Yusuke Matsui | Search bioRxiv

bioRxiv2016

phyC: Clustering cancer evolutionary trees

MotivationMulti-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer sub-clonal evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, the methods developed thus far are not sufficient to characterize and interpret the diversity of cancer sub-clonal evolutionary trees.\n\nResultsWe propose a clustering method (phyC) for cancer sub-clonal evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype.\n\nAvailabilityphyC is implemented with R(>=3.2.2) and is available from https://github.com/ymatts/phyC.\n\nContactymatsui@med.nagoya-u.ac.jp

Bioinformatics↗

bioRxiv2015

D3M: Detection of differential distributions of methylation levels

Motivation: DNA methylation is an important epigenetic modification related to a variety of diseases including cancers. We focus on the methylation data from Illuminas Infinium HumanMethylation450 BeadChip. One of the key issues of methylation analysis is to detect the differential methylation sites between case and control groups. Previous approaches describe data with simple summary statistics and kernel function, and then use statistical tests to determine the difference. However, a summary statistics-based approach cannot capture complicated underlying structure, and a kernel functions-based approach lacks interpretability of results.\n\nResults: We propose a novel method D3M, for detection of differential distribution of methylation, based on distribution-valued data. Our method can detect high-order moments, such as shapes of underlying distributions in methylation profiles, based on the Wasserstein metric. We test the significance of the difference between case and control groups and provide an interpretable summary of the results. The simulation results show that the proposed method achieves promising accuracy and shows favorable results compared with previous methods. Glioblastoma multiforme and lower grade glioma data from The Cancer Genome Atlas show that our method supports recent biological advances and suggests new insights.\n\nAvailability: R implemented code is freely available from\n\nhttps://github.com/ymatts/D3M/\n\nhttps://cran.r-project.org/package=D3M.\n\nContact: ymatsui@med.nagoya-u.ac.jp

Bioinformatics↗