# Lab 3 for the University of Tulsa's CS-6643 Bioinformatics Course
# Expression Exploratory Analysis 
# Professor: Dr. McKinney, Fall 2022
# Noah L. Schrick - 1492657

## Set Working Directory to file directory - RStudio approach
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))

#### Part A: Loading Data
## 1: Loading Gene Expression Data
load("sense.filtered.cpm.Rdata")
dim(sense.filtered.cpm) 
colnames(sense.filtered.cpm)

## 2: Demographic Data
# Loading
subject.attrs <- read.csv("Demographic_symptom.csv", stringsAsFactors = FALSE)
dim(subject.attrs)  # 160 subjects x 40 attributes
colnames(subject.attrs)  # interested in X (sample ids) and Diag (diagnosis)
subject.attrs$X
subject.attrs$Diag

# Matching gene expression samples with their diagnosis
if (!require("dplyr")) install.packages("dplyr")
library(dplyr)
# create a phenotype vector
# grab X (subject ids) and Diag (Diagnosis) from subject.attrs that 
#   intersect %in% with the RNA-Seq data
phenos.df <- subject.attrs %>% 
  filter(X %in% colnames(sense.filtered.cpm)) %>%
  dplyr::select(X, Diag)  
colnames(phenos.df) # $Diag is mdd diagnosis
# grab Diag column and convert character to factor
mddPheno <- as.factor(phenos.df$Diag)  # this is our phenotype/class vector 

summary(mddPheno) # MDD -- major depressive disorder, HC -- healthy control