QCprep_surrogates function

QCprep_surrogates

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

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

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

Returns

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)

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.

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)

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