scMODD: A model-driven algorithm for doublet identification in single-cell RNA-sequencing data
scMODD: A model-driven algorithm for doublet identification in single-cell RNA-sequencing data
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DOI：
10.3389/fsysb.2022.1082309
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发表时间：
2023-01
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影响因子：
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通讯作者：
Xinye Zhao;Alexander Du;Peng-Chao Qiu
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文献类型：
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作者：
Xinye Zhao;Alexander Du;Peng-Chao Qiu
单细胞RNA测序（scRNA-seq）数据通常包含双链，其中一个双链表现为一个细胞条形码，对应于两个或多个细胞的组合基因表达。双重态的存在会导致错误的生物学解释。在这里，我们提出了单细胞模型驱动的双元检测（scMODD），这是一种模型驱动的算法，用于检测scRNA-seq数据中的双元。与现有的主要由数据驱动的双元检测算法相比，ScMODD实现了类似的性能，显示了模型驱动的双元检测方法的前景。当在模拟和真实的scRNA-seq数据中实施scMODD时，我们测试了负二项（NB）模型和零膨胀负二项（ZINB）模型作为scRNA-seq计数数据的基础统计模型，并观察到加入零膨胀并没有提高检测性能，这表明在scRNA-seq的双重检测中没有必要考虑零膨胀。
Single-cell RNA sequencing (scRNA-seq) data often contain doublets, where a doublet manifests as 1 cell barcode that corresponds to combined gene expression of two or more cells. Existence of doublets can lead to spurious biological interpretations. Here, we present single-cell MOdel-driven Doublet Detection (scMODD), a model-driven algorithm to detect doublets in scRNA-seq data. ScMODD achieved similar performance compared to existing doublet detection algorithms which are primarily data-driven, showing the promise of model-driven approach for doublet detection. When implementing scMODD in simulated and real scRNA-seq data, we tested both the negative binomial (NB) model and the zero-inflated negative binomial (ZINB) model to serve as the underlying statistical model for scRNA-seq count data, and observed that incorporating zero inflation did not improve detection performance, suggesting that consideration of zero inflation is not necessary in the context of doublet detection in scRNA-seq.