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It is shown that stage and batch lengths can be estimated in real time with an average error that is not larger than 20% of the inherent batch-to-batch variability, whereas quality estimations can be provided within the accuracy of the hardware instrumentation, but 240 times faster.
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It is also shown that the product quality "builds up" mainly during the first half of a batch, suggesting on the one side that reducing the variability during this period is crucial, and on the other side that the batch length can possibly be shortened.
The length can be shorter on holiday.
Length can be a problem.
Many laboratories buy large batches of serum (held in store by the supplier) to avoid batch variability; batches can be kept at −80 °C without noticeable loss of function.
For a dataset with a large number of queries and references, we can split the dataset into multiple batches, where each batch can be executed sequentially on GPU.
Computing can be done batch by batch or it can be done by individual transaction.
Batch updates can be performed in time that is constant per updated tuple.
Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch.
Batch effects can be a problem with RNAseq data [ 26].
In addition, batch differences can be observed [ 50].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com