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PubMed · 12707371

Analysis of microarray data using Z score transformation.

Chris Cheadle·Marquis P Vawter·William J Freed·Kevin G Becker
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Abstract

High-throughput cDNA microarray technology allows for the simultaneous analysis of gene expression levels for thousands of genes and as such, rapid, relatively simple methods are needed to store, analyze, and cross-compare basic microarray data. The application of a classical method of data normalization, Z score transformation, provides a way of standardizing data across a wide range of experiments and allows the comparison of microarray data independent of the original hybridization intensities. Data normalized by Z score transformation can be used directly in the calculation of significant changes in gene expression between different samples and conditions. We used Z scores to compare several different methods for predicting significant changes in gene expression including fold changes, Z ratios, Z and t statistical tests. We conclude that the Z score transformation normalization method accompanied by either Z ratios or Z tests for significance estimates offers a useful method for the basic analysis of microarray data. The results provided by these methods can be as rigorous and are no more arbitrary than other test methods, and, in addition, they have the advantage that they can be easily adapted to standard spreadsheet programs.

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Cluster AnalysisDNA, ComplementaryData Interpretation, StatisticalDown-RegulationHumansJurkat CellsModels, StatisticalNucleic Acid HybridizationOligonucleotide Array Sequence AnalysisRNAStatistics as TopicUp-RegulationJournal ArticleResearch Support, Non-U.S. Gov't

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BibTeXRIS

Chris Cheadle, Marquis P Vawter, William J Freed, Kevin G Becker. 2003. Analysis of microarray data using Z score transformation.. https://doi.org/10.1016/s1525-1578(10)60455-2

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