Systematic Variation in Genetic Microarray Data
Any type of content formally published in an academic journal, usually following a peer-review process.
The main focus in cDNA microarray analysis is determining which genes are differentially expressed. Scientists apply known statistical methods to model the structure of the experiment or develop new approaches for assessing statistical significance and assume that the data consist of the signal plus random noise. Here, we report the results of some exploratory analyses of such data that show the existence of sources of significant systematic variation which are not necessarily accounted for in standard analyses. In particular, we construct a linearization procedure and compare its effectiveness with that of Yang, et al. (2001). Furthermore, we consider not only the variation due to the pin/print-tip as in previous work, but also the row and column location on the microarray chip, and the relative location from the well-plate. Removal of this extra variation can affect both the size of differential gene expression, and which genes are inferred to be differentially expressed.