Unknown

Dataset Information

0

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


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

altmetric image

Publications

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

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]

Similar Datasets

| S-EPMC4056373 | biostudies-literature
| S-EPMC4088128 | biostudies-literature
| S-EPMC3756668 | biostudies-literature
| S-EPMC5147906 | biostudies-literature
| S-EPMC5728429 | biostudies-literature
| S-EPMC7075697 | biostudies-literature
| S-EPMC10210164 | biostudies-literature
2018-01-10 | GSE98828 | GEO
| S-EPMC4625728 | biostudies-literature
| S-EPMC8495327 | biostudies-literature