Identifying differentially methylated sites in samples with varying tumor purity

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Häkkinen , A , Alkodsi , A , Facciotto , C , Zhang , K , Kaipio , K , Leppa , S , Carpen , O , Grenman , S , Hynninen , J , Hietanen , S , Lehtonen , R & Hautaniemi , S 2018 , ' Identifying differentially methylated sites in samples with varying tumor purity ' , Bioinformatics , vol. 34 , no. 18 , pp. 3078-3085 . https://doi.org/10.1093/bioinformatics/bty310

Title: Identifying differentially methylated sites in samples with varying tumor purity
Author: Häkkinen, Antti; Alkodsi, Amjad; Facciotto, Chiara; Zhang, Kaiyang; Kaipio, Katja; Leppa, Sirpa; Carpen, Olli; Grenman, Seija; Hynninen, Johanna; Hietanen, Sakari; Lehtonen, Rainer; Hautaniemi, Sampsa
Contributor: University of Helsinki, Sampsa Hautaniemi / Principal Investigator
University of Helsinki, Research Programs Unit
University of Helsinki, Research Programs Unit
University of Helsinki, Sampsa Hautaniemi / Principal Investigator
University of Helsinki, Department of Oncology
University of Helsinki, HUSLAB
University of Helsinki, Research Programs Unit
University of Helsinki, Research Programs Unit
Date: 2018-09-15
Language: eng
Number of pages: 8
Belongs to series: Bioinformatics
ISSN: 1367-4803
URI: http://hdl.handle.net/10138/310643
Abstract: Motivation: DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. Results: We use simulations and next-generation methylome, RNA and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources.
Subject: DNA METHYLATION
OVARIAN-CANCER
EPIGENOME
GENOME
3111 Biomedicine
1182 Biochemistry, cell and molecular biology
111 Mathematics
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