Characterising phase variations in MALDI-TOF data and correcting them by peak alignment

The use of MALDI-TOF mass spectrometry as a means of analyzing the proteome has been evaluated extensively in recent years. One of the limitations of this technique that has impeded the development of robust data analysis algorithms is the variability in the location of protein ion signals along the x-axis. We studied technical variations of MALDI-TOF measurements in the context of proteomics profiling. By acquiring a benchmark data set with five replicates, we estimated 76% to 85% of the total variance is due to phase variation. We devised a lobster plot, so named because of the resemblance to a lobster claw, to help detect the phase variation in replicates. We also investigated a peak alignment algorithm to remove the phase variation. This operation is analogous to the normalization step in microarray data analysis. Only after this critical step can features of biological interest be clearly revealed. With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced. We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra. Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.

[1]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[2]  J. Carstensen,et al.  Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping , 1998 .

[3]  M. Campa,et al.  Analysis of human serum proteins by liquid phase isoelectric focusing and matrix‐assisted laser desorption/ionization‐mass spectrometry , 2003, Proteomics.

[4]  E. Petricoin,et al.  Proteomic approaches to the diagnosis, treatment, and monitoring of cancer. , 2003, Advances in experimental medicine and biology.

[5]  Assaf Wool,et al.  Precalibration of matrix‐assisted laser desorption/ionization‐time of flight spectra for peptide mass fingerprinting , 2002, Proteomics.

[6]  P. Schellhammer,et al.  Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. , 2002, Cancer research.

[7]  E. Petricoin,et al.  High-resolution serum proteomic features for ovarian cancer detection. , 2004, Endocrine-related cancer.

[8]  Melanie Hilario,et al.  Machine learning approaches to lung cancer prediction from mass spectra , 2003, Proteomics.

[9]  Xiwu Lin,et al.  Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method , 2003, Proteomics.

[10]  D. Chan,et al.  Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. , 2002, Clinical chemistry.

[11]  P. Eilers.  Parametric time warping. , 2004, Analytical chemistry.

[12]  Naren Ramakrishnan,et al.  Clustering mass spectrometry data using order statistics , 2003, Proteomics.

[13]  Michael J. Campa,et al.  Editorial: Proteomics 9/2003 , 2003 .

[14]  Terence P. Speed,et al.  NORMALIZATION , BASELINE CORRECTION AND ALIGNMENT OF HIGH-THROUGHPUT MASS SPECTROMETRY DATA , 2004 .

[15]  T R Brown,et al.  NMR spectral quantitation by principal component analysis , 2001, NMR in biomedicine.

[16]  Michael J Campa,et al.  Protein expression profiling identifies macrophage migration inhibitory factor and cyclophilin a as potential molecular targets in non-small cell lung cancer. , 2003, Cancer research.

[17]  T R Brown,et al.  NMR spectral quantitation by principal-component analysis. II. Determination of frequency and phase shifts. , 1996, Journal of magnetic resonance. Series B.

[18]  C. Paweletz,et al.  New approaches to proteomic analysis of breast cancer , 2001, Proteomics.

[19]  D. Massart,et al.  A comparison of two algorithms for warping of analytical signals , 2002 .