Interpretable factor models of single-cell RNA-seq via variational autoencoders
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Interpretable factor models of single-cell RNA-seq via variational autoencoders
Authors
Keywords
-
Journal
BIOINFORMATICS
Volume -, Issue -, Pages -
Publisher
Oxford University Press (OUP)
Online
2020-03-14
DOI
10.1093/bioinformatics/btaa169
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Droplet scRNA-seq is not zero-inflated
- (2020) Valentine Svensson NATURE BIOTECHNOLOGY
- Single-cell RNA-seq denoising using a deep count autoencoder
- (2019) Gökcen Eraslan et al. Nature Communications
- A single-cell molecular map of mouse gastrulation and early organogenesis
- (2019) Blanca Pijuan-Sala et al. NATURE
- The single-cell transcriptional landscape of mammalian organogenesis
- (2019) Junyue Cao et al. NATURE
- De novo gene signature identification from single‐cell RNA‐seq with hierarchical Poisson factorization
- (2019) Hanna Mendes Levitin et al. Molecular Systems Biology
- Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis
- (2019) G Durif et al. BIOINFORMATICS
- Exponential scaling of single-cell RNA-seq in the past decade
- (2018) Valentine Svensson et al. Nature Protocols
- A general and flexible method for signal extraction from single-cell RNA-seq data
- (2018) Davide Risso et al. Nature Communications
- Review: Transcriptional Regulation of CD4+ T Cell Differentiation in Experimentally Induced Arthritis and Rheumatoid Arthritis
- (2018) Yuya Kondo et al. Arthritis & Rheumatology
- Deep generative modeling for single-cell transcriptomics
- (2018) Romain Lopez et al. NATURE METHODS
- Cell-Type-Specific Gene Programs of the Normal Human Nephron Define Kidney Cancer Subtypes
- (2017) David Lindgren et al. Cell Reports
- Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states
- (2017) Sumin Jang et al. eLife
- Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
- (2017) Xun Zhu et al. PeerJ
- Comparative Analysis of Gene Regulatory Networks: From Network Reconstruction to Evolution
- (2015) Dawn Thompson et al. Annual Review of Cell and Developmental Biology
- Geometry of the Gene Expression Space of Individual Cells
- (2015) Yael Korem et al. PLoS Computational Biology
- Comparative studies of gene expression and the evolution of gene regulation
- (2012) Irene Gallego Romero et al. NATURE REVIEWS GENETICS
- Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
- (2011) S. Islam et al. GENOME RESEARCH
- Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst
- (2010) Guoji Guo et al. DEVELOPMENTAL CELL
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started