Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders

@article{Malik2021RestrictedMM,
  title={Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders},
  author={Muhammad Ammar Malik and Tom Michoel},
  journal={G3: Genes|Genomes|Genetics},
  year={2021},
  volume={12}
}
Random effect models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data. In applications where some confounding factors are known, estimating simultaneously the contribution of known and latent variance components in random effect models is a challenge that has so far relied on numerical gradient-based optimizers to maximize the likelihood function. This is unsatisfactory because the resulting… 

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