You must login before you can run this tool.
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.
Version 2.2 - published on 19 Sep 2011
This tool is closed source.
Category
Published on
Abstract
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.
Cite this work
Researchers should cite this work as follows: