Open AccessSMILE: mutual information learning for integration of single-cell omics data
Author(s) -
Yang Xu,
Priyojit Das,
Rachel Patton McCord
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab706
Subject(s) - computer science , profiling (computer programming) , python (programming language) , data integration , source code , artificial intelligence , data mining , computational biology , biology , operating system
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).