DOI: 10.18129/B9.bioc.iasva    

Iteratively Adjusted Surrogate Variable Analysis

Bioconductor version: Release (3.8)

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")):


To install this package, start R (version "3.5") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
BiocManager::install("iasva", version = "3.8")

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


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



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biocViews BatchEffect, FeatureExtraction, Preprocessing, QualityControl, RNASeq, Software, StatisticalMethod
Version 1.0.0
License GPL-2
Depends R (>= 3.5)
Imports irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel
Suggests knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer
Depends On Me
Imports Me
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Follow Installation instructions to use this package in your R session.

Source Package iasva_1.0.0.tar.gz
Windows Binary iasva_1.0.0.zip
Mac OS X 10.11 (El Capitan) iasva_1.0.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/iasva
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/iasva
Package Short Url http://bioconductor.org/packages/iasva/
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