TY - JOUR
T1 - ARTS
T2 - Automated randomization of multiple traits for study design
AU - Maienschein-Cline, Mark
AU - Lei, Zhengdeng
AU - Gardeux, Vincent
AU - Abbasi, Taimur
AU - Machado, Roberto F.
AU - Gordeuk, Victor
AU - Desai, Ankit A.
AU - Saraf, Santosh
AU - Bahroos, Neil
AU - Lussier, Yves
N1 - Funding Information:
Funding: National Institutes of Health (grants UL1TR000050, in part) (University of Illinois CTSA); University of Illinois Cancer Center.
PY - 2014/6/1
Y1 - 2014/6/1
N2 - 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.
AB - 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.
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U2 - 10.1093/bioinformatics/btu075
DO - 10.1093/bioinformatics/btu075
M3 - Article
C2 - 24493035
AN - SCOPUS:84901373711
SN - 1367-4803
VL - 30
SP - 1637
EP - 1639
JO - Bioinformatics
JF - Bioinformatics
IS - 11
ER -