Significance Testing of Normalized LC-MS Data
Posted 12 Jan, 2009 in Tools
Available Versions
- 2.0 (published)
- 1.0 (Unpublished)
Supporting Documents
- Pipeline Open Proteomics June 2010 (PDF, 712.39 Kb)
This tool is closed source.
| Version | 2.0 - published on 05 May. 2010 |
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| Contributor(s) | Ann Christine Catlin Rosen Center for Advanced Computing |
| At a glance | 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. |
| Screenshots | |
| Description | 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.
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| credits | The significance software was developed by Xiang Zhang, Jiri Adamec, et al. in 2005. The Purdue Discovery Pipeline was created by the Bindley Biosciences Center under the direction of Charles Buck. The integration of Purdue Discovery Pipeline models as cceHUB tools and the contribution of test datasets to the cceHUB respository are part of a collaborative effort with Jiri Adamec, Amber Jannasch and Catherine P. Riley of the Bindley Biosciences Center and George Howlett of the Rosen Center for Advanced Computing. |
| Cite this work | If you reference this work in a publication, please cite as follows: |
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