changed df to adults df to extract relevant info
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5 changed files with 78 additions and 1007 deletions
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@ -3,7 +3,7 @@ getwd()
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setwd('~/git/mosaic_2020/')
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getwd()
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########################################################################
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# TASK: Extract relevant columns from mosaic data
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# TASK: Extract relevant columns from mosaic adults data
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# sam
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# serum
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# npa
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@ -14,17 +14,17 @@ getwd()
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source("read_data.R")
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# clear unnecessary variables
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#rm()
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rm(all_df)
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########################################################################
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#=========
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# sam
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#=========
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sam_regex = regex(".*_sam[1-3]{1}$", ignore_case = T)
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sam_cols_i = str_extract(colnames(all_df), sam_regex) # not boolean
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#sam_cols_b = colnames(all_df)%in%sam_cols_i # boolean
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sam_cols_i = str_extract(colnames(adult_df), sam_regex) # not boolean
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#sam_cols_b = colnames(adult_df)%in%sam_cols_i # boolean
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sam_cols = colnames(all_df)[colnames(all_df)%in%sam_cols_i]
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sam_cols = colnames(adult_df)[colnames(adult_df)%in%sam_cols_i]
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# this contains log columns + daysamp_samXX: omitting these
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sam_regex_log_days = regex("log|day.*_sam[1-3]{1}$", ignore_case = T, perl = T)
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@ -48,7 +48,7 @@ cat("Extracting SAM cols + metadata_cols")
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if ( length(sam_cols_to_extract) == length(meta_data_cols) + length(sam_cols_clean) ){
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cat("Extracing", length(sam_cols_to_extract), "columns for sam")
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sam_df = all_df[, sam_cols_to_extract]
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sam_df = adult_df[, sam_cols_to_extract]
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}else{
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cat("FAIL: length mismatch"
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, "Expeceted to extract:", length(meta_data_cols) + length(sam_cols_clean), "columns"
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@ -61,10 +61,10 @@ colnames_sam_df = colnames(sam_df); colnames_sam_df
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# serum
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#=========
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serum_regex = regex(".*_serum[1-3]{1}$", ignore_case = T)
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serum_cols_i = str_extract(colnames(all_df), serum_regex) # not boolean
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#serum_cols_b = colnames(all_df)%in%serum_cols_i # boolean
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serum_cols_i = str_extract(colnames(adult_df), serum_regex) # not boolean
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#serum_cols_b = colnames(adult_df)%in%serum_cols_i # boolean
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serum_cols = colnames(all_df)[colnames(all_df)%in%serum_cols_i]
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serum_cols = colnames(adult_df)[colnames(adult_df)%in%serum_cols_i]
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# this contains log columns + dayserump_serumXX: omitting these
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serum_regex_log_days = regex("log|day.*_serum[1-3]{1}$", ignore_case = T, perl = T)
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@ -88,7 +88,7 @@ cat("Extracting SERUM cols + metadata_cols")
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if ( length(serum_cols_to_extract) == length(meta_data_cols) + length(serum_cols_clean) ){
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cat("Extracing", length(serum_cols_to_extract), "columns for serum")
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serum_df = all_df[, serum_cols_to_extract]
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serum_df = adult_df[, serum_cols_to_extract]
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}else{
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cat("FAIL: length mismatch"
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, "Expeceted to extract:", length(meta_data_cols) + length(serum_cols_clean), "columns"
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@ -101,10 +101,10 @@ colnames_serum_df = colnames(serum_df); colnames_serum_df
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# npa
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#=========
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npa_regex = regex(".*_npa[1-3]{1}$", ignore_case = T)
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npa_cols_i = str_extract(colnames(all_df), npa_regex) # not boolean
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#npa_cols_b = colnames(all_df)%in%npa_cols_i # boolean
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npa_cols_i = str_extract(colnames(adult_df), npa_regex) # not boolean
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#npa_cols_b = colnames(adult_df)%in%npa_cols_i # boolean
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npa_cols = colnames(all_df)[colnames(all_df)%in%npa_cols_i]
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npa_cols = colnames(adult_df)[colnames(adult_df)%in%npa_cols_i]
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# this contains log columns + daynpap_npaXX: omitting these
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npa_regex_log_days = regex("log|day|vl_samptime|ct.*_npa[1-3]{1}$", ignore_case = T, perl = T)
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@ -128,7 +128,7 @@ cat("Extracting NPA cols + metadata_cols")
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if ( length(npa_cols_to_extract) == length(meta_data_cols) + length(npa_cols_clean) ){
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cat("Extracing", length(npa_cols_to_extract), "columns for npa")
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npa_df = all_df[, npa_cols_to_extract]
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npa_df = adult_df[, npa_cols_to_extract]
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}else{
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cat("FAIL: length mismatch"
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, "Expeceted to extract:", length(meta_data_cols) + length(npa_cols_clean), "columns"
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@ -137,8 +137,11 @@ if ( length(npa_cols_to_extract) == length(meta_data_cols) + length(npa_cols_cle
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colnames_npa_df = colnames(npa_df); colnames_npa_df
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#==============
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# quick checks
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#==============
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colnames_check = as.data.frame(cbind(colnames_sam_df, colnames_serum_df, colnames_npa_df))
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tail(colnames_check)
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tail(colnames_check) # gives a warning message due to differeing no. of rows for cbind!
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# put NA where a match doesn't exist
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# unmatched lengths
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@ -168,12 +171,16 @@ quick_check = as.data.frame(cbind(metadata_all$mosaic
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, metadata_all$adult
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, metadata_all$age
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, metadata_all$obesity
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, metadata_all$obese2))
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, metadata_all$obese2
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))
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colnames(quick_check) = c("mosaic", "adult", "age", "obesity", "obese2")
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##########################################################################
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# LF data
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##########################################################################
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cols_to_omit = c("adult", "obese2"
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, "height", "height_unit", "weight"
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, "weight_unit", "visual_est_bmi", "bmi_rating")
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#==============
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# lf data: sam
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@ -181,19 +188,11 @@ colnames(quick_check) = c("mosaic", "adult", "age", "obesity", "obese2")
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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$adult == 1,]
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cols_to_omit = c("type"
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#, "flustat"
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#, "obesity"
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#, "obese2"
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, "height", "height_unit", "weight"
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, "weight_unit", "visual_est_bmi", "bmi_rating")
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#sam_df_adults_clean = sam_df_adults[!cols_to_omit]
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#sam_df_adults = sam_df[sam_df$adult == 1,] # resolved at source and only dealing wit age as adult
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sam_df_adults = sam_df
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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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sam_wf = sam_df_adults[wf_cols]
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pivot_cols = meta_data_cols
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# subselect pivot_cols
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@ -208,25 +207,25 @@ 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_clean) * (length(sam_df_adults_clean) - length(pivot_cols)); expected_rows_sam_lf
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expected_rows_sam_lf = nrow(sam_wf) * (length(sam_wf) - length(pivot_cols)); expected_rows_sam_lf
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# using regex:
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sam_adults_lf = sam_df_adults_clean %>%
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sam_lf = sam_wf %>%
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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(sam_adults_lf) == expected_rows_sam_lf) & (sum(table(is.na(sam_adults_lf$mediator))) == expected_rows_sam_lf)
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(nrow(sam_lf) == expected_rows_sam_lf) & (sum(table(is.na(sam_lf$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_adults_lf)
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, "\nNo. of cols: ", ncol(sam_adults_lf)))
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, "\nNo. of rows: ", nrow(sam_lf)
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, "\nNo. of cols: ", ncol(sam_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_adults_lf)
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, "\nGot: ", nrow(sam_lf)
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, "\ncheck expected rows calculation!"))
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quit()
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}
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@ -241,11 +240,11 @@ if (
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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$adult == 1,]
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#serum_df_adults = serum_df[serum_df$adult == 1,] # extract based on age
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serum_df_adults = serum_df
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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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serum_wf = 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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@ -259,25 +258,25 @@ 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_serum_lf = nrow(serum_df_adults_clean) * (length(serum_df_adults_clean) - length(pivot_cols)); expected_rows_serum_lf
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expected_rows_serum_lf = nrow(serum_wf) * (length(serum_wf) - 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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serum_lf = serum_wf %>%
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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$mediator))) == expected_rows_serum_lf)
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(nrow(serum_lf) == expected_rows_serum_lf) & (sum(table(is.na(serum_lf$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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, "\nNo. of rows: ", nrow(serum_lf)
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, "\nNo. of cols: ", ncol(serum_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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, "\nGot: ", nrow(serum_lf)
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, "\ncheck expected rows calculation!"))
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quit()
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}
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@ -288,11 +287,11 @@ if (
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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$adult == 1,]
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#npa_df_adults_clean = npa_df_adults[!cols_to_omit]
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#npa_df_adults = npa_df[npa_df$adult == 1,] # extract based on age
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npa_df_adults = npa_df
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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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npa_wf = 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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@ -306,25 +305,25 @@ 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_npa_lf = nrow(npa_df_adults_clean) * (length(npa_df_adults_clean) - length(pivot_cols)); expected_rows_npa_lf
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expected_rows_npa_lf = nrow(npa_wf) * (length(npa_wf) - 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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npa_lf = npa_wf %>%
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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$mediator))) == expected_rows_npa_lf)
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(nrow(npa_lf) == expected_rows_npa_lf) & (sum(table(is.na(npa_lf$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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, "\nNo. of rows: ", nrow(npa_lf)
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, "\nNo. of cols: ", ncol(npa_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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, "\nGot: ", nrow(npa_lf)
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, "\ncheck expected rows calculation!"))
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quit()
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}
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@ -334,12 +333,14 @@ if (
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rm(sam_regex, sam_regex_log_days, sam_cols, 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(adult_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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rm(i, j
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#, expected_cols
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, start, wf_cols, extra_cols, cols_to_omit)
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# rm not_clean dfs
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rm(sam_df_adults, serum_df_adults, npa_df_adults)
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# rm df containing non-adults
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# rm df
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rm(sam_df, serum_df, npa_df)
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26
read_data.R
26
read_data.R
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@ -26,9 +26,16 @@ all_df <- read.csv("/home/backup/MOSAIC/MEDIATOR_Data/master_file/Mosaic_master_
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, fileEncoding = 'latin1')
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# meta data columns
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meta_data_cols = c("mosaic", "gender", "age", "adult", "flustat", "type"
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, "obesity", "obese2", "height", "height_unit", "weight"
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, "weight_unit", "visual_est_bmi", "bmi_rating")
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meta_data_cols = c("mosaic", "gender", "age"
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, "adult"
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, "flustat", "type"
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, "obesity"
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, "obese2"
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, "height", "height_unit"
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, "weight", "weight_unit"
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, "ia_height_ftin", "ia_height_m", "ia_weight"
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, "visual_est_bmi", "bmi_rating"
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)
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# check if these columns to select are present in the data
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meta_data_cols%in%colnames(all_df)
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@ -36,6 +43,19 @@ all(meta_data_cols%in%colnames(all_df))
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metadata_all = all_df[, meta_data_cols]
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#==============
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# adult patients
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#==============
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adult_df = all_df[all_df$age>=18,]
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if (table(adult_df$adult == 1)[[1]] == nrow(adult_df) ){
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cat ("PASS: adult df extracted successfully")
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} else{
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cat ("FAIL: adult df number mismatch!")
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}
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#============
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# hc
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#============
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@ -1,315 +0,0 @@
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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: unpaired (time) analysis of mediators: NPA
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############################################################
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#=============
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# Input
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#=============
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source("data_extraction_formatting.R")
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# clear variables
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rm(sam_adults_lf, sam_df_adults_clean
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, serum_adults_lf, serum_df_adults_clean)
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rm(colnames_sam_df, expected_rows_sam_lf
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, colnames_serum_df, expected_rows_serum_lf)
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rm(pivot_cols)
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my_sample_type = "npa"
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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("stats_time_unpaired_", my_sample_type, ".csv")
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stats_time_unpaired = paste0(outdir_stats, outfile_name)
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#%%========================================================
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# data assignment for stats
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wf = npa_df_adults_clean
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lf = npa_adults_lf
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#%%========================================================
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||||
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~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$mosaic)]
|
||||
|
||||
stats_un_t1$timepoint = "t1"
|
||||
|
||||
stats_un_t1 = as.data.frame(stats_un_t1)
|
||||
class(stats_un_t1)
|
||||
|
||||
# calculate n_obs for each mediator
|
||||
n_t1 = data.frame(table(lf_t1_comp$mediator))
|
||||
colnames(n_t1) = c("mediator", "n_obs")
|
||||
n_t1$mediator = as.character(n_t1$mediator)
|
||||
|
||||
# merge stats + n_obs df
|
||||
merging_cols = intersect(names(stats_un_t1), names(n_t1)); merging_cols
|
||||
if (all(n_t1$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)
|
||||
stats_un_t2$timepoint = "t2"
|
||||
|
||||
stats_un_t2 = as.data.frame(stats_un_t2)
|
||||
class(stats_un_t2)
|
||||
|
||||
# calculate n_obs for each mediator
|
||||
n_t2 = data.frame(table(lf_t2_comp$mediator))
|
||||
colnames(n_t2) = c("mediator", "n_obs")
|
||||
n_t2$mediator = as.character(n_t2$mediator)
|
||||
|
||||
# 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$value)),]
|
||||
|
||||
stats_un_t3 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t3
|
||||
, data = lf_t3_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
stats_un_t3$timepoint = "t3"
|
||||
|
||||
stats_un_t3 = as.data.frame(stats_un_t3)
|
||||
class(stats_un_t3)
|
||||
|
||||
# calculate n_obs for each mediator
|
||||
n_t3 = data.frame(table(lf_t3_comp$mediator))
|
||||
colnames(n_t3) = c("mediator", "n_obs")
|
||||
n_t3$mediator = as.character(n_t3$mediator)
|
||||
|
||||
# merge stats + n_obs df
|
||||
merging_cols = intersect(names(stats_un_t3), names(n_t3)); merging_cols
|
||||
if (all(n_t3$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"
|
||||
, "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"
|
||||
, "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)
|
||||
|
||||
#******************
|
||||
# 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)
|
||||
|
|
@ -1,319 +0,0 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd("~/git/mosaic_2020/")
|
||||
getwd()
|
||||
############################################################
|
||||
# TASK: unpaired (time) analysis of mediators: SAM
|
||||
############################################################
|
||||
#=============
|
||||
# Input
|
||||
#=============
|
||||
source("data_extraction_formatting.R")
|
||||
|
||||
# clear variables
|
||||
rm(npa_adults_lf, npa_df_adults_clean
|
||||
, serum_adults_lf, serum_df_adults_clean)
|
||||
rm(colnames_npa_df, expected_rows_npa_lf
|
||||
, colnames_serum_df, expected_rows_serum_lf)
|
||||
|
||||
rm(pivot_cols)
|
||||
|
||||
my_sample_type = "sam"
|
||||
#=============
|
||||
# Output: unpaired analysis of time for sam
|
||||
#=============
|
||||
outfile_name = paste0("stats_time_unpaired_", my_sample_type, ".csv")
|
||||
stats_time_unpaired = paste0(outdir_stats, outfile_name)
|
||||
#%%========================================================
|
||||
# data assignment for stats
|
||||
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$value)),]
|
||||
stats_un_t1 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t1
|
||||
, data = lf_t1_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
foo$mosaic[!unique(foo$mosaic)%in%unique(lf_t1_comp$mosaic)]
|
||||
|
||||
stats_un_t1$timepoint = "t1"
|
||||
|
||||
stats_un_t1 = as.data.frame(stats_un_t1)
|
||||
class(stats_un_t1)
|
||||
|
||||
# calculate n_obs for each mediator
|
||||
n_t1 = data.frame(table(lf_t1_comp$mediator))
|
||||
colnames(n_t1) = c("mediator", "n_obs")
|
||||
n_t1$mediator = as.character(n_t1$mediator)
|
||||
|
||||
# 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$value)),]
|
||||
|
||||
stats_un_t2 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t2
|
||||
, data = lf_t2_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
stats_un_t2$timepoint = "t2"
|
||||
|
||||
stats_un_t2 = as.data.frame(stats_un_t2)
|
||||
class(stats_un_t2)
|
||||
|
||||
# calculate n_obs for each mediator
|
||||
n_t2 = data.frame(table(lf_t2_comp$mediator))
|
||||
colnames(n_t2) = c("mediator", "n_obs")
|
||||
n_t2$mediator = as.character(n_t2$mediator)
|
||||
|
||||
# merge stats + n_obs df
|
||||
merging_cols = intersect(names(stats_un_t2), names(n_t2)); merging_cols
|
||||
if (all(n_t2$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)
|
||||
|
||||
stats_un_t3$timepoint = "t3"
|
||||
|
||||
stats_un_t3 = as.data.frame(stats_un_t3)
|
||||
class(stats_un_t3)
|
||||
|
||||
# calculate n_obs for each mediator
|
||||
n_t3 = data.frame(table(lf_t3_comp$mediator))
|
||||
colnames(n_t3) = c("mediator", "n_obs")
|
||||
n_t3$mediator = as.character(n_t3$mediator)
|
||||
|
||||
# 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$mediator]
|
||||
stats_un_t3 = merge(stats_un_t3, n_t3, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t3)
|
||||
, "\nncol:", ncol(stats_un_t3))
|
||||
}
|
||||
|
||||
# check: satisfied!!!!
|
||||
# FIXME: supply the col name automatically?
|
||||
wilcox.test(wf$ifna2a_sam3[wf$obesity == 1], wf$ifna2a_sam3[wf$obesity == 0])
|
||||
|
||||
# add bonferroni adjustment as well
|
||||
stats_un_t3$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"
|
||||
, "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"
|
||||
, "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)
|
||||
|
||||
#******************
|
||||
# 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)
|
||||
|
|
@ -1,316 +0,0 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd("~/git/mosaic_2020/")
|
||||
getwd()
|
||||
############################################################
|
||||
# TASK: unpaired (time) analysis of mediators: serum
|
||||
############################################################
|
||||
#=============
|
||||
# Input
|
||||
#=============
|
||||
source("data_extraction_formatting.R")
|
||||
|
||||
# clear variables
|
||||
rm(sam_adults_lf, sam_df_adults_clean
|
||||
, npa_adults_lf, npa_df_adults_clean)
|
||||
rm(colnames_sam_df, expected_rows_sam_lf
|
||||
, colnames_npa_df, expected_rows_npa_lf)
|
||||
|
||||
rm(pivot_cols)
|
||||
|
||||
my_sample_type = "serum"
|
||||
#=============
|
||||
# Output: unpaired analysis of time for serum
|
||||
#=============
|
||||
outfile_name = paste0("stats_time_unpaired_", my_sample_type, ".csv")
|
||||
stats_time_unpaired = paste0(outdir_stats, outfile_name)
|
||||
#%%========================================================
|
||||
# data assignment for stats
|
||||
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~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$mosaic)]
|
||||
|
||||
stats_un_t1$timepoint = "t1"
|
||||
|
||||
stats_un_t1 = as.data.frame(stats_un_t1)
|
||||
class(stats_un_t1)
|
||||
|
||||
# calculate n_obs for each mediator
|
||||
n_t1 = data.frame(table(lf_t1_comp$mediator))
|
||||
colnames(n_t1) = c("mediator", "n_obs")
|
||||
n_t1$mediator = as.character(n_t1$mediator)
|
||||
|
||||
# merge stats + n_obs df
|
||||
merging_cols = intersect(names(stats_un_t1), names(n_t1)); merging_cols
|
||||
if (all(n_t1$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)
|
||||
stats_un_t2$timepoint = "t2"
|
||||
|
||||
stats_un_t2 = as.data.frame(stats_un_t2)
|
||||
class(stats_un_t2)
|
||||
|
||||
# calculate n_obs for each mediator
|
||||
n_t2 = data.frame(table(lf_t2_comp$mediator))
|
||||
colnames(n_t2) = c("mediator", "n_obs")
|
||||
n_t2$mediator = as.character(n_t2$mediator)
|
||||
|
||||
# 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$value)),]
|
||||
|
||||
stats_un_t3 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t3
|
||||
, data = lf_t3_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
stats_un_t3$timepoint = "t3"
|
||||
|
||||
stats_un_t3 = as.data.frame(stats_un_t3)
|
||||
class(stats_un_t3)
|
||||
|
||||
|
||||
# calculate n_obs for each mediator
|
||||
n_t3 = data.frame(table(lf_t3_comp$mediator))
|
||||
colnames(n_t3) = c("mediator", "n_obs")
|
||||
n_t3$mediator = as.character(n_t3$mediator)
|
||||
|
||||
# merge stats + n_obs df
|
||||
merging_cols = intersect(names(stats_un_t3), names(n_t3)); merging_cols
|
||||
if (all(n_t3$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"
|
||||
, "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"
|
||||
, "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)
|
||||
|
||||
#******************
|
||||
# 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