SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Quality control (QC) is essential for single-cell RNA sequencing (scRNA-seq) data analysis.
• Existing QC methods often assume a single cell type, risking the exclusion of rare cell populations.
• This limitation can introduce bias into downstream analyses.

Purpose of the Study:

• To develop a novel QC method, SampleQC, that addresses the limitations of current approaches.
• To improve the sensitivity and reduce bias in scRNA-seq data processing.
• To ensure the accurate inclusion of all cell types, including rare ones.

Main Methods:

• SampleQC employs a Gaussian mixture model to robustly analyze data across multiple samples.
• The method is designed to be sensitive to variations within and between datasets.
• It was implemented in R for broad accessibility.

Main Results:

• Simulations demonstrated that SampleQC is less prone to excluding rare cell types compared to existing methods.
• Application to a large-scale real dataset (867,000 cells, 172 samples) validated its performance.
• SampleQC showed improved sensitivity and reduced bias in complex, multi-sample scRNA-seq data.

Conclusions:

• SampleQC offers a more robust and less biased approach to scRNA-seq data quality control.
• The method effectively handles datasets with multiple cell types and samples.
• SampleQC's generalizability suggests potential applications beyond scRNA-seq data analysis.