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@ARTICLE{Johann:144575,
      author       = {P. Johann$^*$ and N. Jäger$^*$ and S. M. Pfister$^*$ and
                      M. Sill$^*$},
      title        = {${RF}_{P}urify:$ a novel tool for comprehensive analysis of
                      tumor-purity in methylation array data based on random
                      forest regression.},
      journal      = {BMC bioinformatics},
      volume       = {20},
      number       = {1},
      issn         = {1471-2105},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2019-02018},
      pages        = {428},
      year         = {2019},
      abstract     = {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 .Using two
                      gold standard methods to infer purity - the ABSOLUTE score
                      based on whole genome sequencing data and the ESTIMATE score
                      based on gene expression data- we have optimized Random
                      Forest classifiers to predict tumor purity in entities that
                      were contained in the TCGA project. We validated these
                      classifiers using an independent test data set and
                      cross-compared it to other methods which have been applied
                      to the TCGA datasets (such as ESTIMATE and LUMP). Using
                      Illumina methylation array data of brain tumor entities (as
                      published in Capper et al. (Nature 555:469-474,2018)) we
                      applied this model to estimate tumor purity and find that
                      subgroups of brain tumors display substantial differences in
                      tumor purity.Random forest- based tumor purity prediction is
                      a well suited tool to extrapolate gold standard measures of
                      purity to novel methylation array datasets. In contrast to
                      other available methylation based tumor purity estimation
                      methods, our classifiers do not need a priori knowledge
                      about the tumor entity or matching control tissue to predict
                      tumor purity.},
      cin          = {B062 / L101},
      ddc          = {610},
      cid          = {I:(DE-He78)B062-20160331 / I:(DE-He78)L101-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31419933},
      pmc          = {pmc:PMC6697926},
      doi          = {10.1186/s12859-019-3014-z},
      url          = {https://inrepo02.dkfz.de/record/144575},
}