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

Abstract

Donor multiplexing is a powerful strategy to increase scale, lower the costs, and reduce batch effects in single-cell RNA sequencing (scRNAseq), but clear guidelines for experimental design are lacking, forcing researchers to risk costly demultiplexing failures. To address this, we introduce SNP-Information Content (SNP-IC), a quantitative metric computable from simple unpooled pilot data that accurately predicts the success of genotype-based demultiplexing. Across multiple large-scale datasets using stem cell and organoid models, we establish a robust SNP-IC threshold of approximately 50, above which cells can be reliably assigned to their donor of origin. For more challenging genotype-free approaches, we define a pairwise metric, cpSNP-IC, and demonstrate a much higher requirement of approximately 3,000. Our open-source framework, oddSNP, implements this predictive model, allowing researchers to perform in silico titrations of sequencing depth and donor complexity to optimize experimental design before committing to large-scale studies. oddSNP provides a practical framework, enabling researchers to strategically optimize sequencing depth and donor numbers to maximize experimental success while managing costs and minimizing the risk of catastrophic data loss.