A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
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Title
A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
Authors
Keywords
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Journal
Nature Communications
Volume 11, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-08-28
DOI
10.1038/s41467-020-17900-3
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Note: Only part of the references are listed.- Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data
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- OUP accepted manuscript
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- A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation
- (2016) S. Nestorowa et al. BLOOD
- EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes
- (2015) Sheida Nabavi et al. BIOINFORMATICS
- Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
- (2015) Evan Z. Macosko et al. CELL
- Bayesian approach to single-cell differential expression analysis
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- Smart-seq2 for sensitive full-length transcriptome profiling in single cells
- (2013) Simone Picelli et al. NATURE METHODS
- Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation
- (2012) Davis J. McCarthy et al. NUCLEIC ACIDS RESEARCH
- CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification
- (2012) Tamar Hashimshony et al. Cell Reports
- mRNA-Seq whole-transcriptome analysis of a single cell
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