Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

Power analysis of single-cell RNA-sequencing experiments

(2017) Valentine Svensson et al.   NATURE METHODS

Batch effects and the effective design of single-cell gene expression studies

(2017) Po-Yuan Tung et al.   Scientific Reports

A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation

(2016) S. Nestorowa et al.   BLOOD

Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes

(2016) Åsa Segerstolpe et al.   Cell Metabolism

Resolving early mesoderm diversification through single-cell expression profiling

(2016) Antonio Scialdone et al.   NATURE

Design and Analysis of Single-Cell Sequencing Experiments

(2015) Dominic Grün et al.   CELL

Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells

(2015) Allon M. Klein et al.   CELL

Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

(2015) Evan Z. Macosko et al.   CELL

Combined Single-Cell Functional and Gene Expression Analysis Resolves Heterogeneity within Stem Cell Populations

(2015) Nicola K. Wilson et al.   Cell Stem Cell

Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation

(2015) Aleksandra A. Kolodziejczyk et al.   Cell Stem Cell

Computational assignment of cell-cycle stage from single-cell transcriptome data

(2015) Antonio Scialdone et al.   METHODS

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

(2015) Florian Buettner et al.   NATURE BIOTECHNOLOGY

Computational and analytical challenges in single-cell transcriptomics

(2015) Oliver Stegle et al.   NATURE REVIEWS GENETICS

Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq

(2015) A. Zeisel et al.   SCIENCE

Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex

(2014) Alex A Pollen et al.   NATURE BIOTECHNOLOGY

Normalization of RNA-seq data using factor analysis of control genes or samples

(2014) Davide Risso et al.   NATURE BIOTECHNOLOGY

Validation of noise models for single-cell transcriptomics

(2014) Dominic Grün et al.   NATURE METHODS

Full-length RNA-seq from single cells using Smart-seq2

(2014) Simone Picelli et al.   Nature Protocols

featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

(2013) Y. Liao et al.   BIOINFORMATICS

SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization

(2013) Shintaro Katayama et al.   BIOINFORMATICS

Quantitative single-cell RNA-seq with unique molecular identifiers

(2013) Saiful Islam et al.   NATURE METHODS

Accounting for technical noise in single-cell RNA-seq experiments

(2013) Philip Brennecke et al.   NATURE METHODS

Single-cell sequencing-based technologies will revolutionize whole-organism science

(2013) Ehud Shapiro et al.   NATURE REVIEWS GENETICS

The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote

(2013) Yang Liao et al.   NUCLEIC ACIDS RESEARCH

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

(2012) Davis J. McCarthy et al.   NUCLEIC ACIDS RESEARCH

Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms

(2011) Carlos R. García-Alonso et al.   ANNALS OF OPERATIONS RESEARCH

Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq

(2011) S. Islam et al.   GENOME RESEARCH

A scaling normalization method for differential expression analysis of RNA-seq data

(2010) Mark D Robinson et al.   GENOME BIOLOGY

Differential expression analysis for sequence count data

(2010) Simon Anders et al.   GENOME BIOLOGY

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

(2009) M. D. Robinson et al.   BIOINFORMATICS