Computational Methods for Single-cell Multi-omics Integration and Alignment

作     者：Stefan Stanojevic Yijun Li Aleksandar Ristivojevic Lana X.Garmire Stefan Stanojevic;Yijun Li;Aleksandar Ristivojevic;Lana X.Garmire

作者机构：Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMI 48109USA Department of BiostatisticsUniversity of MichiganAnn ArborMI 48109USA Amherst CollegeAmherstMA 01002USA 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2022年第20卷第5期

页      面：836-849页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 07[理学] 08[工学] 

基　　金：supported by R01 (Grant Nos. LM012373 and LM012907) awarded by the National Library of Medicine R01 (Grant No. HD084633) awarded by the National Institute of Child Health and Human Development to Lana X. Garmire 

主　　题：Single-cell Multi-omics Machine learning Unsupervised learning Integration 

摘      要：Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of ***-omics assays offer even greater opportunities to understand cellular states and biological *** problem of integrating different omics data with very different dimensionality and statistical properties remains,however,quite challenging.A growing body of computational tools is being developed for this task,leveraging ideas ranging from machine translation to the theory of networks,and represents another frontier on the interface of biology and data *** goal in this review is to provide a comprehensive,up-to-date survey of computational techniques for the integration of single-cell multi-omics data,while making the concepts behind each algorithm approachable to a non-expert audience.