Computational and Statistical

Ingvar Eidhammer, Department of Informatics, University of Bergen, Norway

The definitive introduction to data analysis in quantitative proteomics
 
This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments. The author's carefully constructed approach allows readers to easily make the transition into the field of quantitative proteomics. Through detailed descriptions of wet-lab methods, computational approaches and statistical tools, this book covers the full scope of a quantitative experiment, allowing readers to acquire new knowledge as well as acting as a useful reference work for more advanced readers.
 
Computational and Statistical Methods for Protein Quantification by Mass Spectrometry:
* Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs.
* Is illustrated by a large number of figures and examples as well as numerous exercises.
* Provides both clear and rigorous descriptions of methods and approaches.
* Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work.
* Features detailed discussions of both wet-lab approaches and statistical and computational methods.
 
With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field.

The definitive introduction to data analysis in quantitativeproteomics

This book provides all the necessary knowledge about massspectrometry based proteomics methods and computational andstatistical approaches to pursue the planning, design and analysisof quantitative proteomics experiments.

Preface xv
 
Terminology xvii
 
Acknowledgements xix
 
1 Introduction 1
 
1.1 The composition of an organism 1
 
1.2 Homeostasis, physiology, and pathology 4
 
1.3 Protein synthesis 4
 
1.4 Site, sample, state, and environment 4
 
1.5 Abundance and expression - protein and proteome profiles 5
 
1.6 The importance of exact specification of sites and states 6
 
1.7 Relative and absolute quantification 8
 
1.8 In vivo and in vitro experiments 9
 
1.9 Goals for quantitative protein experiments 10
 
1.10 Exercises 10
 
2 Correlations of mRNA and protein abundances 12
 
2.1 Investigating the correlation 12
 
2.2 Codon bias 14
 
2.3 Main results from experiments 15
 
2.4 The ideal case for mRNA-protein comparison 16
 
2.5 Exploring correlation across genes 17
 
2.6 Exploring correlation within one gene 18
 
2.7 Correlation across subsets 18
 
2.8 Comparing mRNA and protein abundances across genes from two situations 19
 
2.9 Exercises 20
 
2.10 Bibliographic notes 21
 
3 Protein level quantification 22
 
3.1 Two-dimensional gels 22
 
3.2 Protein arrays 23
 
3.3 Western blotting 25
 
3.4 ELISA - Enzyme-Linked Immunosorbent Assay 26
 
3.5 Bibliographic notes 26
 
4 Mass spectrometry and protein identification 27
 
4.1 Mass spectrometry 27
 
4.2 Isotope composition of peptides 32
 
4.3 Presenting the intensities - the spectra 36
 
4.4 Peak intensity calculation 38
 
4.5 Peptide identification by MS/MS spectra 38
 
4.6 The protein inference problem 42
 
4.7 False discovery rate for the identifications 44
 
4.8 Exercises 46
 
4.9 Bibliographic notes 47
 
5 Protein quantification by mass spectrometry 48
 
5.1 Situations, protein, and peptide variants 48
 
5.2 Replicates 49
 
5.3 Run - experiment - project 50
 
5.4 Comparing quantification approaches/methods 54
 
5.5 Classification of approaches for quantification using LC-MS/MS 57
 
5.6 The peptide (occurrence) space 60
 
5.7 Ion chromatograms 62
 
5.8 From peptides to protein abundances 62
 
5.9 Protein inference and protein abundance calculation 67
 
5.10 Peptide tables 70
 
5.11 Assumptions for relative quantification 70
 
5.12 Analysis for differentially abundant proteins 71
 
5.13 Normalization of data 71
 
5.14 Exercises 72
 
5.15 Bibliographic notes 74
 
6 Statistical normalization 75
 
6.1 Some illustrative examples 75
 
6.2 Non-normally distributed populations 76
 
6.3 Testing for normality 78
 
6.4 Outliers 82
 
6.5 Variance inequality 90
 
6.6 Normalization and logarithmic transformation 90
 
6.7 Exercises 94
 
6.8 Bibliographic notes 95
 
7 Experimental normalization 96
 
7.1 Sources of variation and level of normalization 96
 
7.2 Spectral normalization 98
 
7.3 Normalization at the peptide and protein level 103
 
7.4 Normalizing using sum, mean, and median 104
 
7.5 MA-plot for normalization 104
 
7.6 Local regression normalization - LOWESS 106
 
7.7 Quantile normalization 107
 
7.8 Overfitting 108
 
7.9 Exercises 109
 
7.10 Bibliographic notes 109
 
8 Statistical analysis 110
 
8.1 Use of replicates for statistical analysis 110
 
8.2 Using a set of proteins for statistical analysis 111
 
8.3 Missing values 116