Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.
|Author||Jun Chen <Chen.Jun2@mayo.edu>, Ehsan Behnam <email@example.com>|
|Date of publication||2017-05-28 07:22:56 UTC|
|Maintainer||Jun Chen <Chen.Jun2@mayo.edu>|
|Package repository||View on CRAN|
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