Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated with the biological variable of interest. 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 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.
Our work has been published in Scientific Reports: Detection of correlated hidden factors from single cell transcriptomes using Iteratively Adjusted-SVA (IA-SVA) Donghyung Lee, Anthony Cheng, Nathan Lawlor, Mohan Bolisetty, Duygu Ucar
Donghyung Lee donghyung.lee@jax.org , Anthony Cheng anthony.cheng@jax.org , and Nathan Lawlor nathan.lawlor@jax.org
To install IA-SVA package, start R and enter the following commands:
library(devtools)
devtools::install_github("UcarLab/iasva")
To load this package, enter the following command to the R console:
library(iasva)
IA-SVA is also available for download from Bioconductor (link to development version here)
To install and load the package, please enter the following into the R console:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("iasva", version = "devel")
library(iasva)
For instructions on how to use IA-SVA, please see the package vignette.
For additional tutorials, please click Quick View for any of the four examples below to view in a web browser.
Example 1) Detecting hidden heterogeneity in human islet alpha cells (Quick View)
Example 2) Detecting cell-cycle stage difference in glioblastoma cells (Quick View)
Example 3) IA-SVA based feature selection improves the performance of clustering algorithms [1] (Quick View)
Example 4) IA-SVA based feature selection improves the performance of clustering algorithms [2] (Quick View)
Example 5) Compare IA-SVA to factor analyses methods in terms of their ability to detect marker genes for different cell types [2] (Quick View)
Example 6) scRNA-seq Data Simulation [1] (Quick View)
Example 7) scRNA-seq Data Simulation [2] (Quick View)
An interactive web version of IA-SVA is publicly hosted on Jackson Laborator dedicated servers (two 24 Core and 192GB RAM servers) here
The github page for this application is available here
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