Work

GDASC: a GPU parallel-based web server for detecting hidden batch factors

Year: 2020

Type: article

Abstract: SUMMARY: We developed GDASC, a web version of our former DASC algorithm implemented with GPU. It provides a user-friendly web interface for detecting batch factors. Based on the good performance of DASC algorithm, it is able to give the most accurate results. For two steps of DASC, data-adaptive shrinkage and semi-non-negative matrix factorization, we designed parallelization strategies facing convex clustering solution and decomposition process. It runs more than 50 times faster than the original version on the representative RNA sequencing quality control dataset. With its accuracy and high speed, this server will be a useful tool for batch effects analysis. AVAILABILITY AND IMPLEMENTATION: http://bioinfo.nankai.edu.cn/gdasc.php. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btaa427

Authors: Xiao Wang, Haidong Yi, Jia Wang, Zhandong Liu, Yanbin Yin, Han Zhang

Institutions: Nankai University, Baylor College of Medicine, University of Nebraska–Lincoln

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Nankai University

Awards: G3556308910, G6016289018, G6047315943