Literature DB >> 27780809

Letter to the Editor response: Nygaard et al.

F Towfic, R Kusko, B Zeskind.   

Abstract

The article by Nygaard and others (2016) proposes that applying batch correction approaches to microarray data from studies with unbalanced designs may inadvertently exaggerate the differences observed. In seeking to illustrate their point, Nygaard and others (2016) utilized a dataset (GSE61901) from a study we published (Towfic and others, 2014) and showed that one analysis pipeline utilizing the traditional approach to batch correction (ComBat) yielded over 1000 differentially expressed probesets, while an alternative approach proposed by Nygaard and others (2016). (utilizing batch as a fixed effect and averaging technical replicates) recovered 11 differentially expressed probesets.
© The Author 2016. Published by Oxford University Press.

Entities:  

Mesh:

Year:  2017        PMID: 27780809      PMCID: PMC5379915          DOI: 10.1093/biostatistics/kxw031

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  8 in total

1.  Analysis of variance components in gene expression data.

Authors:  James J Chen; Robert R Delongchamp; Chen-An Tsai; Huey-miin Hsueh; Frank Sistare; Karol L Thompson; Varsha G Desai; James C Fuscoe
Journal:  Bioinformatics       Date:  2004-02-12       Impact factor: 6.937

2.  The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

Authors:  Jeffrey T Leek; W Evan Johnson; Hilary S Parker; Andrew E Jaffe; John D Storey
Journal:  Bioinformatics       Date:  2012-01-17       Impact factor: 6.937

3.  Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses.

Authors:  Vegard Nygaard; Einar Andreas Rødland; Eivind Hovig
Journal:  Biostatistics       Date:  2015-08-13       Impact factor: 5.899

4.  Use of within-array replicate spots for assessing differential expression in microarray experiments.

Authors:  Gordon K Smyth; Joëlle Michaud; Hamish S Scott
Journal:  Bioinformatics       Date:  2005-01-18       Impact factor: 6.937

5.  Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models.

Authors:  Jorge L Bernal-Rusiel; Douglas N Greve; Martin Reuter; Bruce Fischl; Mert R Sabuncu
Journal:  Neuroimage       Date:  2012-10-30       Impact factor: 6.556

Review 6.  Statistical tests for differential expression in cDNA microarray experiments.

Authors:  Xiangqin Cui; Gary A Churchill
Journal:  Genome Biol       Date:  2003-03-17       Impact factor: 13.583

7.  Illumina WG-6 BeadChip strips should be normalized separately.

Authors:  Wei Shi; Ashish Banerjee; Matthew E Ritchie; Steve Gerondakis; Gordon K Smyth
Journal:  BMC Bioinformatics       Date:  2009-11-11       Impact factor: 3.169

8.  Comparing the biological impact of glatiramer acetate with the biological impact of a generic.

Authors:  Fadi Towfic; Jason M Funt; Kevin D Fowler; Shlomo Bakshi; Eran Blaugrund; Maxim N Artyomov; Michael R Hayden; David Ladkani; Rivka Schwartz; Benjamin Zeskind
Journal:  PLoS One       Date:  2014-01-08       Impact factor: 3.240

  8 in total
  2 in total

1.  Temporal dynamics in meta longitudinal RNA-Seq data.

Authors:  Sunghee Oh; Congjun Li; Ransom L Baldwin; Seongho Song; Fang Liu; Robert W Li
Journal:  Sci Rep       Date:  2019-01-24       Impact factor: 4.379

Review 2.  Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data.

Authors:  Vera-Khlara S Oh; Robert W Li
Journal:  Genes (Basel)       Date:  2021-02-27       Impact factor: 4.096

  2 in total

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