Tests for differential gene expression using weights in oligonucleotide microarray experiments

BMC Genomics
Pingzhao HuCelia M T Greenwood

Abstract

Microarray data analysts commonly filter out genes based on a number of ad hoc criteria prior to any high-level statistical analysis. Such ad hoc approaches could lead to conflicting conclusions with no clear guidance as to which method is most likely to be reproducible. Furthermore, the number of tests performed with concomitant inflation in type I error also plagues the statistical analysis of microarray data, since the number of tested quantities in a study significantly affects the family-wise error rate. It would, therefore, be very useful to develop and adopt strategies that allow quantification of the quality of each probeset, to filter out or give little credence to low-quality or unexpressed probesets, and to incorporate these strategies into gene selection within a multiple testing framework. We have proposed a unified scheme for filtering and gene selection. For Affymetrix gene expression microarrays, we developed new methods for measuring the reliability of a particular probeset in a single array, and we used these to develop measures for a set of arrays. These measures are then used as weights in standard t-statistic calculations, and are incorporated into the multiple testing procedures. We demonstrated the advant...Continue Reading

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Citations

May 31, 2012·Journal of Strength and Conditioning Research·Deydre S TeyhenPhillip J Plisky
Jan 1, 2009·Human Genomics and Proteomics : HGP·Jemila S HamidJoseph Beyene
Dec 26, 2006·Pharmacogenomics·Michail Ignatiadis, Christine Desmedt
Feb 13, 2016·Frontiers in Neurology·Catherine A EdmonsonOwen M Rennert
Jun 16, 2015·Frontiers in Neurology·Yuk Jing LokeJeffrey Mark Craig
Nov 11, 2010·International Journal of Cancer. Journal International Du Cancer·Raffaela BarbanoPaola Parrella

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Methods Mentioned

BETA
biopsies
chip

Software Mentioned

GFC
SAM
PLIER
RMA
dChip
affy
MAS5
Affymetrix
Bioconductor
MathType

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