Literature DB >> 24813215

compcodeR--an R package for benchmarking differential expression methods for RNA-seq data.

Charlotte Soneson1.   

Abstract

UNLABELLED: compcodeR is an R package for benchmarking of differential expression analysis methods, in particular, methods developed for analyzing RNA-seq data. The package provides functionality for simulating realistic RNA-seq count datasets, an interface to several of the most commonly used differential expression analysis methods and extensive functionality for evaluating and comparing different approaches on real and simulated data.
AVAILABILITY AND IMPLEMENTATION: compcodeR is available from http://www.bioconductor.org/packages/release/bioc/html/compcodeR.html.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mesh:

Year:  2014        PMID: 24813215     DOI: 10.1093/bioinformatics/btu324

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  18 in total

1.  iCOBRA: open, reproducible, standardized and live method benchmarking.

Authors:  Charlotte Soneson; Mark D Robinson
Journal:  Nat Methods       Date:  2016-04       Impact factor: 28.547

2.  The fractured landscape of RNA-seq alignment: the default in our STARs.

Authors:  Sara Ballouz; Alexander Dobin; Thomas R Gingeras; Jesse Gillis
Journal:  Nucleic Acids Res       Date:  2018-06-01       Impact factor: 16.971

3.  Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.

Authors:  Aedan G K Roberts; Daniel R Catchpoole; Paul J Kennedy
Journal:  NAR Genom Bioinform       Date:  2022-01-14

4.  Do count-based differential expression methods perform poorly when genes are expressed in only one condition?

Authors:  Xiaobei Zhou; Mark D Robinson
Journal:  Genome Biol       Date:  2015-10-08       Impact factor: 13.583

5.  edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test.

Authors:  Emmanuel Dimont; Jiantao Shi; Rory Kirchner; Winston Hide
Journal:  Bioinformatics       Date:  2015-04-21       Impact factor: 6.937

6.  deGPS is a powerful tool for detecting differential expression in RNA-sequencing studies.

Authors:  Chen Chu; Zhaoben Fang; Xing Hua; Yaning Yang; Enguo Chen; Allen W Cowley; Mingyu Liang; Pengyuan Liu; Yan Lu
Journal:  BMC Genomics       Date:  2015-06-13       Impact factor: 3.969

7.  How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

Authors:  Nicholas J Schurch; Pietá Schofield; Marek Gierliński; Christian Cole; Alexander Sherstnev; Vijender Singh; Nicola Wrobel; Karim Gharbi; Gordon G Simpson; Tom Owen-Hughes; Mark Blaxter; Geoffrey J Barton
Journal:  RNA       Date:  2016-03-28       Impact factor: 4.942

8.  omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.

Authors:  Nestoras Karathanasis; Ioannis Tsamardinos; Vincenzo Lagani
Journal:  PLoS One       Date:  2016-11-03       Impact factor: 3.240

9.  CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates.

Authors:  Joel Z B Low; Tsung Fei Khang; Martti T Tammi
Journal:  BMC Bioinformatics       Date:  2017-12-28       Impact factor: 3.169

10.  Evaluation of methods for differential expression analysis on multi-group RNA-seq count data.

Authors:  Min Tang; Jianqiang Sun; Kentaro Shimizu; Koji Kadota
Journal:  BMC Bioinformatics       Date:  2015-11-04       Impact factor: 3.169

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