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BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference.

ABSTRACT: We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.

SUBMITTER: Rahmani E

PROVIDER: S-EPMC6151042 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference.

Rahmani Elior E Schweiger Regev R Shenhav Liat L Wingert Theodora T Hofer Ira I Gabel Eilon E Eskin Eleazar E Halperin Eran E

Genome biology 20180921 1

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that ea ...[more]

PMID: 30241486

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