Department of Quantitative Health Sciences, Mayo Clinic, 13400 E Shea Blvd, Scottsdale, AZ 85259, United States.
Department of Computer Science, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, United States.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae492.
Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.
单细胞技术使研究人员能够在单个细胞水平上研究细胞功能，并以更高的分辨率研究细胞过程。已经开发了几种多组学单细胞测序技术来探索细胞行为的各个方面。以 NEAT-seq 为例，该方法可以同时为每个细胞获取三种组学数据：基因表达、染色质可及性和转录因子 (TF) 的蛋白质表达。因此，NEAT-seq 提供了对多种模态细胞活动的更全面理解。然而，目前缺乏有效整合这三种组学数据的工具。为了解决这一差距，我们提出了一种名为 MultiSC 的新流水线，用于分析 MULTIomic Single-Cell 数据。我们的流水线利用多模态约束自动编码器（单细胞层次约束自动编码器）在聚类过程中整合多组学数据，并使用基于矩阵分解的模型（scMF）预测 TF 调控的靶基因。此外，我们利用多元线性回归模型从多组学数据中预测基因调控网络。MultiSC 流水线还包含其他功能，包括差异表达、中介分析和因果推断。我们进行了广泛的实验来评估 MultiSC 的性能。结果表明，我们的流水线通过充分利用多组学单细胞数据的潜力，使研究人员能够全面了解细胞活动和基因调控网络。通过使用 MultiSC，研究人员可以有效地整合和分析多种组学数据类型，从而增强对细胞过程的理解。
