A nitty-gritty aspect of correlation and network inference from gene expression data.

Background: All currently available methods of network/association inference from microarray gene expression measurements implicitly assume that such measurements represent the actual expression levels of different genes within each cell included in the biological sample under study. Contrary to this common belief, modern microarray technology produces signals aggregated over a random number of individual cells, a "nitty-gritty" aspect of such arrays, thereby causing a random effect that distorts the correlation structure of intra-cellular gene expression levels.

Results: This paper provides a theoretical consideration of the random effect of signal aggregation and its implications for correlation analysis and network inference.