bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data
Authors
Keywords
-
Journal
BIOINFORMATICS
Volume -, Issue -, Pages -
Publisher
Oxford University Press (OUP)
Online
2019-09-28
DOI
10.1093/bioinformatics/btz726
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Single-cell RNA-seq denoising using a deep count autoencoder
- (2019) Gökcen Eraslan et al. Nature Communications
- Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation
- (2019) Malika Saint et al. Nature Microbiology
- DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data
- (2019) Chengzhong Ye et al. BIOINFORMATICS
- OUP accepted manuscript
- (2018) Briefings in Functional Genomics
- Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
- (2018) David van Dijk et al. CELL
- Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment
- (2018) Elham Azizi et al. CELL
- Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
- (2018) Laleh Haghverdi et al. NATURE BIOTECHNOLOGY
- Integrating single-cell transcriptomic data across different conditions, technologies, and species
- (2018) Andrew Butler et al. NATURE BIOTECHNOLOGY
- scmap: projection of single-cell RNA-seq data across data sets
- (2018) Vladimir Yu Kiselev et al. NATURE METHODS
- SAVER: gene expression recovery for single-cell RNA sequencing
- (2018) Mo Huang et al. NATURE METHODS
- Bias, robustness and scalability in single-cell differential expression analysis
- (2018) Charlotte Soneson et al. NATURE METHODS
- An accurate and robust imputation method scImpute for single-cell RNA-seq data
- (2018) Wei Vivian Li et al. Nature Communications
- Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
- (2018) Jiarui Ding et al. Nature Communications
- zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs
- (2018) Swati Parekh et al. GigaScience
- Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH
- (2018) Eduardo Torre et al. Cell Systems
- Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences
- (2018) Anqi Zhu et al. BIOINFORMATICS
- Deep generative modeling for single-cell transcriptomics
- (2018) Romain Lopez et al. NATURE METHODS
- VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder
- (2018) Dongfang Wang et al. GENOMICS PROTEOMICS & BIOINFORMATICS
- UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy
- (2017) Tom Smith et al. GENOME RESEARCH
- SCnorm: robust normalization of single-cell RNA-seq data
- (2017) Rhonda Bacher et al. NATURE METHODS
- Differential analysis of RNA-seq incorporating quantification uncertainty
- (2017) Harold Pimentel et al. NATURE METHODS
- Normalizing single-cell RNA sequencing data: challenges and opportunities
- (2017) Catalina A Vallejos et al. NATURE METHODS
- Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity
- (2017) Renchao Chen et al. Cell Reports
- Batch effects and the effective design of single-cell gene expression studies
- (2017) Po-Yuan Tung et al. Scientific Reports
- A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure
- (2016) Maayan Baron et al. Cell Systems
- Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells
- (2015) Allon M. Klein et al. CELL
- Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
- (2015) A. Zeisel et al. SCIENCE
- BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
- (2015) Catalina A. Vallejos et al. PLoS Computational Biology
- Bayesian approach to single-cell differential expression analysis
- (2014) Peter V Kharchenko et al. NATURE METHODS
- Accounting for technical noise in single-cell RNA-seq experiments
- (2013) Philip Brennecke et al. NATURE METHODS
- Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
- (2011) S. Islam et al. GENOME RESEARCH
- baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
- (2010) Thomas J Hardcastle et al. BMC BIOINFORMATICS
- A scaling normalization method for differential expression analysis of RNA-seq data
- (2010) Mark D Robinson et al. GENOME BIOLOGY
- Reproducibility-Optimized Test Statistic for Ranking Genes in Microarray Studies
- (2008) L.L. Elo et al. IEEE-ACM Transactions on Computational Biology and Bioinformatics
- Analytical distributions for stochastic gene expression
- (2008) V. Shahrezaei et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started