To begin, download a sample comma-delimited input file containing a list of metabolites with experimental measurements. Double click on the downloaded sample file to open it and verify that it contains labels for both the samples and the metabolites. Next, download the correlation calculator Java application, and double click on the downloaded JAR file to launch the application.
On the input tab, click the browse button to upload the input file. Under specify file format, select samples in rows. Click on the next button from the bottom right of the window to move to the data normalization tab.
Under select methods, check the box next to log to transform data and autoscale data. Under normalized data, click the run button. Once the normalization is complete, click the save button to save the new data file.
Click on the next button to move to the data analysis tab and under Calculate Pearson's Correlation, click run to determine the best Pearson's correlation range for the data. Click on the view histogram button to review the frequency of the maximal Pearson's correlation scores per feature in the view heat map button to review the representation of Pearson's correlation matrix. Under Filter by Pearson's Correlations, leave the default numbers to filter by a range of 0.00 to 1.00.
Then under Select Partial Correlation Method, select the desired method as DSPC method. And under Calculate Partial Correlations, click the run button. Click on the view CSV file to view the results, and click the save button to save the results.
A representative network constructed from a subset of the KORA population study metabolomics data consisting of 151 metabolites across 240 subjects is shown. Consensus network clustering resulted in the identification of nine sub-networks or metabolic modules.
