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

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Summary: 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.

Original languageEnglish (US)
Pages (from-to)1637-1639
Number of pages3
Issue number11
StatePublished - Jun 1 2014

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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