Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing

Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data

(2019) Tianyu Wang et al.   BMC BIOINFORMATICS

DEsingle for detecting three types of differential expression in single-cell RNA-seq data

(2018) Zhun Miao et al.   BIOINFORMATICS

DrImpute: imputing dropout events in single cell RNA sequencing data

(2018) Wuming Gong et al.   BMC BIOINFORMATICS

bigSCale: an analytical framework for big-scale single-cell data

(2018) Giovanni Iacono et al.   GENOME RESEARCH

Bias, robustness and scalability in single-cell differential expression analysis

(2018) Charlotte Soneson et al.   NATURE METHODS

Comparative Analysis of Single-Cell RNA Sequencing Methods

(2017) Christoph Ziegenhain et al.   MOLECULAR CELL

Single-cell mRNA quantification and differential analysis with Census

(2017) Xiaojie Qiu et al.   NATURE METHODS

Salmon provides fast and bias-aware quantification of transcript expression

(2017) Rob Patro et al.   NATURE METHODS

Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer

(2017) Arjun P. Athreya et al.   Oncotarget

Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists

(2017) Xun Zhu et al.   Genome Medicine

Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods

(2017) Alessandra Dal Molin et al.   Frontiers in Genetics

Beta-Poisson model for single-cell RNA-seq data analyses

(2016) Trung Nghia Vu et al.   BIOINFORMATICS

Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data

(2016) Mihails Delmans et al.   BMC BIOINFORMATICS

Comparison of methods to detect differentially expressed genes between single-cell populations

(2016) Maria K. Jaakkola et al.   BRIEFINGS IN BIOINFORMATICS

Single-Cell Transcriptomics of the Human Endocrine Pancreas

(2016) Yue J. Wang et al.   DIABETES

Ballgown bridges the gap between transcriptome assembly and expression analysis

(2015) Alyssa C Frazee et al.   NATURE BIOTECHNOLOGY

Rediscovery rate estimation for assessing the validation of significant findings in high-throughput studies

(2014) Andrea Ganna et al.   BRIEFINGS IN BIOINFORMATICS

Deconstructing transcriptional heterogeneity in pluripotent stem cells

(2014) Roshan M. Kumar et al.   NATURE

Bayesian approach to single-cell differential expression analysis

(2014) Peter V Kharchenko et al.   NATURE METHODS

voom: precision weights unlock linear model analysis tools for RNA-seq read counts

(2014) Charity W Law et al.   GENOME BIOLOGY

Entering the era of single-cell transcriptomics in biology and medicine

(2013) Rickard Sandberg   NATURE METHODS

Do Baseline P-Values Follow a Uniform Distribution in Randomised Trials?

(2013) Martin Bland   PLoS One

Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

(2010) Cole Trapnell et al.   NATURE BIOTECHNOLOGY

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

P-Values are Random Variables

(2008) Duncan J Murdoch et al.   AMERICAN STATISTICIAN

RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays

(2008) J. C. Marioni et al.   GENOME RESEARCH