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Many high dimensional and high-throughput biological datasets have complexsample correlation structures, which include longitudinal and multiple tissuedata, as well as data with multiple treatment conditions or relatedindividuals. These data, as well as nearly all high-throughput `omic' data, areinfluenced by technical and biological factors unknown to the researcher,which, if unaccounted for, can severely obfuscate estimation and inference oneffects due to the known covariate of interest. We therefore developed CBCV andCorrConf: provably accurate and computationally efficient methods to choose thenumber of and estimate latent confounding factors present in high dimensionaldata with correlated or nonexchangeable residuals. We demonstrate each method'ssuperior performance compared to other state of the art methods by analyzingsimulated multi-tissue gene expression data and identifying sex-associated DNAmethylation sites in a real, longitudinal twin study. As far as we are aware,these are the first methods to estimate the number of and correct for latentconfounding factors in data with correlated or nonexchangeable residuals. AnR-package is available for download athttps://github.com/chrismckennan/CorrConf.

Estimating and accounting for unobserved covariates in high dimensional correlated data