Package: iasva
Type: Package
Title: Iteratively Adjusted Surrogate Variable Analysis
Version: 1.20.0
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
git_url: https://git.bioconductor.org/packages/iasva
git_branch: RELEASE_3_18
git_last_commit: e6e4472
git_last_commit_date: 2023-10-24
Date/Publication: 2023-10-26
NeedsCompilation: no
Packaged: 2023-10-27 05:12:17 UTC; biocbuild
Author: Donghyung Lee [aut, cre],
  Anthony Cheng [aut],
  Nathan Lawlor [aut],
  Duygu Ucar [aut]
Built: R 4.3.1; ; 2023-10-28 18:27:42 UTC; unix
