Mar-8.Rd

# Author: Pawar, Date: March 08, 2022; Purpose: To generate box plots for raw microarray datasets

setwd("/Users/pawar/Desktop/GDS223")

library(affy)

eset <- ReadAffy()

eset_Norm <- rma(eset)

rma <- exprs(eset_Norm)

Treatment <- apply(rma[, c("GSM4843.CEL", "GSM4844.CEL")], 1, log)

Control <- apply(rma[, c("GSM4845.CEL", "GSM4846.CEL", "GSM4847.CEL")], 1, log)

# TRANSPOSE: Switch columns to rows and rows to columns!!

# Perform transpose so that samples are your columns and genes are your rows!!

Treatment_T <- t(Treatment)

Control_T <- t(Control)

# To make fold changes, we first need to take mean/average for treatment and for control samples

Treatment_T_Mean <- rowMeans(Treatment_T)
Control_T_Mean <- rowMeans(Control_T)

# Get the subtraction fold change for treatment over control

Fold_Change <- Treatment_T_Mean-Control_T_Mean

# Assignment: Export variable "Fold_Change" as a .csv format file with name "fold_changes.csv"

Hint: https://www.statology.org/export-data-frame-to-csv-in-r/