added stats_unpaired.R for sam, serum and npa
This commit is contained in:
parent
0dab1d5097
commit
93973ed850
4 changed files with 897 additions and 26 deletions
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@ -171,16 +171,22 @@ tail(colnames_check_f)
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# LF data
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##########################################################################
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#=========
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#==============
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# lf data: sam
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#=========
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#==============
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str(sam_df)
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table(sam_df$obesity); table(sam_df$obese2)
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sam_df_adults = sam_df[sam_df$adult == 1,]
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cols_to_omit = c("adult", "flustat", "type", "obesity"
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, "height", "height_unit", "weight", "weight_unit","visual_est_bmi", "bmi_rating")
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cols_to_omit = c("flustat", "type", "obesity"
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, "height", "height_unit", "weight"
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, "weight_unit", "visual_est_bmi", "bmi_rating")
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#sam_df_adults_clean = sam_df_adults[!cols_to_omit]
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wf_cols = colnames(sam_df_adults)[!colnames(sam_df_adults)%in%cols_to_omit]
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sam_df_adults_clean = sam_df_adults[wf_cols]
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pivot_cols = meta_data_cols
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pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
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@ -194,44 +200,144 @@ if (length(pivot_cols) == length(meta_data_cols) - length(cols_to_omit)){
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quit()
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}
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expected_rows_sam_lf = nrow(sam_df_adults) * (length(sam_df_adults) - length(pivot_cols)); expected_rows_sam_lf
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expected_rows_sam_lf = nrow(sam_df_adults_clean) * (length(sam_df_adults_clean) - length(pivot_cols)); expected_rows_sam_lf
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# using regex:
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sam_adults_lf = sam_df_adults %>%
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tidyr::pivot_longer(-all_of(pivot_cols), names_to = c("mediator", "sample_type", "timepoint"),
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names_pattern = "(.*)_(.*)([1-3]{1})",
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values_to = "value")
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sam_adults_lf = sam_df_adults_clean %>%
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tidyr::pivot_longer(-all_of(pivot_cols)
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, names_to = c("mediator", "sample_type", "timepoint")
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, names_pattern = "(.*)_(.*)([1-3]{1})"
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, values_to = "value")
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if ((nrow(sam_lf) == expected_rows_sam_lf) & (sum(table(is.na(sam_adults_lf$mediator))) == expected_rows_sam_lf)) {
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if (
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(nrow(sam_adults_lf) == expected_rows_sam_lf) & (sum(table(is.na(sam_adults_lf$mediator))) == expected_rows_sam_lf)
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) {
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cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
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, "\nNo. of rows: ", nrow(sam_lf)
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, "\nNo. of cols: ", ncol(sam_lf)))
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, "\nNo. of rows: ", nrow(sam_adults_lf)
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, "\nNo. of cols: ", ncol(sam_adults_lf)))
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} else{
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cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
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, "\nExpected no. of rows: ", expected_rows_sam_lf
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, "\nGot: ", nrow(sam_lf)
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, "\nGot: ", nrow(sam_adults_lf)
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, "\ncheck expected rows calculation!"))
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quit()
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}
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#library(data.table)
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#foo = sam_df_adults[1:10]
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#long <- melt(setDT(sam_df_adults), id.vars = pivot_cols, variable.name = "levels")
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#==============
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# lf data: serum
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#==============
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str(serum_df)
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table(serum_df$obesity); table(serum_df$obese2)
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serum_df_adults = serum_df[serum_df$adult == 1,]
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cols_to_omit = c("flustat", "type", "obesity"
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, "height", "height_unit", "weight", "weight_unit","visual_est_bmi", "bmi_rating")
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#serum_df_adults_clean = serum_df_adults[!cols_to_omit]
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wf_cols = colnames(serum_df_adults)[!colnames(serum_df_adults)%in%cols_to_omit]
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serum_df_adults_clean = serum_df_adults[wf_cols]
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pivot_cols = meta_data_cols
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pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
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if (length(pivot_cols) == length(meta_data_cols) - length(cols_to_omit)){
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cat("PASS: pivot cols successfully extracted")
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}else{
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cat("FAIL: length mismatch! pivot cols could not be extracted"
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, "\nExpected length:", length(meta_data_cols) - length(cols_to_omit)
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, "\nGot:",length(pivot_cols) )
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quit()
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}
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expected_rows_serum_lf = nrow(serum_df_adults_clean) * (length(serum_df_adults_clean) - length(pivot_cols)); expected_rows_serum_lf
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# using regex:
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serum_adults_lf = serum_df_adults_clean %>%
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tidyr::pivot_longer(-all_of(pivot_cols)
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, names_to = c("mediator", "sample_type", "timepoint")
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, names_pattern = "(.*)_(.*)([1-3]{1})"
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, values_to = "value")
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if (
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(nrow(serum_adults_lf) == expected_rows_serum_lf) & (sum(table(is.na(serum_adults_lf$mediator))) == expected_rows_serum_lf)
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) {
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cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
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, "\nNo. of rows: ", nrow(serum_adults_lf)
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, "\nNo. of cols: ", ncol(serum_adults_lf)))
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} else{
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cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
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, "\nExpected no. of rows: ", expected_rows_serum_lf
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, "\nGot: ", nrow(serum_adults_lf)
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, "\ncheck expected rows calculation!"))
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quit()
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}
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#==============
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# lf data: npa
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#==============
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str(npa_df)
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table(npa_df$obesity); table(npa_df$obese2)
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npa_df_adults = npa_df[npa_df$adult == 1,]
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cols_to_omit = c("flustat", "type", "obesity"
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, "height", "height_unit", "weight", "weight_unit","visual_est_bmi", "bmi_rating")
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#npa_df_adults_clean = npa_df_adults[!cols_to_omit]
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wf_cols = colnames(npa_df_adults)[!colnames(npa_df_adults)%in%cols_to_omit]
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npa_df_adults_clean = npa_df_adults[wf_cols]
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pivot_cols = meta_data_cols
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pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
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if (length(pivot_cols) == length(meta_data_cols) - length(cols_to_omit)){
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cat("PASS: pivot cols successfully extracted")
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}else{
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cat("FAIL: length mismatch! pivot cols could not be extracted"
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, "\nExpected length:", length(meta_data_cols) - length(cols_to_omit)
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, "\nGot:",length(pivot_cols) )
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quit()
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}
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expected_rows_npa_lf = nrow(npa_df_adults_clean) * (length(npa_df_adults_clean) - length(pivot_cols)); expected_rows_npa_lf
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# using regex:
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npa_adults_lf = npa_df_adults_clean %>%
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tidyr::pivot_longer(-all_of(pivot_cols)
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, names_to = c("mediator", "sample_type", "timepoint")
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, names_pattern = "(.*)_(.*)([1-3]{1})"
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, values_to = "value")
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if (
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(nrow(npa_adults_lf) == expected_rows_npa_lf) & (sum(table(is.na(npa_adults_lf$mediator))) == expected_rows_npa_lf)
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) {
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cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
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, "\nNo. of rows: ", nrow(npa_adults_lf)
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, "\nNo. of cols: ", ncol(npa_adults_lf)))
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} else{
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cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
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, "\nExpected no. of rows: ", expected_rows_npa_lf
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, "\nGot: ", nrow(npa_adults_lf)
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, "\ncheck expected rows calculation!"))
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quit()
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}
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###############################################################################
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# remove unnecessary variables
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rm(sam_regex, sam_regex_log_days, sam_cols, sam_cols_b, sam_cols_clean, sam_cols_i, sam_cols_to_extract, sam_cols_to_omit)
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rm(serum_regex, serum_regex_log_days, serum_cols, serum_cols_clean, serum_cols_i, serum_cols_to_extract, serum_cols_to_omit)
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rm(npa_regex, npa_regex_log_days, npa_cols, npa_cols_clean, npa_cols_i, npa_cols_to_extract, npa_cols_to_omit)
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rm(all_df)
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rm(colnames_check)
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rm(i, j, expected_cols, start, wf_cols, extra_cols, cols_to_omit)
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255
stats_unpaired_npa.R
Normal file
255
stats_unpaired_npa.R
Normal file
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@ -0,0 +1,255 @@
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#!/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 ~ '**'
|
||||
, padjust_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
|
||||
# reorder columns
|
||||
print("preparing to reorder columns...")
|
||||
colnames(combined_unpaired_stats)
|
||||
my_col_order2 = c("mediator"
|
||||
, "timepoint"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "method"
|
||||
, "p"
|
||||
, "p_format"
|
||||
, "p_signif"
|
||||
, "p_adj"
|
||||
, "padjust_signif")
|
||||
|
||||
if( length(my_col_order2) == ncol(combined_unpaired_stats) && isin(my_col_order2, 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()
|
||||
}
|
||||
|
||||
combined_unpaired_stats_f_npa = combined_unpaired_stats_f
|
||||
#******************
|
||||
# write output file
|
||||
#******************
|
||||
cat("UNpaired stats for groups will be:", stats_time_unpaired)
|
||||
write.csv(combined_unpaired_stats_f, stats_time_unpaired, row.names = FALSE)
|
||||
|
||||
255
stats_unpaired_sam.R
Normal file
255
stats_unpaired_sam.R
Normal file
|
|
@ -0,0 +1,255 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd('~/git/mosaic_2020/')
|
||||
getwd()
|
||||
############################################################
|
||||
# TASK: summary stats of mediators by time and outcome
|
||||
############################################################
|
||||
# load libraries and packages
|
||||
|
||||
source("../Header_TT.R")
|
||||
library(tidyverse)
|
||||
library(ggpubr)
|
||||
library(rstatix)
|
||||
library(Hmisc)
|
||||
library(qwraps2)
|
||||
#==========================================================
|
||||
#datadir = "~/git/covid19/Data"
|
||||
source("mosaic_bmi_data_extraction_v2.R")
|
||||
|
||||
|
||||
#=============
|
||||
# Input
|
||||
#=============
|
||||
|
||||
#infile_icu_wf = paste0(datadir,"/icu_covid_wf.csv")
|
||||
#infile_icu_lf = paste0(datadir,"/icu_covid_lf.csv")
|
||||
|
||||
# version 2
|
||||
#infile_icu_wf = paste0(datadir,"/icu_covid_wf_v3.csv")
|
||||
#infile_icu_lf = paste0(datadir,"/icu_covid_lf_v3.csv")
|
||||
|
||||
#sam_adults_lf
|
||||
#=============
|
||||
# Output
|
||||
#=============
|
||||
outdir = paste0("~/git/mosaic_2020/version1")
|
||||
|
||||
# unpaired analysis
|
||||
stats_time_unpaired = paste0(outdir, "stats_unpaired_sam.csv")
|
||||
|
||||
#%%========================================================
|
||||
# read file
|
||||
#wf_data = read.csv(infile_icu_wf , stringsAsFactors = F)
|
||||
#dim(wf_data)
|
||||
|
||||
#lf_data = read.csv(infile_icu_lf , stringsAsFactors = F)
|
||||
#dim(lf_data)
|
||||
|
||||
#%%========================================================
|
||||
# data assignment for stats
|
||||
#wf = wf_data
|
||||
#lf = lf_data
|
||||
wf = sam_df_adults_clean
|
||||
lf = sam_adults_lf
|
||||
table(lf$timepoint)
|
||||
lf$timepoint = paste0("t", lf$timepoint)
|
||||
|
||||
########################################################################
|
||||
# Unpaired stats at each timepoint b/w groups: wilcoxon UNpaired analysis with correction
|
||||
#######################################################################
|
||||
# with adjustment: fdr and BH are identical
|
||||
my_adjust_method = "BH"
|
||||
|
||||
#==============
|
||||
# unpaired: t1
|
||||
#==============
|
||||
lf_t1 = lf[lf$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~obese2
|
||||
, 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$mosaic)]
|
||||
|
||||
stats_un_t1$timepoint = "t1"
|
||||
stats_un_t1$n_obs = length(unique(lf_t1_comp$mosaic)) # CHECK
|
||||
|
||||
stats_un_t1 = as.data.frame(stats_un_t1)
|
||||
class(stats_un_t1)
|
||||
|
||||
# check: satisfied!!!!
|
||||
wilcox.test()
|
||||
|
||||
|
||||
#==============
|
||||
# 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~obese2
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t2
|
||||
, data = lf_t2_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
stats_un_t2$timepoint = "t2"
|
||||
stats_un_t2$n_obs = length(unique(lf_t2_comp$mosaic)) # CHECK
|
||||
|
||||
stats_un_t2 = as.data.frame(stats_un_t2)
|
||||
class(stats_un_t2)
|
||||
|
||||
# check: satisfied!!!!
|
||||
wilcox.test()
|
||||
|
||||
#==============
|
||||
# unpaired: t3
|
||||
#==============
|
||||
lf_t3 = lf[lf$timepoint == "t3",]
|
||||
lf_t3_comp = lf_t3[-which(is.na(lf_t3$value)),]
|
||||
|
||||
stats_un_t3 = compare_means(value~obese2
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t3
|
||||
, data = lf_t3_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
stats_un_t3$timepoint = "t3"
|
||||
stats_un_t3$n_obs = length(unique(lf_t3_comp$mosaic)) # CHECK
|
||||
|
||||
stats_un_t3 = as.data.frame(stats_un_t3)
|
||||
class(stats_un_t3)
|
||||
|
||||
# check: satisfied!!!!
|
||||
wilcox.test()
|
||||
|
||||
#==============
|
||||
# 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.))
|
||||
|
||||
# reflect stats method correctly
|
||||
combined_unpaired_stats$method
|
||||
combined_unpaired_stats$method = gsub("Wilcoxon", "Wilcoxon_unpaired", combined_unpaired_stats$method)
|
||||
combined_unpaired_stats$method
|
||||
|
||||
# replace "." in colnames with "_"
|
||||
colnames(combined_unpaired_stats)
|
||||
#names(combined_unpaired_stats) = gsub("\.", "_", names(combined_unpaired_stats)) # weird!!!!
|
||||
|
||||
colnames(combined_unpaired_stats) = c("mediator"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "p"
|
||||
, "p_adj"
|
||||
, "p_format"
|
||||
, "p_signif"
|
||||
, "method"
|
||||
, "timepoint"
|
||||
, "n_obs")
|
||||
|
||||
colnames(combined_unpaired_stats)
|
||||
combined_unpaired_stats$sample_type = "sam"
|
||||
|
||||
# add an extra column for padjust_signif
|
||||
combined_unpaired_stats$padjust_signif = round(combined_unpaired_stats$p_adj, digits = 2)
|
||||
|
||||
# add appropriate symbols for padjust_signif
|
||||
#combined_unpaired_stats = combined_unpaired_stats %>%
|
||||
# mutate(padjust_signif = case_when(padjust_signif == 0.05 ~ "."
|
||||
# , padjust_signif <0.05 ~ '*'
|
||||
# , padjust_signif <=0.01 ~ '**'
|
||||
# , padjust_signif <=0.001 ~ '***'
|
||||
# , padjust_signif <=0.0001 ~ '****'
|
||||
# , TRUE ~ 'ns'))
|
||||
|
||||
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'))
|
||||
|
||||
# reorder columns
|
||||
print("preparing to reorder columns...")
|
||||
colnames(combined_unpaired_stats)
|
||||
my_col_order2 = c("mediator"
|
||||
, "timepoint"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "method"
|
||||
, "p"
|
||||
, "p_format"
|
||||
, "p_signif"
|
||||
, "p_adj"
|
||||
, "padjust_signif")
|
||||
|
||||
if( length(my_col_order2) == ncol(combined_unpaired_stats) && isin(my_col_order2, 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()
|
||||
}
|
||||
|
||||
combined_unpaired_stats_f_sam = combined_unpaired_stats_f
|
||||
|
||||
#******************
|
||||
# write output file
|
||||
#******************
|
||||
cat("UNpaired stats for groups will be:", stats_time_unpaired)
|
||||
write.csv(combined_unpaired_stats_f, stats_time_unpaired, row.names = FALSE)
|
||||
|
||||
255
stats_unpaired_serum.R
Normal file
255
stats_unpaired_serum.R
Normal file
|
|
@ -0,0 +1,255 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd('~/git/mosaic_2020/')
|
||||
getwd()
|
||||
############################################################
|
||||
# TASK: summary stats of mediators by time and outcome
|
||||
############################################################
|
||||
# load libraries and packages
|
||||
|
||||
#source("../Header_TT.R")
|
||||
library(tidyverse)
|
||||
library(ggpubr)
|
||||
library(rstatix)
|
||||
library(Hmisc)
|
||||
library(qwraps2)
|
||||
#==========================================================
|
||||
#datadir = "~/git/covid19/Data"
|
||||
source("mosaic_bmi_data_extraction_v2.R")
|
||||
|
||||
|
||||
#=============
|
||||
# Input
|
||||
#=============
|
||||
|
||||
#infile_icu_wf = paste0(datadir,"/icu_covid_wf.csv")
|
||||
#infile_icu_lf = paste0(datadir,"/icu_covid_lf.csv")
|
||||
|
||||
# version 2
|
||||
#infile_icu_wf = paste0(datadir,"/icu_covid_wf_v3.csv")
|
||||
#infile_icu_lf = paste0(datadir,"/icu_covid_lf_v3.csv")
|
||||
|
||||
#serum_adults_lf
|
||||
|
||||
#=============
|
||||
# Output
|
||||
#=============
|
||||
outdir = paste0("~/git/mosaic_2020/version1")
|
||||
|
||||
# unpaired analysis
|
||||
stats_time_unpaired = paste0(outdir, "stats_unpaired_serum.csv")
|
||||
|
||||
#%%========================================================
|
||||
# read file
|
||||
#wf_data = read.csv(infile_icu_wf , stringsAsFactors = F)
|
||||
#dim(wf_data)
|
||||
|
||||
#lf_data = read.csv(infile_icu_lf , stringsAsFactors = F)
|
||||
#dim(lf_data)
|
||||
|
||||
#%%========================================================
|
||||
# data assignment for stats
|
||||
#wf = wf_data
|
||||
#lf = lf_data
|
||||
wf = serum_df_adults_clean
|
||||
lf = serum_adults_lf
|
||||
table(lf$timepoint)
|
||||
lf$timepoint = paste0("t", lf$timepoint)
|
||||
|
||||
########################################################################
|
||||
# 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$value)),]
|
||||
stats_un_t1 = compare_means(value~obese2
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t1
|
||||
, data = lf_t1_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
foo$mosaic[!unique(foo$mosaic)%in%unique(lf_t1_comp$mosaic)]
|
||||
|
||||
stats_un_t1$timepoint = "t1"
|
||||
stats_un_t1$n_obs = length(unique(lf_t1_comp$mosaic)) # CHECK
|
||||
|
||||
stats_un_t1 = as.data.frame(stats_un_t1)
|
||||
class(stats_un_t1)
|
||||
|
||||
# check: satisfied!!!!
|
||||
#wilcox.test()
|
||||
|
||||
#==============
|
||||
# unpaired: t2
|
||||
#==============
|
||||
lf_t2 = lf[lf$timepoint == "t2",]
|
||||
lf_t2_comp = lf_t2[-which(is.na(lf_t2$value)),]
|
||||
|
||||
stats_un_t2 = compare_means(value~obese2
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t2
|
||||
, data = lf_t2_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
stats_un_t2$timepoint = "t2"
|
||||
stats_un_t2$n_obs = length(unique(lf_t2_comp$mosaic)) # CHECK
|
||||
|
||||
stats_un_t2 = as.data.frame(stats_un_t2)
|
||||
class(stats_un_t2)
|
||||
|
||||
# check: satisfied!!!!
|
||||
wilcox.test()
|
||||
|
||||
#==============
|
||||
# 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~obese2
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t3
|
||||
, data = lf_t3_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
stats_un_t3$timepoint = "t3"
|
||||
stats_un_t3$n_obs = length(unique(lf_t3_comp$mosaic)) # CHECK
|
||||
|
||||
stats_un_t3 = as.data.frame(stats_un_t3)
|
||||
class(stats_un_t3)
|
||||
|
||||
# check: satisfied!!!!
|
||||
wilcox.test()
|
||||
|
||||
#==============
|
||||
# Rbind these dfs
|
||||
#==============
|
||||
str(stats_un_t1);str(stats_un_t2); str(stats_un_t3)
|
||||
|
||||
n_dfs = 3
|
||||
|
||||
if ( all.equal(nrow(stats_un_t1), nrow(stats_un_t2), nrow(stats_un_t3)) &&
|
||||
all.equal(ncol(stats_un_t1), ncol(stats_un_t2), ncol(stats_un_t3)) ) {
|
||||
expected_rows = nrow(stats_un_t1) * n_dfs
|
||||
expected_cols = ncol(stats_un_t1)
|
||||
print("PASS: expected_rows and cols variables generated for downstream sanity checks")
|
||||
}else{
|
||||
cat("FAIL: dfs have different no. of rows and cols"
|
||||
, "\nCheck harcoded value of n_dfs"
|
||||
, "\nexpected_rows and cols could not be generated")
|
||||
quit()
|
||||
}
|
||||
|
||||
if ( all.equal(colnames(stats_un_t1), colnames(stats_un_t2), colnames(stats_un_t3)) ){
|
||||
print("PASS: colnames match. Rbind the 3 dfs...")
|
||||
combined_unpaired_stats = rbind(stats_un_t1, stats_un_t2, stats_un_t3)
|
||||
} else{
|
||||
cat("FAIL: cannot combined dfs. Colnames don't match!")
|
||||
quit()
|
||||
}
|
||||
|
||||
if ( nrow(combined_unpaired_stats) == expected_rows && ncol(combined_unpaired_stats) == expected_cols ){
|
||||
cat("PASS: combined_df has expected dimension"
|
||||
, "\nNo. of rows in combined_df:", nrow(combined_unpaired_stats)
|
||||
, "\nNo. of cols in combined_df:", ncol(combined_unpaired_stats) )
|
||||
}else{
|
||||
cat("FAIL: combined_df dimension mismatch")
|
||||
quit()
|
||||
}
|
||||
|
||||
#===============================================================
|
||||
# formatting df
|
||||
# delete unnecessary column
|
||||
combined_unpaired_stats = subset(combined_unpaired_stats, select = -c(.y.))
|
||||
|
||||
# reflect stats method correctly
|
||||
combined_unpaired_stats$method
|
||||
combined_unpaired_stats$method = gsub("Wilcoxon", "Wilcoxon_unpaired", combined_unpaired_stats$method)
|
||||
combined_unpaired_stats$method
|
||||
|
||||
# replace "." in colnames with "_"
|
||||
colnames(combined_unpaired_stats)
|
||||
#names(combined_unpaired_stats) = gsub("\.", "_", names(combined_unpaired_stats)) # weird!!!!
|
||||
|
||||
colnames(combined_unpaired_stats) = c("mediator"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "p"
|
||||
, "p_adj"
|
||||
, "p_format"
|
||||
, "p_signif"
|
||||
, "method"
|
||||
, "timepoint"
|
||||
, "n_obs")
|
||||
|
||||
colnames(combined_unpaired_stats)
|
||||
combined_unpaired_stats$sample_type = "serum"
|
||||
|
||||
# add an extra column for padjust_signif
|
||||
combined_unpaired_stats$padjust_signif = round(combined_unpaired_stats$p_adj, digits = 2)
|
||||
|
||||
# add appropriate symbols for padjust_signif
|
||||
#combined_unpaired_stats = combined_unpaired_stats %>%
|
||||
# mutate(padjust_signif = case_when(padjust_signif == 0.05 ~ "."
|
||||
# , padjust_signif <0.05 ~ '*'
|
||||
# , padjust_signif <=0.01 ~ '**'
|
||||
# , padjust_signif <=0.001 ~ '***'
|
||||
# , padjust_signif <=0.0001 ~ '****'
|
||||
# , TRUE ~ 'ns'))
|
||||
|
||||
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'))
|
||||
|
||||
# reorder columns
|
||||
print("preparing to reorder columns...")
|
||||
colnames(combined_unpaired_stats)
|
||||
my_col_order2 = c("mediator"
|
||||
, "timepoint"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "method"
|
||||
, "p"
|
||||
, "p_format"
|
||||
, "p_signif"
|
||||
, "p_adj"
|
||||
, "padjust_signif")
|
||||
|
||||
if( length(my_col_order2) == ncol(combined_unpaired_stats) && isin(my_col_order2, 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()
|
||||
}
|
||||
|
||||
combined_unpaired_stats_f_serum = combined_unpaired_stats_f
|
||||
|
||||
#******************
|
||||
# write output file
|
||||
#******************
|
||||
cat("UNpaired stats for groups will be:", stats_time_unpaired)
|
||||
write.csv(combined_unpaired_stats_f, stats_time_unpaired, row.names = FALSE)
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue