IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) generates high-dimensional data to study cellular heterogeneity.
• Analyzing scRNA-seq data requires specialized expertise due to its complexity and numerous analytical steps.
• Existing tools vary in performance based on data type and complexity, necessitating robust benchmarking.

Purpose of the Study:

• To present the Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP) for flexible and comparative analysis of scRNA-seq data.
• To enable users to benchmark different analytical components and identify optimal pipeline combinations for their specific datasets.
• To compare reference-based cell annotation with unsupervised methods for improved cell type identification.

Main Methods:

• IBRAP incorporates interchangeable analytical components and multiple benchmarking metrics.
• The pipeline was applied to single- and multi-sample integration analyses using pancreatic tissue, cancer cell lines, and simulated data.
• Reference-based cell annotation was compared against unsupervised analysis within IBRAP.

Main Results:

• Optimal scRNA-seq analysis pipelines are sample- and study-dependent.
• IBRAP demonstrated its interchangeable and benchmarking capabilities across diverse datasets.
• Reference-based cell annotation proved superior in identifying both major and minor cell types compared to unsupervised methods.

Conclusions:

• IBRAP provides a valuable tool for integrating multiple samples and studies to create comprehensive reference maps.
• The pipeline facilitates novel biological discoveries from large scRNA-seq datasets.
• IBRAP supports the creation of reference maps for normal and diseased tissues, aiding research in various biological contexts.