Automatic preprocessing of electrophoretic images

Abstract Analysis of two-dimensional (2D) electrophoretic images is a multi-step approach, enabling application of a variety of methods at different stages of data processing. The choice of these, as well as input parameters, leads to software-induced variations. Effective preprocessing methods, which do not require optimization of input parameters, are potent in eliminating software-induced variations. As a general method for background elimination and image scaling, robust Orthogonal Regression (rOR) is proposed and compared with Orthogonal Regression. This comparison is based on the univariate and multivariate approaches of feature selection, exploring the idea developed for significance analysis of microarray data [V. Goss Tusher, R. Tibshirani, G. Chu, Significance analysis of microarrays applied to the ionizing radiation response, P. Natl. Acad. Sci. U. S. A., 98 (2001) 5116–5121] and adapted to the analysis of proteomic data. All calculations are performed at the pixel level.

[1]  J. Vohradský,et al.  Point pattern matching in the analysis of two‐dimensional gel electropherograms , 1999, Electrophoresis.

[2]  P. Lemkin.  Comparing two‐dimensional electrophoretic gel images across the Internet , 1997, Electrophoresis.

[3]  D. G. Simpson,et al.  Robust principal component analysis for functional data , 2007 .

[4]  Paul H. C. Eilers,et al.  Fast and compact smoothing on large multidimensional grids , 2006, Comput. Stat. Data Anal..

[5]  Y. Heyden,et al.  Robust statistics in data analysis — A review: Basic concepts , 2007 .

[6]  Bjørn K. Alsberg,et al.  Cross model validation and optimisation of bilinear regression models , 2008 .

[7]  G. Lubec,et al.  Proteomic analysis of rat cerebral cortex, hippocampus and striatum after exposure to morphine. , 2006, International journal of molecular medicine.

[8]  T. Næs,et al.  The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy , 1988 .

[9]  J. Garrels.  The QUEST system for quantitative analysis of two-dimensional gels. , 1989, The Journal of biological chemistry.

[10]  Fabio Pastorino,et al.  Spot overlapping in two‐dimensional maps: A serious problem ignored for much too long , 2005, Proteomics.

[11]  M Daszykowski,et al.  Target selection for alignment of chromatographic signals obtained using monochannel detectors. , 2007, Journal of chromatography. A.

[12]  Beata Walczak,et al.  Preprocessing of two‐dimensional gel electrophoresis images , 2004, Proteomics.

[13]  M Schultz,et al.  Software aids for the analysis of 2D gel electrophoresis images. , 1979, Computers and biomedical research, an international journal.

[14]  Z. Smilansky,et al.  Automatic registration for images of two‐dimensional protein gels , 2001, Electrophoresis.

[15]  Gregory R. Phillips,et al.  Comparison of conventional and robust regression in analysis of chemical data , 1983 .

[16]  T. Isaksson,et al.  Determination of Particle Size in Powders by Scatter Correction in Diffuse Near-Infrared Reflectance , 1988 .

[17]  Beata Walczak,et al.  Classification of genomic data: some aspects of feature selection. , 2008, Talanta.

[18]  K. Kaczmarek,et al.  Feature Based Fuzzy Matching of 2D Gel Electrophoresis Images , 2002, J. Chem. Inf. Comput. Sci..

[19]  P. Geladi,et al.  Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat , 1985 .

[20]  P F Lemkin,et al.  Some extensions to the GELLAB two-dimensional electrophoretic gel analysis system. , 1982, Clinical chemistry.

[21]  Beata Walczak,et al.  Pixel‐based analysis of multiple images for the identification of changes: A novel approach applied to unravel proteome patters of 2‐D electrophoresis gel images , 2007 .

[22]  Knut Conradsen,et al.  Analysis of Two-Dimensional Electrophoretic Gels , 1992 .

[23]  Carola Wenk,et al.  An Applied Point Pattern Matching Problem: Comparing 2D Patterns of Protein Ppots , 1999, Discret. Appl. Math..

[24]  H Alt,et al.  New algorithmic approaches to protein spot detection and pattern matching in two‐dimensional electrophoresis gel databases , 1999, Electrophoresis.

[25]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[26]  R D Appel,et al.  Melanie II – a third‐generation software package for analysis of two‐dimensional electrophoresis images: II. Algorithms , 1997, Electrophoresis.

[27]  Peter D Wentzell,et al.  Bootstrap method for the estimation of measurement uncertainty in spotted dual-color DNA microarrays , 2007, Analytical and bioanalytical chemistry.

[28]  Harald Martens,et al.  Challenges related to analysis of protein spot volumes from two-dimensional gel electrophoresis as revealed by replicate gels. , 2006, Journal of proteome research.

[29]  Beata Walczak,et al.  Baseline reduction in two dimensional gel electrophoresis images , 2005 .

[30]  Tormod Næs,et al.  Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data , 1995 .

[31]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Asa M Wheelock,et al.  Software‐induced variance in two‐dimensional gel electrophoresis image analysis , 2005, Electrophoresis.

[33]  K. Kaczmarek,et al.  Matching 2D Gel Electrophoresis Images , 2003, J. Chem. Inf. Comput. Sci..

[34]  Stanley R. Sternberg,et al.  Biomedical Image Processing , 1983, Computer.

[35]  B. Walczak,et al.  Fuzzy warping of chromatograms , 2005 .

[36]  M Daszykowski,et al.  Start-to-end processing of two-dimensional gel electrophoretic images. , 2007, Journal of chromatography. A.

[37]  S Veeser,et al.  Multiresolution image registration for two‐dimensional gel electrophoresis , 2001, Proteomics.

[38]  Sabine Van Huffel,et al.  Total least squares problem - computational aspects and analysis , 1991, Frontiers in applied mathematics.