Treffer: SMILE: mutual information learning for integration of single-cell omics data.

Title:
SMILE: mutual information learning for integration of single-cell omics data.
Authors:
Xu Y; UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA., Das P; UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA., McCord RP; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2022 Jan 03; Vol. 38 (2), pp. 476-486.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Software* , Algorithms*, RNA-Seq ; Transcriptome ; DNA Methylation ; Single-Cell Analysis
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Grant Information:
R35 GM133557 United States GM NIGMS NIH HHS
Entry Date(s):
Date Created: 20211008 Date Completed: 20230202 Latest Revision: 20230401
Update Code:
20250114
PubMed Central ID:
PMC10060712
DOI:
10.1093/bioinformatics/btab706
PMID:
34623402
Database:
MEDLINE

Weitere Informationen

Motivation: Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single-cell omics data to be integrated across sources, types and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning).
Results: Using a unique cell-pairing design, SMILE successfully integrates multisource single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint-profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome-wide peaks for ATAC-seq. Integrated representations learned from joint-profiling technologies can then be used as a framework for comparing independent single source data.
Availability and Implementation: The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE, implemented in Python.
Supplementary Information: Supplementary data are available at Bioinformatics online.
(© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)