Maleki Farhad, Kusalik Anthony
Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada.
Microarrays (Basel). 2015 Oct 16;4(4):432-53. doi: 10.3390/microarrays4040432.
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.
细胞通路涉及蛋白质的磷酸化和去磷酸化。称为激酶组阵列的肽微阵列有助于在单个实验中测量数百种蛋白质的磷酸化活性。分析激酶组微阵列的数据是一个多步骤过程。通常，特定步骤可能有多种技术，有必要对它们进行比较和评估。此类评估需要已知正确分析结果的数据。不幸的是，此类激酶组数据在该领域不容易获得。此外，还没有既定的技术来创建具有已知结果且与真实激酶组数据集具有相同特征的人工激酶组数据集。本文提出了一种生成合成激酶组阵列数据的方法。该方法依赖于激酶组微阵列实验的实际强度测量，并保留其细微特征。通过评估消除激酶组微阵列数据中异质方差的方法，证明了该方法的实用性。激酶组微阵列的磷酸化强度通常表现出这种异质方差，其存在会对依赖方差同质性的下游统计技术产生负面影响。结果表明，使用所提出的合成数据生成器的输出，可以严格比较两种方差稳定方法。
