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Data complexity analysis quantifies the hardness of constructing a predictivemodel on a given dataset. However, the effectiveness of existing datacomplexity measures can be challenged by the existence of irrelevant featuresand feature interactions in biological micro-array data. We propose a noveldata complexity measure, depth, that leverages an evolutionary inspired featureselection algorithm to quantify the complexity of micro-array data. Byexamining feature subsets of varying sizes, the approach offers a novelperspective on data complexity analysis. Unlike traditional metrics, depth isrobust to irrelevant features and effectively captures complexity stemming fromfeature interactions. On synthetic micro-array data, depth outperforms existingmethods in robustness to irrelevant features and identifying complexity fromfeature interactions. Applied to case-control genotype and gene-expressionmicro-array datasets, the results reveal that a single feature ofgene-expression data can account for over 90% of the performance ofmulti-feature model, confirming the adequacy of the commonly useddifferentially expressed gene (DEG) feature selection method for the geneexpression data. Our study also demonstrates that constructing predictivemodels for genotype data is harder than gene expression data. The results inthis paper provide evidence for the use of interpretable machine learningalgorithms on microarray data.

How complex is the microarray dataset? A novel data complexity metric for biological high-dimensional microarray data