DOI: 10.18129/B9.bioc.iasva    

This package is for version 3.12 of Bioconductor; for the stable, up-to-date release version, see iasva.

Iteratively Adjusted Surrogate Variable Analysis

Bioconductor version: 3.12

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]

Maintainer: Donghyung Lee <Donghyung.Lee at jax.org>, Anthony Cheng <Anthony.Cheng at jax.org>

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biocViews BatchEffect, FeatureExtraction, ImmunoOncology, Preprocessing, QualityControl, RNASeq, Software, StatisticalMethod
Version 1.8.0
In Bioconductor since BioC 3.8 (R-3.5) (2.5 years)
License GPL-2
Depends R (>= 3.5)
Imports irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel
Suggests knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer
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Source Package iasva_1.8.0.tar.gz
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