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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "iasva" in publications use:'
type: software
license: GPL-2.0-only
title: 'iasva: Iteratively Adjusted Surrogate Variable Analysis'
version: 1.23.0
doi: 10.32614/CRAN.package.iasva
abstract: 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.
authors:
- family-names: Lee
  given-names: Donghyung
  email: Donghyung.Lee@jax.org
- family-names: Cheng
  given-names: Anthony
  email: Anthony.Cheng@jax.org
- family-names: Lawlor
  given-names: Nathan
  email: Nathan.Lawlor@jax.org
- family-names: Ucar
  given-names: Duygu
  email: Duygu.Ucar@jax.org
repository: https://bioc.r-universe.dev
commit: 5f0aaf46b30e336d2819d573f6286429d83942ab
date-released: '2018-11-29'
contact:
- family-names: Lee
  given-names: Donghyung
  email: Donghyung.Lee@jax.org