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.
A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes, Donghyung Lee, Anthony Cheng, Duygu Ucar, bioRxiv. 2017; doi: https://doi.org/10.1101/151217
Donghyung Lee donghyung.lee@jax.org and Anthony Cheng anthony.cheng@jax.org
To install IA-SVA package, start R and enter the following commands:
library(devtools)
devtools::install_github("UcarLab/IA-SVA")
To load this package, enter the following command to the R console:
library(iasva)
Click Quick View to view each vignette 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)
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