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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
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]