Publisher Correction: Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data
| Autor: | Yue Li, Yifan Zhao, Jian Tang, Huiyu Cai, Zuobai Zhang |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Science Cell General Physics and Astronomy Computational biology General Biochemistry Genetics and Molecular Biology Retina Transcriptome Functional clustering Mice Text mining Alzheimer Disease Machine learning Databases Genetic RNA Small Cytoplasmic medicine Animals Humans Depressive Disorder Major Multidisciplinary Models Genetic business.industry Sequence Analysis RNA Gene Expression Profiling General Chemistry Gene signature Publisher Correction medicine.anatomical_structure Genes Mitochondrial Neural Networks Computer Single-Cell Analysis business |
| Zdroj: | Nature Communications Nature Communications, Vol 12, Iss 1, Pp 1-1 (2021) |
| ISSN: | 2041-1723 |
| Popis: | 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 existing computational methods. We present single-cell Embedded Topic Model (scETM). Our key contribution is the utilization of a transferable neural-network-based encoder while having an interpretable linear decoder via a matrix tri-factorization. In particular, scETM simultaneously learns an encoder network to infer cell type mixture and a set of highly interpretable gene embeddings, topic embeddings, and batch-effect linear intercepts from multiple scRNA-seq datasets. scETM is scalable to over 10 |
| Databáze: | OpenAIRE |
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