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 organization: | Sampsa Hautaniemi / Principal Investigator Genome-Scale Biology (GSB) Research Program Research Programs Unit Medicum Department of Biochemistry and Developmental Biology Faculty of Medicine University of Helsinki Department of Oncology HUSLAB Precision Cancer Pathology Department of Pathology Clinicum Bioinformatics HUS Comprehensive Cancer Center |
Date: | 2018-09-15 |
Language: | eng |
Number of pages: | 8 |
Belongs to series: | Bioinformatics |
ISSN: | 1367-4803 |
DOI: | https://doi.org/10.1093/bioinformatics/bty310 |
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 |
Peer reviewed: | Yes |
Rights: | unspecified |
Usage restriction: | openAccess |
Self-archived version: | acceptedVersion |
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