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

BMC Bioinformatics
Allan A SiosonRuth Grene

Abstract

Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compar...Continue Reading

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Citations

Jun 7, 2008·Journal of Experimental Botany·Cecilia Vasquez-RobinetRuth Grene
Sep 26, 2012·BMC Bioinformatics·Cristina BaciuJennifer W Weller
Sep 7, 2012·PloS One·Christos BazakosPanagiotis Kalaitzis
Nov 28, 2009·Developmental and Comparative Immunology·Chul-Hong KimErik P Lillehoj
Oct 1, 2008·Functional Plant Biology : FPB·Shrinivasrao P ManeRuth Grene

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Methods Mentioned

BETA
PCR

Software Mentioned

Microarray Data Analysis Software ( MIDAS )
SAS
Expresso
ScanArray Express
Multi Experiment Viewer ( MEV )
STAT
BioArray Software Environment ( BASE )
Bioconductor
Microarray Experiment Viewer ( MEV )
GOT

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