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.
|Author||Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor [aut], Duygu Ucar [aut]|
|Bioconductor views||BatchEffect FeatureExtraction ImmunoOncology Preprocessing QualityControl RNASeq Software StatisticalMethod|
|Maintainer||Donghyung Lee <[email protected]>, Anthony Cheng <[email protected]>|
|Package repository||View on Bioconductor|
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