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Significance Testing of Normalized LC-MS Data
Several statistical significance tests are employed to identify peptide or metabolite peaks that either make significant contributions to the molecular profile of a sample or distinguish a group of samples from others.
The Significance Test Tool uses several statistical significance test methods to identify data elements that either contribute to the proteomic profile of a sample or that distinguish groups of samples. Some peaks may be present across sample groups but with differing intensity between the groups. The quantitative difference identifies the case where a peak is present in most (or all) samples, but with different intensities from group to group.
Methods implemented in the tool include:
- Two-tailed t-test and
- Mann-Whitney tests.
Input for the Significance Test Tool includes:
- the normalized data file generated from the Normalization Tool for a collection of LC-MS datasets aligned through the Peak Alignmend Tool.
- input parameters to provide information about the number of groups and group sizes in the normalized dataset.
Researchers should cite this work as follows: