SMILE: mutual information learning for integration of single-cell omics data.
Bioinformatics
; 38(2): 476-486, 2022 01 03.
Article
in En
| MEDLINE
| ID: mdl-34623402
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Software
Language:
En
Journal:
Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2022
Document type:
Article
Affiliation country:
United States
Publication country:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
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SCOTLAND
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UK
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UNITED KINGDOM