The systematic, non-biological differences between batches in genomics experiment are referred as batch effects. It can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideally balanced designs.Ī sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. It can also optimize the homogeneous distribution of confounding factors across batches. 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. OSAT is developed to facilitate the allocation of collected samples to different batches in genomics study. We describe OSAT (Optimal Sample Assignment Tool), a bioconductor package designed for automated sample-to-batch allocations in genomics experiments. 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. 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. To minimize the impact of batch effects, an ideal experiment design should ensure the even distribution of biological groups and confounding factors across batches. Batch effect is one type of variability that is not of primary interest but ubiquitous in sizable genomic experiments.
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