OSAT: a tool for sample-to-batch allocations in genomics experiments

BMC Genomics

Software

Jeffrey M Conroy¹  Christine B Ambrosone²  Lara E Sucheston²  Jianmin Wang³  Dan Wang³  Li Yan³  Maochun Qin³  Qiang Hu³  Song Liu³  Changxing Ma⁴  Candace S Johnson⁵ 

[1] Cancer Genetics, Roswell Park Cancer Institute, 14263, Buffalo, NY, USA;Cancer Prevention and Control, Roswell Park Cancer Institute, 14263, Buffalo, NY, USA;Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, 14263, Buffalo, NY, USA;Department of Biostatistics, SUNY University at Buffalo, 14214, Buffalo, NY, USA;Pharmacology and Therapeutics, Roswell Park Cancer Institute, 14263, Buffalo, NY, USA;

关键词: Genomic Study;    Optimization Step;    Batch Effect;    Randomized Complete Block Design;    Alternative Algorithm;   

DOI  :  10.1186/1471-2164-13-689

 received in 2012-07-10, accepted in 2012-12-04,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundBatch effect is one type of variability that is not of primary interest but ubiquitous in sizable genomic experiments. To minimize the impact of batch effects, an ideal experiment design should ensure the even distribution of biological groups and confounding factors across batches. However, due to the practical complications, the availability of the final collection of samples in genomics study might be unbalanced and incomplete, which, without appropriate attention in sample-to-batch allocation, could lead to drastic batch effects. Therefore, it is necessary to develop effective and handy tool to assign collected samples across batches in an appropriate way in order to minimize the impact of batch effects.ResultsWe describe OSAT (Optimal Sample Assignment Tool), a bioconductor package designed for automated sample-to-batch allocations in genomics experiments.ConclusionsOSAT is developed to facilitate the allocation of collected samples to different batches in genomics study. Through optimizing the even distribution of samples in groups of biological interest into different batches, it can reduce the confounding or correlation between batches and the biological variables of interest. It can also optimize the homogeneous distribution of confounding factors across batches. It can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideally balanced designs.

【 授权许可】

CC BY   
© Yan et al.; licensee BioMed Central Ltd. 2012