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added non asthmatics analysis data_extraction_formatting_non_asthmatics.R and flu_stats_unpaired_na_npa.R

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Tanushree Tunstall 2020-11-20 11:43:56 +00:00
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data_extraction_formatting_non_asthmatics.R Normal file
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#!/usr/bin/Rscript
getwd()
setwd('~/git/mosaic_2020/')
getwd()
########################################################################
# TASK: Extract relevant columns from mosaic adults data
# sam
# serum
# npa
########################################################################
#====================
# Input: source data
#====================
source("read_data.R")
#============================
# Data to use: Important step
#============================
# select df to use
my_data = fp_adults_na
# clear unnecessary variables
rm(all_df, adult_df, fp_adults)
########################################################################
if ( sum(table(my_data$asthma)) && sum(table(my_data$asthma, my_data$ia_exac_copd)) && sum(table(my_data$obesity)) == nrow(my_data) ){
cat("PASS: fp adults WITHOUT asthma extracted sucessfully")
}else{
cat("FAIL: numbers mismatch. Please check")
quit()
}
#=========
# sam
#=========
sam_regex = regex(".*_sam[1-3]{1}$", ignore_case = T)
sam_cols_i = str_extract(colnames(my_data), sam_regex) # not boolean
#sam_cols_b = colnames(my_data)%in%sam_cols_i # boolean
sam_cols = colnames(my_data)[colnames(my_data)%in%sam_cols_i]
sam_cols
# this contains log columns + daysamp_samXX: omitting these
sam_regex_log_days = regex("log|day.*_sam[1-3]{1}$", ignore_case = T, perl = T)
sam_cols_to_omit = sam_cols[grepl(sam_regex_log_days, sam_cols)]; sam_cols_to_omit
sam_cols_to_omit
sam_cols_clean = sam_cols[!sam_cols%in%sam_cols_to_omit]; sam_cols_clean
length(sam_cols_clean)
if( length(sam_cols_clean) == length(sam_cols) - length(sam_cols_to_omit) ){
cat("PASS: clean cols extracted"
, "\nNo. of clean SAM cols to extract:", length(sam_cols_clean))
}else{
cat("FAIL: length mismatch. Aborting further cols extraction"
, "Expected length:", length(sam_cols) - length(sam_cols_to_omit)
, "Got:", length(sam_cols_clean) )
}
sam_cols_to_extract = c(meta_data_cols, sam_cols_clean)
cat("Extracting SAM cols + metadata_cols")
if ( length(sam_cols_to_extract) == length(meta_data_cols) + length(sam_cols_clean) ){
cat("Extracing", length(sam_cols_to_extract), "columns for sam")
sam_df = my_data[, sam_cols_to_extract]
}else{
cat("FAIL: length mismatch"
, "Expeceted to extract:", length(meta_data_cols) + length(sam_cols_clean), "columns"
, "Got:", length(sam_cols_to_extract))
}
colnames_sam_df = colnames(sam_df); colnames_sam_df
#=========
# serum
#=========
serum_regex = regex(".*_serum[1-3]{1}$", ignore_case = T)
serum_cols_i = str_extract(colnames(my_data), serum_regex) # not boolean
#serum_cols_b = colnames(my_data)%in%serum_cols_i # boolean
serum_cols = colnames(my_data)[colnames(my_data)%in%serum_cols_i]
# this contains log columns + dayserump_serumXX: omitting these
serum_regex_log_days = regex("log|day.*_serum[1-3]{1}$", ignore_case = T, perl = T)
serum_cols_to_omit = serum_cols[grepl(serum_regex_log_days, serum_cols)]; serum_cols_to_omit
serum_cols_clean = serum_cols[!serum_cols%in%serum_cols_to_omit]; serum_cols_clean
length(serum_cols_clean)
if( length(serum_cols_clean) == length(serum_cols) - length(serum_cols_to_omit) ){
cat("PASS: clean cols extracted"
, "\nNo. of clean serum cols to extract:", length(serum_cols_clean))
}else{
cat("FAIL: length mismatch. Aborting further cols extraction"
, "Expected length:", length(serum_cols) - length(serum_cols_to_omit)
, "Got:", length(serum_cols_clean) )
}
serum_cols_to_extract = c(meta_data_cols, serum_cols_clean)
cat("Extracting SERUM cols + metadata_cols")
if ( length(serum_cols_to_extract) == length(meta_data_cols) + length(serum_cols_clean) ){
cat("Extracing", length(serum_cols_to_extract), "columns for serum")
serum_df = my_data[, serum_cols_to_extract]
}else{
cat("FAIL: length mismatch"
, "Expeceted to extract:", length(meta_data_cols) + length(serum_cols_clean), "columns"
, "Got:", length(serum_cols_to_extract))
}
colnames_serum_df = colnames(serum_df); colnames_serum_df
#=========
# npa
#=========
npa_regex = regex(".*_npa[1-3]{1}$", ignore_case = T)
npa_cols_i = str_extract(colnames(my_data), npa_regex) # not boolean
#npa_cols_b = colnames(my_data)%in%npa_cols_i # boolean
npa_cols = colnames(my_data)[colnames(my_data)%in%npa_cols_i]
# this contains log columns + daynpap_npaXX: omitting these
npa_regex_log_days = regex("log|day|vl_samptime|ct.*_npa[1-3]{1}$", ignore_case = T, perl = T)
npa_cols_to_omit = npa_cols[grepl(npa_regex_log_days, npa_cols)]; npa_cols_to_omit
npa_cols_clean = npa_cols[!npa_cols%in%npa_cols_to_omit]; npa_cols_clean
length(npa_cols_clean)
if( length(npa_cols_clean) == length(npa_cols) - length(npa_cols_to_omit) ){
cat("PASS: clean cols extracted"
, "\nNo. of clean npa cols to extract:", length(npa_cols_clean))
}else{
cat("FAIL: length mismatch. Aborting further cols extraction"
, "Expected length:", length(npa_cols) - length(npa_cols_to_omit)
, "Got:", length(npa_cols_clean) )
}
npa_cols_to_extract = c(meta_data_cols, npa_cols_clean)
cat("Extracting NPA cols + metadata_cols")
if ( length(npa_cols_to_extract) == length(meta_data_cols) + length(npa_cols_clean) ){
cat("Extracing", length(npa_cols_to_extract), "columns for npa")
npa_df = my_data[, npa_cols_to_extract]
}else{
cat("FAIL: length mismatch"
, "Expeceted to extract:", length(meta_data_cols) + length(npa_cols_clean), "columns"
, "Got:", length(npa_cols_to_extract))
}
colnames_npa_df = colnames(npa_df); colnames_npa_df
#==============
# quick checks
#==============
colnames_check = as.data.frame(cbind(colnames_sam_df, colnames_serum_df, colnames_npa_df))
tail(colnames_check) # gives a warning message due to differing no. of rows for cbind!
# put NA where a match doesn't exist
# unmatched lengths
#colnames_check[117,1] <- NA
#colnames_check[117,2] <- NA
if ( ncol(sam_df) == ncol(serum_df) ){
start = ncol(sam_df)+1
extra_cols = start:ncol(npa_df)
}
colnames_check_f = colnames_check
tail(colnames_check_f)
for (i in extra_cols){
for (j in 1:2) {
cat("\ni:", i
,"\nj:", j)
colnames_check_f[i,j] <- NA
#colnames_check_f[i, j]< - NA
}
}
tail(colnames_check_f)
##########################################################################
# LF data
##########################################################################
cols_to_omit = c("adult"
#, "obese2"
#, "height", "height_unit", "weight"
#, "weight_unit", "visual_est_bmi", "bmi_rating"
)
pivot_cols = meta_data_cols
# subselect pivot_cols
pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
ncols_omitted = table(meta_data_cols%in%cols_to_omit)[[2]]
ncols_omitted
#==============
# lf data: sam
#==============
str(sam_df)
table(sam_df$obesity)
#table(sam_df$obese2)
#sam_df_adults = sam_df[sam_df$adult == 1,] # resolved at source and only dealing wit age as adult
sam_df_adults = sam_df
wf_cols = colnames(sam_df_adults)[!colnames(sam_df_adults)%in%cols_to_omit]
sam_wf = sam_df_adults[wf_cols]
if (length(pivot_cols) == length(meta_data_cols) - ncols_omitted){
cat("PASS: pivot cols successfully extracted")
}else{
cat("FAIL: length mismatch! pivot cols could not be extracted"
, "\nExpected length:", length(meta_data_cols) - ncols_omitted
, "\nGot:",length(pivot_cols) )
quit()
}
expected_rows_sam_lf = nrow(sam_wf) * (length(sam_wf) - length(pivot_cols)); expected_rows_sam_lf
# using regex:
sam_lf = sam_wf %>%
tidyr::pivot_longer(-all_of(pivot_cols)
, names_to = c("mediator", "sample_type", "timepoint")
, names_pattern = "(.*)_(.*)([1-3]{1})"
, values_to = "value")
if (
(nrow(sam_lf) == expected_rows_sam_lf) & (sum(table(is.na(sam_lf$mediator))) == expected_rows_sam_lf)
) {
cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
, "\nNo. of rows: ", nrow(sam_lf)
, "\nNo. of cols: ", ncol(sam_lf)))
} else{
cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
, "\nExpected no. of rows: ", expected_rows_sam_lf
, "\nGot: ", nrow(sam_lf)
, "\ncheck expected rows calculation!"))
quit()
}
#library(data.table)
#foo = sam_df_adults[1:10]
#long <- melt(setDT(sam_df_adults), id.vars = pivot_cols, variable.name = "levels")
#==============
# lf data: serum
#==============
str(serum_df)
table(serum_df$obesity)
#table(serum_df$obese2)
#serum_df_adults = serum_df[serum_df$adult == 1,] # extract based on age
serum_df_adults = serum_df
wf_cols = colnames(serum_df_adults)[!colnames(serum_df_adults)%in%cols_to_omit]
serum_wf = serum_df_adults[wf_cols]
if (length(pivot_cols) == length(meta_data_cols) - ncols_omitted){
cat("PASS: pivot cols successfully extracted")
}else{
cat("FAIL: length mismatch! pivot cols could not be extracted"
, "\nExpected length:", length(meta_data_cols) - ncols_omitted
, "\nGot:",length(pivot_cols) )
quit()
}
expected_rows_serum_lf = nrow(serum_wf) * (length(serum_wf) - length(pivot_cols)); expected_rows_serum_lf
# using regex:
serum_lf = serum_wf %>%
tidyr::pivot_longer(-all_of(pivot_cols)
, names_to = c("mediator", "sample_type", "timepoint")
, names_pattern = "(.*)_(.*)([1-3]{1})"
, values_to = "value")
if (
(nrow(serum_lf) == expected_rows_serum_lf) & (sum(table(is.na(serum_lf$mediator))) == expected_rows_serum_lf)
) {
cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
, "\nNo. of rows: ", nrow(serum_lf)
, "\nNo. of cols: ", ncol(serum_lf)))
} else{
cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
, "\nExpected no. of rows: ", expected_rows_serum_lf
, "\nGot: ", nrow(serum_lf)
, "\ncheck expected rows calculation!"))
quit()
}
#==============
# lf data: npa
#==============
str(npa_df)
table(npa_df$obesity)
#table(npa_df$obese2)
#npa_df_adults = npa_df[npa_df$adult == 1,] # extract based on age
npa_df_adults = npa_df
wf_cols = colnames(npa_df_adults)[!colnames(npa_df_adults)%in%cols_to_omit]
npa_wf = npa_df_adults[wf_cols]
if (length(pivot_cols) == length(meta_data_cols) - ncols_omitted){
cat("PASS: pivot cols successfully extracted")
}else{
cat("FAIL: length mismatch! pivot cols could not be extracted"
, "\nExpected length:", length(meta_data_cols) - ncols_omitted
, "\nGot:",length(pivot_cols) )
quit()
}
expected_rows_npa_lf = nrow(npa_wf) * (length(npa_wf) - length(pivot_cols)); expected_rows_npa_lf
# using regex:
npa_lf = npa_wf %>%
tidyr::pivot_longer(-all_of(pivot_cols)
, names_to = c("mediator", "sample_type", "timepoint")
, names_pattern = "(.*)_(.*)([1-3]{1})"
, values_to = "value")
if (
(nrow(npa_lf) == expected_rows_npa_lf) & (sum(table(is.na(npa_lf$mediator))) == expected_rows_npa_lf)
) {
cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
, "\nNo. of rows: ", nrow(npa_lf)
, "\nNo. of cols: ", ncol(npa_lf)))
} else{
cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
, "\nExpected no. of rows: ", expected_rows_npa_lf
, "\nGot: ", nrow(npa_lf)
, "\ncheck expected rows calculation!"))
quit()
}
###############################################################################
# remove unnecessary variables
rm(sam_regex, sam_regex_log_days, sam_cols, sam_cols_clean, sam_cols_i, sam_cols_to_extract, sam_cols_to_omit)
rm(serum_regex, serum_regex_log_days, serum_cols, serum_cols_clean, serum_cols_i, serum_cols_to_extract, serum_cols_to_omit)
rm(npa_regex, npa_regex_log_days, npa_cols, npa_cols_clean, npa_cols_i, npa_cols_to_extract, npa_cols_to_omit)
rm(my_data)
rm(colnames_check)
rm(i, j
#, expected_cols
, start, wf_cols, extra_cols, cols_to_omit)
# rm not_clean dfs
rm(sam_df_adults, serum_df_adults, npa_df_adults)
# rm df
rm(sam_df, serum_df, npa_df)
rm(colnames_check_f, fp_adults_na)

381
flu_stats_unpaired_na_npa.R Executable file
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#!/usr/bin/Rscript
getwd()
setwd("~/git/mosaic_2020/")
getwd()
############################################################
# TASK: unpaired (time) analysis of mediators:
# sample type: NPA
# data: Flu positive adult patients
# group: obesity
############################################################
my_sample_type = "npa"
#=============
# Input
#=============
source("data_extraction_formatting_non_asthmatics.R")
# check: adult variable and age variable discrepancy!
metadata_all$mosaic[metadata_all$adult==1 & metadata_all$age<=18]
#=============
# Output
#=============
outfile_name = paste0("flu_stats_time_unpaired_NA_", my_sample_type, ".csv")
flu_stats_time_unpaired_na = paste0(outdir_stats, outfile_name)
#===============================
# data assignment for stats
#================================
wf = npa_wf[npa_wf$flustat == 1,]
lf = npa_lf[npa_lf$flustat == 1,]
lf$timepoint = paste0("t", lf$timepoint)
lf = lf[!lf$mediator == "vitd",]
########################################################################
# clear variables
rm(sam_lf, sam_wf
, serum_lf, serum_wf)
rm(colnames_sam_df, expected_rows_sam_lf
, colnames_serum_df, expected_rows_serum_lf)
rm(pivot_cols)
# sanity checks
table(lf$timepoint)
#if (table(lf$flustat) == table(npa_lf$flustat)[[2]]){
# cat("Analysing Flu positive patients for:", my_sample_type)
#}else{
# cat("FAIL: problem with subsetting data for:", my_sample_type)
# quit()
#}
########################################################################
# Unpaired stats at each timepoint b/w groups: wilcoxon UNpaired analysis
# with correction
########################################################################
# with adjustment: fdr and BH are identical
my_adjust_method = "BH"
#==============
# unpaired: t1
#==============
lf_t1 = lf[lf$timepoint == "t1",]
sum(is.na(lf_t1$value))
foo = lf_t1[which(is.na(lf_t1$value)),]
#ci = which(is.na(lf_t1$value))
#lf_t1_comp = lf_t1[-ci,]
lf_t1_comp = lf_t1[-which(is.na(lf_t1$value)),]
stats_un_t1 = compare_means(value~obesity
, group.by = "mediator"
#, data = lf_t1
, data = lf_t1_comp
, paired = FALSE
, p.adjust.method = my_adjust_method)
foo$mosaic[!unique(foo$mosaic)%in%unique(lf_t1_comp$mosaic)]
# add timepoint and convert to df
stats_un_t1$timepoint = "t1"
stats_un_t1 = as.data.frame(stats_un_t1)
class(stats_un_t1)
#----------------------------------------
# calculate n_obs for each mediator: t1
#----------------------------------------
#n_t1 = data.frame(table(lf_t1_comp$mediator))
n_t1_all = data.frame(table(lf_t1$mediator))
colnames(n_t1_all) = c("mediator", "n_obs")
n_t1_all$mediator = as.character(n_t1_all$mediator)
n_t1_comp = data.frame(table(lf_t1_comp$mediator))
colnames(n_t1_comp) = c("mediator", "n_obs_complete")
n_t1_comp$mediator = as.character(n_t1_comp$mediator)
merge_cols = intersect(names(n_t1_all), names(n_t1_comp)); merge_cols
n_t1= merge(n_t1_all, n_t1_comp, by = merge_cols, all = T)
#==================================
# Merge: merge stats + n_obs df
#===================================
merging_cols = intersect(names(stats_un_t1), names(n_t1)); merging_cols
if (all(n_t1$mediator%in%stats_un_t1$mediator)) {
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
stats_un_t1 = merge(stats_un_t1, n_t1, by = merging_cols, all = T)
cat("\nsuccessfull merge:"
, "\nnrow:", nrow(stats_un_t1)
, "\nncol:", ncol(stats_un_t1))
}else{
nf = n_t1$mediator[!n_t1$mediator%in%stats_un_t1$mediator]
stats_un_t1 = merge(stats_un_t1, n_t1, by = merging_cols, all.y = T)
cat("\nMerged with caution:"
, "\nnrows mismatch:", nf
, "not found in stats possibly due to all obs being LLODs"
, "\nintroduced NAs for:", nf
, "\nnrow:", nrow(stats_un_t1)
, "\nncol:", ncol(stats_un_t1))
}
# add bonferroni adjustment as well
stats_un_t1$p_adj_bonferroni = p.adjust(stats_un_t1$p, method = "bonferroni")
rm(n_t1)
rm(lf_t1_comp)
#==============
# unpaired: t2
#==============
lf_t2 = lf[lf$timepoint == "t2",]
lf_t2_comp = lf_t2[-which(is.na(lf_t2$value)),]
stats_un_t2 = compare_means(value~obesity
, group.by = "mediator"
#, data = lf_t2
, data = lf_t2_comp
, paired = FALSE
, p.adjust.method = my_adjust_method)
# add timepoint and convert to df
stats_un_t2$timepoint = "t2"
stats_un_t2 = as.data.frame(stats_un_t2)
class(stats_un_t2)
#----------------------------------------
# calculate n_obs for each mediator: t2
#----------------------------------------
#n_t2 = data.frame(table(lf_t2_comp$mediator))
n_t2_all = data.frame(table(lf_t2$mediator))
colnames(n_t2_all) = c("mediator", "n_obs")
n_t2_all$mediator = as.character(n_t2_all$mediator)
n_t2_comp = data.frame(table(lf_t2_comp$mediator))
colnames(n_t2_comp) = c("mediator", "n_obs_complete")
n_t2_comp$mediator = as.character(n_t2_comp$mediator)
merge_cols = intersect(names(n_t2_all), names(n_t2_comp)); merge_cols
n_t2= merge(n_t2_all, n_t2_comp, by = merge_cols, all = T)
#==================================
# Merge: merge stats + n_obs df
#==================================
merging_cols = intersect(names(stats_un_t2), names(n_t2)); merging_cols
if (all(n_t2$mediator%in%stats_un_t2$mediator)) {
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
stats_un_t2 = merge(stats_un_t2, n_t2, by = merging_cols, all = T)
cat("\nsuccessfull merge:"
, "\nnrow:", nrow(stats_un_t2)
, "\nncol:", ncol(stats_un_t2))
}else{
nf = n_t2$mediator[!n_t2$mediator%in%stats_un_t2$mediator]
stats_un_t2 = merge(stats_un_t2, n_t2, by = merging_cols, all.y = T)
cat("\nMerged with caution:"
, "\nnrows mismatch:", nf
, "not found in stats possibly due to all obs being LLODs"
, "\nintroduced NAs for:", nf
, "\nnrow:", nrow(stats_un_t2)
, "\nncol:", ncol(stats_un_t2))
}
# add bonferroni adjustment as well
stats_un_t2$p_adj_bonferroni = p.adjust(stats_un_t2$p, method = "bonferroni")
rm(n_t2)
rm(lf_t2_comp)
#==============
# unpaired: t3
#==============
lf_t3 = lf[lf$timepoint == "t3",]
lf_t3_comp = lf_t3[-which(is.na(lf_t3$value)),]
stats_un_t3 = compare_means(value~obesity
, group.by = "mediator"
#, data = lf_t3
, data = lf_t3_comp
, paired = FALSE
, p.adjust.method = my_adjust_method)
# add timepoint and convert to df
stats_un_t3$timepoint = "t3"
stats_un_t3 = as.data.frame(stats_un_t3)
class(stats_un_t3)
#----------------------------------------
# calculate n_obs for each mediator: t3
#----------------------------------------
#n_t3 = data.frame(table(lf_t3_comp$mediator))
n_t3_all = data.frame(table(lf_t3$mediator))
colnames(n_t3_all) = c("mediator", "n_obs")
n_t3_all$mediator = as.character(n_t3_all$mediator)
n_t3_comp = data.frame(table(lf_t3_comp$mediator))
colnames(n_t3_comp) = c("mediator", "n_obs_complete")
n_t3_comp$mediator = as.character(n_t3_comp$mediator)
merge_cols = intersect(names(n_t3_all), names(n_t3_comp)); merge_cols
n_t3= merge(n_t3_all, n_t3_comp, by = merge_cols, all = T)
#==================================
# Merge: merge stats + n_obs df
#==================================
merging_cols = intersect(names(stats_un_t3), names(n_t3)); merging_cols
if (all(n_t3$mediator%in%stats_un_t3$mediator)) {
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
stats_un_t3 = merge(stats_un_t3, n_t3, by = merging_cols, all = T)
cat("\nsuccessfull merge:"
, "\nnrow:", nrow(stats_un_t3)
, "\nncol:", ncol(stats_un_t3))
}else{
nf = n_t3$mediator[!n_t3$mediator%in%stats_un_t3$mediator]
stats_un_t3 = merge(stats_un_t3, n_t3, by = merging_cols, all.y = T)
cat("\nMerged with caution:"
, "\nnrows mismatch:", nf
, "not found in stats possibly due to all obs being LLODs"
, "\nintroduced NAs for:", nf
, "\nnrow:", nrow(stats_un_t3)
, "\nncol:", ncol(stats_un_t3))
}
# add bonferroni adjustment as well
stats_un_t3$p_adj_bonferroni = p.adjust(stats_un_t3$p, method = "bonferroni")
rm(n_t3)
rm(lf_t3_comp)
########################################################################
#==============
# Rbind these dfs
#==============
str(stats_un_t1);str(stats_un_t2); str(stats_un_t3)
n_dfs = 3
if ( all.equal(nrow(stats_un_t1), nrow(stats_un_t2), nrow(stats_un_t3)) &&
all.equal(ncol(stats_un_t1), ncol(stats_un_t2), ncol(stats_un_t3)) ) {
expected_rows = nrow(stats_un_t1) * n_dfs
expected_cols = ncol(stats_un_t1)
print("PASS: expected_rows and cols variables generated for downstream sanity checks")
}else{
cat("FAIL: dfs have different no. of rows and cols"
, "\nCheck harcoded value of n_dfs"
, "\nexpected_rows and cols could not be generated")
quit()
}
if ( all.equal(colnames(stats_un_t1), colnames(stats_un_t2), colnames(stats_un_t3)) ){
print("PASS: colnames match. Rbind the 3 dfs...")
combined_unpaired_stats = rbind(stats_un_t1, stats_un_t2, stats_un_t3)
} else{
cat("FAIL: cannot combined dfs. Colnames don't match!")
quit()
}
if ( nrow(combined_unpaired_stats) == expected_rows && ncol(combined_unpaired_stats) == expected_cols ){
cat("PASS: combined_df has expected dimension"
, "\nNo. of rows in combined_df:", nrow(combined_unpaired_stats)
, "\nNo. of cols in combined_df:", ncol(combined_unpaired_stats) )
}else{
cat("FAIL: combined_df dimension mismatch")
quit()
}
#######################################################################
#=================
# formatting df
#=================
# delete: unnecessary column
combined_unpaired_stats = subset(combined_unpaired_stats, select = -c(.y.))
# add sample_type
cat("Adding sample type info as a column", my_sample_type, "...")
combined_unpaired_stats$sample_type = my_sample_type
# add: reflect stats method correctly i.e paired or unpaired
# incase there are NA due to LLODs, the gsub won't work!
#combined_unpaired_stats$method = gsub("Wilcoxon", "Wilcoxon_unpaired", combined_unpaired_stats$method)
combined_unpaired_stats$method = "wilcoxon unpaired"
combined_unpaired_stats$method
# add an extra column for padjust_signif: my_adjust_method
combined_unpaired_stats$padjust_signif = combined_unpaired_stats$p.adj
# add appropriate symbols for padjust_signif: my_adjust_method
combined_unpaired_stats = dplyr::mutate(combined_unpaired_stats, padjust_signif = case_when(padjust_signif == 0.05 ~ "."
, padjust_signif <=0.0001 ~ '****'
, padjust_signif <=0.001 ~ '***'
, padjust_signif <=0.01 ~ '**'
, padjust_signif <0.05 ~ '*'
, TRUE ~ 'ns'))
# add an extra column for p_bon_signif
combined_unpaired_stats$p_bon_signif = combined_unpaired_stats$p_adj_bonferroni
# add appropriate symbols for p_bon_signif
combined_unpaired_stats = dplyr::mutate(combined_unpaired_stats, p_bon_signif = case_when(p_bon_signif == 0.05 ~ "."
, p_bon_signif <=0.0001 ~ '****'
, p_bon_signif <=0.001 ~ '***'
, p_bon_signif <=0.01 ~ '**'
, p_bon_signif <0.05 ~ '*'
, TRUE ~ 'ns'))
# reorder columns
print("preparing to reorder columns...")
colnames(combined_unpaired_stats)
my_col_order2 = c("mediator"
, "timepoint"
, "sample_type"
, "n_obs"
, "n_obs_complete"
, "group1"
, "group2"
, "method"
, "p"
, "p.format"
, "p.signif"
, "p.adj"
, "padjust_signif"
, "p_adj_bonferroni"
, "p_bon_signif")
if( length(my_col_order2) == ncol(combined_unpaired_stats) && (all(my_col_order2%in%colnames(combined_unpaired_stats))) ){
print("PASS: Reordering columns...")
combined_unpaired_stats_f = combined_unpaired_stats[, my_col_order2]
print("Successful: column reordering")
print("formatted df called:'combined_unpaired_stats_f'")
cat('\nformatted df has the following dimensions\n')
print(dim(combined_unpaired_stats_f ))
} else{
cat(paste0("FAIL:Cannot reorder columns, length mismatch"
, "\nExpected column order for: ", ncol(combined_unpaired_stats)
, "\nGot:", length(my_col_order2)))
quit()
}
# assign nice column names like replace "." with "_"
colnames(combined_unpaired_stats_f) = c("mediator"
, "timepoint"
, "sample_type"
, "n_obs"
, "n_obs_complete"
, "group1"
, "group2"
, "method"
, "p"
, "p_format"
, "p_signif"
, paste0("p_adj_fdr_", my_adjust_method)
, paste0("p_", my_adjust_method, "_signif")
, "p_adj_bonferroni"
, "p_bon_signif")
colnames(combined_unpaired_stats_f)
# count how many meds are significant
n_sig = length(combined_unpaired_stats_f$mediator[combined_unpaired_stats_f$p_signif<0.05])
cat("\nTotal no. of statistically significant mediators in", toupper(my_sample_type)
, "are:", n_sig)
sig_meds = combined_unpaired_stats_f[combined_unpaired_stats_f$p_signif<0.05,]
########################################################################
#******************
# write output file
#******************
cat("UNpaired stats for groups will be:", flu_stats_time_unpaired_na)
write.csv(combined_unpaired_stats_f, flu_stats_time_unpaired_na, row.names = FALSE)