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A novel method to classify human cells is presented in this work based on thetransform-domain method on DNA methylation data. DNA methylation profilevariations are observed in human cells with the progression of disease stages,and the proposal is based on this DNA methylation variation to classify normaland disease cells including cancer cells. The cancer cell types investigated inthis work cover hepatocellular (sample size n = 40), colorectal (n = 44), lung(n = 70) and endometrial (n = 87) cancer cells. A new pipeline is proposedintegrating the DNA methylation intensity measurements on all the CpG islandsby the transformation of Walsh-Hadamard Transform (WHT). The study reveals thethree-step properties of the DNA methylation transform-domain data and the stepvalues of association with the cell status. Further assessments have beencarried out on the proposed machine learning pipeline to perform classificationof the normal and cancer tissue cells. A number of machine learning classifiersare compared for whole sequence and WHT sequence classification based on publicWhole-Genome Bisulfite Sequencing (WGBS) DNA methylation datasets. TheWHT-based method can speed up the computation time by more than one order ofmagnitude compared with whole original sequence classification, whilemaintaining comparable classification accuracy by the selected machine learningclassifiers. The proposed method has broad applications in expedited diseaseand normal human cell classifications by the epigenome and genome datasets.

Transform-Domain Classification of Human Cells based on DNA Methylation Datasets