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Related Experiment Video

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data.

Yaru Zhang¹, Yunlong Ma¹, Yukuan Huang¹

¹Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.

Computational and Structural Biotechnology Journal

|November 19, 2020

Summary

This study evaluates pathway analysis algorithms for single-cell RNA sequencing (scRNA-seq) data. Pagoda2 demonstrated the best overall performance in accuracy, scalability, and stability for scRNA-seq functional annotation.

Keywords:

Benchmark Gene expression Pathway analysis scRNA-seq

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates high-dimensional gene expression data.
Biological pathway analysis is crucial for interpreting scRNA-seq data, aiding cell clustering and functional annotation.
Numerous algorithms exist to transform gene-level data into pathway-based functional gene sets.

Purpose of the Study:

To comprehensively evaluate the accuracy, stability, and scalability of widely-used pathway activity transformation algorithms for scRNA-seq data.
To assess the impact of different scRNA-seq preprocessing steps on pathway analysis.
To identify the optimal algorithms and preprocessing strategies for robust scRNA-seq functional interpretation.

Main Methods:

Collected seven pathway activity transformation algorithms and 32 scRNA-seq datasets covering 16 different scRNA-seq techniques.
Developed a comprehensive framework to systematically assess algorithm performance.
Evaluated the impact of preprocessing steps, including cell filtering and data normalization (sctransform, scran).

Main Results:

Data normalization using sctransform and scran consistently showed a positive impact across all evaluated tools.
Cell filtering had a minimal effect on the overall accuracy of scRNA-seq pathway analysis.
Pagoda2 achieved the highest overall performance, excelling in accuracy, scalability, and stability.
PLAGE demonstrated the highest stability, with moderate accuracy and scalability.

Conclusions:

Pagoda2 is recommended as a top-performing tool for scRNA-seq pathway analysis due to its superior accuracy, scalability, and stability.
PLAGE is a highly stable alternative for pathway analysis in scRNA-seq studies.
Effective data normalization is critical for reliable functional interpretation of scRNA-seq data.