SiftCell: A robust framework to detect and isolate cell-containing droplets from single-cell RNA sequence reads.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptome profiling of individual cells.
• scRNA-seq data often contains cell-free droplets capturing ambient RNA from lysed cells.
• Accurate filtering of cell-free droplets is crucial for reliable downstream analysis.

Purpose of the Study:

• To develop a robust computational method for identifying and filtering cell-free droplets in scRNA-seq data.
• To improve the accuracy and comprehensiveness of upstream quality control in scRNA-seq analysis.

Main Methods:

• SiftCell, a software suite utilizing randomization (SiftCell-Shuffle) for droplet visualization.
• Classification of droplet types using SiftCell-Boost.
• Quantification of ambient RNA contribution per droplet with SiftCell-Mix.

Main Results:

• SiftCell effectively identifies and visualizes cell-containing and cell-free droplets.
• The method demonstrates superior accuracy and comprehensiveness compared to existing approaches.
• Successful application across diverse single-cell platforms.

Conclusions:

• SiftCell offers a streamlined and accurate solution for upstream quality control in scRNA-seq.
• Improved droplet filtering enhances the reliability of scRNA-seq data analysis.
• The software suite is broadly applicable to various scRNA-seq datasets.