OddSNP: a predictive framework for optimizing multiplexed single-cell RNA-seq experiments.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Single-cell RNA sequencing (scRNAseq) is crucial for biological research.
• Donor multiplexing enhances scRNAseq efficiency but lacks clear experimental design guidelines.
• This leads to potential demultiplexing failures and increased costs.

Purpose of the Study:

• To develop a quantitative metric for predicting the success of genotype-based donor demultiplexing in scRNAseq.
• To establish guidelines for experimental design in donor multiplexing to minimize failure risks.
• To introduce a practical computational framework for optimizing scRNAseq experiments.

Main Methods:

• Introduction of SNP-Information Content (SNP-IC), a metric calculated from pilot data.
• Definition of a pairwise metric, cpSNP-IC, for genotype-free demultiplexing.
• Development of the open-source framework 'oddSNP' for in silico experimental design optimization.

Main Results:

• SNP-IC accurately predicts demultiplexing success, with a threshold of ~50 for reliable donor assignment.
• A higher cpSNP-IC requirement of ~3,000 was determined for genotype-free methods.
• The 'oddSNP' framework allows for in silico titration of sequencing depth and donor complexity.

Conclusions:

• SNP-IC provides a robust method for predicting and optimizing donor multiplexing in scRNAseq.
• 'oddSNP' empowers researchers to design cost-effective and successful scRNAseq experiments.
• This framework mitigates the risk of data loss and enhances the scalability of single-cell studies.