Peak alignment addresses retention time shift by recongnizing and aligning significant peaks; it then uses discrete deconvolution to align overlapped peaks.
Screenshots
Description
In an ideal experiment, the same peptide or metabolite detected using the same analysis workflow should produce the same signal. For example, a peptide measured on the LC-MS system should have the same retention time and molecular weight for different samples. However, experimental variations may result in differences in the measured values. Peak alignment processing recognizes peaks from the same molecule occurring in different samples from the millions of peaks detected during the course of an experiment.
The xAlign software implemented in the Proteome Discovery Pipeline uses a two-step alignment approach. The first step addresses systematic retention time shift by recognizing and aligning significant peaks. A significant peak refers to a peak that is present in every sample and is the most intense peak in a certain m/z and retention time range. The second step uses discrete deconvolution to align overlapped peaks. The peak alignment tool generates the following datasets:
Alignment table listing the isotope label, charge state, m/z, retention time and peak intensities for each sample, as well as the number of samples in which the peak is identified and the mean intensity for each peak. This dataset serves as the input for the next step in the pipeline, the normalization,
Mass charge (m/z) and retention time variation,
Quality assessments of the data prior to moving on to the statistical testing. The assessment is based on the D value of the K-S test, the number of peaks identified in each sample and the number of peaks aligned in each sample.
credits
The xAlign algorithm and software were 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 Paige Riley of the Bindley Biosciences Center and George Howlett of the Rosen Center for Advanced Computing.
references
Zhang X., Assara J.M., Adamec J., Ouzzani M, Elmagarmid A.K.: Data Preprocessing in liquid chromatography-mass spectrometry-based proteomics. Bioinformatics 2005, 21(21):4054-4059.
Cite this work
If you reference this work in a publication, please cite as follows:
Ann Christine Catlin; George Howlett (2008), "Peak Alignment of LC-MS Data," http://ccehub.org/resources/xalign.
