Dissecting differential signals in high-throughput data from complex tissues

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


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Research Design*