Year: 2019
Type: article
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.
Source: Bioinformatics
Authors: Ziyi Li, Zhijin Wu, Peng Jin, Hao Wu
Institutions: Emory University, Brown University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2952420117