Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA.
Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
Genome Biol. 2024 Jan 30;25(1):37. doi: 10.1186/s13059-024-03177-y.
Sample multiplexing enables pooled analysis during single-cell RNA sequencing workflows, thereby increasing throughput and reducing batch effects. A challenge for all multiplexing techniques is to link sample-specific barcodes with cell-specific barcodes, then demultiplex sample identity post-sequencing. However, existing demultiplexing tools fail under many real-world conditions where barcode cross-contamination is an issue. We therefore developed deMULTIplex2, an algorithm inspired by a mechanistic model of barcode cross-contamination. deMULTIplex2 employs generalized linear models and expectation-maximization to probabilistically determine the sample identity of each cell. Benchmarking reveals superior performance across various experimental conditions, particularly on large or noisy datasets with unbalanced sample compositions.
样品多路复用可在单细胞 RNA 测序工作流程中实现混合分析，从而提高通量并减少批次效应。所有多路复用技术的一个挑战是将样品特有的条形码与细胞特有的条形码相关联，然后在测序后对样品身份进行解复用。然而，现有的解复用工具在许多存在条形码交叉污染问题的实际情况下都会失败。因此，我们开发了 deMULTIplex2，这是一种受条形码交叉污染机制模型启发的算法。deMULTIplex2 使用广义线性模型和期望最大化来概率性地确定每个细胞的样品身份。基准测试显示，该算法在各种实验条件下都具有优越的性能，特别是在具有不平衡样品组成的大型或嘈杂数据集上。
