rox: A statistical model for regression with missing values
rox: A statistical model for regression with missing values
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Cold Spring Harbor: Cold Spring Harbor Laboratory Press
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English
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Cold Spring Harbor: Cold Spring Harbor Laboratory Press
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High-dimensional omics datasets frequently contain missing data points, which typically occur due to concentrations below the limit of detection (LOD) of the profiling platform. The presence of such missing values significantly limits downstream statistical analysis and result interpretation. Two common techniques to deal with this issue include the removal of samples with missing values, and imputation approaches which substitute the missing measurements with reasonable estimates. Both approaches, however, suffer from various shortcomings and pitfalls. In this paper, we present "rox", a novel statistical model for the analysis of omics data with missing values without the need for imputation. The model directly incorporates missing values as "low" concentrations into the calculation. We show the superiority of rox over common approaches on simulated data and on six metabolomics datasets. Fully leveraging the information contained in LOD-based missing values, rox provides a powerful tool for the statistical analysis of omics data. Competing Interest Statement Jan Krumsiek holds equity in Chymia LLC and IP in PsyProtix. Footnotes * https://github.com/krumsieklab/rox...
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rox: A statistical model for regression with missing values
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TN_cdi_proquest_journals_2650970860
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2650970860
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E-ISSN
2692-8205
DOI
10.1101/2022.04.15.488427
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https://www.proquest.com/docview/2650970860?pq-origsite=primo&accountid=13902