MiMIR (version 1.5)

prep_met_for_scores: prep_met_for_scores

Description

Helper function to pre-process the Nightingale Health metabolomics data-set before applying the mortality, Type-2-diabetes and CVD scores.

Usage

prep_met_for_scores(dat, featID, plusone = FALSE, quiet = FALSE)

Value

The Nightingale-metabolomics data-frame after pre-processing (checked for zeros, zscale and log-transformed) according to what has been done by the authors of the original papers.

Arguments

dat

numeric data-frame with Nightingale-metabolomics

featID

vector of strings with the names of metabolic features included in the score selected

plusone

logical to determine if a value of 1.0 should be added to all metabolic features (TRUE) or only to the ones featuring zeros before log-transforming (FALSE)

quiet

logical to suppress the messages in the console

References

This function is constructed to be able to follow the pre-processing steps described in: Deelen,J. et al. (2019) A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nature Communications, 10, 1-8, doi:10.1038/s41467-019-11311-9.

Ahola-Olli,A.V. et al. (2019) Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts. Diabetologia, 62, 2298-2309, doi:10.1007/s00125-019-05001-w

Wurtz,P. et al. (2015) Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation, 131, 774-785, doi:10.1161/CIRCULATIONAHA.114.013116

See Also

comp.mort_score, mort_betas, comp.T2D_Ahola_Olli, comp.CVD_score

Examples

library(MiMIR)

#load the Nightingale metabolomics dataset
metabolic_measures <- synthetic_metabolic_dataset
#Prepare the metabolic features fo the mortality score
prepped_met <- prep_met_for_scores(metabolic_measures,featID=MiMIR::mort_betas$Abbreviation)