![]() We developed OSAT to facilitate the allocation of collected samples into different batches in genomics studies. 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. The resulted unbalance and incompleteness nature of sample availability in genomics study, without appropriate attention in sample-to-batch allocation, could lead to drastic batch effects. In clinical genomics study, samples may be rare, difficult or expensive to collect, irreplaceable or fail QC before profiling. This is due to the fact that these studies are mostly observational or quasi-experimental since we usually do not have full control over sample availability. However, despite all best effort, it is often than not that the collected samples are not complying with the original ideal RCBD design. It makes the separation of the real biological effect of our interests and effects by other confounding factors statistically more powerful. In an ideal genomics design, the groups of the main interest, as well as important confounding variables should be balanced and replicated across the batches to form a Randomized Complete Block Design (RCBD). It would be problematic if one batch run contains most samples of a particular biological group. ![]() To minimize the impact of batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. This especially important issue is unfortunately often overlooked, partially due to the complexity and multiple steps involved in genomics studies. Without sound experiment designs and statistical analysis methods to handle batch effects, misleading or even erroneous conclusions could be made. ![]() Batch effects are wide-spread occurrences in genomic studies, and it has been shown that noticeable variation between different batch runs can be a real concern, sometimes even larger than the biological differences. The systematic, non-biological differences between batches in genomics experiment are referred as batch effects. ![]() A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. ![]()
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