Journal
BIOINFORMATICS
Volume 35, Issue 20, Pages 3898-3905Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btz196
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Funding
- National Institute of Health [R01GM122083, P50AG025688, P20GM103645, NS097206, MH116441, AG052476]
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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.
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