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IndepthPathway: an integrated tool for in-depth pathway enrichment analysis based on single-cell sequencing data
Sanghoon Lee1,2,3, Letian Deng1,2, Yue Wang1,2
1UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, United States.
Bioinformatics (Oxford, England)
|May 27, 2023
Summary
A new method, Weighted Concept Signature Enrichment Analysis, improves pathway enrichment for noisy single-cell RNA sequencing data. The IndepthPathway R package offers robust analysis for biological discoveries.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Single-cell sequencing offers unprecedented cellular resolution but faces challenges with noisy data and low gene coverage.
- Existing pathway enrichment methods struggle with the high technical variability inherent in single-cell transcriptomics.
- Detecting enriched pathways in rare or low-abundance cell populations is particularly difficult due to sparse signals.
Purpose of the Study:
- To develop a novel pathway enrichment method robust to the noise and sparsity of single-cell RNA sequencing (scRNA-seq) data.
- To create an accessible R package, IndepthPathway, for applying this method to both single-cell and bulk RNA sequencing data.
- To enhance the reliability and depth of pathway analysis in single-cell studies.
Main Methods:
- Developed Weighted Concept Signature Enrichment Analysis, a method that assesses functional relationships between pathway gene sets and differentially expressed genes.
- Leveraged a 'universal concept signature' from highly differentially expressed genes to mitigate noise and low gene coverage.
- Integrated this analysis into the IndepthPathway R package, designed for broad usability by biologists.
Main Results:
- IndepthPathway demonstrated outstanding stability and depth in pathway enrichment results, even under simulated scRNA-seq technical variability and dropouts.
- Benchmarking on matched single-cell and bulk RNA sequencing data confirmed the method's superior performance.
- The package effectively handles the stochastic nature of scRNA-seq data, improving analytical rigor.
Conclusions:
- IndepthPathway provides a robust solution for pathway enrichment analysis in single-cell transcriptomics.
- The developed method and package significantly improve the scientific rigor and reliability of pathway discoveries from scRNA-seq data.
- This tool empowers biologists to gain deeper insights from complex single-cell datasets.
