Transformations, background estimation, and process effects in the statistical analysis of microarrays
Microarray technology has made available large data sets that can provide information on gene expression when cells are subjected to various treatments. Before proceeding with a formal statistical analysis, many biological and procedural aspects should be considered. These aspects may guide the analysis and subsequent statistical inference. Several of these issues are discussed in connection with the analysis of oligonucleotide and cDNA microarray experiments. The particular focus in this article is on effects caused by the cDNA slide manufacturing process, appropriate transformations of the data, and on adjustments for background. A prescription for the analysis of microarray data is proposed and demonstrated using data from a cDNA experiment comparing the genetic expressions in two mouse cell lines; a candidate set of genes is identified for further study. The prescription may be modified for oligonucleotide microarray data. (C) 2003 Elsevier B.V. All rights reserved.
background variation fluorescence lognormal distribution median polish process variation smoothing tukey's g-family of distributions