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Differential Analysis of 2-D Maps by Pixel-Based Approaches

  • Emilio MarengoEmail author
  • Elisa Robotti
  • Fabio Quasso
Part of the Methods in Molecular Biology book series (MIMB, volume 1384)

Abstract

Two approaches to the analysis of 2-D maps are available: the first one involves a step of spot detection on each gel image; the second one is based instead on the direct differential analysis of 2-D map images, following a pixel-based procedure. Both approaches strongly depend on the proper alignment of the gel images, but the pixel-based approach allows to solve important drawbacks of the spot-volume procedure, i.e., the problem of missing data and of overlapping spots. However, this approach is quite computationally intensive and requires the use of algorithms able to separate the information (i.e., spot-related information) from the background. Here, the most recent pixel-based approaches are described.

Key words

Pixel-based approach Gel-electrophoresis Fuzzy logic Three-way PCA 

References

  1. 1.
    Daszykowski M, Wróbel MS, Bierczynska-Krzysik A, Silberring J, Lubec G, Walczak B (2009) Automatic preprocessing of electrophoretic images. Chemometr Intell Lab Syst 97:132–140CrossRefGoogle Scholar
  2. 2.
    Færgestad EM, Rye M, Walczak B, Gidskehaug L, Wold JP, Grove H, Jia X, Hollung K, Indahl UG, Westad F, van den Berg F, Martens H (2007) 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. Proteomics 7:3450–3461CrossRefPubMedGoogle Scholar
  3. 3.
    Grove H, Hollung K, Uhlen AK, Martens H, Færgestad EM (2006) Challenges related to analysis of protein spot volumes from two-dimensional gel electrophoresis as revealed by replicate gels. J Proteome Res 5:3399–3410CrossRefPubMedGoogle Scholar
  4. 4.
    Rye MB, Faergestad EM, Martens H, Wold JP, Alsberg BK (2008) An improved pixel-based approach for analyzing images in two-dimensional gel electrophoresis. Electrophoresis 29:1382–1393CrossRefPubMedGoogle Scholar
  5. 5.
    Van Belle W, Ånensen N, Haaland I, Bruserud O, Høgda K-A, Gjertsen BT (2006) Correlation analysis of two-dimensional gel electrophoretic protein patterns and biological variables. BMC Bioinformatics 7:198PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Øye OK, Jørgensen KM, Hjelle SM, Sulen A, Ulvang DM, Gjertsen BT (2013) Gel2DE—a software tool for correlation analysis of 2D gel electrophoresis data. BMC Bioinformatics 14:215PubMedCentralCrossRefPubMedGoogle Scholar
  7. 7.
    Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Upper Saddle River, NJ, pp 432–438Google Scholar
  8. 8.
    Hough P (1962) Methods and means for recognizing complex patterns. US Patent 3,069,654 1962Google Scholar
  9. 9.
    Conradsen K, Pedersen J (1992) Analysis of two-dimensional electrophoresis gels. Biometrics 48:1273–1287CrossRefGoogle Scholar
  10. 10.
    Van Belle W, Sjøholt G, Ånensen N, Høgda KA, Gjertsen BT (2006) Adaptive contrast enhancement of two-dimensional electrophoretic gels facilitates visualization, orientation and alignment. Electrophoresis 27(20):4086–4095CrossRefPubMedGoogle Scholar
  11. 11.
    Veterling WT, Flannery BP (2002) Numerical recipes in C++, 2nd edn. Cambridge University Press, Cambridge, UKGoogle Scholar
  12. 12.
    Kenny J, Keeping E (1962) The standard deviation and calculation of the standard deviation (Volume chap 6.5–6.6), 3rd edn. Princeton NJ, pp 77–80Google Scholar
  13. 13.
    Eilers PHC (2004) Parametric time warping. Anal Chem 76:404–411CrossRefPubMedGoogle Scholar
  14. 14.
    Eilers PHC, Currie ID, Durban M (2006) Fast and compact smoothing on large multidimensional grids. Comput Stat Data Anal 50:61–76CrossRefGoogle Scholar
  15. 15.
    Kaczmarek K, Walczak B, de Jong S, Vandeginste BGM (2005) Baseline reduction in two dimensional gel electrophoresis images. Acta Chromatogr 15:82–96Google Scholar
  16. 16.
    Lieber CA, Jansen AM (2003) Automated method for subtraction of fluorescence from biological Raman spectra. Appl Spectrosc 57:1363–1367CrossRefPubMedGoogle Scholar
  17. 17.
    Martens H, Martens M (2000) Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Qual Prefer 11:5–16CrossRefGoogle Scholar
  18. 18.
    Wheelock Å, Buchpitt AR (2005) Software-induced variance in two-dimensional gel electrophoresis image analysis. Electrophoresis 26:4508–4520CrossRefPubMedGoogle Scholar
  19. 19.
    Daszykowski M, Stanimirova I, Bodzon-Kulakowsk A, Silberring J, Lubec G, Walczak B (2007) Start-to-end processing of two-dimensional gel electrophoretic images. J Chromatogr A 1158:306–317CrossRefPubMedGoogle Scholar
  20. 20.
    Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693CrossRefGoogle Scholar
  21. 21.
    Hubbard BB (1998) The World according to wavelets. A K Peters, Wellesley, MAGoogle Scholar
  22. 22.
    Walczak B (ed) (2000) Wavelets in chemistry. Elsevier, AmsterdamGoogle Scholar
  23. 23.
    Walczak B, Massart DL (1997) Noise suppression and signal compression using the wavelet packet transform. Chemom Intell Lab Syst 36:81–94CrossRefGoogle Scholar
  24. 24.
    Kaczmarek K, Walczak B, de Jong S, Vandeginste BGM (2004) Preprocessing of two-dimensional gel electrophoresis images. Proteomics 4:2377–2389CrossRefPubMedGoogle Scholar
  25. 25.
    Chang SG, Yu B, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process 9:1532–1546CrossRefGoogle Scholar
  26. 26.
    Kaczmarek K, Walczak B, de Jong S, Vandeginste BGM (2002) Feature based fuzzy matching of 2D gel electrophoresis images. J Chem Inf Comput Sci 6:1431–1442CrossRefGoogle Scholar
  27. 27.
    Kaczmarek K, Walczak B, de Jong S, Vandeginste BGM (2003) Matching 2D gel electrophoresis images. J Chem Inf Comput Sci 43:978–986CrossRefPubMedGoogle Scholar
  28. 28.
    Walczak B, Wu W (2005) Fuzzy warping of chromatograms. Chemom Intell Lab Syst 77:173–180CrossRefGoogle Scholar
  29. 29.
    Sinkhorn RA (1964) A relationship between arbitrary positive matrices and doubly stochastic matrices. Ann Math Stat 35:876–879CrossRefGoogle Scholar
  30. 30.
    Kaczmarek K, Walczak B, de Jong S, Vandeginste BGM (2003) Comparison of image-transformation methods used in matching 2D gel electrophoresis images. Acta Chromatogr 13:7–21Google Scholar
  31. 31.
    Beucher S (1992) The watershed transformation applied to image segmentation. Scanning Microsc 6:299–314Google Scholar
  32. 32.
    Skolnick MM (1986) Application of morphological transformations to the analysis of two-dimensional electrophoretic gels of biological materials. Comput Vis Graph Image Process 35:306–332CrossRefGoogle Scholar
  33. 33.
    Sternberg SR (1986) Grayscale morphology. Comput Vis Graph Image Process 35:333–355CrossRefGoogle Scholar
  34. 34.
    Otsu N (1979) A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern B 9:62–66CrossRefGoogle Scholar
  35. 35.
    Martens H, Næs T (1989) Mutivariate calibration. Wiley, ChichesterGoogle Scholar
  36. 36.
    Walczak B, Massart DL (1996) The radial basis functions—partial least squares approach as a flexible non-linear regression technique. Anal Chim Acta 331:177–185CrossRefGoogle Scholar
  37. 37.
    Walczak B, Massart DL (1996) Application of radial basis functions—partial least squares to non-linear pattern recognition problems: diagnosis of process faults. Anal Chim Acta 331:187–193CrossRefGoogle Scholar
  38. 38.
    Walczak B, Massart DL (2000) Local modelling with radial basis function networks. Chemom Intell Lab Syst 51:179–198CrossRefGoogle Scholar
  39. 39.
    Czekaj T, Wu W, Walczak B (2005) About kernel latent variable approaches and SVM. J Chemometrics 19:341–354CrossRefGoogle Scholar
  40. 40.
    Centner V, Massart DL, de Noord OE, de Jong S, Vandeginste BGM, Sterna C (1996) Elimination of uninformative variables for multivariate calibration. Anal Chem 68:3851–3858CrossRefPubMedGoogle Scholar
  41. 41.
    Daszykowski M, Bierczynska-Krzysik A, Silberring J, Walczak B (2010) Avoiding spots detection in analysis of electrophoretic gel images. Chemometr Intell Lab Syst 104:2–7CrossRefGoogle Scholar
  42. 42.
    Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98:5116–5121PubMedCentralCrossRefPubMedGoogle Scholar
  43. 43.
    Marengo E, Bobba M, Liparota MC, Robotti E, Righetti PG (2005) Use of Legendre moments for the fast comparison of two-dimensional polyacrylamide gel electrophoresis maps images. J Chromatogr A 1096(1-2):86–91CrossRefPubMedGoogle Scholar
  44. 44.
    Marengo E, Robotti E, Bobba M, Demartini M, Righetti PG (2008) A new method for comparing 2-D-PAGE maps based on the computation of Zernike moments and multivariate statistical tools. Anal Bioanal Chem 391(4):1163–1173CrossRefPubMedGoogle Scholar
  45. 45.
    Marengo E, Leardi R, Robotti E, Righetti PG, Antonucci F, Cecconi D (2003) Application of three-way principal component analysis to the evaluation of two-dimensional maps in proteomics. J Proteome Res 2(4):351–360CrossRefPubMedGoogle Scholar
  46. 46.
    Marengo E, Robotti E, Gianotti V, Righetti PG, Cecconi D, Domenici E (2003) A new integrated statistical approach to the diagnostic use of proteomic two-dimensional maps. Electrophoresis 24(1-2):225–236CrossRefPubMedGoogle Scholar
  47. 47.
    Marengo E, Robotti E, Righetti PG, Antonucci F (2003) New approach based on fuzzy logic and principal component analysis for the classification of two-dimensional maps in health and disease: application to lymphomas. J Chromatogr A 1004(1-2):13–28CrossRefPubMedGoogle Scholar
  48. 48.
    Tucker LR (1966) Some mathematical notes on three-mode factor analysis. Psychometrika 31:279–311CrossRefPubMedGoogle Scholar
  49. 49.
    Kroonenberg PM (1983) Three-mode principal component analysis. DSWO Press, LeidenGoogle Scholar
  50. 50.
    Geladi P (1989) Analysis of multi-way (multi-mode) data. Chemometr Intell Lab 7:11–30CrossRefGoogle Scholar
  51. 51.
    Smilde AK (1989) Three-way analyses problems and prospects. Chemometr Intell Lab 15:143–157CrossRefGoogle Scholar
  52. 52.
    Henrion R (1993) Body diagonalization of core matrices in three-way principal component analysis: theoretical bounds and simulations. Chemometr Intell Lab 6:477–494Google Scholar
  53. 53.
    Henrion R (1994) N-way principal component analysis. Theory, algorithms and applications. Chemometr Intell Lab 25:1–23CrossRefGoogle Scholar
  54. 54.
    Henrion R, Andersson CA (1999) A new criterion for simple-structure transformations of core arrays in N-way principal component analysis. Chemometr Intell Lab 47:189–204CrossRefGoogle Scholar
  55. 55.
    Tulp A, Verwoerd D, Neefjes J (1999) Electromigration for separations of protein complexes. J Chromatogr B 722:141–151CrossRefGoogle Scholar
  56. 56.
    Young G, Householder AS (1930) Discussion of a set of points in terms of their mutual distances. Psychometrika 3:19–22CrossRefGoogle Scholar
  57. 57.
    Cox TF, Cox MAA (1994) Multidimensional Scaling. Chapman & Hall, LondonGoogle Scholar
  58. 58.
    Schoenberg IJ (1935) Remarks to Maurice Fréchet’s article “Sur la définition axiomatique d’une classe d’espace distanciés vectoriellement applicable sur l’espace de Hilbert”. Ann Math 36:724–732CrossRefGoogle Scholar
  59. 59.
    Young G, Householder AS (1938) Discussion of a set of points in terms of their mutual distances. Psycometrika 3:19–22CrossRefGoogle Scholar
  60. 60.
    Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325–338CrossRefGoogle Scholar
  61. 61.
    Shepard RN (1962) The analysis of proximities: multidimensional scaling with an unknown distance function, I. Psycometrika 27:125–140CrossRefGoogle Scholar
  62. 62.
    Shepard RN (1962) The analysis of proximities: multidimensional scaling with an unknown distance function, II. Psycometrika 27:219–246CrossRefGoogle Scholar
  63. 63.
    Kruskal JB (1964) Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psycometrika 29:1–27CrossRefGoogle Scholar
  64. 64.
    Wang X, Feng DD (2005) Hybrid registration for two-dimensional gel protein images. Third Asia Pacific bioinformatics conference (APBC2005), paper 241, pp 201–210Google Scholar
  65. 65.
    Allison DB, Cui XQ, Page GP, Sabripour M (2006) Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 7:55–65CrossRefPubMedGoogle Scholar
  66. 66.
    Gidskehaug L, Anderssen E, Alsberg BK (2006) Cross model validated feature selection based on gene clusters. Chemometrics Intell Lab Syst 84:172–176CrossRefGoogle Scholar
  67. 67.
    Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41:613–627CrossRefGoogle Scholar
  68. 68.
    Serneels S, Croux C, Filzmoser P, Van Espen PJ (2005) Partial robust M-regression. Chemom Intell Lab Syst 79:55–64CrossRefGoogle Scholar
  69. 69.
    Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148CrossRefGoogle Scholar
  70. 70.
    Massart DL, Vandeginste BGM, Deming SM, Michotte Y, Kaufman L (1988) Chemometrics: a textbook. Elsevier, AmsterdamGoogle Scholar
  71. 71.
    Vandeginste BGM, Massart DL, Buydens LMC, De Yong S, Lewi PJ, Smeyers-Verbeke J (1988) Handbook of chemometrics and qualimetrics: part B. Elsevier, AmsterdamGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Sciences and Technological InnovationUniversity of Piemonte OrientaleAlessandriaItaly

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