pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) analysis involves complex pipelines with multiple interdependent steps.
• Evaluating and comparing these pipelines is crucial for accurate biological insights but challenging due to inter-step dependencies and varied metrics.

Purpose of the Study:

• To introduce pipeComp, a flexible R framework designed for comprehensive pipeline comparison.
• To establish a standardized and extensible benchmarking system for scRNA-seq analysis pipelines.

Main Methods:

• Developed pipeComp, an R framework that models interactions between analysis steps.
• Utilized multi-level evaluation metrics for assessing pipeline performance.
• Applied pipeComp to benchmark common scRNA-seq analysis steps using simulated and real datasets.
• Demonstrated extensibility by applying pipeComp to assess the impact of unwanted variation removal on differential expression analysis.

Main Results:

• pipeComp successfully handles inter-step dependencies in analysis pipelines.
• The framework provides a flexible and extensible approach to benchmarking.
• Evaluated common scRNA-seq methods, revealing performance differences based on chosen metrics and data types.
• Showcased the utility of pipeComp in assessing the impact of specific data processing steps on downstream analyses.

Conclusions:

• pipeComp offers a robust and adaptable solution for benchmarking complex biological analysis pipelines.
• The framework facilitates rigorous evaluation of single-cell RNA sequencing methods and other bioinformatics workflows.
• Enables reproducible and extensible scientific benchmarking across various biological research fields.