cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• 10x Genomics Chromium technology enables high-throughput single-cell RNA sequencing, generating vast amounts of data.
• Quantifying gene expression from massive single-cell RNA sequencing datasets presents significant computational challenges.

Purpose of the Study:

• To introduce cellCounts, a novel R-based tool for efficient and accurate gene expression quantification from 10x Genomics Chromium data.
• To address the challenges posed by large data volumes in single-cell RNA sequencing analysis.

Main Methods:

• cellCounts utilizes a seed-and-vote strategy for read alignment to a reference genome.
• It collapses reads to Unique Molecular Identifiers (UMIs) and assigns UMIs to genes using the featureCounts program.
• The tool is implemented within the R package Rsubread for seamless integration with other R-based bioinformatics workflows.

Main Results:

• Evaluation using simulated and real datasets demonstrated that cellCounts provides favorable performance compared to established tools such as cellRanger and STARsolo.
• cellCounts offers an efficient and accurate solution for quantifying gene expression at the single-cell level.

Conclusions:

• cellCounts provides a valuable and accessible tool for researchers working with 10x Genomics single-cell RNA sequencing data.
• Its efficient processing and accurate quantification capabilities facilitate deeper insights into cellular gene expression profiles.