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Bioinformatics. 2019 Oct 15;35(20):3898-3905. doi: 10.1093/bioinformatics/btz196.

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

Author information

1
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
2
Department of Biostatistics, Brown University, Providence, RI, USA.
3
Department of Human Genetics, Emory University, Atlanta, GA, USA.

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.

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