iasva: Iteratively Adjusted Surrogate Variable Analysis

Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors.

Package details

AuthorDonghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor [aut], Duygu Ucar [aut]
Bioconductor views BatchEffect FeatureExtraction ImmunoOncology Preprocessing QualityControl RNASeq Software StatisticalMethod
MaintainerDonghyung Lee <[email protected]>, Anthony Cheng <[email protected]>
Package repositoryView on Bioconductor
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iasva documentation built on Oct. 31, 2019, 9:56 a.m.