UcarLab/iasva: 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

Package details

Bioconductor views BatchEffect FeatureExtraction ImmunoOncology Preprocessing QualityControl RNASeq Software StatisticalMethod
MaintainerDonghyung Lee <Donghyung.Lee@jax.org>, Anthony Cheng <Anthony.Cheng@jax.org>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
UcarLab/iasva documentation built on Feb. 3, 2020, 8:28 a.m.