scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data

Abstract Single-cell RNA sequencing (scRNA-seq) allows quantitative analysis of gene expression at the level of single cells, beneficial to study cell heterogeneity. The recognition of cell types facilitates the construction of cell atlas in complex tissues or organisms, which is the basis of almost...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 23; no. 1
Main Authors: Yin, Qingyang, Wang, Yang, Guan, Jinting, Ji, Guoli
Format: Journal Article
Language:English
Published: England Oxford University Press 17.01.2022
Oxford Publishing Limited (England)
Subjects:
Annotations
Classification
Data Analysis
Feature extraction
Gene expression
Gene Expression Profiling
Gene sequencing
Heterogeneity
Medical treatment
Predictions
Representations
Ribonucleic acid
RNA
RNA-Seq
Sequence Analysis, RNA
Single-Cell Analysis - methods
Whole Exome Sequencing
ensemble learning
scRNA-seq data
feature extraction
integrative autoencoder
classification framework
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary:Abstract Single-cell RNA sequencing (scRNA-seq) allows quantitative analysis of gene expression at the level of single cells, beneficial to study cell heterogeneity. The recognition of cell types facilitates the construction of cell atlas in complex tissues or organisms, which is the basis of almost all downstream scRNA-seq data analyses. Using disease-related scRNA-seq data to perform the prediction of disease status can facilitate the specific diagnosis and personalized treatment of disease. Since single-cell gene expression data are high-dimensional and sparse with dropouts, we propose scIAE, an integrative autoencoder-based ensemble classification framework, to firstly perform multiple random projections and apply integrative and devisable autoencoders (integrating stacked, denoising and sparse autoencoders) to obtain compressed representations. Then base classifiers are built on the lower-dimensional representations and the predictions from all base models are integrated. The comparison of scIAE and common feature extraction methods shows that scIAE is effective and robust, independent of the choice of dimension, which is beneficial to subsequent cell classification. By testing scIAE on different types of data and comparing it with existing general and single-cell–specific classification methods, it is proven that scIAE has a great classification power in cell type annotation intradataset, across batches, across platforms and across species, and also disease status prediction. The architecture of scIAE is flexible and devisable, and it is available at https://github.com/JGuan-lab/scIAE.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbab508