scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
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DOI：
10.1186/s13059-019-1766-4
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发表时间：
2019-08-14
期刊：
影响因子：
12.3
通讯作者：
Quon, Gerald
中科院分区：
文献类型：
--
作者：
Johansen, Nelson;Quon, Gerald
scRNA-seq数据集整合发生在不同的背景下，例如在不同条件或物种中识别基因表达的细胞类型特异性差异，或批量效应校正。我们提出了scAlign，这是一种用于数据集成的无监督深度学习方法，可以合并部分、重叠或完整的细胞标签集，并估计数据集中基因表达的每个细胞差异。scAlign的性能是最先进的，并且对细胞类型特异性表达和细胞类型组成的跨数据集变化具有鲁棒性。我们证明scAlign揭示了疟疾寄生虫罕见种群的基因表达程序。我们的框架广泛适用于其他领域的集成挑战。
scRNA-seq dataset integration occurs in different contexts, such as the identification of cell type-specific differences in gene expression across conditions or species, or batch effect correction. We present scAlign, an unsupervised deep learning method for data integration that can incorporate partial, overlapping, or a complete set of cell labels, and estimate per-cell differences in gene expression across datasets. scAlign performance is state-of-the-art and robust to cross-dataset variation in cell type-specific expression and cell type composition. We demonstrate that scAlign reveals gene expression programs for rare populations of malaria parasites. Our framework is widely applicable to integration challenges in other domains.