  A novel method to classify human cells is presented in this work based on the
transform-domain method on DNA methylation data. DNA methylation profile
variations are observed in human cells with the progression of disease stages,
and the proposal is based on this DNA methylation variation to classify normal
and disease cells including cancer cells. The cancer cell types investigated in
this work cover hepatocellular (sample size n = 40), colorectal (n = 44), lung
(n = 70) and endometrial (n = 87) cancer cells. A new pipeline is proposed
integrating the DNA methylation intensity measurements on all the CpG islands
by the transformation of Walsh-Hadamard Transform (WHT). The study reveals the
three-step properties of the DNA methylation transform-domain data and the step
values of association with the cell status. Further assessments have been
carried out on the proposed machine learning pipeline to perform classification
of the normal and cancer tissue cells. A number of machine learning classifiers
are compared for whole sequence and WHT sequence classification based on public
Whole-Genome Bisulfite Sequencing (WGBS) DNA methylation datasets. The
WHT-based method can speed up the computation time by more than one order of
magnitude compared with whole original sequence classification, while
maintaining comparable classification accuracy by the selected machine learning
classifiers. The proposed method has broad applications in expedited disease
and normal human cell classifications by the epigenome and genome datasets.
