oddSNP 发布预测框架，用于优化多重单细胞 RNA-seq 实验设计
oddSNP: a predictive framework for optimizing multiplexed single-cell RNA-seq experiments
研究团队发布开源框架 oddSNP，用可量化的 SNP-IC 指标预测多重 scRNA-seq 实验中基因型去多重化能否成功。SNP-IC 可从简单的未混样预实验数据计算，作者在干细胞与类器官模型的多套大规模数据集上得出约 50 的阈值，高于该值时细胞可可靠归属到供体来源；针对不依赖基因型的更困难方案，作者定义了成对指标 cpSNP-IC，其要求高得多，约为 3,000。oddSNP 支持在计算层面调整测序深度与供体复杂度，让研究者在正式开展大规模实验前优化设计、控制成本并降低去多重化失败的风险。
来源：Bioinformatics Advances · doi.org
Motivation
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.
Results
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.
Availability and Implementation
Our open-source framework, oddSNP, implements this predictive model to enable in-silico titration of sequencing depth and donor complexity, allowing researchers to optimize study design before large-scale deployment, while managing costs and minimizing the risk of catastrophic data loss. oddSNP can be installed directly from PyPI and Bioconda. Source code is also freely available from GitHub at https://www.github.com/nemoto-lab/oddSNP (with release versions also available in Zenodo at DOI 10.5281/zenodo.19378022). Novel scRNAseq data used as part of this study is available at the Gene Expression Omnibus (GEO) website under accession GSE334633.