Related references
Note: Only part of the references are listed.Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data
Lihua Zhang et al.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2020)
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
Michael B. Cole et al.
CELL SYSTEMS (2019)
Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage
Dvir Aran et al.
NATURE IMMUNOLOGY (2019)
GENCODE reference annotation for the human and mouse genomes
Adam Frankish et al.
NUCLEIC ACIDS RESEARCH (2019)
DrImpute: imputing dropout events in single cell RNA sequencing data
Wuming Gong et al.
BMC BIOINFORMATICS (2018)
Limitations of alignment-free tools in total RNA-seq quantification
Douglas C. Wu et al.
BMC GENOMICS (2018)
Quantitative single-cell transcriptomics
Christoph Ziegenhain et al.
BRIEFINGS IN FUNCTIONAL GENOMICS (2018)
Identifying cell populations with scRNASeq
Tallulah S. Andrews et al.
MOLECULAR ASPECTS OF MEDICINE (2018)
SAVER: gene expression recovery for single-cell RNA sequencing
Mo Huang et al.
NATURE METHODS (2018)
Bias, robustness and scalability in single-cell differential expression analysis
Charlotte Soneson et al.
NATURE METHODS (2018)
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs
Swati Parekh et al.
GIGASCIENCE (2018)
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Koen Van den Berge et al.
GENOME BIOLOGY (2018)
Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions
Ciaran Evans et al.
BRIEFINGS IN BIOINFORMATICS (2018)
Simulation-based comprehensive benchmarking of RNA-seq aligners
Giacomo Baruzzo et al.
NATURE METHODS (2017)
UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy
Tom Smith et al.
GENOME RESEARCH (2017)
Comparative Analysis of Single-Cell RNA Sequencing Methods
Christoph Ziegenhain et al.
MOLECULAR CELL (2017)
SCnorm: robust normalization of single-cell RNA-seq data
Rhonda Bacher et al.
NATURE METHODS (2017)
Normalizing single-cell RNA sequencing data: challenges and opportunities
Catalina A. Vallejos et al.
NATURE METHODS (2017)
Power analysis of single-cell RNA-sequencing experiments
Valentine Svensson et al.
NATURE METHODS (2017)
Single-cell mRNA quantification and differential analysis with Census
Xiaojie Qiu et al.
NATURE METHODS (2017)
Linnorm: improved statistical analysis for single cell RNA-seq expression data
Shun H. Yip et al.
NUCLEIC ACIDS RESEARCH (2017)
Massively parallel digital transcriptional profiling of single cells
Grace X. Y. Zheng et al.
NATURE COMMUNICATIONS (2017)
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
Sabri Boughorbel et al.
PLOS ONE (2017)
powsimR: power analysis for bulk and single cell RNA-seq experiments
Beate Vieth et al.
BIOINFORMATICS (2017)
Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation
Nuala A. O'Leary et al.
NUCLEIC ACIDS RESEARCH (2016)
The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery
Hendrik G. Stunnenberg et al.
CELL (2016)
Revealing the vectors of cellular identity with single-cell genomics
Allon Wagner et al.
NATURE BIOTECHNOLOGY (2016)
Near-optimal probabilistic RNA-seq quantification
Nicolas L. Bray et al.
NATURE BIOTECHNOLOGY (2016)
iCOBRA: open, reproducible, standardized and live method benchmarking
Charlotte Soneson et al.
NATURE METHODS (2016)
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
Aaron T. L. Lun et al.
GENOME BIOLOGY (2016)
A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification
Shanrong Zhao et al.
BMC GENOMICS (2015)
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko et al.
CELL (2015)
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Amit Zeisel et al.
SCIENCE (2015)
Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression
Jong Kyoung Kim et al.
NATURE COMMUNICATIONS (2015)
Beta Regression inR
Francisco Cribari-Neto et al.
Journal of Statistical Software (2015)
MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
Greg Finak et al.
GENOME BIOLOGY (2015)
Full-length RNA-seq from single cells using Smart-seq2
Simone Picelli et al.
NATURE PROTOCOLS (2014)
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Charity W. Law et al.
GENOME BIOLOGY (2014)
STAR: ultrafast universal RNA-seq aligner
Alexander Dobin et al.
BIOINFORMATICS (2013)
CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification
Tamar Hashimshony et al.
CELL REPORTS (2012)
GC-Content Normalization for RNA-Seq Data
Davide Risso et al.
BMC BIOINFORMATICS (2011)
Synthetic spike-in standards for RNA-seq experiments
Lichun Jiang et al.
GENOME RESEARCH (2011)
A scaling normalization method for differential expression analysis of RNA-seq data
Mark D. Robinson et al.
GENOME BIOLOGY (2010)
Differential expression analysis for sequence count data
Simon Anders et al.
GENOME BIOLOGY (2010)
Fast and accurate short read alignment with Burrows-Wheeler transform
Heng Li et al.
BIOINFORMATICS (2009)
The vertebrate genome annotation (Vega) database
L. G. Wilming et al.
NUCLEIC ACIDS RESEARCH (2008)
A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables
M Smithson et al.
PSYCHOLOGICAL METHODS (2006)