MiMIR (version 1.5)

calculate_surrogate_scores: calculate_surrogate_scores

Description

Function to compute the surrogate scores by Bizzarri et al. from the Nightingale metabolomics matrix

Usage

calculate_surrogate_scores(
  met,
  pheno,
  PARAM_surrogates,
  bin_names = c("sex", "diabetes"),
  Nmax_miss = 1,
  Nmax_zero = 1,
  post = TRUE,
  roc = FALSE,
  quiet = FALSE
)

Value

if pheno is not available: list with the surrogates and the Nightingale metabolomics matrix after QC. if pheno is available: list with the surrogates, ROC curves, phenotypes, binarized phenotypes and the Nightingale metabolomics matrix after QC,

Arguments

met

numeric data-frame with Nightingale-metabolomics

pheno

phenotypic data.frame including this clinical variables (with the same nomenclature): "sex","diabetes", "lipidmed", "blood_pressure_lowering_med", "current_smoking", "metabolic_syndrome", "alcohol_consumption", "age","BMI", "ln_hscrp","waist_circumference", "weight","height", "triglycerides", "ldl_chol", "hdlchol", "totchol", "eGFR","wbc","hgb"

PARAM_surrogates

list containing the parameters to compute the metabolomics-based surrogates

bin_names

vector of strings containing the names of the binary variables

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)

post

logical to indicate if the function should calculate the posterior probabilities

roc

logical to plot ROC curves for the metabolomics surrogate (available only for the phenotypes included)

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

QCprep_surrogates

Examples

require(MiMIR)
require(foreach)
require(pROC)
require(foreach)

#load dataset
m <- synthetic_metabolic_dataset
p <- synthetic_phenotypic_dataset
#Apply the surrogates
sur<-calculate_surrogate_scores(met=m,pheno=p,MiMIR::PARAM_surrogates,bin_names=c("sex","diabetes"))