Log in to save to my catalogue

rox: A statistical model for regression with missing values

rox: A statistical model for regression with missing values

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2650970860

rox: A statistical model for regression with missing values

About this item

Full title

rox: A statistical model for regression with missing values

Publisher

Cold Spring Harbor: Cold Spring Harbor Laboratory Press

Journal title

bioRxiv, 2022-04

Language

English

Formats

Publication information

Publisher

Cold Spring Harbor: Cold Spring Harbor Laboratory Press

More information

Scope and Contents

Contents

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...

Alternative Titles

Full title

rox: A statistical model for regression with missing values

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2650970860

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2650970860

Other Identifiers

E-ISSN

2692-8205

DOI

10.1101/2022.04.15.488427