Differential Expression Analysis with DESeq2 in R: A Step-by-Step Guide

The code provided is using the DESeq2 package in R to perform differential expression analysis on gene expression data. Here are the steps:
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1. Load the DESeq2 package: library("DESeq2")
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2. Create the condition variable: The condition variable is created as a factor, with "Normal" repeated 113 times and "Tumor" repeated 1118 times.
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3. Create the coldata dataframe: This dataframe contains the row names from the GBMdata matrix and the condition variable.
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4. Create the DESeq dataset: The DESeqDataSetFromMatrix function is used to create a DESeq dataset from the count data in the GBMdata matrix and the coldata dataframe. The formula ~ condition specifies the design formula for the differential expression analysis.
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5. Perform differential expression analysis: The DESeq function is used to perform the differential expression analysis on the DESeq dataset (dds).
   
   After these steps, you can proceed with further analysis, such as generating differential expression results, performing hypothesis testing, or visualizing the results.