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TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis.

ABSTRACT: In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the "reference-free deconvolution" methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at https://bioconductor.org/packages/TOAST .

SUBMITTER: Li Z

PROVIDER: S-EPMC6727351 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis.

Li Ziyi Z Wu Hao H

Genome biology 20190904 1

In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the "reference-free deconvolution" methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition ...[more]

PMID: 31484546

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