Gene Expression Analysis Using R

This project involves analyzing gene expression data to understand the effects of a BET inhibitor treatment on a pancreatic cell line. The analysis includes data preprocessing, quality control, differential expression analysis, and functional enrichment analysis using R and Bioconductor packages. The goal is to identify significantly overexpressed and underexpressed genes and understand the biological processes affected by the treatment.

Key Features:
- Data Import and Preprocessing: Imported gene expression data from Affymetrix microarrays and preprocessed it using the Robust Multi-array Average (RMA) algorithm. This step involved background correction, normalization, and summarization of the raw data.
- Quality Control: Conducted quality control assessments using Principal Component Analysis (PCA) and boxplots to visualize the raw and processed data. Ensured no significant outliers were present in the dataset.
- Differential Expression Analysis: Performed differential expression analysis using Generalized Linear Models (GLMs) to identify genes that are significantly overexpressed or underexpressed between treated and control samples. Applied a 0.001 p-value threshold for significance.
- Functional Enrichment Analysis: Conducted Gene Set Enrichment Analysis (GSEA) to identify the top 10 upregulated and downregulated Gene Ontology (GO) terms for biological processes affected by the treatment.
- Visualization: Created various visualizations, including PCA plots, boxplots, volcano plots, and GO term enrichment plots, to interpret and present the results effectively.