Cellular pathways involve the phosphorylation and dephosphorylation of proteins. Peptide microarrays called kinome arrays facilitate the measurement of the phosphorylation activity of hundreds of proteins in a single experiment. Analyzing the data from kinome microarrays is a multi-step process. Typically, various techniques are possible for a particular step, and it is necessary to compare and evaluate them. Such evaluations require data for which correct analysis results are known. Unfortunately, such kinome data is not readily available in the community. Further, there are no established techniques for creating artificial kinome datasets with known results and with the same characteristics as real kinome datasets. In this paper, a methodology for generating synthetic kinome array data is proposed. The methodology relies on actual intensity measurements from kinome microarray experiments and preserves their subtle characteristics. The utility of the methodology is demonstrated by evaluating methods for eliminating heterogeneous variance in kinome microarray data. Phosphorylation intensities from kinome microarrays often exhibit such heterogeneous variance and its presence can negatively impact downstream statistical techniques that rely on homogeneity of variance. It is shown that using the output from the proposed synthetic data generator, it is possible to critically compare two variance stabilization methods.
Keywords: heteroscedasticity of variance; kinome array; normalization; synthetic data.