CimpleG: Finding simple CpG methylation signatures

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract DNA methylation (DNAm) at specific CG dinucleotides (CpG sites) in the genome provides powerful biomarkers for a wide range of applications, such as epigenetic clocks, cell type deconvolution, and stratification of cancer patients. Such epigenetic signatures are usually based on multivariate approaches that require hundreds of DNAm sites for predictions. In contrast, targeted assays for only a few selected CpGs may facilitate faster turnover, better standardisation, and reduced costs. Here, we propose a computational framework named CimpleG for the detection of small CpG methylation signatures, exemplarily used for cell type classification. We evaluate the performance of CimpleG and competing methods on two distinct datasets in regard to the cell-type classification of blood cells and of other somatic cells. Furthermore, we evaluate how well these classifiers perform cellular deconvolution of blood cell mixtures. We show that CimpleG is both time efficient and performs as well as Elastic Net, while basing its prediction on a single DNAm site per cell type. Altogether, CimpleG provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution.

Authors 4

1. Tiago Maié Aachen Institute for Computational Genomics Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   RWTH Aachen University

   Affiliation as printed

   Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School

   Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074 Aachen, Germany

2. Myriam Erz Aachen Institute for Computational Genomics Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   RWTH Aachen University

   Affiliation as printed

   Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School

   Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074 Aachen, Germany

3. Wolfgang Wagner Aachen Institute for Stem Cell Biology

   RWTH Aachen University

   Affiliation as printed

   Helmholtz Institute for Biomedical Engineering, RWTH Aachen University

   Institute for Stem Cell Biology, RWTH Aachen University Medical School

   Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany

   Institute for Stem Cell Biology, RWTH Aachen University Medical School, Aachen, 52074, Germany

4. Ivan G. Costa corresponding Aachen Institute for Computational Genomics Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   RWTH Aachen University

   Affiliation as printed

   Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School

   Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074 Aachen, Germany