Literature DB >> 18629220

Reassessing design and analysis of two-colour microarray experiments using mixed effects models.

Guilherme J M Rosa1, Juan P Steibel, Robert J Tempelman.   

Abstract

Gene expression microarray studies have led to interesting experimental design and statistical analysis challenges. The comparison of expression profiles across populations is one of the most common objectives of microarray experiments. In this manuscript we review some issues regarding design and statistical analysis for two-colour microarray platforms using mixed linear models, with special attention directed towards the different hierarchical levels of replication and the consequent effect on the use of appropriate error terms for comparing experimental groups. We examine the traditional analysis of variance (ANOVA) models proposed for microarray data and their extensions to hierarchically replicated experiments. In addition, we discuss a mixed model methodology for power and efficiency calculations of different microarray experimental designs.

Year:  2005        PMID: 18629220      PMCID: PMC2447516          DOI: 10.1002/cfg.464

Source DB:  PubMed          Journal:  Comp Funct Genomics        ISSN: 1531-6912


  14 in total

1.  Assessing gene significance from cDNA microarray expression data via mixed models.

Authors:  R D Wolfinger; G Gibson; E D Wolfinger; L Bennett; H Hamadeh; P Bushel; C Afshari; R S Paules
Journal:  J Comput Biol       Date:  2001       Impact factor: 1.479

Review 2.  Statistical design and the analysis of gene expression microarray data.

Authors:  M K Kerr; G A Churchill
Journal:  Genet Res       Date:  2001-04       Impact factor: 1.588

3.  Analysis of variance for gene expression microarray data.

Authors:  M K Kerr; M Martin; G A Churchill
Journal:  J Comput Biol       Date:  2000       Impact factor: 1.479

Review 4.  Fundamentals of experimental design for cDNA microarrays.

Authors:  Gary A Churchill
Journal:  Nat Genet       Date:  2002-12       Impact factor: 38.330

5.  Comparison of microarray designs for class comparison and class discovery.

Authors:  K Dobbin; R Simon
Journal:  Bioinformatics       Date:  2002-11       Impact factor: 6.937

6.  Power and sample size for DNA microarray studies.

Authors:  Mei-Ling Ting Lee; G A Whitmore
Journal:  Stat Med       Date:  2002-12-15       Impact factor: 2.373

7.  Statistical design of reverse dye microarrays.

Authors:  K Dobbin; J H Shih; R Simon
Journal:  Bioinformatics       Date:  2003-05-01       Impact factor: 6.937

8.  The effect of replication on gene expression microarray experiments.

Authors:  Paul Pavlidis; Qinghong Li; William Stafford Noble
Journal:  Bioinformatics       Date:  2003-09-01       Impact factor: 6.937

Review 9.  Design issues for cDNA microarray experiments.

Authors:  Yee Hwa Yang; Terry Speed
Journal:  Nat Rev Genet       Date:  2002-08       Impact factor: 53.242

10.  Genes regulated by learning in the hippocampus.

Authors:  Tarek A Leil; Alex Ossadtchi; Thomas E Nichols; Richard M Leahy; Desmond J Smith
Journal:  J Neurosci Res       Date:  2003-03-15       Impact factor: 4.164

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  16 in total

1.  Borrowing strength: a likelihood ratio test for related sparse signals.

Authors:  Ernst C Wit; David J G Bakewell
Journal:  Bioinformatics       Date:  2012-06-04       Impact factor: 6.937

Review 2.  Design of microarray experiments for genetical genomics studies.

Authors:  Júlio S S Bueno Filho; Steven G Gilmour; Guilherme J M Rosa
Journal:  Genetics       Date:  2006-08-03       Impact factor: 4.562

3.  Mosquito transcriptome profiles and filarial worm susceptibility in Armigeres subalbatus.

Authors:  Matthew T Aliota; Jeremy F Fuchs; Thomas A Rocheleau; Amanda K Clark; Julián F Hillyer; Cheng-Chen Chen; Bruce M Christensen
Journal:  PLoS Negl Trop Dis       Date:  2010-04-20

Review 4.  Challenges and approaches to statistical design and inference in high-dimensional investigations.

Authors:  Gary L Gadbury; Karen A Garrett; David B Allison
Journal:  Methods Mol Biol       Date:  2009

5.  Genome-wide linkage analysis of global gene expression in loin muscle tissue identifies candidate genes in pigs.

Authors:  Juan Pedro Steibel; Ronald O Bates; Guilherme J M Rosa; Robert J Tempelman; Valencia D Rilington; Ashok Ragavendran; Nancy E Raney; Antonio Marcos Ramos; Fernando F Cardoso; David B Edwards; Catherine W Ernst
Journal:  PLoS One       Date:  2011-02-08       Impact factor: 3.240

6.  Microarray analysis of peripheral blood lymphocytes from ALS patients and the SAFE detection of the KEGG ALS pathway.

Authors:  Jean-Luc C Mougeot; Zhen Li; Andrea E Price; Fred A Wright; Benjamin R Brooks
Journal:  BMC Med Genomics       Date:  2011-10-25       Impact factor: 3.063

7.  The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison.

Authors:  Allan A Sioson; Shrinivasrao P Mane; Pinghua Li; Wei Sha; Lenwood S Heath; Hans J Bohnert; Ruth Grene
Journal:  BMC Bioinformatics       Date:  2006-04-20       Impact factor: 3.169

8.  Characterizing differential individual response to porcine reproductive and respiratory syndrome virus infection through statistical and functional analysis of gene expression.

Authors:  Maria E Arceo; Catherine W Ernst; Joan K Lunney; Igseo Choi; Nancy E Raney; Tinghua Huang; Christopher K Tuggle; R R R Rowland; Juan P Steibel
Journal:  Front Genet       Date:  2013-01-16       Impact factor: 4.599

9.  Ductal carcinoma in situ of the breast (DCIS) with heterogeneity of nuclear grade: prognostic effects of quantitative nuclear assessment.

Authors:  Judith-Anne W Chapman; Naomi A Miller; H Lavina A Lickley; Jin Qian; William A Christens-Barry; Yuejiao Fu; Yan Yuan; David E Axelrod
Journal:  BMC Cancer       Date:  2007-09-10       Impact factor: 4.430

10.  Statistical analysis of efficient unbalanced factorial designs for two-color microarray experiments.

Authors:  Robert J Tempelman
Journal:  Int J Plant Genomics       Date:  2008
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