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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\u2013based 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.<\/jats:p>","DOI":"10.1093\/bib\/bbae492","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T05:31:56Z","timestamp":1728365516000},"source":"Crossref","is-referenced-by-count":6,"title":["MultiSC: a deep learning pipeline for analyzing multiomics single-cell data"],"prefix":"10.1093","volume":"25","author":[{"given":"Xiang","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Quantitative Health Sciences, Mayo Clinic , 13400 E Shea Blvd, Scottsdale, AZ 85259,","place":["United States"]},{"name":"Department of Computer Science, New Jersey Institute of Technology , 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqi","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department 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