Analysis of Microarray Data Using Z Score Transformation

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. cDNA microarray technologies are rapidly being applied in biology and medicine.

Molecular characterization of the transition to mid-life in Caenorhabditis elegans

“…Raw data were subjected to Z normalization as described previously (Cheadle et al 2003). The sample quality was first assessed with principal component analysis and hierarchical clustering based on all gene sample z-scores (Fig.…”

“…For the purposes of discussing up-and down-regulated genes, state I was considered as the baseline level of gene expression. We used a z-ratio method (Cheadle et al 2003) to analyze our raw hybridization data and identify significantly changed transcripts. State II upregulated and state II down-regulated genes were defined by z-scores of 1.50 or higher and −1.50 or lower.…”

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

“…In the majority of cases an adaption of the raw values is necessary before pooling different datasets. To this end common methods like scaling by Z-transformation [5] or magnitude normalization [6] have been applied. In some studies normalization across genes has also been performed [7].…”

Transcriptome analysis of age-, gender- and diet-associated changes in murine thymus

“…All data were processed in Excel spreadsheets using a Z score statistical analysis method developed at NIA [45]. In order to be selected for the final gene list, the expression value of a particular gene had to be at least 1.5 times different from the Z score of the control.…”