PT  - JOURNAL ARTICLE
AU  - Antti Häkkinen
AU  - Amjad Alkodsi
AU  - Chiara Facciotto
AU  - Kaiyang Zhang
AU  - Katja Kaipio
AU  - Sirpa Leppä
AU  - Olli Carpén
AU  - Seija Grénman
AU  - Johanna Hynninen
AU  - Sakari Hietanen
AU  - Rainer Lehtonen
AU  - Sampsa Hautaniemi
TI  - Identifying differentially methylated sites in samples with varying tumor purity
AID  - 10.1101/248781
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 248781
4099  - http://biorxiv.org/content/early/2018/01/16/248781.short
4100  - http://biorxiv.org/content/early/2018/01/16/248781.full
AB  - 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. 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. The method is freely available at https://bitbucket.org/anthakki/dmml.