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SKQ is able to pool information across multiple data sources, accommodate general data features, and provide more informative estimation/inference from given data.
SKQ's strength lies in its ability to pool information across multiple subpopulations/data sources.
BCA is able to pool information across compatible fragments, thus improving the resolution of the inferred tree at each locus.
These methods separately model each source of data and make no effort at all to pool information across different sources.
Rather than considering each gene/probe separately, these Bayesian modified approaches pool information across genes to achieve a more accurate and stable variance estimation thus improving the results of the tests.
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This variation in how different tags are expected to reduce theft also suggests that pooling information across tag types (in, say, a meta-analysis) to generate an overall conclusion is inappropriate.
This approach effectively pools information across all five groups to estimate the genetic parameters.
ComBat applies an empirical Bayes approach, by pooling information across genes and shrinking the batch effect parameter toward the overall mean of the batch estimates across genes.
Through empirical studies, we have demonstrated SKQ's ability to efficiently model exposure response surfaces by pooling information across multiple data sources.
Case 1: A multi-source dose time response case is developed to show SKQ's modeling efficiency by pooling information across multiple data sources.
ComBat, proposed by Johnson et al., applies an empirical Bayes approach by pooling information across genes and shrinks the batch effect parameter toward the overall mean of the batch estimates across genes [ 6].
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