ARTS: automated randomization of multiple traits for study design

Mark Maienschein-Cline; Zhengdeng Lei; Vincent Gardeux; Taimur Abbasi; Roberto F Machado; Victor Gordeuk; Ankit A Desai; Santosh Saraf; Neil Bahroos; Yves Lussier

doi:10.1093/bioinformatics/btu075

ARTS: automated randomization of multiple traits for study design

Bioinformatics. 2014 Jun 1;30(11):1637-9. doi: 10.1093/bioinformatics/btu075. Epub 2014 Feb 3.

Affiliations

¹ Center for Research Informatics, Institute for Interventional Health Informatics, Department of Medicine, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, Computation Institute of The University of Chicago, Chicago and Argonne National Laboratory, The University of Chicago, Lemont, IL, USA.
² Center for Research Informatics, Institute for Interventional Health Informatics, Department of Medicine, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, Computation Institute of The University of Chicago, Chicago and Argonne National Laboratory, The University of Chicago, Lemont, IL, USACenter for Research Informatics, Institute for Interventional Health Informatics, Department of Medicine, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, Computation Institute of The University of Chicago, Chicago and Argonne National Laboratory, The University of Chicago, Lemont, IL, USACenter for Research Informatics, Institute for Interventional Health Informatics, Department of Medicine, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, Computation Institute of The University of Chicago, Chicago and Argonne National Laboratory, The University of Chicago, Lemont, IL, USA.
³ Center for Research Informatics, Institute for Interventional Health Informatics, Department of Medicine, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, Computation Institute of The University of Chicago, Chicago and Argonne National Laboratory, The University of Chicago, Lemont, IL, USACenter for Research Informatics, Institute for Interventional Health Informatics, Department of Medicine, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, Computation Institute of The University of Chicago, Chicago and Argonne National Laboratory, The University of Chicago, Lemont, IL, USACenter for Research Informatics, Institute for Interventional Health Informatics, Department of Medicine, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, Computation Institute of The University of Chicago, Chicago and Argonne National Laboratory, The University of Chicago, Lemont, IL, USACenter for Research Informatics, Institute for Interventional Health Informatics, Department of Medicine, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, Computation Institute of The University of Chicago, Chicago and Argonne National Laboratory, The University of Chicago, Lemont, IL, USACenter for Research Informatics, Institute for Interventional Health Informatics, Department of Medicine, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, Computation Institute of The University of Chicago, Chicago and Argonne National Laboratory, The University of Chicago, Lemont, IL, USA.

Abstract

Collecting data from large studies on high-throughput platforms, such as microarray or next-generation sequencing, typically requires processing samples in batches. There are often systematic but unpredictable biases from batch-to-batch, so proper randomization of biologically relevant traits across batches is crucial for distinguishing true biological differences from experimental artifacts. When a large number of traits are biologically relevant, as is common for clinical studies of patients with varying sex, age, genotype and medical background, proper randomization can be extremely difficult to prepare by hand, especially because traits may affect biological inferences, such as differential expression, in a combinatorial manner. Here we present ARTS (automated randomization of multiple traits for study design), which aids researchers in study design by automatically optimizing batch assignment for any number of samples, any number of traits and any batch size.

Availability and implementation: ARTS is implemented in Perl and is available at github.com/mmaiensc/ARTS. ARTS is also available in the Galaxy Tool Shed, and can be used at the Galaxy installation hosted by the UIC Center for Research Informatics (CRI) at galaxy.cri.uic.edu.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Female
High-Throughput Nucleotide Sequencing
Humans
Male
Microarray Analysis
Middle Aged
Random Allocation*
Software*

ARTS: automated randomization of multiple traits for study design

Authors

Affiliations

Abstract

Publication types

MeSH terms

Grants and funding