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:

• Matrix Format: Your data should be in a matrix format, where rows represent genes (or features) and columns represent samples (cells or conditions).
• Normalization: Normalize the data to account for technical variation using log transformation or scaling methods.
• Subpopulation Labeling: Assign subpopulation labels based on prior clustering or metadata.

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:

• Clustering: Use methods like kmeans, hierarchical clustering (linkage and cluster), or fitgmdist for subpopulation identification.
• Manual Annotation: If you have metadata available, assign subpopulation labels based on known characteristics.

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:

• Statistical Testing: Use ttest2 for simple two-group comparisons or ANOVA (anova1) for multi-group comparisons.
• Built-in Functions: MATLAB’s Statistics and Machine Learning Toolbox provides functions like ranksum for non-parametric testing.