Package: iasva
Type: Package
Title: Iteratively Adjusted Surrogate Variable Analysis
Version: 1.31.0
Date: 2018-11-29
Authors@R: c(person("Donghyung", "Lee", email = "Donghyung.Lee@jax.org",
    role = c("aut", "cre")), person("Anthony", "Cheng", 
    email = "Anthony.Cheng@jax.org", role = "aut"),
    person("Nathan", "Lawlor", email = "Nathan.Lawlor@jax.org",
    role = "aut"), person("Duygu", "Ucar",
    email = "Duygu.Ucar@jax.org", role = "aut"))
Maintainer: Donghyung Lee <Donghyung.Lee@jax.org>, Anthony Cheng
 <Anthony.Cheng@jax.org>
Description: 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.
Depends: R (>= 3.5),
Imports: irlba, stats, cluster, graphics, SummarizedExperiment,
        BiocParallel
License: GPL-2
biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq,
        Software, StatisticalMethod, FeatureExtraction, ImmunoOncology
Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot,
        DescTools, RColorBrewer
VignetteBuilder: knitr
RoxygenNote: 6.0.1
Config/pak/sysreqs: zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 12:48:04 UTC
RemoteUrl: https://github.com/bioc/iasva
RemoteRef: HEAD
RemoteSha: cf5a38d1247b85582a06e3d43c513fb128ce12f5
NeedsCompilation: no
Packaged: 2026-07-10 05:56:10 UTC; root
Author: Donghyung Lee [aut, cre],
  Anthony Cheng [aut],
  Nathan Lawlor [aut],
  Duygu Ucar [aut]