Abstract
BackgroundInterrogation of chromatographic data for biomarker discovery becomes a tedious task due to stochastic variability in retention times arising from solvent and column performance. The difficulty is further compounded when the effects of exposure (e.g. to environmental contaminants) and biological variability result in varying numbers and intensities of peaks among chromatograms.ResultsWe developed a software tool to correct the stochastic time shifts in chromatographic data through iterative selection of landmark peaks and isometric interpolation to improve alignment of all chromatographic peaks. To illustrate application of the tool, plasma peptides from Fischer rats exposed for 4 h to clean air or Ottawa urban particles (EHC-93) were separated by HPLC with autofluorescence detection, and the retention time shifts between chromatograms were corrected (dewarped). Both dewarped and non-dewarped datasets were then mined for models containing peptide peaks that best discriminate among the treatment groups using ClinproTools™. In general, models generated by dewarped datasets were able to better classify test sample chromatograms into either clean air or EHC-93 exposure groups, and 0 or 24 h post-recovery time groups. Peak areas of peptides in a model that produced the best discrimination of treatment groups were analyzed by two-way ANOVA with exposure (clean air, EHC-93) and recovery time (0 h, 24 h) as factors. Statistically significant (p < 0.05) time-dependent and exposure-dependent increases and decreases were noted establishing these as biomarker candidates for further validation.ConclusionOur software tool provides a simple and portable approach for alignment of chromatograms with complex, bi-directional retention time shifts prior to data mining. Reliable biomarker discovery can be achieved through chromatographic dewarping using our software followed by pattern recognition by commercial data mining applications.
Highlights
Interrogation of chromatographic data for biomarker discovery becomes a tedious task due to stochastic variability in retention times arising from solvent and column performance
Various approaches of peak alignment such as local shifting and peak matching [10], target peak alignment [9,11], rank alignment [12], correlation optimized warping [9,13,14,15], dynamic time dewarping [16], parametric time warping [15,17], semi-parametric time warping [9,15] and fuzzy warping [18] have been applied to dewarp liquid or gas chromatograms with varying degrees of efficiency
Implementation and validation of dewarping approach Our dewarping approach consisted of a software assisted iterative selection and alignment of landmark peaks by interpolation or removal of data points between peaks for dewarping of all peaks across chromatograms. This was implemented as a Windows based software tool ("DewarpTool")
Summary
Interrogation of chromatographic data for biomarker discovery becomes a tedious task due to stochastic variability in retention times arising from solvent and column performance. In the case of liquid chromatography, an integral component of several proteomics platforms, retention time shifts across a series of chromatographic runs can arise due to changes in quality of mobile phases or column performance This makes the data less amenable for direct differential comparisons of treatment responses or pattern recognition for sample source identification or exposure determination [9]. Tomasi et al [19] compared correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic datasets prior to analysis by PCA and concluded that dynamic time warping with rigid slope constraints and correlation optimized warping were superior to unconstrained dynamic time warping These results suggest that in addition to the demonstrated differences in the efficiency and applicability of these approaches to specific chromatographic datasets, there is a requirement for the definition and experimentation with a number of dewarping parameters to obtain good alignments. All of the above algorithms are routinely implemented in a mathematical computing environment (e.g. subroutines on MatLab)
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