I want to correct metabolite data for BMI and age

I can't wrap my head around what to do here, what is the best way to Normalise or correct my metabolite data for BMI and/or age of participant in either matlab or R

Answer 1

[Ishaan]

Hey @Daniel,

I see that you intend to normalize/correct metabolite data for variables like BMI and age.

Here is a general step by step workflow you can follow to achieve the same.

1. Load and standardize the data:

• Load your data into a table with metabolites, BMI and/or age as columns and samples as rows.
• Standardize the data to have zero mean and unit variance.

dataTable = readtable('dataFile.csv');

standardizedData = zscore(dataTable.Metabolites);

2. Correct for BMI and Age:

• You can use linear or polynomial regression to remove the effects of BMI and age from your metabolite data.

for i = 1:size(standardizedData, 2)

lm = fitlm(dataTable, ['Metabolites' num2str(i) ' ~ BMI + Age']);

residuals(:, i) = lm.Residuals.Raw;

end

• The residuals from the model can be used as the corrected metabolite data, as they represent the part of the metabolite levels not explained by BMI and age.
• You might want to verify if the residuals are independent of BMI and age by plotting or calculating correlations.

corrplot([residuals, dataTable.BMI, dataTable.Age]);

3. Save the Corrected Data:

• Save your corrected data for further analysis.

correctedDataTable = array2table(residuals, 'VariableNames', dataTable.Properties.VariableNames(1:end-2));

writetable(correctedDataTable, 'correctedMetaboliteData.csv');

Hope this helps you in normalizing your metabolite data for BMI and age using MATLAB.