Open AccessSemisupervised Generative Autoencoder for Single-Cell Data
Author(s) -
Trung Ngô Trọng,
Juha Mehtonen,
Gerardo González,
Roger Kramer,
Ville Hautamäki,
Merja Heinäniemi
Publication year - 2020
Publication title -
journal of computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.585
H-Index - 95
eISSN - 1557-8666
pISSN - 1066-5277
DOI - 10.1089/cmb.2019.0337
Subject(s) - autoencoder , artificial intelligence , generative model , deep learning , transcriptome , computer science , computational biology , generative grammar , artificial neural network , machine learning , phenotype , pattern recognition (psychology) , biology , gene , gene expression , genetics
Single-cell transcriptomics offers a tool to study the diversity of cell phenotypes through snapshots of the abundance of mRNA in individual cells. Often there is additional information available besides the single-cell gene expression counts, such as bulk transcriptome data from the same tissue, or quantification of surface protein levels from the same cells. In this study, we propose models based on the Bayesian deep learning approach, where protein quantification, available as CITE-seq counts, from the same cells is used to constrain the learning process, thus forming a SemI-SUpervised generative Autoencoder (SISUA) model. The generative model is based on the deep variational autoencoder (VAE) neural network architecture.