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Dissecting differential signals in high-throughput data from complex tissues.

ABSTRACT: MOTIVATION:Samples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for. RESULTS:We develop a method to model the high-throughput data from mixed, heterogeneous samples, and to detect differential signals. Our method allows flexible statistical inference for detecting a variety of cell-type specific changes. Extensive simulation studies and analyses of two real datasets demonstrate the favorable performance of our proposed method compared with existing ones serving similar purpose. AVAILABILITY AND IMPLEMENTATION:The proposed method is implemented as an R package and is freely available on GitHub (https://github.com/ziyili20/TOAST). SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Li Z

PROVIDER: S-EPMC6931351 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Dissecting differential signals in high-throughput data from complex tissues.

Li Ziyi Z Wu Zhijin Z Jin Peng P Wu Hao H

Bioinformatics (Oxford, England) 20191001 20

<h4>Motivation</h4>Samples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for.<h4>Results</h4>We develop a method to model the high-throughput data from mixed, heterogeneous samples, and to detect differential signals. Our method allows flexible statistical inference for detecting a vari ...[more]

PMID: 30903684

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Dissecting differential signals in high-throughput data from complex tissues.

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Dissecting differential signals in high-throughput data from complex tissues.

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