_stats = combined_unpaired_stats %>%
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# mutate(padjust_signif = case_when(padjust_signif == 0.05 ~ "."
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# , padjust_signif <0.05 ~ '*'
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# , padjust_signif <=0.01 ~ '**'
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# , padjust_signif <=0.001 ~ '***'
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# , padjust_signif <=0.0001 ~ '****'
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# , TRUE ~ 'ns'))
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combined_unpaired_stats = dplyr::mutate(combined_unpaired_stats, padjust_signif = case_when(padjust_signif == 0.05 ~ "."
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, padjust_signif <=0.0001 ~ '****'
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, padjust_signif <=0.001 ~ '***'
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, padjust_signif <=0.01 ~ '**'
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, padjust_signif <0.05 ~ '*'
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, TRUE ~ 'ns'))
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# reorder columns
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print("preparing to reorder columns...")
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colnames(combined_unpaired_stats)
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my_col_order2 = c("mediator"
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, "timepoint"
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, "group1"
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, "group2"
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, "method"
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, "p"
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, "p_format"
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, "p_signif"
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, "p_adj"
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, "padjust_signif")
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if( length(my_col_order2) == ncol(combined_unpaired_stats) && isin(my_col_order2, colnames(combined_unpaired_stats)) ){
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print("PASS: Reordering columns...")
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combined_unpaired_stats_f = combined_unpaired_stats[, my_col_order2]
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print("Successful: column reordering")
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print("formatted df called:'combined_unpaired_stats_f'")
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cat('\nformatted df has the following dimensions\n')
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print(dim(combined_unpaired_stats_f ))
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} else{
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cat(paste0("FAIL:Cannot reorder columns, length mismatch"
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, "\nExpected column order for: ", ncol(combined_unpaired_stats)
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, "\nGot:", length(my_col_order2)))
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quit()
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}
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combined_unpaired_stats_f_npa = combined_unpaired_stats_f
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#******************
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# write output file
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#******************
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cat("UNpaired stats for groups will be:", stats_time_unpaired)
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write.csv(combined_unpaired_stats_f, stats_time_unpaired, row.names = FALSE)
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