Empirical evaluation of data transformations and ranking statistics for microarray analysis.

Published

Journal Article

There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics outperform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.

Full Text

Duke Authors

Cited Authors

  • Qin, L-X; Kerr, KF; Contributing Members of the Toxicogenomics Research Consortium,

Published Date

  • January 2004

Published In

Volume / Issue

  • 32 / 18

Start / End Page

  • 5471 - 5479

PubMed ID

  • 15479783

Pubmed Central ID

  • 15479783

Electronic International Standard Serial Number (EISSN)

  • 1362-4962

International Standard Serial Number (ISSN)

  • 0305-1048

Digital Object Identifier (DOI)

  • 10.1093/nar/gkh866

Language

  • eng