Normalization of Aligned LC-MS Data
Posted 12 Jan, 2009 in Tools
Available Versions
- 2.0 (published)
- 1.1 (Unpublished)
- 1.0 (Unpublished)
This tool is closed source.
| Version | 2.0 - published on 06 Jul. 2009 |
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| Contributor(s) | Ann Christine Catlin Rosen Center for Advanced Computing George Howlett Purdue University |
| At a glance | Normalization attempts to quantitatively filter overall peak intensity variations due to experiment errors such as systematic variable injection volumes loaded onto LC-MS. |
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| Description | For multi-experiment analysis, aligned data must be normalizalized for sample comparison. Normalization attempts to filter overall peak intensity variations resulting from experimental errors. Several normalization methods have been proposed. One approach is to choose one dataset as a reference and normalize all others relative to this reference. The intensity ratio of each aligned peak pair in reference and sample is calculated. The normalization constant for the sample being considered is then taken as the median of the ratio of intensities for all components between the sample in question and the reference sample.
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| credits | The normalization 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. |
| references | Wang W., Zhou H., Lin H., Roy S., Shaler T.A., Hill L.R., Norton S., Kumar P., Anderle M., Becker C.H: Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem 2003, 75(18):4818-4826. Zhu W., Wang X., Ma Y., Rao M., Glimm J., Lovach J.S.: Detection of cancer-specific markers amid massive mass spectral data. Proc Natl Acad Sci USA 2003, 100(25):14666-14671. Hartemink A.J., Jaakola T.S., Young R.A.: Maximum likelihood estimation of optimal scaling factors for expression array normalization. Proceedings of SPIE:4266 |
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