MiMIR (version 1.5)

predictions_surrogates: predictions_surrogates

Description

Helper function that apply a surrogate model and plot a ROC curve the accuracy

Usage

predictions_surrogates(FIT, data, title_img = FALSE, plot = TRUE)

Value

If plot==TRUE The surrogate predictions and the roc curve. If plot==F only the surrogate predictions

Arguments

FIT

numeric vector with betas of the logistic regressions composing the surrogates by Bizzarri et al.

data

numeric data-frame with Nightingale-metabolomics and the binarized phenotype to predict

title_img

string with title of the image

plot

logical to obtain the ROC curve

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, calculate_surrogate_scores, subset_samples_sd_surrogates, apply.fit_surro

Examples

if (FALSE) {
library(MiMIR)

#load the Nightignale metabolomics dataset
metabolic_measures <- read.csv("Nightingale_file_path",header = TRUE, row.names = 1)
# Do the pre-processing steps to the metabolic measures
metabolic_measures<-QCprep_surrogates(as.matrix(metabolic_measures), Nmax_miss=1,Nmax_zero=1)

#load the phenotypic dataset
phenotypes <- read.csv("phenotypes_file_path",header = TRUE, row.names = 1)
#Calculating the binarized surrogates
bin_pheno<-binarize_all_pheno(phenotypes)

#Apply a surrogate models and plot the ROC curve
data<-data.frame(out=factor(phenotypes_names$bin_names[,1]), metabo_measures)
colnames(data)[1]<-"out"
pred<-predictions_surrogates(PARAM_surrogates$models_betas["s_sex",], data=data, title_img="s_sex")

}