Literature DB >> 15335204

Using ANOVA to analyze microarray data.

Gary A Churchill1.   

Abstract

ANOVA provides a general approach to the analysis of single and multiple factor experiments on both one- and two-color microarray platforms. Mixed model ANOVA is important because in many microarray experiments there are multiple sources of variation that must be taken into consideration when constructing tests for differential expression of a gene. The genome is large, and the signals of expression change can be small, so we must rely on rigorous statistical methods to distinguish signal from noise. We apply statistical tests to ensure that we are not just making up stories based on seeing patterns where there may be none.

Mesh:

Year:  2004        PMID: 15335204     DOI: 10.2144/04372TE01

Source DB:  PubMed          Journal:  Biotechniques        ISSN: 0736-6205            Impact factor:   1.993


  60 in total

1.  Gene expression analysis of mouse chromosome substitution strains.

Authors:  Keith R Shockley; Gary A Churchill
Journal:  Mamm Genome       Date:  2006-06-12       Impact factor: 2.957

Review 2.  Unraveling the dynamic transcriptome.

Authors:  Siobhan M Brady; Terri A Long; Philip N Benfey
Journal:  Plant Cell       Date:  2006-09       Impact factor: 11.277

3.  Psychrobacter arcticus 273-4 uses resource efficiency and molecular motion adaptations for subzero temperature growth.

Authors:  Peter W Bergholz; Corien Bakermans; James M Tiedje
Journal:  J Bacteriol       Date:  2009-01-23       Impact factor: 3.490

4.  High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation.

Authors:  Emily Breeze; Elizabeth Harrison; Stuart McHattie; Linda Hughes; Richard Hickman; Claire Hill; Steven Kiddle; Youn-Sung Kim; Christopher A Penfold; Dafyd Jenkins; Cunjin Zhang; Karl Morris; Carol Jenner; Stephen Jackson; Brian Thomas; Alexandra Tabrett; Roxane Legaie; Jonathan D Moore; David L Wild; Sascha Ott; David Rand; Jim Beynon; Katherine Denby; Andrew Mead; Vicky Buchanan-Wollaston
Journal:  Plant Cell       Date:  2011-03-29       Impact factor: 11.277

Review 5.  Bioinformatics and systems biology of the lipidome.

Authors:  Shankar Subramaniam; Eoin Fahy; Shakti Gupta; Manish Sud; Robert W Byrnes; Dawn Cotter; Ashok Reddy Dinasarapu; Mano Ram Maurya
Journal:  Chem Rev       Date:  2011-09-23       Impact factor: 60.622

6.  A multi-layer inference approach to reconstruct condition-specific genes and their regulation.

Authors:  Ming Wu; Li Liu; Hussein Hijazi; Christina Chan
Journal:  Bioinformatics       Date:  2013-04-22       Impact factor: 6.937

7.  Assessing and selecting gene expression signals based upon the quality of the measured dynamics.

Authors:  Eric Yang; Ioannis P Androulakis
Journal:  BMC Bioinformatics       Date:  2009-02-10       Impact factor: 3.169

8.  Importance of replication in analyzing time-series gene expression data: corticosteroid dynamics and circadian patterns in rat liver.

Authors:  Tung T Nguyen; Richard R Almon; Debra C DuBois; William J Jusko; Ioannis P Androulakis
Journal:  BMC Bioinformatics       Date:  2010-05-26       Impact factor: 3.169

9.  Importance of randomization in microarray experimental designs with Illumina platforms.

Authors:  Ricardo A Verdugo; Christian F Deschepper; Gloria Muñoz; Daniel Pomp; Gary A Churchill
Journal:  Nucleic Acids Res       Date:  2009-07-17       Impact factor: 16.971

10.  A search for small noncoding RNAs in Staphylococcus aureus reveals a conserved sequence motif for regulation.

Authors:  Thomas Geissmann; Clément Chevalier; Marie-Josée Cros; Sandrine Boisset; Pierre Fechter; Céline Noirot; Jacques Schrenzel; Patrice François; François Vandenesch; Christine Gaspin; Pascale Romby
Journal:  Nucleic Acids Res       Date:  2009-11       Impact factor: 16.971

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