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

Li Yan,Candace S Johnson,Qiang Hu,Song Liu,Dan Wang,Maochun Qin,Changxing Ma,Christine B Ambrosone,Jianmin Wang,Jeffrey M Conroy,Lara E Sucheston

doi:10.1186/1471-2164-13-689

Abstract

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.

Highlights

Batch effect is one type of variability that is not of primary interest but ubiquitous in sizable genomic experiments
We developed OSAT to facilitate the allocation of collected samples into different batches in genomics studies
OSTA is developed to address one practical issue in genomics studies – when the available experimental samples ready to be profiled in the genomics instruments are collected, how should one allocate these samples to different batches in a proper way to achieve an optimal setup minimizing the impact of batch effects at the genomic profiling stage? With a block randomization step followed by an optimization step, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest

Summary

Introduction

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. A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. Without sound experiment designs and statistical analysis methods to handle batch effects, misleading or even erroneous conclusions could be made. This especially important issue is often overlooked, partially due to the complexity and multiple steps involved in genomics studies. To minimize the impact of batch effects, a careful experiment design should ensure the even distribution

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