{"doi":"10.64898/2025.12.08.692882","title":"OddSNP: a predictive framework for optimizing multiplexed single-cell RNA-seq experiments","abstract":"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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":584949,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9525,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":576964,"name":"T. Nishimura","orcid":"0000-0002-8925-5944","position":1,"is_corresponding":false},{"id":1498387,"name":"Yuichi Shigihara","orcid":null,"position":2,"is_corresponding":false},{"id":231613,"name":"Masaki Kimura","orcid":"0000-0001-5530-2629","position":3,"is_corresponding":false},{"id":231623,"name":"Takanori Takebe","orcid":"0000-0002-6989-3041","position":4,"is_corresponding":false},{"id":1498042,"name":"Takahiro Nemoto","orcid":"0000-0003-2981-4035","position":5,"is_corresponding":false},{"id":1498041,"name":"Rodolfo S. Allendes Osorio","orcid":"0000-0002-9627-9338","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-19T02:59:16.166424Z","pmid":"41415468","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}