Comparison of high-throughput single-cell RNA sequencing data processing pipelines.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables large-scale transcript profiling.
• Numerous analysis pipelines exist, posing a challenge for users selecting appropriate workflows.
• Integrated analysis of scRNA-seq data is growing, but potential biases from different upstream pipelines are unclear.

Purpose of the Study:

• To evaluate the performance of seven high-throughput scRNA-seq data processing pipelines.
• To provide guidelines for selecting efficient, robust, and reliable scRNA-seq workflows.
• To establish a framework for evaluating future scRNA-seq data processing developments.

Main Methods:

• Encapsulated seven existing scRNA-seq pipelines using Nextflow.
• Evaluated pipeline performance on eight public datasets from five different scRNA-seq platforms.
• Assessed running time, computational resource consumption, and data analysis consistency.

Main Results:

• Performance varied across pipelines and datasets.
• Identified pipelines with optimal efficiency and consistency for specific platforms.
• Quantified resource usage and running times for each pipeline.

Conclusions:

• The study provides practical guidelines for selecting scRNA-seq data processing pipelines.
• Performance evaluation is essential for robust and reliable transcriptomic analysis.
• The developed framework aids in choosing optimal workflows for diverse scRNA-seq data.