OSAT: a tool for sample-to-batch allocations in genomics experiments.
OSAT: a tool for sample-to-batch allocations in genomics experiments.
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OSAT：基因组学实验中样品到批次分配的工具。
DOI：
10.1186/1471-2164-13-689
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发表时间：
2012-12-10
期刊：
影响因子：
4.4
通讯作者：
Liu S
中科院分区：
文献类型：
--
作者：
Yan L;Ma C;Wang D;Hu Q;Qin M;Conroy JM;Sucheston LE;Ambrosone CB;Johnson CS;Wang J;Liu S
批量效应是一种变异性，它不是主要的兴趣，但在相当大的基因组实验中普遍存在。为了最大限度地减少批效应的影响，理想的实验设计应确保生物组和混杂因素在批间的均匀分布。然而，由于实际的复杂性，基因组学研究中最终收集的样本的可用性可能是不平衡和不完整的，如果在样本到批次的分配中没有适当的注意，可能会导致严重的批次效应。因此，有必要开发有效和方便的工具，以适当的方式分配收集的样品在批次之间，以尽量减少批次效应的影响。我们描述了OSAT（最佳样品分配工具），一个生物导体包设计的自动样品到批次分配在基因组学实验。OSAT是为了方便基因组学研究中将采集的样本分配到不同批次而开发的。通过将生物学感兴趣组中的样本均匀分布到不同批次中，可以减少批次与感兴趣生物学变量之间的混杂或相关性。它还可以优化批次间混杂因素的均匀分布。它可以处理具有挑战性的情况下，不完整和不平衡的样本收集，以及理想的平衡设计。
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. We describe OSAT (Optimal Sample Assignment Tool), a bioconductor package designed for automated sample-to-batch allocations in genomics experiments. 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.
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