FUNCellA: A Tool for Single-Sample Enrichment Analysis and Relative Pathway Activity Estimation in Single-Cell RNA...

Area of Science:

• Computational biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-Seq) reveals cellular heterogeneity but faces challenges with data sparsity and noise.
• Existing pathway enrichment methods are often unsuitable for scRNA-Seq data due to variability and dropout.
• Current approaches lack robust methods for clustering pathway activity to detect cell subpopulations.

Purpose of the Study:

• To develop a novel framework, FUNCellA, for estimating relative pathway activity scores in single cells.
• To enable unsupervised clustering of pathway activity vectors for subpopulation detection.
• To improve functional interpretation and discovery of cellular heterogeneity in scRNA-Seq data.

Main Methods:

• FUNCellA integrates 7 single-sample enrichment algorithms with relative activation thresholding.
• Unsupervised learning techniques like k-means and Gaussian mixture modeling are employed.
• The framework estimates relative pathway activity scores for individual cells.

Main Results:

• FUNCellA effectively identifies active, inactive, and intermediate cellular states.
• Benchmarking on diverse datasets (scRNA-Seq, bulk, microarray) shows superior performance in relative pathway activation detection.
• The method outperforms existing tools in detecting pathway activation patterns.

Conclusions:

• FUNCellA provides a robust solution for pathway activity analysis in single-cell data.
• It enables functional cell classification beyond marker-based clustering.
• The framework uncovers nuanced cellular heterogeneity, including sub-activation states and disease-specific responses.