MiMIR (version 1.5)

QCprep_surrogates: QCprep_surrogates

Description

Helper function to pre-process the Nightingale Health metabolomics data-set before applying metabolomics-based surrogates by Bizzarri et al.

Usage

QCprep_surrogates(
  mat,
  PARAM_surrogates,
  Nmax_miss = 1,
  Nmax_zero = 1,
  quiet = FALSE
)

Value

Nightingale-metabolomics data-frame after pre-processing (checked for zeros, missing values, samples>5SD from the BBMRI-mean, imputing the missing values and z-scaled)

Arguments

mat

numeric data-frame Nightingale metabolomics matrix.

PARAM_surrogates

is a list holding the parameters to compute the surrogates

Nmax_miss

numeric value indicating the maximum number of missing values allowed per sample (Number suggested=1)

Nmax_zero

numeric value indicating the maximum number of zeros allowed per sample (Number suggested=1)

quiet

logical to suppress the messages in the console

Details

Bizzarri et al. built multivariate models,using 56 metabolic features quantified by Nightingale, to predict the 19 binary characteristics of an individual. The binary variables are: sex, diabetes status, metabolic syndrome status, lipid medication usage, blood pressure lowering medication, current smoking, alcohol consumption, high age, middle age, low age, high hsCRP, high triglycerides, high ldl cholesterol, high total cholesterol, low hdl cholesterol, low eGFR, low white blood cells, low hemoglobin levels.

References

This function was made to vidualize the binarized variables calculated following the rules indicated in the article: Bizzarri,D. et al. (2022) 1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints. EBioMedicine, 75, 103764, doi:10.1016/j.ebiom.2021.103764

See Also

binarize_all_pheno

Examples

library(MiMIR)

#load the Nightignale metabolomics dataset
metabolic_measures <- synthetic_metabolic_dataset
#Pre-process the metabolic features
prepped_met<-QCprep_surrogates(as.matrix(metabolic_measures), MiMIR::PARAM_surrogates)