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BMC Bioinformatics. 2005 Mar 31;6:80.

Correlation test to assess low-level processing of high-density oligonucleotide microarray data.

Author information

  • 1Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Alexander.Ploner@meb.ki.se

Abstract

BACKGROUND:

There are currently a number of competing techniques for low-level processing of oligonucleotide array data. The choice of technique has a profound effect on subsequent statistical analyses, but there is no method to assess whether a particular technique is appropriate for a specific data set, without reference to external data.

RESULTS:

We analyzed coregulation between genes in order to detect insufficient normalization between arrays, where coregulation is measured in terms of statistical correlation. In a large collection of genes, a random pair of genes should have on average zero correlation, hence allowing a correlation test. For all data sets that we evaluated, and the three most commonly used low-level processing procedures including MAS5, RMA and MBEI, the housekeeping-gene normalization failed the test. For a real clinical data set, RMA and MBEI showed significant correlation for absent genes. We also found that a second round of normalization on the probe set level improved normalization significantly throughout.

CONCLUSION:

Previous evaluation of low-level processing in the literature has been limited to artificial spike-in and mixture data sets. In the absence of a known gold-standard, the correlation criterion allows us to assess the appropriateness of low-level processing of a specific data set and the success of normalization for subsets of genes.

PMID:
15799785
[PubMed - indexed for MEDLINE]
PMCID:
PMC1084343
Free PMC Article

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