UcarLab/IA-SVA: Iteratively Adjusted Surrogate Variable Analysis
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.
Getting started
Browse package contents
Package details |
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| Bioconductor views | BatchEffect FeatureExtraction ImmunoOncology Preprocessing QualityControl RNASeq Software StatisticalMethod |
| Maintainer | Donghyung Lee <Donghyung.Lee@jax.org>, Anthony Cheng <Anthony.Cheng@jax.org> |
| License | GPL-2 |
| Version | 1.11.1 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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