Microbial Single-Cell Analysis (E. coli Transcriptomics)

🦠 Project Overview

This project analyzes RNA-seq data to study gene expression changes in E. coli across different conditions: Wild Type (WT), Mutant, and Biofilm. We used a Pseudo-bulk approach with Salmon for quantification and Seurat (R) for analysis.

📊 Key Results

1. PCA Analysis (Clustering)

The PCA plot shows clear separation between the conditions. Notably, the Biofilm samples (Green) are distinctly separated from Planktonic samples, indicating a major transcriptomic shift.

2. Gene Expression Heatmap

We identified the top variable genes driving these differences. The heatmap below highlights specific gene clusters activated only during Biofilm formation (bottom yellow block).

🛠️ Pipeline

1. QC : FastQC & MultiQC.
2. Alignment : Salmon (Mapping to MG1655 Reference).
3. Analysis: Seurat (Normalization, PCA, Differential Expression).

📂 Repository Structure

• notebooks/: Jupyter Notebooks containing the code.
• E_coli_Top_Variable_Genes.csv: List of identified marker genes.
• E_coli_Normalized_Counts.csv: Processed expression matrix.