1. Spectrum Deconvolution of LC-MS Data

   9.9 out of 5 stars 

   05 Sep. 2008 | Tools | Contributor(s): Ann Christine Catlin, George Howlett

   Spectral deconvolution differentiates analyte signals from contaminants or instrumental noise, and reduces data dimensionality to benefit downstream statistical analysis.

   http://ccehub.org/resources/xmass

2. Peak Alignment of LC-MS Data

   0.0 out of 5 stars 

   15 Sep. 2008 | Tools | Contributor(s): Ann Christine Catlin, George Howlett

   Peak alignment addresses retention time shift by recongnizing and aligning significant peaks; it then uses discrete deconvolution to align overlapped peaks.

   http://ccehub.org/resources/xalign

3. Normalization of Aligned LC-MS Data

   0.0 out of 5 stars 

   12 Jan. 2009 | Tools | Contributor(s): Ann Christine Catlin, George Howlett

   Normalization attempts to quantitatively filter overall peak intensity variations due to experiment errors such as systematic variable injection volumes loaded onto LC-MS.

   http://ccehub.org/resources/normal

4. Significance Testing of Normalized LC-MS Data

   0.0 out of 5 stars 

   12 Jan. 2009 | Tools | Contributor(s): Ann Christine Catlin

   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 …

   http://ccehub.org/resources/significance

5. Pattern Recognition for Normalized LC-MS Data

   0.0 out of 5 stars 

   12 Jan. 2009 | Tools | Contributor(s): Ann Christine Catlin, George Howlett

   This tool provides principal component analysis (PCA), linear discriminate analysis (LDA), and canonical discriminate analysis (CDA) for data clustering on aligned, normalized LC-MS datasets.

   http://ccehub.org/resources/pattern

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