Perform differential expression analysis separately for each identified cell cluster to determine if certain subpopulations exhibit distinct responses. This approach helps identify genes or pathways specifically affected within sensitive subpopulations.

🗯️MATLAB snippet

Differential expression analysis (DEA) in subpopulations is critical for identifying how specific perturbations affect gene or feature expression. MATLAB offers various tools and methods for conducting DEA, particularly suited for analyzing datasets from experiments like single-cell RNA sequencing or other high-dimensional studies. Here's how to approach differential expression analysis in subpopulations with MATLAB:

1. Data Preparation

Ensure your data is appropriately formatted and preprocessed before running differential expression analysis:

Example of Normalizing Data:

 % Assuming `expressionData` is a matrix of raw gene expression data
 expressionDataNorm = log2(expressionData + 1);  % Log-transform to reduce skewness

2. Identifying Subpopulations

To analyze differential expression within subpopulations, you must first identify and label these groups:

Example of Clustering for Subpopulation Identification:

 % Perform K-means clustering to identify subpopulations
 [idx, ~] = kmeans(expressionDataNorm', 3);  % Transpose for samples as rows
 subpopulationLabels = idx;

3. Differential Expression Analysis

To perform DEA between subpopulations: