Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis

By Jeffrey T. Leek, John D. Storey. 2007

631 citations recorded by the source corpus.

It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce “surrogate variable analysis” (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.

Subfields: Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Learning Explainable Models Using Attribution Priors (2019, methodology): “This can be extended in the sparse group lasso (SGL) to penalize all weights connected to a given feature [8, 30].”
A simple new approach to variable selection in regression, with application to genetic fine mapping (2018, methodology): “(2016), we preprocessed the intron ratios by regressing out the first three principal components of the matrix of intron ratios; the intent is to control for unmeasured confounders (Leek and Storey, 2007).”
LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies (2019, methodology): “The matrix W is a “latent matrix” of rank K , defined by K latent factors where K can be determined by model choice procedures (Leek and Storey 2007; Frichot et al. 2013).”
Improving performance of deep learning models with axiomatic attribution priors and expected gradients (2020, background): “For example, prior work in bioinformatics has shown that protein-protein interaction networks contain valuable information for improving performance on biological prediction tasks [6].”
Challenges of Big Data Analysis (2013, neutral): “They have been shown to have substantial effects on gene expression levels, and failing to taking them into consideration may lead towrong scientific conclusions [37].”
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018, positive): “Surrogate variable analysis was performed to identify hidden confounding factors (43).”
Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi (2016, methodology): “…methods such as Noob (Triche et al., 2013) and functional normalization (Fortin, Labbe, et al., 2014), and using the batch effect correction tools ComBat (Johnson, Li, and Rabinovic, 2007), SVA (Leek and Storey, 2007; Leek and Storey, 2008) and RUVm (Maksimovic, Gagnon-Bartsch, et al., 2015).”
Fast, sensitive, and accurate integration of single cell data with Harmony (2019, background): “…Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information, details of author contributions and competing interests, and statements of code and data availability are available at https://doi.org/10.1038/s41592-019-0619-0.”
Harmonization of multi-site diffusion tensor imaging data (2017, neutral): “In genomics, latent factor approaches that estimate unknown source of variation have been successfully used, such as SVA [Leek and Storey, 2007] and RUV [Gagnon-Bartsch and Speed, 2012].”
Removal of batch effects using distribution‐matching residual networks (2016, background): “he former is multivariate, and can take into account dependencies, while the latter assumes that the joint distributions is a product of its marginals Finak et al. (2014). Surrogate variable Analysis Leek and Storey (2007) is popular approach for batch eect adjustment, primarily in gene expression d”
A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies (2010, methodology): “often have an influence on large numbers of genes. We and others have proposed methods to detect and correct for such effects”