logistic regression for outcome and meds
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10 changed files with 265 additions and 1904 deletions
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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: boxplots at T1
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# FIXME: currently not rendering, problem with NAs for stats?
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############################################################
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my_samples = "npa_sam_serum"
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#=============
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# Input
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#=============
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#source("data_extraction_formatting_non_asthmatics.R")
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source("plot_data_na.R")
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# check: adult variable and age variable discrepancy!
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metadata_all$mosaic[metadata_all$adult==1 & metadata_all$age<=18]
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#=============
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# Output:
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#=============
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outfile_bp = paste0("boxplots_linear_NA_", my_samples, ".pdf")
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output_boxplot = paste0(outdir_plots, outfile_bp); output_boxplot
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#===============================
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# data assignment for plots
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#================================
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#-----------
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# npa
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#-----------
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##wf_fp_npa = npa_wf[npa_wf$flustat == 1,]
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#lf_fp_npa = npa_lf[npa_lf$flustat == 1,]
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#lf_fp_npa$timepoint = paste0("t", lf_fp_npa$timepoint)
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#lf_fp_npa$timepoint = as.factor(lf_fp_npa$timepoint)
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#lf_fp_npa$obesity = as.factor(lf_fp_npa$obesity)
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#table(lf_fp_npa$mediator)
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#head(lf_fp_npa$value[lf_fp_npa$mediator == "vitd"])
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#lf_fp_npa = lf_fp_npa[!lf_fp_npa$mediator == "vitd",]
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#-----------
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# sam
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#-----------
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##wf_fp_sam = samm_wf[samm_wf$flustat == 1,]
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#lf_fp_sam = sam_lf[sam_lf$flustat == 1,]
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#lf_fp_sam$timepoint = paste0("t", lf_fp_sam$timepoint)
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#lf_fp_sam$timepoint = as.factor(lf_fp_sam$timepoint)
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#lf_fp_sam$obesity = as.factor(lf_fp_sam$obesity)
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#table(lf_fp_sam$mediator)
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#head(lf_fp_sam$value[lf_fp_sam$mediator == "vitd"])
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#lf_fp_sam = lf_fp_sam[!lf_fp_sam$mediator == "vitd",]
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#-----------
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# serum
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#-----------
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##wf_fp_serum = serum_wf[serum_wf$flustat == 1,]
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#lf_fp_serum = serum_lf[serum_lf$flustat == 1,]
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#lf_fp_serum$timepoint = paste0("t", lf_fp_serum$timepoint)
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#lf_fp_serum$timepoint = as.factor(lf_fp_serum$timepoint)
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#lf_fp_serum$obesity = as.factor(lf_fp_serum$obesity)
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########################################################################
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cat("Output plots will be in:", output_boxplot)
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pdf(output_boxplot, width = 20, height = 15)
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#=======================================================================
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# NPA
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#=======================================================================
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if (is.factor(lf_fp_npa$timepoint) && is.factor(lf_fp_npa$timepoint)){
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cat ("PASS: required groups are factors")
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}
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#------------------------------------------
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title_npa_linear = "NPA (Linear)"
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#-----------------------------------------
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bxp_npa_linear <- ggboxplot(lf_fp_npa, x = "timepoint", y = "value",
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color = "obesity", palette = c("#00BFC4", "#F8766D")) +
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facet_wrap(~mediator, nrow = 7, ncol = 5, scales = "free_y", shrink = T)+
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#scale_y_log10() +
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theme(axis.text.x = element_text(size = 15)
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, axis.text.y = element_text(size = 15
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 15)
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, axis.title.y = element_text(size = 15)
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, plot.title = element_text(size = 20, hjust = 0.5)
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, strip.text.x = element_text(size = 15, colour = "black")
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, legend.title = element_text(color = "black", size = 20)
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, legend.text = element_text(size = 15)
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, legend.direction = "horizontal") +
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labs(title = title_npa_linear
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, x = ""
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, y = "Levels")
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bxp_npa_linear
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#=======================================================================
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# SAM
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#=======================================================================
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if (is.factor(lf_fp_sam$timepoint) && is.factor(lf_fp_sam$timepoint)){
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cat ("PASS: required groups are factors")
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}
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#------------------------------------------
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title_sam_linear = "SAM (Linear)"
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#-----------------------------------------
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bxp_sam_linear <- ggboxplot(lf_fp_sam, x = "timepoint", y = "value",
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color = "obesity", palette = c("#00BFC4", "#F8766D")) +
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facet_wrap(~mediator, nrow = 7, ncol = 5, scales = "free_y", shrink = T)+
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#scale_y_log10() +
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theme(axis.text.x = element_text(size = 15)
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, axis.text.y = element_text(size = 15
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 15)
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, axis.title.y = element_text(size = 15)
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, plot.title = element_text(size = 20, hjust = 0.5)
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, strip.text.x = element_text(size = 15, colour = "black")
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, legend.title = element_text(color = "black", size = 20)
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, legend.text = element_text(size = 15)
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, legend.direction = "horizontal") +
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labs(title = title_sam_linear
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, x = ""
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, y = "Levels")
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bxp_sam_linear
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#=======================================================================
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# SERUM
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#=======================================================================
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if (is.factor(lf_fp_serum$timepoint) && is.factor(lf_fp_serum$timepoint)){
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cat ("PASS: required groups are factors")
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}
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table(lf_fp_serum$mediator)
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#------------------------------------------
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title_serum_linear = "SERUM (Linear)"
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#-----------------------------------------
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bxp_serum_linear <- ggboxplot(lf_fp_serum, x = "timepoint", y = "value",
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color = "obesity", palette = c("#00BFC4", "#F8766D")) +
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facet_wrap(~mediator, nrow = 7, ncol = 5, scales = "free_y", shrink = T)+
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#scale_y_log10() +
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theme(axis.text.x = element_text(size = 15)
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, axis.text.y = element_text(size = 15
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 15)
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, axis.title.y = element_text(size = 15)
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, plot.title = element_text(size = 20, hjust = 0.5)
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, strip.text.x = element_text(size = 15, colour = "black")
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, legend.title = element_text(color = "black", size = 20)
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, legend.text = element_text(size = 15)
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, legend.direction = "horizontal") +
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labs(title = title_serum_linear
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, x = ""
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, y = "Levels")
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bxp_serum_linear
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#==========================================================================
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dev.off()
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############################################################################
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170
boxplot_log_na.R
170
boxplot_log_na.R
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@ -1,170 +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: boxplots at T1
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# FIXME: currently not rendering, problem with NAs for stats?
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############################################################
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my_samples = "npa_sam_serum"
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#=============
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# Input
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#=============
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#source("data_extraction_formatting_non_asthmatics.R")
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source("plot_data_na.R")
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# check: adult variable and age variable discrepancy!
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metadata_all$mosaic[metadata_all$adult==1 & metadata_all$age<=18]
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#=============
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# Output:
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#=============
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outfile_bp_log = paste0("boxplots_log_NA_", my_samples, ".pdf")
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output_boxplot_log = paste0(outdir_plots, outfile_bp_log); output_boxplot_log
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#===============================
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# data assignment for plots
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#================================
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#-----------
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# npa
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#-----------
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#wf_fp_npa = npa_wf[npa_wf$flustat == 1,]
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lf_fp_npa = npa_lf[npa_lf$flustat == 1,]
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lf_fp_npa$timepoint = paste0("t", lf_fp_npa$timepoint)
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lf_fp_npa$timepoint = as.factor(lf_fp_npa$timepoint)
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lf_fp_npa$obesity = as.factor(lf_fp_npa$obesity)
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table(lf_fp_npa$mediator)
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head(lf_fp_npa$value[lf_fp_npa$mediator == "vitd"])
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lf_fp_npa = lf_fp_npa[!lf_fp_npa$mediator == "vitd",]
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#-----------
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# sam
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#-----------
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#wf_fp_sam = samm_wf[samm_wf$flustat == 1,]
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lf_fp_sam = sam_lf[sam_lf$flustat == 1,]
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lf_fp_sam$timepoint = paste0("t", lf_fp_sam$timepoint)
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lf_fp_sam$timepoint = as.factor(lf_fp_sam$timepoint)
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lf_fp_sam$obesity = as.factor(lf_fp_sam$obesity)
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table(lf_fp_sam$mediator)
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head(lf_fp_sam$value[lf_fp_sam$mediator == "vitd"])
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lf_fp_sam = lf_fp_sam[!lf_fp_sam$mediator == "vitd",]
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#-----------
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# serum
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#-----------
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#wf_fp_serum = serum_wf[serum_wf$flustat == 1,]
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lf_fp_serum = serum_lf[serum_lf$flustat == 1,]
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lf_fp_serum$timepoint = paste0("t", lf_fp_serum$timepoint)
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lf_fp_serum$timepoint = as.factor(lf_fp_serum$timepoint)
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lf_fp_serum$obesity = as.factor(lf_fp_serum$obesity)
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########################################################################
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cat("Output plots will be in:", output_boxplot_log)
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pdf(output_boxplot_log, width = 20, height = 15)
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#=======================================================================
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# NPA
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#=======================================================================
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if (is.factor(lf_fp_npa$timepoint) && is.factor(lf_fp_npa$timepoint)){
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cat ("PASS: required groups are factors")
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}
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#------------------------------------
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title_npa_log = "NPA (Log)"
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#-----------------------------------
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bxp_npa_log <- ggboxplot(lf_fp_npa, x = "timepoint", y = "value",
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color = "obesity", palette = c("#00BFC4", "#F8766D")) +
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facet_wrap(~mediator, nrow = 7, ncol = 5, scales = "free_y", shrink = F)+
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scale_y_log10() +
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theme(axis.text.x = element_text(size = 15)
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, axis.text.y = element_text(size = 15
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 15)
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, axis.title.y = element_text(size = 15)
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, plot.title = element_text(size = 20, hjust = 0.5)
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, strip.text.x = element_text(size = 15, colour = "black")
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, legend.title = element_text(color = "black", size = 20)
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, legend.text = element_text(size = 15)
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, legend.direction = "horizontal") +
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labs(title = title_npa_log
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, x = ""
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, y = "Levels (Log)")
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bxp_npa_log
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#=======================================================================
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# SAM
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#=======================================================================
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if (is.factor(lf_fp_sam$timepoint) && is.factor(lf_fp_sam$timepoint)){
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cat ("PASS: required groups are factors")
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}
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#------------------------------------
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title_sam_log = "SAM (Log)"
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#-----------------------------------
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bxp_sam_log <- ggboxplot(lf_fp_sam, x = "timepoint", y = "value",
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color = "obesity", palette = c("#00BFC4", "#F8766D")) +
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facet_wrap(~mediator, nrow = 7, ncol = 5, scales = "free_y", shrink = T)+
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scale_y_log10() +
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theme(axis.text.x = element_text(size = 15)
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, axis.text.y = element_text(size = 15
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 15)
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, axis.title.y = element_text(size = 15)
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, plot.title = element_text(size = 20, hjust = 0.5)
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, strip.text.x = element_text(size = 15, colour = "black")
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, legend.title = element_text(color = "black", size = 20)
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, legend.text = element_text(size = 15)
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, legend.direction = "horizontal") +
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labs(title = title_sam_log
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, x = ""
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, y = "Levels (Log)")
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bxp_sam_log
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#=======================================================================
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# SERUM
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#=======================================================================
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if (is.factor(lf_fp_serum$timepoint) && is.factor(lf_fp_serum$timepoint)){
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cat ("PASS: required groups are factors")
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}
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table(lf_fp_serum$mediator)
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#------------------------------------
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title_serum_log = "SERUM (Log)"
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#-----------------------------------
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bxp_serum_log <- ggboxplot(lf_fp_serum, x = "timepoint", y = "value",
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color = "obesity", palette = c("#00BFC4", "#F8766D")) +
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facet_wrap(~mediator, nrow = 7, ncol = 5, scales = "free_y", shrink = T)+
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scale_y_log10() +
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theme(axis.text.x = element_text(size = 15)
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, axis.text.y = element_text(size = 15
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = 15)
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, axis.title.y = element_text(size = 15)
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, plot.title = element_text(size = 20, hjust = 0.5)
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, strip.text.x = element_text(size = 15, colour = "black")
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, legend.title = element_text(color = "black", size = 20)
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, legend.text = element_text(size = 15)
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, legend.direction = "horizontal") +
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labs(title = title_serum_log
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, x = ""
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, y = "Levels (Log)")
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bxp_serum_log
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#==========================================================================
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dev.off()
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############################################################################
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# rm df
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rm(sam_df, serum_df, npa_df)
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rm(colnames_check_f, fp_adults)
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rm(colnames_check_f
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#, fp_adults
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#, fp_adults_na)
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)
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@ -172,6 +172,12 @@ table(clinical_df$asthma, clinical_df$age_bins)
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#1 11 8 12 9
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sum(table(clinical_df$asthma, clinical_df$age_bins)) == nrow(clinical_df)
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table(clinical_df$age_int)
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clinical_df = subset(clinical_df, select = -c(age_int))
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table(clinical_df$age_int)
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class(clinical_df$age_bins)
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clinical_df$age_bins
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#===========================
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# O2 saturation binning
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@ -214,7 +220,6 @@ tot_onset2ini
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onset_initial_bin = cut(clinical_df$onset_2_initial, c(min_in, 4, max_in))
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clinical_df$onset_initial_bin = onset_initial_bin
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sum(table(clinical_df$onset_initial_bin)) == tot_onset2ini
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#=======================
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@ -357,10 +362,6 @@ clinical_df_ics = merge(clinical_df, clinical_ics, by = merging_cols, all = T);
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colnames(clinical_df_ics)
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# change colname of logistic_outcome
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c1 = which(colnames(clinical_df_ics) == "logistic_outcome")
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colnames(clinical_df_ics)[c1] <- "t1_resp_recoded"
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if (nrow(clinical_df_ics) == nrow(clinical_df) & nrow(clinical_ics)){
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cat("\nPASS: No. of rows match, nrow =", nrow(clinical_df_ics)
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, "\nChecking ncols...")
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@ -376,14 +377,33 @@ if (nrow(clinical_df_ics) == nrow(clinical_df) & nrow(clinical_ics)){
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, "\nExpected nrows:", nrow(fp_adults))
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}
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# change the factor vars to integers
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str(clinical_df_ics)
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factor_vars = lapply(clinical_df_ics, class) == "factor"
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table(factor_vars)
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clinical_df_ics[, factor_vars] <- lapply(clinical_df_ics[, factor_vars], as.integer)
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table(factor_vars)
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#=========================
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# add binary outcome for T1 resp score
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#=========================
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table(clinical_df_ics$T1_resp_score)
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str(clinical_df_ics)
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clinical_df_ics$t1_resp_recoded = ifelse(clinical_df_ics$T1_resp_score <3, 0, 1)
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table(clinical_df_ics$t1_resp_recoded)
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#table(clinical_df_ics$steroid)
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table(clinical_df_ics$steroid_ics)
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#=========================
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# change the factor vars to integers
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#=========================
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#str(clinical_df_ics)
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#factor_vars = lapply(clinical_df_ics, class) == "factor"
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#table(factor_vars)
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#clinical_df_ics[, factor_vars] <- lapply(clinical_df_ics[, factor_vars], as.integer)
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#table(factor_vars)
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#str(clinical_df_ics)
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#=========================
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# remove cols
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#=========================
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|
||||
clinical_df_ics = subset(clinical_df_ics, select = -c(onset_2_initial))
|
||||
|
||||
#======================
|
||||
# writing output file
|
||||
|
|
@ -392,10 +412,17 @@ outfile_name_reg = "clinical_df_recoded.csv"
|
|||
outfile_reg = paste0(outdir, outfile_name_reg)
|
||||
|
||||
cat("\nWriting clinical file for regression:", outfile_reg)
|
||||
|
||||
#write.csv(clinical_df_ics, file = outfile_reg)
|
||||
|
||||
#=========================
|
||||
# clinical_df_ics: without asthma
|
||||
#=========================
|
||||
clinical_df_ics_na = clinical_df_ics[clinical_df_ics$asthma == 0,]
|
||||
|
||||
################################################################
|
||||
rm(age_bins, max_age_interval, max_in, min_in
|
||||
, o2_sat_bin, onset_initial_bin, tot_o2
|
||||
, n_text_code, n1, n2, tot_onset2ini, infile_ics
|
||||
, tot_onset2ini, meta_data_cols
|
||||
, clinical_df)
|
||||
, clinical_df, clinical_ics)
|
||||
################################################################
|
||||
|
|
|
|||
|
|
@ -1,355 +0,0 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd('~/git/mosaic_2020/')
|
||||
getwd()
|
||||
########################################################################
|
||||
# TASK: Extract relevant columns from mosaic adults data
|
||||
# sam
|
||||
# serum
|
||||
# npa
|
||||
########################################################################
|
||||
#====================
|
||||
# Input: source data
|
||||
#====================
|
||||
source("read_data.R")
|
||||
|
||||
#============================
|
||||
# Data to use: Important step
|
||||
#============================
|
||||
# select df to use
|
||||
my_data = fp_adults_na
|
||||
|
||||
# clear unnecessary variables
|
||||
rm(all_df, adult_df, fp_adults)
|
||||
|
||||
########################################################################
|
||||
if ( sum(table(my_data$asthma)) && sum(table(my_data$asthma, my_data$ia_exac_copd)) && sum(table(my_data$obesity)) == nrow(my_data) ){
|
||||
cat("PASS: fp adults WITHOUT asthma extracted sucessfully")
|
||||
}else{
|
||||
cat("FAIL: numbers mismatch. Please check")
|
||||
quit()
|
||||
}
|
||||
|
||||
#=========
|
||||
# sam
|
||||
#=========
|
||||
sam_regex = regex(".*_sam[1-3]{1}$", ignore_case = T)
|
||||
sam_cols_i = str_extract(colnames(my_data), sam_regex) # not boolean
|
||||
#sam_cols_b = colnames(my_data)%in%sam_cols_i # boolean
|
||||
|
||||
sam_cols = colnames(my_data)[colnames(my_data)%in%sam_cols_i]
|
||||
sam_cols
|
||||
|
||||
# this contains log columns + daysamp_samXX: omitting these
|
||||
sam_regex_log_days = regex("log|day.*_sam[1-3]{1}$", ignore_case = T, perl = T)
|
||||
sam_cols_to_omit = sam_cols[grepl(sam_regex_log_days, sam_cols)]; sam_cols_to_omit
|
||||
sam_cols_to_omit
|
||||
|
||||
sam_cols_clean = sam_cols[!sam_cols%in%sam_cols_to_omit]; sam_cols_clean
|
||||
length(sam_cols_clean)
|
||||
|
||||
if( length(sam_cols_clean) == length(sam_cols) - length(sam_cols_to_omit) ){
|
||||
cat("PASS: clean cols extracted"
|
||||
, "\nNo. of clean SAM cols to extract:", length(sam_cols_clean))
|
||||
}else{
|
||||
cat("FAIL: length mismatch. Aborting further cols extraction"
|
||||
, "Expected length:", length(sam_cols) - length(sam_cols_to_omit)
|
||||
, "Got:", length(sam_cols_clean) )
|
||||
}
|
||||
|
||||
sam_cols_to_extract = c(meta_data_cols, sam_cols_clean)
|
||||
|
||||
cat("Extracting SAM cols + metadata_cols")
|
||||
|
||||
if ( length(sam_cols_to_extract) == length(meta_data_cols) + length(sam_cols_clean) ){
|
||||
cat("Extracing", length(sam_cols_to_extract), "columns for sam")
|
||||
sam_df = my_data[, sam_cols_to_extract]
|
||||
}else{
|
||||
cat("FAIL: length mismatch"
|
||||
, "Expeceted to extract:", length(meta_data_cols) + length(sam_cols_clean), "columns"
|
||||
, "Got:", length(sam_cols_to_extract))
|
||||
}
|
||||
|
||||
colnames_sam_df = colnames(sam_df); colnames_sam_df
|
||||
|
||||
#=========
|
||||
# serum
|
||||
#=========
|
||||
serum_regex = regex(".*_serum[1-3]{1}$", ignore_case = T)
|
||||
serum_cols_i = str_extract(colnames(my_data), serum_regex) # not boolean
|
||||
#serum_cols_b = colnames(my_data)%in%serum_cols_i # boolean
|
||||
|
||||
serum_cols = colnames(my_data)[colnames(my_data)%in%serum_cols_i]
|
||||
|
||||
# this contains log columns + dayserump_serumXX: omitting these
|
||||
serum_regex_log_days = regex("log|day.*_serum[1-3]{1}$", ignore_case = T, perl = T)
|
||||
serum_cols_to_omit = serum_cols[grepl(serum_regex_log_days, serum_cols)]; serum_cols_to_omit
|
||||
|
||||
serum_cols_clean = serum_cols[!serum_cols%in%serum_cols_to_omit]; serum_cols_clean
|
||||
length(serum_cols_clean)
|
||||
|
||||
if( length(serum_cols_clean) == length(serum_cols) - length(serum_cols_to_omit) ){
|
||||
cat("PASS: clean cols extracted"
|
||||
, "\nNo. of clean serum cols to extract:", length(serum_cols_clean))
|
||||
}else{
|
||||
cat("FAIL: length mismatch. Aborting further cols extraction"
|
||||
, "Expected length:", length(serum_cols) - length(serum_cols_to_omit)
|
||||
, "Got:", length(serum_cols_clean) )
|
||||
}
|
||||
|
||||
serum_cols_to_extract = c(meta_data_cols, serum_cols_clean)
|
||||
|
||||
cat("Extracting SERUM cols + metadata_cols")
|
||||
|
||||
if ( length(serum_cols_to_extract) == length(meta_data_cols) + length(serum_cols_clean) ){
|
||||
cat("Extracing", length(serum_cols_to_extract), "columns for serum")
|
||||
serum_df = my_data[, serum_cols_to_extract]
|
||||
}else{
|
||||
cat("FAIL: length mismatch"
|
||||
, "Expeceted to extract:", length(meta_data_cols) + length(serum_cols_clean), "columns"
|
||||
, "Got:", length(serum_cols_to_extract))
|
||||
}
|
||||
|
||||
colnames_serum_df = colnames(serum_df); colnames_serum_df
|
||||
|
||||
#=========
|
||||
# npa
|
||||
#=========
|
||||
npa_regex = regex(".*_npa[1-3]{1}$", ignore_case = T)
|
||||
npa_cols_i = str_extract(colnames(my_data), npa_regex) # not boolean
|
||||
#npa_cols_b = colnames(my_data)%in%npa_cols_i # boolean
|
||||
|
||||
npa_cols = colnames(my_data)[colnames(my_data)%in%npa_cols_i]
|
||||
|
||||
# this contains log columns + daynpap_npaXX: omitting these
|
||||
npa_regex_log_days = regex("log|day|vl_samptime|ct.*_npa[1-3]{1}$", ignore_case = T, perl = T)
|
||||
npa_cols_to_omit = npa_cols[grepl(npa_regex_log_days, npa_cols)]; npa_cols_to_omit
|
||||
|
||||
npa_cols_clean = npa_cols[!npa_cols%in%npa_cols_to_omit]; npa_cols_clean
|
||||
length(npa_cols_clean)
|
||||
|
||||
if( length(npa_cols_clean) == length(npa_cols) - length(npa_cols_to_omit) ){
|
||||
cat("PASS: clean cols extracted"
|
||||
, "\nNo. of clean npa cols to extract:", length(npa_cols_clean))
|
||||
}else{
|
||||
cat("FAIL: length mismatch. Aborting further cols extraction"
|
||||
, "Expected length:", length(npa_cols) - length(npa_cols_to_omit)
|
||||
, "Got:", length(npa_cols_clean) )
|
||||
}
|
||||
|
||||
npa_cols_to_extract = c(meta_data_cols, npa_cols_clean)
|
||||
|
||||
cat("Extracting NPA cols + metadata_cols")
|
||||
|
||||
if ( length(npa_cols_to_extract) == length(meta_data_cols) + length(npa_cols_clean) ){
|
||||
cat("Extracing", length(npa_cols_to_extract), "columns for npa")
|
||||
npa_df = my_data[, npa_cols_to_extract]
|
||||
}else{
|
||||
cat("FAIL: length mismatch"
|
||||
, "Expeceted to extract:", length(meta_data_cols) + length(npa_cols_clean), "columns"
|
||||
, "Got:", length(npa_cols_to_extract))
|
||||
}
|
||||
|
||||
colnames_npa_df = colnames(npa_df); colnames_npa_df
|
||||
|
||||
#==============
|
||||
# quick checks
|
||||
#==============
|
||||
colnames_check = as.data.frame(cbind(colnames_sam_df, colnames_serum_df, colnames_npa_df))
|
||||
tail(colnames_check) # gives a warning message due to differing no. of rows for cbind!
|
||||
|
||||
# put NA where a match doesn't exist
|
||||
# unmatched lengths
|
||||
#colnames_check[117,1] <- NA
|
||||
#colnames_check[117,2] <- NA
|
||||
|
||||
if ( ncol(sam_df) == ncol(serum_df) ){
|
||||
start = ncol(sam_df)+1
|
||||
extra_cols = start:ncol(npa_df)
|
||||
}
|
||||
|
||||
colnames_check_f = colnames_check
|
||||
tail(colnames_check_f)
|
||||
|
||||
for (i in extra_cols){
|
||||
for (j in 1:2) {
|
||||
cat("\ni:", i
|
||||
,"\nj:", j)
|
||||
colnames_check_f[i,j] <- NA
|
||||
#colnames_check_f[i, j]< - NA
|
||||
|
||||
}
|
||||
}
|
||||
tail(colnames_check_f)
|
||||
|
||||
##########################################################################
|
||||
# LF data
|
||||
##########################################################################
|
||||
cols_to_omit = c("adult"
|
||||
#, "obese2"
|
||||
#, "height", "height_unit", "weight"
|
||||
#, "weight_unit", "visual_est_bmi", "bmi_rating"
|
||||
)
|
||||
|
||||
pivot_cols = meta_data_cols
|
||||
# subselect pivot_cols
|
||||
pivot_cols = meta_data_cols[!meta_data_cols%in%cols_to_omit];pivot_cols
|
||||
ncols_omitted = table(meta_data_cols%in%cols_to_omit)[[2]]
|
||||
ncols_omitted
|
||||
|
||||
#==============
|
||||
# lf data: sam
|
||||
#==============
|
||||
str(sam_df)
|
||||
table(sam_df$obesity)
|
||||
#table(sam_df$obese2)
|
||||
|
||||
#sam_df_adults = sam_df[sam_df$adult == 1,] # resolved at source and only dealing wit age as adult
|
||||
sam_df_adults = sam_df
|
||||
|
||||
wf_cols = colnames(sam_df_adults)[!colnames(sam_df_adults)%in%cols_to_omit]
|
||||
sam_wf = sam_df_adults[wf_cols]
|
||||
|
||||
if (length(pivot_cols) == length(meta_data_cols) - ncols_omitted){
|
||||
cat("PASS: pivot cols successfully extracted")
|
||||
}else{
|
||||
cat("FAIL: length mismatch! pivot cols could not be extracted"
|
||||
, "\nExpected length:", length(meta_data_cols) - ncols_omitted
|
||||
, "\nGot:",length(pivot_cols) )
|
||||
quit()
|
||||
}
|
||||
|
||||
expected_rows_sam_lf = nrow(sam_wf) * (length(sam_wf) - length(pivot_cols)); expected_rows_sam_lf
|
||||
|
||||
# using regex:
|
||||
sam_lf = sam_wf %>%
|
||||
tidyr::pivot_longer(-all_of(pivot_cols)
|
||||
, names_to = c("mediator", "sample_type", "timepoint")
|
||||
, names_pattern = "(.*)_(.*)([1-3]{1})"
|
||||
, values_to = "value")
|
||||
|
||||
if (
|
||||
(nrow(sam_lf) == expected_rows_sam_lf) & (sum(table(is.na(sam_lf$mediator))) == expected_rows_sam_lf)
|
||||
) {
|
||||
cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
|
||||
, "\nNo. of rows: ", nrow(sam_lf)
|
||||
, "\nNo. of cols: ", ncol(sam_lf)))
|
||||
} else{
|
||||
cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
|
||||
, "\nExpected no. of rows: ", expected_rows_sam_lf
|
||||
, "\nGot: ", nrow(sam_lf)
|
||||
, "\ncheck expected rows calculation!"))
|
||||
quit()
|
||||
}
|
||||
|
||||
#library(data.table)
|
||||
#foo = sam_df_adults[1:10]
|
||||
#long <- melt(setDT(sam_df_adults), id.vars = pivot_cols, variable.name = "levels")
|
||||
|
||||
#==============
|
||||
# lf data: serum
|
||||
#==============
|
||||
str(serum_df)
|
||||
table(serum_df$obesity)
|
||||
#table(serum_df$obese2)
|
||||
|
||||
#serum_df_adults = serum_df[serum_df$adult == 1,] # extract based on age
|
||||
serum_df_adults = serum_df
|
||||
|
||||
wf_cols = colnames(serum_df_adults)[!colnames(serum_df_adults)%in%cols_to_omit]
|
||||
serum_wf = serum_df_adults[wf_cols]
|
||||
|
||||
if (length(pivot_cols) == length(meta_data_cols) - ncols_omitted){
|
||||
cat("PASS: pivot cols successfully extracted")
|
||||
}else{
|
||||
cat("FAIL: length mismatch! pivot cols could not be extracted"
|
||||
, "\nExpected length:", length(meta_data_cols) - ncols_omitted
|
||||
, "\nGot:",length(pivot_cols) )
|
||||
quit()
|
||||
}
|
||||
|
||||
expected_rows_serum_lf = nrow(serum_wf) * (length(serum_wf) - length(pivot_cols)); expected_rows_serum_lf
|
||||
|
||||
# using regex:
|
||||
serum_lf = serum_wf %>%
|
||||
tidyr::pivot_longer(-all_of(pivot_cols)
|
||||
, names_to = c("mediator", "sample_type", "timepoint")
|
||||
, names_pattern = "(.*)_(.*)([1-3]{1})"
|
||||
, values_to = "value")
|
||||
|
||||
if (
|
||||
(nrow(serum_lf) == expected_rows_serum_lf) & (sum(table(is.na(serum_lf$mediator))) == expected_rows_serum_lf)
|
||||
) {
|
||||
cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
|
||||
, "\nNo. of rows: ", nrow(serum_lf)
|
||||
, "\nNo. of cols: ", ncol(serum_lf)))
|
||||
} else{
|
||||
cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
|
||||
, "\nExpected no. of rows: ", expected_rows_serum_lf
|
||||
, "\nGot: ", nrow(serum_lf)
|
||||
, "\ncheck expected rows calculation!"))
|
||||
quit()
|
||||
}
|
||||
|
||||
#==============
|
||||
# lf data: npa
|
||||
#==============
|
||||
str(npa_df)
|
||||
table(npa_df$obesity)
|
||||
#table(npa_df$obese2)
|
||||
|
||||
#npa_df_adults = npa_df[npa_df$adult == 1,] # extract based on age
|
||||
npa_df_adults = npa_df
|
||||
|
||||
wf_cols = colnames(npa_df_adults)[!colnames(npa_df_adults)%in%cols_to_omit]
|
||||
npa_wf = npa_df_adults[wf_cols]
|
||||
|
||||
if (length(pivot_cols) == length(meta_data_cols) - ncols_omitted){
|
||||
cat("PASS: pivot cols successfully extracted")
|
||||
}else{
|
||||
cat("FAIL: length mismatch! pivot cols could not be extracted"
|
||||
, "\nExpected length:", length(meta_data_cols) - ncols_omitted
|
||||
, "\nGot:",length(pivot_cols) )
|
||||
quit()
|
||||
}
|
||||
|
||||
expected_rows_npa_lf = nrow(npa_wf) * (length(npa_wf) - length(pivot_cols)); expected_rows_npa_lf
|
||||
|
||||
# using regex:
|
||||
npa_lf = npa_wf %>%
|
||||
tidyr::pivot_longer(-all_of(pivot_cols)
|
||||
, names_to = c("mediator", "sample_type", "timepoint")
|
||||
, names_pattern = "(.*)_(.*)([1-3]{1})"
|
||||
, values_to = "value")
|
||||
|
||||
if (
|
||||
(nrow(npa_lf) == expected_rows_npa_lf) & (sum(table(is.na(npa_lf$mediator))) == expected_rows_npa_lf)
|
||||
) {
|
||||
cat(paste0("PASS: long format data has correct no. of rows and NA in mediator:"
|
||||
, "\nNo. of rows: ", nrow(npa_lf)
|
||||
, "\nNo. of cols: ", ncol(npa_lf)))
|
||||
} else{
|
||||
cat(paste0("FAIL:long format data has unexpected no. of rows or NAs in mediator"
|
||||
, "\nExpected no. of rows: ", expected_rows_npa_lf
|
||||
, "\nGot: ", nrow(npa_lf)
|
||||
, "\ncheck expected rows calculation!"))
|
||||
quit()
|
||||
}
|
||||
|
||||
###############################################################################
|
||||
# remove unnecessary variables
|
||||
rm(sam_regex, sam_regex_log_days, sam_cols, sam_cols_clean, sam_cols_i, sam_cols_to_extract, sam_cols_to_omit)
|
||||
rm(serum_regex, serum_regex_log_days, serum_cols, serum_cols_clean, serum_cols_i, serum_cols_to_extract, serum_cols_to_omit)
|
||||
rm(npa_regex, npa_regex_log_days, npa_cols, npa_cols_clean, npa_cols_i, npa_cols_to_extract, npa_cols_to_omit)
|
||||
rm(my_data)
|
||||
rm(colnames_check)
|
||||
rm(i, j
|
||||
#, expected_cols
|
||||
, start, wf_cols, extra_cols, cols_to_omit)
|
||||
|
||||
# rm not_clean dfs
|
||||
rm(sam_df_adults, serum_df_adults, npa_df_adults)
|
||||
|
||||
# rm df
|
||||
rm(sam_df, serum_df, npa_df)
|
||||
rm(colnames_check_f, fp_adults_na)
|
||||
|
|
@ -214,7 +214,7 @@ comb_stats_categ_df_f = comb_stats_categ_df[order(comb_stats_categ_df$p_signif
|
|||
# write output file
|
||||
#******************
|
||||
cat("Chisq and fishers test results in:", outfile_clin_categ)
|
||||
#write.csv(comb_stats_categ_df_f, outfile_clin_categ, row.names = FALSE)
|
||||
write.csv(comb_stats_categ_df_f, outfile_clin_categ, row.names = FALSE)
|
||||
|
||||
#==================
|
||||
#0 date not recorded
|
||||
|
|
@ -222,7 +222,8 @@ cat("Chisq and fishers test results in:", outfile_clin_categ)
|
|||
#-2 n/a specified by clinician
|
||||
#-3 unknown specified by
|
||||
|
||||
|
||||
########################################################################
|
||||
# checks!
|
||||
chisq_test(table(clinical_df_ics$obesity, clinical_df_ics$smoking))
|
||||
|
||||
chisq_test(table(clinical_df_ics$obesity, clinical_df_ics$max_resp_score))
|
||||
|
|
|
|||
|
|
@ -1,381 +0,0 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd("~/git/mosaic_2020/")
|
||||
getwd()
|
||||
############################################################
|
||||
# TASK: unpaired (time) analysis of mediators:
|
||||
# sample type: NPA
|
||||
# data: Flu positive adult patients
|
||||
# group: obesity
|
||||
############################################################
|
||||
my_sample_type = "npa"
|
||||
|
||||
#=============
|
||||
# Input
|
||||
#=============
|
||||
source("data_extraction_formatting_non_asthmatics.R")
|
||||
|
||||
# check: adult variable and age variable discrepancy!
|
||||
metadata_all$mosaic[metadata_all$adult==1 & metadata_all$age<=18]
|
||||
|
||||
#=============
|
||||
# Output
|
||||
#=============
|
||||
outfile_name = paste0("flu_stats_time_unpaired_NA_", my_sample_type, ".csv")
|
||||
flu_stats_time_unpaired_na = paste0(outdir_stats, outfile_name)
|
||||
|
||||
#===============================
|
||||
# data assignment for stats
|
||||
#================================
|
||||
wf = npa_wf[npa_wf$flustat == 1,]
|
||||
lf = npa_lf[npa_lf$flustat == 1,]
|
||||
lf$timepoint = paste0("t", lf$timepoint)
|
||||
lf = lf[!lf$mediator == "vitd",]
|
||||
########################################################################
|
||||
# clear variables
|
||||
rm(sam_lf, sam_wf
|
||||
, serum_lf, serum_wf)
|
||||
rm(colnames_sam_df, expected_rows_sam_lf
|
||||
, colnames_serum_df, expected_rows_serum_lf)
|
||||
|
||||
rm(pivot_cols)
|
||||
|
||||
# sanity checks
|
||||
table(lf$timepoint)
|
||||
|
||||
########################################################################
|
||||
# Unpaired stats at each timepoint b/w groups: wilcoxon UNpaired analysis
|
||||
# with correction
|
||||
########################################################################
|
||||
# with adjustment: fdr and BH are identical
|
||||
my_adjust_method = "BH"
|
||||
|
||||
#==============
|
||||
# unpaired: t1
|
||||
#==============
|
||||
lf_t1 = lf[lf$timepoint == "t1",]
|
||||
sum(is.na(lf_t1$value))
|
||||
|
||||
foo = lf_t1[which(is.na(lf_t1$value)),]
|
||||
#ci = which(is.na(lf_t1$value))
|
||||
#lf_t1_comp = lf_t1[-ci,]
|
||||
|
||||
lf_t1_comp = lf_t1[-which(is.na(lf_t1$value)),]
|
||||
stats_un_t1 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t1
|
||||
, data = lf_t1_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
foo$mosaic[!unique(foo$mosaic)%in%unique(lf_t1_comp$mosaic)]
|
||||
|
||||
# add timepoint and convert to df
|
||||
stats_un_t1$timepoint = "t1"
|
||||
stats_un_t1 = as.data.frame(stats_un_t1)
|
||||
class(stats_un_t1)
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each mediator: t1
|
||||
#----------------------------------------
|
||||
#n_t1 = data.frame(table(lf_t1_comp$mediator))
|
||||
n_t1_all = data.frame(table(lf_t1$mediator))
|
||||
colnames(n_t1_all) = c("mediator", "n_obs")
|
||||
n_t1_all$mediator = as.character(n_t1_all$mediator)
|
||||
|
||||
n_t1_comp = data.frame(table(lf_t1_comp$mediator))
|
||||
colnames(n_t1_comp) = c("mediator", "n_obs_complete")
|
||||
n_t1_comp$mediator = as.character(n_t1_comp$mediator)
|
||||
|
||||
merge_cols = intersect(names(n_t1_all), names(n_t1_comp)); merge_cols
|
||||
n_t1= merge(n_t1_all, n_t1_comp, by = merge_cols, all = T)
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#===================================
|
||||
merging_cols = intersect(names(stats_un_t1), names(n_t1)); merging_cols
|
||||
if (all(n_t1$mediator%in%stats_un_t1$mediator)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_t1 = merge(stats_un_t1, n_t1, by = merging_cols, all = T)
|
||||
cat("\nsuccessfull merge:"
|
||||
, "\nnrow:", nrow(stats_un_t1)
|
||||
, "\nncol:", ncol(stats_un_t1))
|
||||
}else{
|
||||
nf = n_t1$mediator[!n_t1$mediator%in%stats_un_t1$mediator]
|
||||
stats_un_t1 = merge(stats_un_t1, n_t1, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t1)
|
||||
, "\nncol:", ncol(stats_un_t1))
|
||||
}
|
||||
|
||||
# add bonferroni adjustment as well
|
||||
stats_un_t1$p_adj_bonferroni = p.adjust(stats_un_t1$p, method = "bonferroni")
|
||||
|
||||
rm(n_t1)
|
||||
rm(lf_t1_comp)
|
||||
|
||||
#==============
|
||||
# unpaired: t2
|
||||
#==============
|
||||
lf_t2 = lf[lf$timepoint == "t2",]
|
||||
lf_t2_comp = lf_t2[-which(is.na(lf_t2$value)),]
|
||||
|
||||
stats_un_t2 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t2
|
||||
, data = lf_t2_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
# add timepoint and convert to df
|
||||
stats_un_t2$timepoint = "t2"
|
||||
stats_un_t2 = as.data.frame(stats_un_t2)
|
||||
class(stats_un_t2)
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each mediator: t2
|
||||
#----------------------------------------
|
||||
#n_t2 = data.frame(table(lf_t2_comp$mediator))
|
||||
n_t2_all = data.frame(table(lf_t2$mediator))
|
||||
colnames(n_t2_all) = c("mediator", "n_obs")
|
||||
n_t2_all$mediator = as.character(n_t2_all$mediator)
|
||||
|
||||
n_t2_comp = data.frame(table(lf_t2_comp$mediator))
|
||||
colnames(n_t2_comp) = c("mediator", "n_obs_complete")
|
||||
n_t2_comp$mediator = as.character(n_t2_comp$mediator)
|
||||
|
||||
merge_cols = intersect(names(n_t2_all), names(n_t2_comp)); merge_cols
|
||||
n_t2= merge(n_t2_all, n_t2_comp, by = merge_cols, all = T)
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#==================================
|
||||
merging_cols = intersect(names(stats_un_t2), names(n_t2)); merging_cols
|
||||
if (all(n_t2$mediator%in%stats_un_t2$mediator)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_t2 = merge(stats_un_t2, n_t2, by = merging_cols, all = T)
|
||||
cat("\nsuccessfull merge:"
|
||||
, "\nnrow:", nrow(stats_un_t2)
|
||||
, "\nncol:", ncol(stats_un_t2))
|
||||
}else{
|
||||
nf = n_t2$mediator[!n_t2$mediator%in%stats_un_t2$mediator]
|
||||
stats_un_t2 = merge(stats_un_t2, n_t2, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t2)
|
||||
, "\nncol:", ncol(stats_un_t2))
|
||||
}
|
||||
|
||||
# add bonferroni adjustment as well
|
||||
stats_un_t2$p_adj_bonferroni = p.adjust(stats_un_t2$p, method = "bonferroni")
|
||||
|
||||
rm(n_t2)
|
||||
rm(lf_t2_comp)
|
||||
|
||||
#==============
|
||||
# unpaired: t3
|
||||
#==============
|
||||
lf_t3 = lf[lf$timepoint == "t3",]
|
||||
lf_t3_comp = lf_t3[-which(is.na(lf_t3$value)),]
|
||||
|
||||
stats_un_t3 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t3
|
||||
, data = lf_t3_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
# add timepoint and convert to df
|
||||
stats_un_t3$timepoint = "t3"
|
||||
stats_un_t3 = as.data.frame(stats_un_t3)
|
||||
class(stats_un_t3)
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each mediator: t3
|
||||
#----------------------------------------
|
||||
#n_t3 = data.frame(table(lf_t3_comp$mediator))
|
||||
n_t3_all = data.frame(table(lf_t3$mediator))
|
||||
colnames(n_t3_all) = c("mediator", "n_obs")
|
||||
n_t3_all$mediator = as.character(n_t3_all$mediator)
|
||||
|
||||
n_t3_comp = data.frame(table(lf_t3_comp$mediator))
|
||||
colnames(n_t3_comp) = c("mediator", "n_obs_complete")
|
||||
n_t3_comp$mediator = as.character(n_t3_comp$mediator)
|
||||
|
||||
merge_cols = intersect(names(n_t3_all), names(n_t3_comp)); merge_cols
|
||||
n_t3= merge(n_t3_all, n_t3_comp, by = merge_cols, all = T)
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#==================================
|
||||
merging_cols = intersect(names(stats_un_t3), names(n_t3)); merging_cols
|
||||
if (all(n_t3$mediator%in%stats_un_t3$mediator)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_t3 = merge(stats_un_t3, n_t3, by = merging_cols, all = T)
|
||||
cat("\nsuccessfull merge:"
|
||||
, "\nnrow:", nrow(stats_un_t3)
|
||||
, "\nncol:", ncol(stats_un_t3))
|
||||
}else{
|
||||
nf = n_t3$mediator[!n_t3$mediator%in%stats_un_t3$mediator]
|
||||
stats_un_t3 = merge(stats_un_t3, n_t3, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t3)
|
||||
, "\nncol:", ncol(stats_un_t3))
|
||||
}
|
||||
|
||||
# add bonferroni adjustment as well
|
||||
stats_un_t3$p_adj_bonferroni = p.adjust(stats_un_t3$p, method = "bonferroni")
|
||||
|
||||
rm(n_t3)
|
||||
rm(lf_t3_comp)
|
||||
|
||||
########################################################################
|
||||
#==============
|
||||
# Rbind these dfs
|
||||
#==============
|
||||
str(stats_un_t1);str(stats_un_t2); str(stats_un_t3)
|
||||
|
||||
n_dfs = 3
|
||||
|
||||
if ( all.equal(nrow(stats_un_t1), nrow(stats_un_t2), nrow(stats_un_t3)) &&
|
||||
all.equal(ncol(stats_un_t1), ncol(stats_un_t2), ncol(stats_un_t3)) ) {
|
||||
expected_rows = nrow(stats_un_t1) * n_dfs
|
||||
expected_cols = ncol(stats_un_t1)
|
||||
print("PASS: expected_rows and cols variables generated for downstream sanity checks")
|
||||
}else{
|
||||
cat("FAIL: dfs have different no. of rows and cols"
|
||||
, "\nCheck harcoded value of n_dfs"
|
||||
, "\nexpected_rows and cols could not be generated")
|
||||
quit()
|
||||
}
|
||||
|
||||
if ( all.equal(colnames(stats_un_t1), colnames(stats_un_t2), colnames(stats_un_t3)) ){
|
||||
print("PASS: colnames match. Rbind the 3 dfs...")
|
||||
combined_unpaired_stats = rbind(stats_un_t1, stats_un_t2, stats_un_t3)
|
||||
} else{
|
||||
cat("FAIL: cannot combined dfs. Colnames don't match!")
|
||||
quit()
|
||||
}
|
||||
|
||||
if ( nrow(combined_unpaired_stats) == expected_rows && ncol(combined_unpaired_stats) == expected_cols ){
|
||||
cat("PASS: combined_df has expected dimension"
|
||||
, "\nNo. of rows in combined_df:", nrow(combined_unpaired_stats)
|
||||
, "\nNo. of cols in combined_df:", ncol(combined_unpaired_stats) )
|
||||
}else{
|
||||
cat("FAIL: combined_df dimension mismatch")
|
||||
quit()
|
||||
}
|
||||
|
||||
#######################################################################
|
||||
#=================
|
||||
# formatting df
|
||||
#=================
|
||||
# delete: unnecessary column
|
||||
combined_unpaired_stats = subset(combined_unpaired_stats, select = -c(.y.))
|
||||
|
||||
# add sample_type
|
||||
cat("Adding sample type info as a column", my_sample_type, "...")
|
||||
combined_unpaired_stats$sample_type = my_sample_type
|
||||
|
||||
# add: reflect stats method correctly i.e paired or unpaired
|
||||
# incase there are NA due to LLODs, the gsub won't work!
|
||||
#combined_unpaired_stats$method = gsub("Wilcoxon", "Wilcoxon_unpaired", combined_unpaired_stats$method)
|
||||
combined_unpaired_stats$method = "wilcoxon unpaired"
|
||||
combined_unpaired_stats$method
|
||||
|
||||
# add an extra column for padjust_signif: my_adjust_method
|
||||
combined_unpaired_stats$padjust_signif = combined_unpaired_stats$p.adj
|
||||
# add appropriate symbols for padjust_signif: my_adjust_method
|
||||
combined_unpaired_stats = dplyr::mutate(combined_unpaired_stats, padjust_signif = case_when(padjust_signif == 0.05 ~ "."
|
||||
, padjust_signif <=0.0001 ~ '****'
|
||||
, padjust_signif <=0.001 ~ '***'
|
||||
, padjust_signif <=0.01 ~ '**'
|
||||
, padjust_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# add an extra column for p_bon_signif
|
||||
combined_unpaired_stats$p_bon_signif = combined_unpaired_stats$p_adj_bonferroni
|
||||
# add appropriate symbols for p_bon_signif
|
||||
combined_unpaired_stats = dplyr::mutate(combined_unpaired_stats, p_bon_signif = case_when(p_bon_signif == 0.05 ~ "."
|
||||
, p_bon_signif <=0.0001 ~ '****'
|
||||
, p_bon_signif <=0.001 ~ '***'
|
||||
, p_bon_signif <=0.01 ~ '**'
|
||||
, p_bon_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# reorder columns
|
||||
print("preparing to reorder columns...")
|
||||
colnames(combined_unpaired_stats)
|
||||
my_col_order2 = c("mediator"
|
||||
, "timepoint"
|
||||
, "sample_type"
|
||||
, "n_obs"
|
||||
, "n_obs_complete"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "method"
|
||||
, "p"
|
||||
, "p.format"
|
||||
, "p.signif"
|
||||
, "p.adj"
|
||||
, "padjust_signif"
|
||||
, "p_adj_bonferroni"
|
||||
, "p_bon_signif")
|
||||
|
||||
if( length(my_col_order2) == ncol(combined_unpaired_stats) && (all(my_col_order2%in%colnames(combined_unpaired_stats))) ){
|
||||
print("PASS: Reordering columns...")
|
||||
combined_unpaired_stats_f = combined_unpaired_stats[, my_col_order2]
|
||||
print("Successful: column reordering")
|
||||
print("formatted df called:'combined_unpaired_stats_f'")
|
||||
cat('\nformatted df has the following dimensions\n')
|
||||
print(dim(combined_unpaired_stats_f ))
|
||||
} else{
|
||||
cat(paste0("FAIL:Cannot reorder columns, length mismatch"
|
||||
, "\nExpected column order for: ", ncol(combined_unpaired_stats)
|
||||
, "\nGot:", length(my_col_order2)))
|
||||
quit()
|
||||
}
|
||||
# assign nice column names like replace "." with "_"
|
||||
colnames(combined_unpaired_stats_f) = c("mediator"
|
||||
, "timepoint"
|
||||
, "sample_type"
|
||||
, "n_obs"
|
||||
, "n_obs_complete"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "method"
|
||||
, "p"
|
||||
, "p_format"
|
||||
, "p_signif"
|
||||
, paste0("p_adj_fdr_", my_adjust_method)
|
||||
, paste0("p_", my_adjust_method, "_signif")
|
||||
, "p_adj_bonferroni"
|
||||
, "p_bon_signif")
|
||||
|
||||
colnames(combined_unpaired_stats_f)
|
||||
|
||||
#---------------
|
||||
# quick summary
|
||||
#---------------
|
||||
# count how many meds are significant
|
||||
n_sig = length(combined_unpaired_stats_f$mediator[combined_unpaired_stats_f$p_signif<0.05 & !is.na(combined_unpaired_stats_f$p_signif<0.05)])
|
||||
sig_meds = combined_unpaired_stats_f[(combined_unpaired_stats_f$p_signif<0.05 & !is.na(combined_unpaired_stats_f$p_signif<0.05)),]
|
||||
|
||||
sig_meds$med_time = paste0(sig_meds$mediator, "@", sig_meds$timepoint)
|
||||
|
||||
cat("\nTotal no. of statistically significant mediators in", toupper(my_sample_type)
|
||||
, "are:", n_sig
|
||||
, "\nThese are:", sig_meds$med_time)
|
||||
|
||||
########################################################################
|
||||
#******************
|
||||
# write output file
|
||||
#******************
|
||||
cat("\nUNpaired stats for groups will be:", flu_stats_time_unpaired_na)
|
||||
#write.csv(combined_unpaired_stats_f, flu_stats_time_unpaired_na, row.names = FALSE)
|
||||
|
|
@ -1,383 +0,0 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd("~/git/mosaic_2020/")
|
||||
getwd()
|
||||
############################################################
|
||||
# TASK: unpaired (time) analysis of mediators:
|
||||
# sample type: SAM
|
||||
# data: Flu positive adult patients
|
||||
# group: obesity
|
||||
############################################################
|
||||
my_sample_type = "sam"
|
||||
|
||||
#=============
|
||||
# Input
|
||||
#=============
|
||||
source("data_extraction_formatting_non_asthmatics.R")
|
||||
|
||||
# check: adult variable and age variable discrepancy!
|
||||
metadata_all$mosaic[metadata_all$adult==1 & metadata_all$age<=18]
|
||||
|
||||
#=============
|
||||
# Output
|
||||
#=============
|
||||
outfile_name = paste0("flu_stats_time_unpaired_NA_", my_sample_type, ".csv")
|
||||
flu_stats_time_unpaired_na = paste0(outdir_stats, outfile_name)
|
||||
|
||||
#===============================
|
||||
# data assignment for stats
|
||||
#================================
|
||||
wf = sam_wf[sam_wf$flustat == 1,]
|
||||
lf = sam_lf[sam_lf$flustat == 1,]
|
||||
lf$timepoint = paste0("t", lf$timepoint)
|
||||
lf = lf[!lf$mediator == "vitd",]
|
||||
########################################################################
|
||||
# clear variables
|
||||
rm(npa_lf, npa_wf
|
||||
, serum_lf, serum_wf)
|
||||
rm(colnames_npa_df, expected_rows_npa_lf
|
||||
, colnames_serum_df, expected_rows_serum_lf)
|
||||
|
||||
rm(pivot_cols)
|
||||
|
||||
# sanity checks
|
||||
table(lf$timepoint)
|
||||
length(unique(lf$mosaic))
|
||||
|
||||
########################################################################
|
||||
# Unpaired stats at each timepoint b/w groups: wilcoxon UNpaired analysis
|
||||
# with correction
|
||||
#######################################################################
|
||||
# with adjustment: fdr and BH are identical
|
||||
my_adjust_method = "BH"
|
||||
|
||||
#==============
|
||||
# unpaired: t1
|
||||
#==============
|
||||
lf_t1 = lf[lf$timepoint == "t1",]
|
||||
sum(is.na(lf_t1$value))
|
||||
|
||||
foo = lf_t1[which(is.na(lf_t1$value)),]
|
||||
#ci = which(is.na(lf_t1$value))
|
||||
#lf_t1_comp = lf_t1[-ci,]
|
||||
|
||||
lf_t1_comp = lf_t1[-which(is.na(lf_t1$value)),]
|
||||
stats_un_t1 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t1
|
||||
, data = lf_t1_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
foo$mosaic[!unique(foo$mosaic)%in%unique(lf_t1_comp$mosaic)]
|
||||
|
||||
# add timepoint and convert to df
|
||||
stats_un_t1$timepoint = "t1"
|
||||
stats_un_t1 = as.data.frame(stats_un_t1)
|
||||
class(stats_un_t1)
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each mediator: t1
|
||||
#----------------------------------------
|
||||
#n_t1 = data.frame(table(lf_t1_comp$mediator))
|
||||
n_t1_all = data.frame(table(lf_t1$mediator))
|
||||
colnames(n_t1_all) = c("mediator", "n_obs")
|
||||
n_t1_all$mediator = as.character(n_t1_all$mediator)
|
||||
|
||||
n_t1_comp = data.frame(table(lf_t1_comp$mediator))
|
||||
colnames(n_t1_comp) = c("mediator", "n_obs_complete")
|
||||
n_t1_comp$mediator = as.character(n_t1_comp$mediator)
|
||||
|
||||
merge_cols = intersect(names(n_t1_all), names(n_t1_comp)); merge_cols
|
||||
n_t1= merge(n_t1_all, n_t1_comp, by = merge_cols, all = T)
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#===================================
|
||||
merging_cols = intersect(names(stats_un_t1), names(n_t1)); merging_cols
|
||||
if (all(n_t1$mediator%in%stats_un_t1$mediator)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_t1 = merge(stats_un_t1, n_t1, by = merging_cols, all = T)
|
||||
cat("\nsuccessfull merge:"
|
||||
, "\nnrow:", nrow(stats_un_t1)
|
||||
, "\nncol:", ncol(stats_un_t1))
|
||||
}else{
|
||||
nf = n_t1$mediator[!n_t1$mediator%in%stats_un_t1$mediator]
|
||||
stats_un_t1 = merge(stats_un_t1, n_t1, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t1)
|
||||
, "\nncol:", ncol(stats_un_t1))
|
||||
}
|
||||
|
||||
# add bonferroni adjustment as well
|
||||
stats_un_t1$p_adj_bonferroni = p.adjust(stats_un_t1$p, method = "bonferroni")
|
||||
|
||||
rm(n_t1)
|
||||
rm(lf_t1_comp)
|
||||
|
||||
#==============
|
||||
# unpaired: t2
|
||||
#==============
|
||||
lf_t2 = lf[lf$timepoint == "t2",]
|
||||
lf_t2_comp = lf_t2[-which(is.na(lf_t2$value)),]
|
||||
|
||||
stats_un_t2 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t2
|
||||
, data = lf_t2_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
# add timepoint and convert to df
|
||||
stats_un_t2$timepoint = "t2"
|
||||
stats_un_t2 = as.data.frame(stats_un_t2)
|
||||
class(stats_un_t2)
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each mediator: t2
|
||||
#----------------------------------------
|
||||
#n_t2 = data.frame(table(lf_t2_comp$mediator))
|
||||
n_t2_all = data.frame(table(lf_t2$mediator))
|
||||
colnames(n_t2_all) = c("mediator", "n_obs")
|
||||
n_t2_all$mediator = as.character(n_t2_all$mediator)
|
||||
|
||||
n_t2_comp = data.frame(table(lf_t2_comp$mediator))
|
||||
colnames(n_t2_comp) = c("mediator", "n_obs_complete")
|
||||
n_t2_comp$mediator = as.character(n_t2_comp$mediator)
|
||||
|
||||
merge_cols = intersect(names(n_t2_all), names(n_t2_comp)); merge_cols
|
||||
n_t2= merge(n_t2_all, n_t2_comp, by = merge_cols, all = T)
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#===================================
|
||||
merging_cols = intersect(names(stats_un_t2), names(n_t2)); merging_cols
|
||||
if (all(n_t2$mediator%in%stats_un_t2$mediator)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_t2 = merge(stats_un_t2, n_t2, by = merging_cols, all = T)
|
||||
cat("\nsuccessfull merge:"
|
||||
, "\nnrow:", nrow(stats_un_t2)
|
||||
, "\nncol:", ncol(stats_un_t2))
|
||||
}else{
|
||||
nf = n_t2$mediator[!n_t2$mediator%in%stats_un_t2$mediator]
|
||||
stats_un_t2 = merge(stats_un_t2, n_t2, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t2)
|
||||
, "\nncol:", ncol(stats_un_t2))
|
||||
}
|
||||
|
||||
# add bonferroni adjustment as well
|
||||
stats_un_t2$p_adj_bonferroni = p.adjust(stats_un_t2$p, method = "bonferroni")
|
||||
|
||||
rm(n_t2)
|
||||
rm(lf_t2_comp)
|
||||
|
||||
#==============
|
||||
# unpaired: t3
|
||||
#==============
|
||||
lf_t3 = lf[lf$timepoint == "t3",]
|
||||
lf_t3_comp = lf_t3[-which(is.na(lf_t3$value)),]
|
||||
|
||||
stats_un_t3 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t3
|
||||
, data = lf_t3_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
# add timepoint and convert to df
|
||||
stats_un_t3$timepoint = "t3"
|
||||
stats_un_t3 = as.data.frame(stats_un_t3)
|
||||
class(stats_un_t3)
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each mediator: t3
|
||||
#----------------------------------------
|
||||
#n_t3 = data.frame(table(lf_t3_comp$mediator))
|
||||
n_t3_all = data.frame(table(lf_t3$mediator))
|
||||
colnames(n_t3_all) = c("mediator", "n_obs")
|
||||
n_t3_all$mediator = as.character(n_t3_all$mediator)
|
||||
|
||||
n_t3_comp = data.frame(table(lf_t3_comp$mediator))
|
||||
colnames(n_t3_comp) = c("mediator", "n_obs_complete")
|
||||
n_t3_comp$mediator = as.character(n_t3_comp$mediator)
|
||||
|
||||
merge_cols = intersect(names(n_t3_all), names(n_t3_comp)); merge_cols
|
||||
n_t3= merge(n_t3_all, n_t3_comp, by = merge_cols, all = T)
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#==================================
|
||||
merging_cols = intersect(names(stats_un_t3), names(n_t3)); merging_cols
|
||||
if (all(n_t3$mediator%in%stats_un_t3$mediator)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_t3 = merge(stats_un_t3, n_t3, by = merging_cols, all = T)
|
||||
cat("\nsuccessfull merge:"
|
||||
, "\nnrow:", nrow(stats_un_t3)
|
||||
, "\nncol:", ncol(stats_un_t3))
|
||||
}else{
|
||||
nf = n_t3$mediator[!n_t3$mediator%in%stats_un_t3$mediator]
|
||||
stats_un_t3 = merge(stats_un_t3, n_t3, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t3)
|
||||
, "\nncol:", ncol(stats_un_t3))
|
||||
}
|
||||
|
||||
# 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"
|
||||
, "n_obs_complete"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "method"
|
||||
, "p"
|
||||
, "p.format"
|
||||
, "p.signif"
|
||||
, "p.adj"
|
||||
, "padjust_signif"
|
||||
, "p_adj_bonferroni"
|
||||
, "p_bon_signif")
|
||||
|
||||
if( length(my_col_order2) == ncol(combined_unpaired_stats) && (all(my_col_order2%in%colnames(combined_unpaired_stats))) ){
|
||||
print("PASS: Reordering columns...")
|
||||
combined_unpaired_stats_f = combined_unpaired_stats[, my_col_order2]
|
||||
print("Successful: column reordering")
|
||||
print("formatted df called:'combined_unpaired_stats_f'")
|
||||
cat('\nformatted df has the following dimensions\n')
|
||||
print(dim(combined_unpaired_stats_f ))
|
||||
} else{
|
||||
cat(paste0("FAIL:Cannot reorder columns, length mismatch"
|
||||
, "\nExpected column order for: ", ncol(combined_unpaired_stats)
|
||||
, "\nGot:", length(my_col_order2)))
|
||||
quit()
|
||||
}
|
||||
# assign nice column names like replace "." with "_"
|
||||
colnames(combined_unpaired_stats_f) = c("mediator"
|
||||
, "timepoint"
|
||||
, "sample_type"
|
||||
, "n_obs"
|
||||
, "n_obs_complete"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "method"
|
||||
, "p"
|
||||
, "p_format"
|
||||
, "p_signif"
|
||||
, paste0("p_adj_fdr_", my_adjust_method)
|
||||
, paste0("p_", my_adjust_method, "_signif")
|
||||
, "p_adj_bonferroni"
|
||||
, "p_bon_signif")
|
||||
|
||||
colnames(combined_unpaired_stats_f)
|
||||
|
||||
#---------------
|
||||
# quick summary
|
||||
#---------------
|
||||
# count how many meds are significant
|
||||
n_sig = length(combined_unpaired_stats_f$mediator[combined_unpaired_stats_f$p_signif<0.05 & !is.na(combined_unpaired_stats_f$p_signif<0.05)])
|
||||
sig_meds = combined_unpaired_stats_f[(combined_unpaired_stats_f$p_signif<0.05 & !is.na(combined_unpaired_stats_f$p_signif<0.05)),]
|
||||
|
||||
sig_meds$med_time = paste0(sig_meds$mediator, "@", sig_meds$timepoint)
|
||||
|
||||
cat("\nTotal no. of statistically significant mediators in", toupper(my_sample_type)
|
||||
, "are:", n_sig
|
||||
, "\nThese are:", sig_meds$med_time)
|
||||
|
||||
#######################################################################
|
||||
#******************
|
||||
# write output file
|
||||
#******************
|
||||
cat("\nUNpaired stats for groups will be:", flu_stats_time_unpaired_na)
|
||||
#write.csv(combined_unpaired_stats_f, flu_stats_time_unpaired_na, row.names = FALSE)
|
||||
|
|
@ -1,376 +0,0 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd("~/git/mosaic_2020/")
|
||||
getwd()
|
||||
############################################################
|
||||
# TASK: unpaired (time) analysis of mediators: serum
|
||||
############################################################
|
||||
my_sample_type = "serum"
|
||||
|
||||
#=============
|
||||
# Input
|
||||
#=============
|
||||
source("data_extraction_formatting_non_asthmatics.R")
|
||||
|
||||
# check: adult variable and age variable discrepancy!
|
||||
metadata_all$mosaic[metadata_all$adult==1 & metadata_all$age<=18]
|
||||
|
||||
#=============
|
||||
# Output
|
||||
#=============
|
||||
outfile_name = paste0("flu_stats_time_unpaired_NA_", my_sample_type, ".csv")
|
||||
flu_stats_time_unpaired_na = paste0(outdir_stats, outfile_name)
|
||||
|
||||
#===============================
|
||||
# data assignment for stats
|
||||
#================================
|
||||
wf = serum_wf[serum_wf$flustat == 1,]
|
||||
lf = serum_lf[serum_lf$flustat == 1,]
|
||||
lf$timepoint = paste0("t", lf$timepoint)
|
||||
|
||||
########################################################################
|
||||
# clear variables
|
||||
rm(sam_lf, sam_wf
|
||||
, npa_lf, npa_wf)
|
||||
rm(colnames_sam_df, expected_rows_sam_lf
|
||||
, colnames_npa_df, expected_rows_npa_lf)
|
||||
|
||||
rm(pivot_cols)
|
||||
|
||||
# sanity checks
|
||||
table(lf$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)]
|
||||
|
||||
# add timepoint and convert to df
|
||||
stats_un_t1$timepoint = "t1"
|
||||
stats_un_t1 = as.data.frame(stats_un_t1)
|
||||
class(stats_un_t1)
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each mediator: t1
|
||||
#----------------------------------------
|
||||
#n_t1 = data.frame(table(lf_t1_comp$mediator))
|
||||
n_t1_all = data.frame(table(lf_t1$mediator))
|
||||
colnames(n_t1_all) = c("mediator", "n_obs")
|
||||
n_t1_all$mediator = as.character(n_t1_all$mediator)
|
||||
|
||||
n_t1_comp = data.frame(table(lf_t1_comp$mediator))
|
||||
colnames(n_t1_comp) = c("mediator", "n_obs_complete")
|
||||
n_t1_comp$mediator = as.character(n_t1_comp$mediator)
|
||||
|
||||
merge_cols = intersect(names(n_t1_all), names(n_t1_comp)); merge_cols
|
||||
n_t1= merge(n_t1_all, n_t1_comp, by = merge_cols, all = T)
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#==================================
|
||||
merging_cols = intersect(names(stats_un_t1), names(n_t1)); merging_cols
|
||||
if (all(n_t1$mediator%in%stats_un_t1$mediator)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_t1 = merge(stats_un_t1, n_t1, by = merging_cols, all = T)
|
||||
cat("\nsuccessfull merge:"
|
||||
, "\nnrow:", nrow(stats_un_t1)
|
||||
, "\nncol:", ncol(stats_un_t1))
|
||||
}else{
|
||||
nf = n_t1$mediator[!n_t1$mediator%in%stats_un_t1$mediator]
|
||||
stats_un_t1 = merge(stats_un_t1, n_t1, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t1)
|
||||
, "\nncol:", ncol(stats_un_t1))
|
||||
}
|
||||
|
||||
# add bonferroni adjustment as well
|
||||
stats_un_t1$p_adj_bonferroni = p.adjust(stats_un_t1$p, method = "bonferroni")
|
||||
|
||||
rm(n_t1)
|
||||
rm(lf_t1_comp)
|
||||
|
||||
#==============
|
||||
# unpaired: t2
|
||||
#==============
|
||||
lf_t2 = lf[lf$timepoint == "t2",]
|
||||
lf_t2_comp = lf_t2[-which(is.na(lf_t2$value)),]
|
||||
|
||||
stats_un_t2 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t2
|
||||
, data = lf_t2_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
# add timepoint and convert to df
|
||||
stats_un_t2$timepoint = "t2"
|
||||
stats_un_t2 = as.data.frame(stats_un_t2)
|
||||
class(stats_un_t2)
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each mediator: t2
|
||||
#----------------------------------------
|
||||
#n_t2 = data.frame(table(lf_t2_comp$mediator))
|
||||
n_t2_all = data.frame(table(lf_t2$mediator))
|
||||
colnames(n_t2_all) = c("mediator", "n_obs")
|
||||
n_t2_all$mediator = as.character(n_t2_all$mediator)
|
||||
|
||||
n_t2_comp = data.frame(table(lf_t2_comp$mediator))
|
||||
colnames(n_t2_comp) = c("mediator", "n_obs_complete")
|
||||
n_t2_comp$mediator = as.character(n_t2_comp$mediator)
|
||||
|
||||
merge_cols = intersect(names(n_t2_all), names(n_t2_comp)); merge_cols
|
||||
n_t2= merge(n_t2_all, n_t2_comp, by = merge_cols, all = T)
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#==================================
|
||||
merging_cols = intersect(names(stats_un_t2), names(n_t2)); merging_cols
|
||||
if (all(n_t2$mediator%in%stats_un_t2$mediator)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_t2 = merge(stats_un_t2, n_t2, by = merging_cols, all = T)
|
||||
cat("\nsuccessfull merge:"
|
||||
, "\nnrow:", nrow(stats_un_t2)
|
||||
, "\nncol:", ncol(stats_un_t2))
|
||||
}else{
|
||||
nf = n_t2$mediator[!n_t2$mediator%in%stats_un_t2$mediator]
|
||||
stats_un_t2 = merge(stats_un_t2, n_t2, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t2)
|
||||
, "\nncol:", ncol(stats_un_t2))
|
||||
}
|
||||
|
||||
# add bonferroni adjustment as well
|
||||
stats_un_t2$p_adj_bonferroni = p.adjust(stats_un_t2$p, method = "bonferroni")
|
||||
|
||||
rm(n_t2)
|
||||
rm(lf_t2_comp)
|
||||
|
||||
#==============
|
||||
# unpaired: t3
|
||||
#==============
|
||||
lf_t3 = lf[lf$timepoint == "t3",]
|
||||
lf_t3_comp = lf_t3[-which(is.na(lf_t3$value)),]
|
||||
|
||||
stats_un_t3 = compare_means(value~obesity
|
||||
, group.by = "mediator"
|
||||
#, data = lf_t3
|
||||
, data = lf_t3_comp
|
||||
, paired = FALSE
|
||||
, p.adjust.method = my_adjust_method)
|
||||
|
||||
# add timepoint and convert to df
|
||||
stats_un_t3$timepoint = "t3"
|
||||
stats_un_t3 = as.data.frame(stats_un_t3)
|
||||
class(stats_un_t3)
|
||||
|
||||
#----------------------------------------
|
||||
# calculate n_obs for each mediator: t3
|
||||
#----------------------------------------
|
||||
#n_t3 = data.frame(table(lf_t3_comp$mediator))
|
||||
n_t3_all = data.frame(table(lf_t3$mediator))
|
||||
colnames(n_t3_all) = c("mediator", "n_obs")
|
||||
n_t3_all$mediator = as.character(n_t3_all$mediator)
|
||||
|
||||
n_t3_comp = data.frame(table(lf_t3_comp$mediator))
|
||||
colnames(n_t3_comp) = c("mediator", "n_obs_complete")
|
||||
n_t3_comp$mediator = as.character(n_t3_comp$mediator)
|
||||
|
||||
merge_cols = intersect(names(n_t3_all), names(n_t3_comp)); merge_cols
|
||||
n_t3 = merge(n_t3_all, n_t3_comp, by = merge_cols, all = T)
|
||||
|
||||
#==================================
|
||||
# Merge: merge stats + n_obs df
|
||||
#==================================
|
||||
merging_cols = intersect(names(stats_un_t3), names(n_t3)); merging_cols
|
||||
if (all(n_t3$mediator%in%stats_un_t3$mediator)) {
|
||||
cat("PASS: merging stats and n_obs on column/s:", merging_cols)
|
||||
stats_un_t3 = merge(stats_un_t3, n_t3, by = merging_cols, all = T)
|
||||
cat("\nsuccessfull merge:"
|
||||
, "\nnrow:", nrow(stats_un_t3)
|
||||
, "\nncol:", ncol(stats_un_t3))
|
||||
}else{
|
||||
nf = n_t3$mediator[!n_t3$mediator%in%stats_un_t3$mediator]
|
||||
stats_un_t3 = merge(stats_un_t3, n_t3, by = merging_cols, all.y = T)
|
||||
cat("\nMerged with caution:"
|
||||
, "\nnrows mismatch:", nf
|
||||
, "not found in stats possibly due to all obs being LLODs"
|
||||
, "\nintroduced NAs for:", nf
|
||||
, "\nnrow:", nrow(stats_un_t3)
|
||||
, "\nncol:", ncol(stats_un_t3))
|
||||
}
|
||||
|
||||
# add bonferroni adjustment as well
|
||||
stats_un_t3$p_adj_bonferroni = p.adjust(stats_un_t3$p, method = "bonferroni")
|
||||
|
||||
rm(n_t3)
|
||||
rm(lf_t3_comp)
|
||||
########################################################################
|
||||
#==============
|
||||
# Rbind these dfs
|
||||
#==============
|
||||
str(stats_un_t1);str(stats_un_t2); str(stats_un_t3)
|
||||
|
||||
n_dfs = 3
|
||||
|
||||
if ( all.equal(nrow(stats_un_t1), nrow(stats_un_t2), nrow(stats_un_t3)) &&
|
||||
all.equal(ncol(stats_un_t1), ncol(stats_un_t2), ncol(stats_un_t3)) ) {
|
||||
expected_rows = nrow(stats_un_t1) * n_dfs
|
||||
expected_cols = ncol(stats_un_t1)
|
||||
print("PASS: expected_rows and cols variables generated for downstream sanity checks")
|
||||
}else{
|
||||
cat("FAIL: dfs have different no. of rows and cols"
|
||||
, "\nCheck harcoded value of n_dfs"
|
||||
, "\nexpected_rows and cols could not be generated")
|
||||
quit()
|
||||
}
|
||||
|
||||
if ( all.equal(colnames(stats_un_t1), colnames(stats_un_t2), colnames(stats_un_t3)) ){
|
||||
print("PASS: colnames match. Rbind the 3 dfs...")
|
||||
combined_unpaired_stats = rbind(stats_un_t1, stats_un_t2, stats_un_t3)
|
||||
} else{
|
||||
cat("FAIL: cannot combined dfs. Colnames don't match!")
|
||||
quit()
|
||||
}
|
||||
|
||||
if ( nrow(combined_unpaired_stats) == expected_rows && ncol(combined_unpaired_stats) == expected_cols ){
|
||||
cat("PASS: combined_df has expected dimension"
|
||||
, "\nNo. of rows in combined_df:", nrow(combined_unpaired_stats)
|
||||
, "\nNo. of cols in combined_df:", ncol(combined_unpaired_stats) )
|
||||
}else{
|
||||
cat("FAIL: combined_df dimension mismatch")
|
||||
quit()
|
||||
}
|
||||
|
||||
#######################################################################
|
||||
#=================
|
||||
# formatting df
|
||||
#=================
|
||||
# delete: unnecessary column
|
||||
combined_unpaired_stats = subset(combined_unpaired_stats, select = -c(.y.))
|
||||
|
||||
# add sample_type
|
||||
cat("Adding sample type info as a column", my_sample_type, "...")
|
||||
combined_unpaired_stats$sample_type = my_sample_type
|
||||
|
||||
# add: reflect stats method correctly i.e paired or unpaired
|
||||
# incase there are NA due to LLODs, the gsub won't work!
|
||||
#combined_unpaired_stats$method = gsub("Wilcoxon", "Wilcoxon_unpaired", combined_unpaired_stats$method)
|
||||
combined_unpaired_stats$method = "wilcoxon unpaired"
|
||||
combined_unpaired_stats$method
|
||||
|
||||
# add an extra column for padjust_signif: my_adjust_method
|
||||
combined_unpaired_stats$padjust_signif = combined_unpaired_stats$p.adj
|
||||
# add appropriate symbols for padjust_signif: my_adjust_method
|
||||
combined_unpaired_stats = dplyr::mutate(combined_unpaired_stats, padjust_signif = case_when(padjust_signif == 0.05 ~ "."
|
||||
, padjust_signif <=0.0001 ~ '****'
|
||||
, padjust_signif <=0.001 ~ '***'
|
||||
, padjust_signif <=0.01 ~ '**'
|
||||
, padjust_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# add an extra column for p_bon_signif
|
||||
combined_unpaired_stats$p_bon_signif = combined_unpaired_stats$p_adj_bonferroni
|
||||
# add appropriate symbols for p_bon_signif
|
||||
combined_unpaired_stats = dplyr::mutate(combined_unpaired_stats, p_bon_signif = case_when(p_bon_signif == 0.05 ~ "."
|
||||
, p_bon_signif <=0.0001 ~ '****'
|
||||
, p_bon_signif <=0.001 ~ '***'
|
||||
, p_bon_signif <=0.01 ~ '**'
|
||||
, p_bon_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# reorder columns
|
||||
print("preparing to reorder columns...")
|
||||
colnames(combined_unpaired_stats)
|
||||
my_col_order2 = c("mediator"
|
||||
, "timepoint"
|
||||
, "sample_type"
|
||||
, "n_obs"
|
||||
, "n_obs_complete"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "method"
|
||||
, "p"
|
||||
, "p.format"
|
||||
, "p.signif"
|
||||
, "p.adj"
|
||||
, "padjust_signif"
|
||||
, "p_adj_bonferroni"
|
||||
, "p_bon_signif")
|
||||
|
||||
if( length(my_col_order2) == ncol(combined_unpaired_stats) && (all(my_col_order2%in%colnames(combined_unpaired_stats))) ){
|
||||
print("PASS: Reordering columns...")
|
||||
combined_unpaired_stats_f = combined_unpaired_stats[, my_col_order2]
|
||||
print("Successful: column reordering")
|
||||
print("formatted df called:'combined_unpaired_stats_f'")
|
||||
cat('\nformatted df has the following dimensions\n')
|
||||
print(dim(combined_unpaired_stats_f ))
|
||||
} else{
|
||||
cat(paste0("FAIL:Cannot reorder columns, length mismatch"
|
||||
, "\nExpected column order for: ", ncol(combined_unpaired_stats)
|
||||
, "\nGot:", length(my_col_order2)))
|
||||
quit()
|
||||
}
|
||||
# assign nice column names like replace "." with "_"
|
||||
colnames(combined_unpaired_stats_f) = c("mediator"
|
||||
, "timepoint"
|
||||
, "sample_type"
|
||||
, "n_obs"
|
||||
, "n_obs_complete"
|
||||
, "group1"
|
||||
, "group2"
|
||||
, "method"
|
||||
, "p"
|
||||
, "p_format"
|
||||
, "p_signif"
|
||||
, paste0("p_adj_fdr_", my_adjust_method)
|
||||
, paste0("p_", my_adjust_method, "_signif")
|
||||
, "p_adj_bonferroni"
|
||||
, "p_bon_signif")
|
||||
|
||||
colnames(combined_unpaired_stats_f)
|
||||
#---------------
|
||||
# quick summary
|
||||
#---------------
|
||||
# count how many meds are significant
|
||||
n_sig = length(combined_unpaired_stats_f$mediator[combined_unpaired_stats_f$p_signif<0.05 & !is.na(combined_unpaired_stats_f$p_signif<0.05)])
|
||||
sig_meds = combined_unpaired_stats_f[(combined_unpaired_stats_f$p_signif<0.05 & !is.na(combined_unpaired_stats_f$p_signif<0.05)),]
|
||||
|
||||
sig_meds$med_time = paste0(sig_meds$mediator, "@", sig_meds$timepoint)
|
||||
|
||||
cat("\nTotal no. of statistically significant mediators in", toupper(my_sample_type)
|
||||
, "are:", n_sig
|
||||
, "\nThese are:", sig_meds$med_time)
|
||||
|
||||
|
||||
########################################################################
|
||||
#******************
|
||||
# write output file
|
||||
#******************
|
||||
cat("\nUNpaired stats for groups will be:", flu_stats_time_unpaired_na)
|
||||
#write.csv(combined_unpaired_stats_f, flu_stats_time_unpaired_na, row.names = FALSE)
|
||||
259
logistic_regression.R
Executable file → Normal file
259
logistic_regression.R
Executable file → Normal file
|
|
@ -18,80 +18,241 @@ getwd()
|
|||
#====================
|
||||
source("data_extraction_formatting_clinical.R")
|
||||
|
||||
rm(fp_adults, metadata_all)
|
||||
# quick sanity checks
|
||||
table(clinical_df_ics$ia_exac_copd==1 & clinical_df_ics$asthma == 1)
|
||||
table(fp_adults$ia_exac_copd==1 & fp_adults$asthma == 1)
|
||||
table(fp_adults_na$ia_exac_copd==1 & fp_adults_na$asthma == 1)
|
||||
|
||||
table(clinical_df_ics$asthma)
|
||||
|
||||
#--------------------
|
||||
# Data reassignment
|
||||
#--------------------
|
||||
my_data = clinical_df_ics
|
||||
my_data_na = clinical_df_ics_na
|
||||
|
||||
table(my_data$ia_exac_copd==1 & my_data$asthma == 1)
|
||||
table(my_data_na$ia_exac_copd==1 & my_data_na$asthma == 1)
|
||||
|
||||
# clear variables
|
||||
#rm(fp_adults, fp_adults_na)
|
||||
|
||||
########################################################################
|
||||
my_data = reg_data
|
||||
#########################################################################
|
||||
# check factor of each column
|
||||
lapply(my_data, class)
|
||||
|
||||
character_vars <- lapply(my_data, class) == "character"
|
||||
character_vars
|
||||
table(character_vars)
|
||||
if ( names(which(lapply(my_data, class) == "character")) == "mosaic" ){
|
||||
cat("Character class for 1 column only:", "mosaic")
|
||||
}else{
|
||||
cat("More than one character class detected: Resolve!")
|
||||
quit()
|
||||
}
|
||||
|
||||
factor_vars <- lapply(my_data, class) == "factor"
|
||||
table(factor_vars)
|
||||
#============================
|
||||
# Identifying column types: Reg data
|
||||
#===========================
|
||||
cols_to_omit = c("mosaic", "flustat", "onset_2_initial", "ia_exac_copd")
|
||||
|
||||
my_data[, character_vars] <- lapply(my_data[, character_vars], as.factor)
|
||||
factor_vars <- lapply(my_data, class) == "factor"
|
||||
factor_vars
|
||||
my_reg_data = my_data[!colnames(my_data)%in%cols_to_omit]
|
||||
|
||||
my_vars = colnames(my_reg_data)
|
||||
my_vars
|
||||
|
||||
lapply(my_reg_data, class)
|
||||
numerical_vars = c("age"
|
||||
, "vl_pfu_ul_npa1"
|
||||
, "los"
|
||||
, "onset2final"
|
||||
, "onsfindeath"
|
||||
, "o2_sat_admis")
|
||||
|
||||
my_reg_data[numerical_vars] <- lapply(my_reg_data[numerical_vars], as.numeric)
|
||||
|
||||
my_reg_params = my_vars
|
||||
|
||||
na_count = sapply(my_reg_data, function(x) sum(is.na(x)));na_count
|
||||
names(na_count)[na_count>0]
|
||||
|
||||
vars_to_factor = my_vars[!my_vars%in%numerical_vars]
|
||||
|
||||
# convert to factor
|
||||
lapply(my_reg_data, class)
|
||||
my_reg_data[vars_to_factor] <- lapply(my_reg_data[vars_to_factor], as.factor)
|
||||
factor_vars <- colnames(my_reg_data)[lapply(my_reg_data, class) == "factor"]
|
||||
table(factor_vars)
|
||||
|
||||
# check again
|
||||
lapply(my_data, class)
|
||||
lapply(my_reg_data, class)
|
||||
|
||||
table(my_data$ethnicity)
|
||||
my_data$ethnicity = as.factor(my_data$ethnicity)
|
||||
class(my_data$ethnicity)
|
||||
|
||||
colnames(my_data)
|
||||
reg_param = c("age"
|
||||
, "age_bins"
|
||||
#, "death" # outcome
|
||||
, "asthma"
|
||||
, "obesity"
|
||||
, "gender"
|
||||
# all parasm for reg
|
||||
my_reg_params = c("age"
|
||||
, "vl_pfu_ul_npa1"
|
||||
, "los"
|
||||
, "o2_sat_admis"
|
||||
#, "logistic_outcome"
|
||||
#, "steroid_ics"
|
||||
, "ethnicity"
|
||||
, "smoking"
|
||||
, "onset2final"
|
||||
#, "onsfindeath"
|
||||
#, "o2_sat_admis"
|
||||
, "death"
|
||||
, "obesity"
|
||||
, "sfluv"
|
||||
, "h1n1v"
|
||||
, "gender"
|
||||
, "asthma"
|
||||
, "ethnicity"
|
||||
, "smoking"
|
||||
, "ia_cxr"
|
||||
, "max_resp_score"
|
||||
, "T1_resp_score"
|
||||
, "com_noasthma"
|
||||
, "onset_initial_bin")
|
||||
, "T2_resp_score"
|
||||
, "inresp_sev"
|
||||
, "steroid"
|
||||
, "age_bins"
|
||||
, "o2_sat_bin"
|
||||
, "onset_initial_bin"
|
||||
, "steroid_ics"
|
||||
, "t1_resp_recoded")
|
||||
|
||||
for(i in reg_param) {
|
||||
# print (i)
|
||||
p_form = as.formula(paste("death ~ ", i ,sep = ""))
|
||||
model_reg = glm(p_form , family = binomial, data = my_data)
|
||||
#=================
|
||||
# reg data prepare
|
||||
#=================
|
||||
pv1 = "death"
|
||||
pv2 = "t1_resp_recoded"
|
||||
|
||||
#reg_params1 = factor_vars[!factor_vars%in%pv1]
|
||||
#reg_params_mixed = my_vars[!my_vars%in%pv1]
|
||||
|
||||
########################################################################
|
||||
#=================
|
||||
# outcome2
|
||||
#=================
|
||||
#-----------------------------
|
||||
# outcome: death + obesity
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
my_reg_params1 = my_reg_params[!my_reg_params%in%c("death", "obesity")]
|
||||
|
||||
for(i in my_reg_params1) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("death ~ obesity + ", i ,sep = ""))
|
||||
print(p_form)
|
||||
model_reg = glm(p_form , family = binomial, data = my_reg_data)
|
||||
print(summary(model_reg))
|
||||
print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
|
||||
#print (PseudoR2(model_reg))
|
||||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
|
||||
#-----------------------------
|
||||
# outcome: death
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
my_reg_params1v2 = my_reg_params[!my_reg_params%in%c("death")]
|
||||
|
||||
for(i in my_reg_params1v2) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("death ~ ", i ,sep = ""))
|
||||
print(p_form)
|
||||
model_reg = glm(p_form , family = binomial, data = my_reg_data)
|
||||
print(summary(model_reg))
|
||||
print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
|
||||
#print (PseudoR2(model_reg))
|
||||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
########################################################################
|
||||
#=================
|
||||
# outcome2
|
||||
#=================
|
||||
#-----------------------------
|
||||
# outcome: t1_resp_recoded + obesity
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
my_reg_params2 = my_reg_params[!my_reg_params%in%c("death"
|
||||
, "obesity"
|
||||
, "t1_resp_recoded"
|
||||
, "T1_resp_score")]
|
||||
|
||||
for(i in my_reg_params2) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("t1_resp_recoded ~ obesity + ", i ,sep = ""))
|
||||
print(p_form)
|
||||
model_reg = glm(p_form , family = binomial, data = my_reg_data)
|
||||
print(summary(model_reg))
|
||||
print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
|
||||
#print (PseudoR2(model_reg))
|
||||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
|
||||
|
||||
full_mod = glm(death ~ asthma +
|
||||
gender +
|
||||
age_bins +
|
||||
los +
|
||||
#ethnicity +
|
||||
onset_initial_bin +
|
||||
o2_sat_bin +
|
||||
com_noasthma +
|
||||
#-----------------------------
|
||||
# outcome: t1_resp_recoded
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
my_reg_params2v2 = my_reg_params[!my_reg_params%in%c("death"
|
||||
#, "obesity"
|
||||
, "t1_resp_recoded"
|
||||
, "T1_resp_score")]
|
||||
|
||||
for(i in my_reg_params2v2) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("t1_resp_recoded ~ ", i ,sep = ""))
|
||||
print(p_form)
|
||||
model_reg = glm(p_form , family = binomial, data = my_reg_data)
|
||||
print(summary(model_reg))
|
||||
print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
|
||||
#print (PseudoR2(model_reg))
|
||||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
|
||||
########################################################################
|
||||
# Full model
|
||||
########################################################################
|
||||
|
||||
full_mod = glm(death ~ obesity +
|
||||
age +
|
||||
#age_bins +
|
||||
obesity +
|
||||
#ia_cxr +
|
||||
smoking +
|
||||
#sfluv +
|
||||
#h1n1v
|
||||
max_resp_score +
|
||||
T1_resp_score +
|
||||
, family = "binomial", data = my_data)
|
||||
asthma +
|
||||
t1_resp_recoded +
|
||||
#ia_cxr
|
||||
, family = "binomial", data = my_reg_data)
|
||||
|
||||
summary(full_mod)
|
||||
|
||||
|
||||
|
||||
########################################################################
|
||||
# mediators
|
||||
########################################################################
|
||||
sig_npa_cols = c("mosaic", sig_npa_cols)
|
||||
|
||||
my_med_sig = fp_adults[, sig_npa_cols]
|
||||
|
||||
my_reg_data_med = merge(clinical_df_ics, my_med_sig
|
||||
, by = intersect(names(clinical_df_ics), names(my_med_sig))
|
||||
)
|
||||
|
||||
#my_reg_params_meds = c(my_reg_params, sig_npa_cols)
|
||||
my_reg_params_meds = colnames(my_reg_data_med)
|
||||
my_reg_params_meds1 = my_reg_params_meds[!my_reg_params_meds%in%c("mosaic", "flustat"
|
||||
, "onset_2_initial"
|
||||
, "onsfindeath"
|
||||
, "ia_exac_copd"
|
||||
, "death"
|
||||
, "obesity")]
|
||||
|
||||
|
||||
|
||||
for(i in my_reg_params_meds1) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("death ~ obesity + ", i ,sep = ""))
|
||||
print(p_form)
|
||||
model_reg = glm(p_form , family = binomial, data = my_reg_data_med)
|
||||
print(summary(model_reg))
|
||||
print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
|
||||
#print (PseudoR2(model_reg))
|
||||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue