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.
