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In many cases, however, multiple sampling is not clinically feasible.
Whether multiple sampling is needed for exposure assessment in specific situations (e.g., during pregnancy) will depend mainly on the intraperson variability at the sampling time (e.g., noon) and throughout the study period (e.g., 1 week).
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Since multiple sampling was carried out an O-PLS-DA model could be constructed and cross-validated without risk of over-fitting data.
Despite the technical challenges faced, protein abundance determination over multiple samples is still an area of interest.
Since the least-squares estimator for multiple samples is the sample mean estimator [13], the estimate of channel coefficient for a frame becomes (3).
Therefore, detection of the microorganism in multiple samples is usually necessary to make a definitive diagnosis of B. cereus pneumonia [3].
Joint handling of multiple samples is computationally and conceptually challenging, see e.g. [ 17, 18].
Creating a baseline from multiple samples is essential for population and family studies where a matched control is not available.
Therefore, the straightforward approach for dealing with multiple samples is not a good way to treat multiple sample RNA-Seq data.
However, when such segments are common to several samples, joint segmentation of multiple samples is an additional mechanism to increase sensitivity.
Because of the scarcity of coalescent events in the recent history, the simultaneous analysis of multiple samples is needed to infer recent demographics.
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