DataSheet1_Destin2: Integrative and cross-modality analysis of single-cell chromatin accessibility data.PDF

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Destin2，一种面向单细胞ATAC测序（single-cell ATAC-seq）数据的跨模态降维、聚类与轨迹重构新型统计与计算方法。该框架整合了来自染色质峰可及性、基序偏差得分以及伪基因活性的细胞级表观基因组谱，并利用多模态输入学习共享流形，随后完成聚类与/或轨迹推断。我们将Destin2应用于同时包含离散细胞类型与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开展了基准测试研究。通过从非匹配的单细胞RNA测序数据中以高置信度迁移得到的细胞类型标签，我们采用四项性能评估指标，阐明了Destin2如何验证并优于现有方法。借助单细胞RNA与ATAC多组学数据，我们以匹配细胞对作为真实基准，进一步展示了Destin2的跨模态整合分析如何保留真实的细胞间相似性。Destin2已封装为可免费获取的R软件包，下载地址为https://github.com/yuchaojiang/Destin2。