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2-D PAGE Map Analysis

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Cover of '2-D PAGE Map Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Sources of Experimental Variation in 2-D Maps: The Importance of Experimental Design in Gel-Based Proteomics
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    Chapter 2 Decoding 2-D Maps by Autocovariance Function
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    Chapter 3 Two-Dimensional Gel Electrophoresis Image Analysis via Dedicated Software Packages
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    Chapter 4 Comparative Evaluation of Software Features and Performances
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    Chapter 5 Image Pretreatment Tools I: Algorithms for Map Denoising and Background Subtraction Methods
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    Chapter 6 Image Pretreatment Tools II: Normalization Techniques for 2-DE and 2-D DIGE
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    Chapter 7 Spot Matching of 2-DE Images Using Distance, Intensity, and Pattern Information
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    Chapter 8 Algorithms for Warping of 2-D PAGE Maps
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    Chapter 9 2-DE Gel Analysis: The Spot Detection
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    Chapter 10 2-D PAGE Map Analysis
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    Chapter 11 Detection and Quantification of Protein Spots by Pinnacle
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    Chapter 12 A Novel Gaussian Extrapolation Approach for 2-D Gel Electrophoresis Saturated Protein Spots
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    Chapter 13 Multiple Testing and Pattern Recognition in 2-DE Proteomics
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    Chapter 14 Chemometric Multivariate Tools for Candidate Biomarker Identification: LDA, PLS-DA, SIMCA, Ranking-PCA
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    Chapter 15 The Use of Legendre and Zernike Moment Functions for the Comparison of 2-D PAGE Maps
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    Chapter 16 Nonlinear Dimensionality Reduction by Minimum Curvilinearity for Unsupervised Discovery of Patterns in Multidimensional Proteomic Data
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    Chapter 17 Differential Analysis of 2-D Maps by Pixel-Based Approaches
Attention for Chapter 6: Image Pretreatment Tools II: Normalization Techniques for 2-DE and 2-D DIGE
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Chapter title
Image Pretreatment Tools II: Normalization Techniques for 2-DE and 2-D DIGE
Chapter number 6
Book title
2-D PAGE Map Analysis
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3255-9_6
Pubmed ID
Book ISBNs
978-1-4939-3254-2, 978-1-4939-3255-9
Authors

Elisa Robotti, Emilio Marengo, Fabio Quasso

Abstract

Gel electrophoresis is usually applied to identify different protein expression profiles in biological samples (e.g., control vs. pathological, control vs. treated). Information about the effect to be investigated (a pathology, a drug, a ripening effect, etc.) is however generally confounded with experimental variability that is quite large in 2-DE and may arise from small variations in the sample preparation, reagents, sample loading, electrophoretic conditions, staining and image acquisition. Obtaining valid quantitative estimates of protein abundances in each map, before the differential analysis, is therefore fundamental to provide robust candidate biomarkers.Normalization procedures are applied to reduce experimental noise and make the images comparable, improving the accuracy of differential analysis. Certainly, they may deeply influence the final results, and to this respect they have to be applied with care. Here, the most widespread normalization procedures are described both for what regards the applications to 2-DE and 2D Difference Gel-electrophoresis (2-D DIGE) maps.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 33%
Unknown 2 67%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 33%
Unknown 2 67%