RF_Purify: a novel tool for comprehensive analysis of tumor-purity in methylation array data based on random forest regression
With the advent of array-based techniques to measure methylation levels in primary tumor samples, systematic investigations of methylomes have widely been performed on a large number of tumor entities. Most of these approaches are not based on measuring individual cell methylation but rather the bul...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
16 August 2019
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| In: |
BMC bioinformatics
Year: 2019, Jahrgang: 20 |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-019-3014-z |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1186/s12859-019-3014-z Verlag: https://doi.org/10.1186/s12859-019-3014-z 
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| Verfasserangaben: | Pascal David Johann, Natalie Jäger, Stefan M. Pfister and Martin Sill |
MARC
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| 520 | |a With the advent of array-based techniques to measure methylation levels in primary tumor samples, systematic investigations of methylomes have widely been performed on a large number of tumor entities. Most of these approaches are not based on measuring individual cell methylation but rather the bulk tumor sample DNA, which contains a mixture of tumor cells, infiltrating immune cells and other stromal components. This raises questions about the purity of a certain tumor sample, given the varying degrees of stromal infiltration in different entities. Previous methods to infer tumor purity require or are based on the use of matching control samples which are rarely available. Here we present a novel, reference free method to quantify tumor purity, based on two Random Forest classifiers, which were trained on ABSOLUTE as well as ESTIMATE purity values from TCGA tumor samples. We subsequently apply this method to a previously published, large dataset of brain tumors, proving that these models perform well in datasets that have not been characterized with respect to tumor purity . | ||
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