iasva

DOI: 10.18129/B9.bioc.iasva    

This package is for version 3.13 of Bioconductor; for the stable, up-to-date release version, see iasva.

Iteratively Adjusted Surrogate Variable Analysis

Bioconductor version: 3.13

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>

Installation

To install this package, start R (version "4.1") and enter:

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

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")

 

HTML	R Script	Detecting hidden heterogeneity in single cell RNA-Seq data
PDF		Reference Manual
Text		NEWS

Details

biocViews	BatchEffect, FeatureExtraction, ImmunoOncology, Preprocessing, QualityControl, RNASeq, Software, StatisticalMethod
Version	1.10.0
In Bioconductor since	BioC 3.8 (R-3.5) (3 years)
License	GPL-2
Depends	R (>= 3.5)
Imports	irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel
LinkingTo
Suggests	knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

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

Source Package	iasva_1.10.0.tar.gz
Windows Binary	iasva_1.10.0.zip
macOS 10.13 (High Sierra)	iasva_1.10.0.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	https://bioconductor.org/packages/iasva/
Package Downloads Report	Download Stats