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Open Access Software

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

Li Yan1*, Changxing Ma2, Dan Wang1, Qiang Hu1, Maochun Qin1, Jeffrey M Conroy3, Lara E Sucheston4, Christine B Ambrosone4, Candace S Johnson5, Jianmin Wang1 and Song Liu1

Author Affiliations

1 Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA

2 Department of Biostatistics, SUNY University at Buffalo, Buffalo, NY, 14214, USA

3 Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA

4 Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA

5 Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA

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BMC Genomics 2012, 13:689  doi:10.1186/1471-2164-13-689

Published: 10 December 2012

Abstract

Background

Batch 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.

Results

We describe OSAT (Optimal Sample Assignment Tool), a bioconductor package designed for automated sample-to-batch allocations in genomics experiments.

Conclusions

OSAT 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.