Guan Peter Y, Lee Jin Seok, Wang Lihao, Lin Kevin Z, Mei Wenwen, Chen Li, Jiang Yuchao
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, Unites States.
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, Unites States.
Front Genet. 2023 Feb 17;14:1089936. doi: 10.3389/fgene.2023.1089936. eCollection 2023.
We propose Destin2, a novel statistical and computational method for cross-modality dimension reduction, clustering, and trajectory reconstruction for single-cell ATAC-seq data. The framework integrates cellular-level epigenomic profiles from peak accessibility, motif deviation score, and pseudo-gene activity and learns a shared manifold using the multimodal input, followed by clustering and/or trajectory inference. We apply Destin2 to real scATAC-seq datasets with both discretized cell types and transient cell states and carry out benchmarking studies against existing methods based on unimodal analyses. Using cell-type labels transferred with high confidence from unmatched single-cell RNA sequencing data, we adopt four performance assessment metrics and demonstrate how Destin2 corroborates and improves upon existing methods. Using single-cell RNA and ATAC multiomic data, we further exemplify how Destin2's cross-modality integrative analyses preserve true cell-cell similarities using the matched cell pairs as ground truths. Destin2 is compiled as a freely available R package available at https://github.com/yuchaojiang/Destin2.
我们提出了Destin2，这是一种用于单细胞ATAC-seq数据的跨模态降维、聚类和轨迹重建的新型统计和计算方法。该框架整合了来自峰值可及性、基序偏差分数和假基因活性的细胞水平表观基因组概况，并使用多模态输入学习共享流形，随后进行聚类和/或轨迹推断。我们将Destin2应用于具有离散细胞类型和瞬时细胞状态的真实scATAC-seq数据集，并针对基于单模态分析的现有方法进行基准研究。利用从不匹配的单细胞RNA测序数据中高置信度转移的细胞类型标签，我们采用了四个性能评估指标，并展示了Destin2如何证实并改进现有方法。使用单细胞RNA和ATAC多组学数据，我们进一步举例说明了Destin2的跨模态综合分析如何以匹配的细胞对作为基本事实来保留真实的细胞间相似性。Destin2被编译为一个可在https://github.com/yuchaojiang/Destin2上免费获取的R包。
