The purpose of our software is to provide a user-friendly, intuitive tool for analyzing targeted and untargeted metabolomics data. Our software is designed to overcome the following challenges often encountered when analyzing metabolomics data:
For this version of our software we are currently accepting .cdf, .mzxml, .mzml, and .mzdata.xml. However, if you have a different file type we can still analyze your data. Please contact us about it.
Yes. Our software is integrated with Amazon Web Services to provide all security and features provided through Amazon. AWS supports both server-side encryption and client-side encryption for data uploads and flexible security features to block unauthorized users from accessing your data and results once uploaded. All files can be deleted from the AWS at any time and no archives or backups are kept. To ensure you don’t accidently lose your results, it is recommended that you download your results provided through email immediately.
Please make sure that the format of the covariate file matches the format of the example demo data provided. The first column should be labeled ‘File’ and contain the file names for your analysis, all additional columns should be labeled covariates. If you are still experiencing difficulties, please contact us about the issue.
Please make sure that the data files are in the correct format (.cdf, .mzxml, .mzml, and .mzdata.xml) and that they match the rows in the ‘File’ column in the uploaded covariate file. If you still get an error, please contact us about the issue.
Our software will automatically impute missing values based on the data.
Our software drops samples with missing covariates. Please make sure all samples have values for every covariate to prevent this.
Our software automatically handles outliers so they do not affect the p-value calculations.
Yes. The covariate file requires at least 1 labeled covariate in addition to the ‘File’ column. By making every sample have the same covariate value, only a feature detection algorithm will be run.
Technical replicates are treated as independent individual samples.
Using the ‘Repeated Measures’ analysis, our software will take into account all duplicated samples when calculating p-values. This analysis type requires an additional ‘Subject’ column in the covariate file to identify which sample belongs to which subject. This analysis type also allows for time series analysis by including an additional ‘Time’ column in the covariate file.
Our software does not normalize data nor does it require normalization. However, if you would like your data normalized, please contact us about it.
Any values at or below zero will be treated the same as all other intensity values in the low signal range. This low signal or ‘noise’ range is automatically calculated from the data.
These are filters for your feature results. Any features that don’t meet the intensity or width requirement will be dropped and not shown in your results. Using larger values will also decrease the run time of your analysis. The values to use will depend on your data and how it was collected.
Check out the Upload Guide
Check out the How to Link Buckets Guide
Check out the Analysis Guide
You will receive an email once your results are ready. From there you can directly download your PDF and excel files or you can review your results in the user interface by clicking on ‘results dashboard’. You can also check the Results Page
Check out the Results Guide