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iasva

This is the development version of iasva; for the stable release version, see iasva.

Iteratively Adjusted Surrogate Variable Analysis

Bioconductor version: Development (3.18)

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>

Citation (from within R, enter citation("iasva")):

Installation

To install this package, start R (version "4.3") and enter: 

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("iasva")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("iasva")
Detecting hidden heterogeneity in single cell RNA-Seq data HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews BatchEffect, FeatureExtraction, ImmunoOncology, Preprocessing, QualityControl, RNASeq, Software, StatisticalMethod
Version 1.19.0
In Bioconductor since BioC 3.8 (R-3.5) (5 years)
License GPL-2
Depends R (>= 3.5)
Imports irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel
Linking To
Suggests knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer
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Enhances
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package iasva_1.19.0.tar.gz
Windows Binary iasva_1.19.0.zip
macOS Binary (x86_64) iasva_1.19.0.tgz
macOS Binary (arm64) iasva_1.19.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/iasva
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/iasva
Bioc Package Browser https://code.bioconductor.org/browse/iasva/
Package Short Url https://bioconductor.org/packages/iasva/
Package Downloads Report Download Stats