Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, large-scale integrative analysis of scRNA-seq data remains a challenge largely due to unwanted batch effects and the limited transferabilty, interpretability, and scalability of the...

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Bibliographic Details
Published in:	Nature communications Vol. 12; no. 1; pp. 5261 - 15
Main Authors:	Zhao, Yifan, Cai, Huiyu, Zhang, Zuobai, Tang, Jian, Li, Yue
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 06.09.2021 Nature Publishing Group Nature Portfolio
Subjects:	631/114/1305 631/114/2404 Alzheimer Disease - genetics Alzheimer Disease - pathology Animals Clustering Coders Computer applications Computer science Data analysis Databases, Genetic Datasets Depressive Disorder, Major - genetics Depressive Disorder, Major - pathology Gene expression Gene Expression Profiling - methods Gene Expression Profiling - statistics & numerical data Gene sequencing Gene set enrichment analysis Genes, Mitochondrial Humanities and Social Sciences Humans Learning Mathematical analysis Mice Models, Genetic multidisciplinary Neural networks Neural Networks, Computer Retina - cytology Retina - physiology Ribonucleic acid RNA RNA, Small Cytoplasmic Science Science (multidisciplinary) Sequence Analysis, RNA - methods Sequence Analysis, RNA - statistics & numerical data Single-Cell Analysis - methods Transcriptomics Transfer learning
ISSN:	2041-1723, 2041-1723
Online Access:	Get full text
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