An associative analysis of gene expression array data

Igor Dozmorov, Michael Centola

Research output: Contribution to journalArticle

95 Citations (Scopus)

Abstract

Motivation: We face the absence of optimized standards to guide normalization, comparative analysis, and interpretation of data sets. One aspect of this is that current methods of statistical analysis do not adequately utilize the information inherent in the large data sets generated in a microarray experiment and require a tradeoff between detection sensitivity and specificity. Results: We present a multistep procedure for analysis of mRNA expression data obtained from cDNA array methods. To identify and classify differentially expressed genes, results from standard paired t-test of normalized data are compared with those from a novel method, denoted an associative analysis. This method associates experimental gene expressions presented as residuals in regression analysis against control averaged expressions to a common standard-the family of similarly computed residuals for low variability genes derived from control experiments. By associating changes in expression of a given gene to a large family of equally expressed genes of the control group, this method utilizes the large data sets inherent in microarray experiments to increase both specificity and sensitivity. The overall procedure is illustrated by tabulation of genes whose expression differs significantly between Snell dwarf mice (dw/dw) and their phenotypically normal littermates (dw/+, +/+). Of the 2352 genes examined only 450-500 were expressed above the background levels observed in nonexpressed genes and of these 120 were established as differentially expressed in dwarf mice at a significance level that excludes appearance of false positive determinations.

Original languageEnglish (US)
Pages (from-to)204-211
Number of pages8
JournalBioinformatics
Volume19
Issue number2
DOIs
StatePublished - Feb 1 2003

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Gene expression
Gene Expression
Genes
Gene
Microarrays
Large Data Sets
Microarray
Specificity
Mouse
Experiment
Significance level
t-test
Experiments
Sensitivity and Specificity
CDNA
False Positive
Comparative Analysis
Regression Analysis
Regression analysis
Messenger RNA

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

An associative analysis of gene expression array data. / Dozmorov, Igor; Centola, Michael.

In: Bioinformatics, Vol. 19, No. 2, 01.02.2003, p. 204-211.

Research output: Contribution to journalArticle

Dozmorov, Igor ; Centola, Michael. / An associative analysis of gene expression array data. In: Bioinformatics. 2003 ; Vol. 19, No. 2. pp. 204-211.
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