A gene expression matrix is a fundamental data structure in genomics and transcriptomics, particularly in analyses like single-cell RNA sequencing (scRNA-seq).

Gene Expression Matrix Analysis: A Step-by-Step Guide

1. Data Preprocessing

Before analysis, the raw expression matrix needs normalization and quality control.

✅ Steps:

• Check for missing values → Remove or impute missing data.
• Filter low-expression genes → Remove genes with very low counts across samples.
• Normalization → Adjust for sequencing depth and library size (e.g., TPM, RPKM, FPKM, or log-transformation).

💡 Tools: DESeq2, edgeR, limma, Seurat (for single-cell data)

2. Exploratory Data Analysis (EDA)

Check data structure and detect batch effects.

✅ Key methods:

• Principal Component Analysis (PCA) → Identify sample clusters.
• Hierarchical Clustering / Heatmaps → Group similar samples and genes.
• Correlation Analysis → Identify co-expressed genes.

💡 Tools: PCAtools, ggplot2 (R), Seaborn, matplotlib (Python)

3. Differential Expression Analysis (DEA)

Find genes that are differentially expressed between conditions (e.g., disease vs. control).