tidy scripts and using age as criteria for adults
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3 changed files with 110 additions and 115 deletions
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@ -8,7 +8,6 @@ getwd()
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# data: Flu positive adult patients
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# group: obesity
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############################################################
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my_sample_type = "npa"
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#=============
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@ -19,12 +18,20 @@ source("data_extraction_formatting.R")
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# check: adult variable and age variable discrepancy!
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metadata_all$mosaic[metadata_all$adult==1 & metadata_all$age<=18]
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#=========================================================
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#=============
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# Output
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#=============
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outfile_name = paste0("flu_stats_time_unpaired_", my_sample_type, ".csv")
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flu_stats_time_unpaired = paste0(outdir_stats, outfile_name)
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#===============================
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# data assignment for stats
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#================================
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wf = npa_wf[npa_wf$flustat == 1,]
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lf = npa_lf[npa_lf$flustat == 1,]
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lf$timepoint = paste0("t", lf$timepoint)
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#=========================================================
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########################################################################
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# clear variables
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rm(sam_lf, sam_wf
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, serum_lf, serum_wf)
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@ -33,13 +40,6 @@ rm(colnames_sam_df, expected_rows_sam_lf
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rm(pivot_cols)
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#=============
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# Output: unpaired analysis of time for npa
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#=============
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outfile_name = paste0("flu_stats_time_unpaired_", my_sample_type, ".csv")
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flu_stats_time_unpaired = paste0(outdir_stats, outfile_name)
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########################################################################
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# sanity checks
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table(lf$timepoint)
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@ -52,24 +52,20 @@ if (table(lf$flustat) == table(npa_lf$flustat)[[2]]){
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########################################################################
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# Unpaired stats at each timepoint b/w groups: wilcoxon UNpaired analysis
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# with correction
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#######################################################################
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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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#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$value)),]
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#--------------------
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# unpaired stats: t1
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#--------------------
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stats_un_t1 = compare_means(value~obesity
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, group.by = "mediator"
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#, data = lf_t1
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@ -89,9 +85,9 @@ n_t1 = data.frame(table(lf_t1_comp$mediator))
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colnames(n_t1) = c("mediator", "n_obs")
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n_t1$mediator = as.character(n_t1$mediator)
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#=========================
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# Merge1: merge stats + n_obs df
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#=========================
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#==================================
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# Merge: merge stats + n_obs df
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#===================================
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merging_cols = intersect(names(stats_un_t1), names(n_t1)); merging_cols
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if (all(n_t1$mediator%in%stats_un_t1$mediator)) {
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cat("PASS: merging stats and n_obs on column/s:", merging_cols)
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@ -113,39 +109,6 @@ if (all(n_t1$mediator%in%stats_un_t1$mediator)) {
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# add bonferroni adjustment as well
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stats_un_t1$p_adj_bonferroni = p.adjust(stats_un_t1$p, method = "bonferroni")
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#--------------------
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# summary stats: t1
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#--------------------
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gp_stats_t1 = groupedstats::grouped_summary(
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data = lf_t1_comp,
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grouping.vars = c(mediator, obesity),
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measures = value,
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measures.type = "numeric")
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#====================================
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# Merge 2: Merge1 + summary_stats
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#====================================
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merging_cols2 = intersect(names(stats_un_t1), names(gp_stats_t1)); merging_cols2
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if (all(gp_stats_t1$mediator%in%stats_un_t1$mediator)) {
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cat("PASS: merging summary stats with unpaired analysis on column/s:", merging_cols2)
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stats_un_t1 = merge(stats_un_t1, gp_stats_t1, by = merging_cols2, all = T)
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cat("\nsuccessfull merge:"
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, "\nnrow:", nrow(stats_un_t1)
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, "\nncol:", ncol(stats_un_t1))
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}else{
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nf = gp_stats_t1$mediator[!gp_stats_t1$mediator%in%stats_un_t1$mediator]
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stats_un_t1 = merge(stats_un_t1, gp_stats_t1, by = merging_cols2, all.y = T)
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cat("\nMerged with caution:"
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, "\nnrows mismatch:", nf
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, "not found in stats possibly due to all obs being LLODs"
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, "\nintroduced NAs for:", nf
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, "\nnrow:", nrow(stats_un_t1)
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, "\nncol:", ncol(stats_un_t1))
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}
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rm(n_t1)
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rm(lf_t1_comp)
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@ -161,8 +124,9 @@ stats_un_t2 = compare_means(value~obesity
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, data = lf_t2_comp
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, paired = FALSE
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, p.adjust.method = my_adjust_method)
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stats_un_t2$timepoint = "t2"
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# add timepoint and convert to df
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stats_un_t2$timepoint = "t2"
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stats_un_t2 = as.data.frame(stats_un_t2)
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class(stats_un_t2)
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@ -171,7 +135,9 @@ n_t2 = data.frame(table(lf_t2_comp$mediator))
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colnames(n_t2) = c("mediator", "n_obs")
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n_t2$mediator = as.character(n_t2$mediator)
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# merge stats + n_obs df
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#==================================
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# Merge: merge stats + n_obs df
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#==================================
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merging_cols = intersect(names(stats_un_t2), names(n_t2)); merging_cols
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if (all(n_t2$mediator%in%stats_un_t2$mediator)) {
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cat("PASS: merging stats and n_obs on column/s:", merging_cols)
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@ -209,8 +175,8 @@ stats_un_t3 = compare_means(value~obesity
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, paired = FALSE
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, p.adjust.method = my_adjust_method)
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# add timepoint and convert to df
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stats_un_t3$timepoint = "t3"
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stats_un_t3 = as.data.frame(stats_un_t3)
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class(stats_un_t3)
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@ -219,7 +185,9 @@ n_t3 = data.frame(table(lf_t3_comp$mediator))
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colnames(n_t3) = c("mediator", "n_obs")
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n_t3$mediator = as.character(n_t3$mediator)
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# merge stats + n_obs df
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#==================================
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# Merge: merge stats + n_obs df
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#==================================
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merging_cols = intersect(names(stats_un_t3), names(n_t3)); merging_cols
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if (all(n_t3$mediator%in%stats_un_t3$mediator)) {
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cat("PASS: merging stats and n_obs on column/s:", merging_cols)
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@ -244,7 +212,7 @@ stats_un_t3$p_adj_bonferroni = p.adjust(stats_un_t3$p, method = "bonferroni")
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rm(n_t3)
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rm(lf_t3_comp)
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###########################################################################################
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########################################################################
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#==============
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# Rbind these dfs
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#==============
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@ -364,7 +332,7 @@ colnames(combined_unpaired_stats_f) = c("mediator"
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, "p_bon_signif")
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colnames(combined_unpaired_stats_f)
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########################################################################
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#******************
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# write output file
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#******************
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