Analysis of Data from Viral DNA Microchips

Abstract

Viral DNA microchips, arrays of viral genes printed over a glass slide, are powerful tools for rapidly characterizing the expression pattern of these genes in an infection. The chips are exposed to a solution of fluorescently labeled cDNAs prepared from either mock or true infected human fibroblast cells and the expression levels of the various genes are recorded with the objective of detecting which viral genes are expressed to a significantly higher degree when exposed to the true infection as compared to the mock infection. The data were initially examined visually via image plots and scatterplots. These reveal that analysis of such data presents many challenges owing to, among other problems, high interchip and intrachip variability with low signal-to-noise ratio, differential intensity scales that have to be adjusted nonlinearly, nonGaussian data, data for a large number of genes with little replication, scratches and dark spots on the chips, dust, outliers, and an inability to quantitate intensities below a detection limit, or above a threshold. The first step of the analysis was to standardize the chips to a single intensity scale using a photograph analogy. Next, the average expression level of each gene was estimated using a highly resistant repeated median estimator to avoid being misled by aberrant values. Finally, a simulation-based approach was used to make a distribution-free assessment of significance.