Maienschein-Cline M.,Institute for Interventional Health Informatics |
Lei Z.,Institute for Interventional Health Informatics |
Gardeux V.,Institute for Interventional Health Informatics |
Gardeux V.,University of Illinois at Chicago |
And 10 more authors.
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. © 2014 The Author 2014. Source