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MethylResolver-a method for deconvoluting bulk DNA methylation profiles into known and unknown cell contents.

ABSTRACT: Bulk tissue DNA methylation profiling has been used to examine epigenetic mechanisms and biomarkers of complex diseases such as cancer. However, heterogeneity of cellular content in tissues complicates result interpretation and utility. In silico deconvolution of cellular fractions from bulk tissue data offers a fast and inexpensive alternative to experimentally measuring such fractions. In this study, we report the design, implementation, and benchmarking of MethylResolver, a Least Trimmed Squares regression-based method for inferring leukocyte subset fractions from methylation profiles of tumor admixtures. Compared to previous approaches MethylResolver is more accurate as unknown cellular content in the mixture increases and is able to resolve tumor purity-scaled immune cell-type fractions without a cancer-specific signature. We also present a pan-cancer deconvolution of TCGA, recapitulating that high eosinophil fraction predicts improved cervical carcinoma survival and identifying elevated B cell fraction as a previously unreported predictor of poor survival for papillary renal cell carcinoma.

SUBMITTER: Arneson D

PROVIDER: S-EPMC7400544 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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MethylResolver-a method for deconvoluting bulk DNA methylation profiles into known and unknown cell contents.

Arneson Douglas D Yang Xia X Wang Kai K

Communications biology 20200803 1

Bulk tissue DNA methylation profiling has been used to examine epigenetic mechanisms and biomarkers of complex diseases such as cancer. However, heterogeneity of cellular content in tissues complicates result interpretation and utility. In silico deconvolution of cellular fractions from bulk tissue data offers a fast and inexpensive alternative to experimentally measuring such fractions. In this study, we report the design, implementation, and benchmarking of MethylResolver, a Least Trimmed Squa ...[more]

PMID: 32747663

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Project description:BackgroundComprehensive characterization of exposures and immune responses to viral infections is critical to a basic understanding of human health and disease. We previously developed the VirScan system, a programmable phage-display technology for profiling antibody binding to a library of peptides designed to span the human virome. Previous VirScan analytical approaches did not carefully account for antibody cross-reactivity among sequences shared by related viruses or for the disproportionate representation of individual viruses in the library.MethodsHere we present the AntiViral Antibody Response Deconvolution Algorithm (AVARDA), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection with species-level resolution by considering sequence alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. We further assessed AVARDA's utility in associating viral infection with type 1 diabetes and lupus.FindingsBy comparing acute and convalescent sera, AVARDA successfully confirmed or detected encephalitis-associated responses to human herpesviruses 1, 3, 4, 5, and 6, improving the rate of diagnosing viral encephalitis in this cohort by 44%. AVARDA analyses of VirScan data from the type 1 diabetes and lupus cohorts implicated enterovirus and herpesvirus infections, respectively.InterpretationAVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases.FundingThis work was made possible by support from the National Institutes of Health (NIH), the US Army Research Office, the Singapore Infectious Diseases Initiative (SIDI), the Singapore Ministry of Health's National Medical Research Council (NMRC) and the Singapore National Research Foundation (NRF).

Project description:BACKGROUND:To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. METHODS:Our deconvolution method was developed by choosing ?-support vector regression (?-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. RESULTS:In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. CONCLUSIONS:We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

Dataset Information

MethylResolver-a method for deconvoluting bulk DNA methylation profiles into known and unknown cell contents.

Publications

MethylResolver-a method for deconvoluting bulk DNA methylation profiles into known and unknown cell contents.

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