deMULTIplex2: robust sample demultiplexing for scRNA-seq.
deMULTIplex2: robust sample demultiplexing for scRNA-seq.
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deMULTIplex2：用于 scRNA-seq 的强大样本解复用。
DOI：
10.1101/2023.04.11.536275
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Gartner,ZevJ
中科院分区：
文献类型：
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作者：
Zhu,Qin;Conrad,DanielN;Gartner,ZevJ
样品多路复用可在单细胞RNA测序工作流程中进行合并分析，从而提高通量并减少批次效应。所有多路复用技术的挑战是将样品特异性条形码与细胞特异性条形码连接，然后在测序后解多路复用样品身份。然而，现有的解复用工具在条形码交叉污染是一个问题的许多现实条件下失败。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采用广义线性模型和期望最大化来概率地确定每个细胞的样本身份。基准测试揭示了在各种实验条件下的上级性能，特别是在具有不平衡样本组成的大型或噪声数据集上。
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.