From abe72f45da34f807b3b55b1b3865867908a3483e Mon Sep 17 00:00:00 2001
From: noah <noah@theschricks.com>
Date: Thu, 13 Oct 2022 15:08:33 -0500
Subject: [PATCH] Data pre-processing

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 Schrick-Noah_CS-6643_Lab-5.R  | 51 +++++++++++++++++++++++++++++++++++
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 create mode 100644 Schrick-Noah_CS-6643_Lab-5.R

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+,noah,NovaArchSys,13.10.2022 15:00,file:///home/noah/.config/libreoffice/4;
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+# Lab 5 for the University of Tulsa's CS-6643 Bioinformatics Course
+# Gene Expression Statistical Learning
+# Professor: Dr. McKinney, Fall 2022
+# Noah L. Schrick - 1492657
+
+## Set Working Directory to file directory - RStudio approach
+setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
+
+#### 0: Process and filter data
+# load gene expression data
+load("sense.filtered.cpm.Rdata")  # setwd!
+
+# load phenotype (mdd/hc) data
+subject.attrs <- read.csv("Demographic_symptom.csv", 
+                          stringsAsFactors = FALSE)
+
+if (!require("dplyr")) install.packages("dplyr")
+library(dplyr)
+# grab intersecting X (subject ids) and Diag (Diagnosis) from columns
+phenos.df <- subject.attrs %>% 
+  filter(X %in% colnames(sense.filtered.cpm)) %>%
+  dplyr::select(X, Diag)  
+mddPheno <- as.factor(phenos.df$Diag)  
+
+# Normalized and transform
+if (!require("preprocessCore")) install.packages("preprocessCore")
+library(preprocessCore)
+mddExprData_quantile <- normalize.quantiles(sense.filtered.cpm)
+mddExprData_quantileLog2 <- log2(mddExprData_quantile)
+# attach phenotype names and gene names to data 
+colnames(mddExprData_quantileLog2) <- mddPheno  
+rownames(mddExprData_quantileLog2) <- rownames(sense.filtered.cpm)
+
+# coefficient of variation filter sd(x)/abs(mean(x))
+CoV_values <- apply(mddExprData_quantileLog2,1,
+                    function(x) {sd(x)/abs(mean(x))})
+# smaller threshold, the higher the experimental effect relative to the 
+# measurement precision
+sum(CoV_values<.045)
+# there is one gene that has 0 variation -- remove
+sd_values <- apply(mddExprData_quantileLog2,1, function(x) {sd(x)})
+rownames(mddExprData_quantileLog2)[sd_values==0]  
+# filter the data matrix 
+GxS.covfilter <- mddExprData_quantileLog2[CoV_values<.045 & sd_values>0,]
+dim(GxS.covfilter)
+
+# convert phenotype to factor
+pheno.factor <- as.factor(colnames(GxS.covfilter))
+pheno.factor
+str(pheno.factor)
+levels(pheno.factor)
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