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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.
Highlights
There have been an increasing number of large-scale clinical studies using high-throughput technologies to profile biological samples collected from human subjects, in order to identify molecular biomarkers and therapeutic targets for different diseases [1, 2]
Method overview In this work, we develop an iterative algorithm to improve feature selection
Step (b) is to identify cell type-specific features using cross-cell type differential analysis. These features are used for the RF deconvolution in step (a) in a new iteration
Summary
There have been an increasing number of large-scale clinical studies using high-throughput technologies to profile biological samples collected from human subjects, in order to identify molecular biomarkers and therapeutic targets for different diseases [1, 2]. These samples (e.g., blood, tumor, or brain tissues) are often mixtures of different cell types. Adjusting for cell composition is especially emphasized in epigenome-wide association studies (EWAS), where ignoring the composition has been shown to produce biased results [4]. As a result, adjusting for cell composition has become a standard procedure in EWAS studies [6, 9,10,11]
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