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Found 4 papers, 2 papers with code
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS
The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of hundreds to thousands of related phenotypes.
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification
To address these issues, we then develop a novel framework and method that treats peptide-spectrum matching as a Bayesian model selection problem with an incomplete model space, which are, to our knowledge, the first to account for all sources of PSM error without relying on the aforementioned assumptions.
Estimation and inference in metabolomics with non-random missing data and latent factors
However, current methods to analyze these data can only account for the missing data or unobserved factors, but not both.
Methodology
Accounting for unobserved covariates with varying degrees of estimability in high dimensional biological data
Lastly, we use previously published DNA methylation data to show our method can more accurately estimate the direct effect of asthma on methylation than previously published methods, which underestimate the correlation between asthma and latent cell type heterogeneity.
Methodology
