Computational and Statistical Methods for Protein Quantification by Mass Spectrometry
by Eidhammer, Ingvar; Barsnes, Harald; Eide, Geir Egil; Martens, LennartEdition: 1st
Format: Hardcover
Pub. Date: 2013-02-11
Publisher(s): Wiley
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Summary
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. 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. 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.
Author Biography
Ingvar Eidhammer, Department of Informatics, University of Bergen, Norway
Harald Barsnes, Department of Biomedicine, University of Bergen, Norway
Geir Egil Eide, Centre for Clinical Research, Haukeland University,Norway
Lennart Martens, Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Belgium
Table of Contents
Preface
1 Introduction
1.1 The composition of an organism
1.1.1 A simple model of an organism
1.1.2 Composition of cells
1.2 Homeostasis, physiology and pathology
1.3 Protein synthesis
1.4 Site, sample, state and environment
1.5 Abundance and expression - protein and proteome profiles
1.5.1 The protein dynamic range
1.6 The importance of exact specification of sites and states
1.6.1 Biological features
1.6.2 Physiological and pathological features
1.6.3 Input features
1.6.4 External features
1.6.5 Activity features
1.6.6 The cell cycle .
1.7 Relative and absolute quantification
1.7.1 Relative quantification
1.7.2 Absolute quantification
1.8 In vivo and in vitro experiments
1.9 Goals for quantitative protein experiments
1.10 Exercises
2 Correlations of mRNA and Protein Abundances
2.1 Investigating the correlation
2.2 Codon bias
2.3 Main results from experiments
2.4 The ideal case for mRNA-protein comparison
2.5 Exploring correlation across genes
2.6 Exploring correlation within one gene
2.7 Correlation across subsets
2.8 Comparing mRNA and protein abundances across genes from
two situations
2.9 Exercises
2.10 Bibliographic notes
3 Protein-level Quantification
3.1 Two-dimensional gels
3.1.1 Comparing results from different experiments - DIGE
3.2 Protein arrays
3.2.1 Forward arrays
3.2.2 Reverse arrays
3.2.3 Detection of binding molecules
3.2.4 Analysis of protein array readouts
3.3 Western blotting
3.4 ELISA - Enzyme-Linked Immunosorbent Assay
3.5 Bibliographic notes
4 Mass Spectrometry and Protein Identification
4.1 Mass spectrometry
4.1.1 Peptide mass fingerprinting (PMF)
4.1.2 MS/MS - Tandem MS
4.1.3 Mass spectrometers
4.2 Isotope composition of peptides
4.2.1 Predicting the isotope intensity distribution .
4.2.2 Estimating the charge
4.2.3 Revealing isotope patterns
4.3 Presenting the intensities - the spectra
4.4 Peak intensity calculation
4.5 Peptide identification by MS/MS spectra
4.5.1 Spectral comparison
4.5.2 Sequential comparison
4.5.3 Scoring
4.5.4 Statistical significance
4.6 The protein inference problem
4.6.1 Determining maximal explanatory sets
4.6.2 Determining minimal explanatory sets
4.7 False discovery rate for the identifications .
4.7.1 Constructing the decoy database
4.7.2 Separate or composite search
4.8 Exercises
4.9 Bibliographic notes
5 Protein Quantification by Mass Spectrometry
5.1 Situations, protein and peptide variants
5.1.1 Situation
5.1.2 Protein variants - peptide variants
5.2 Replicates
5.3 Run - experiment - project
5.3.1 LC-MS/MS run
5.3.2 Quantification run
5.3.3 Quantification experiment
5.3.4 Quantification project
5.3.5 Planning quantification experiments
5.4 Comparing quantification approaches/methods
5.4.1 Accuracy
5.4.2 Precision
5.4.3 Repeatability and reproducibility
5.4.4 Dynamic range and linear dynamic range
5.4.5 Limit of blank - LOB
5.4.6 Limit of detection - LOD
5.4.7 Limit of quantification - LOQ
5.4.8 Sensitivity
5.4.9 Selectivity
5.5 Classification of approaches for quantification using LC-MS/MS
5.5.1 Discovery or targeted protein quantification .
5.5.2 Label-based vs. label-free quantification
5.5.3 Abundance determination - ion current vs. peptide identification
5.5.4 Classification
5.6 The peptide (occurrence) space
5.7 Ion chromatograms
5.8 From peptides to protein abundances
5.8.1 Combined single abundance from single abundances
5.8.2 Relative abundance from single abundances
5.8.3 Combined relative abundance from relative abundances
5.9 Protein inference and protein abundance calculation
5.9.1 Use of the peptides in protein abundance calculation
5.9.2 Classifying the proteins
5.9.3 Can shared peptides be used for quantification?
5.10 Peptide tables
5.11 Assumptions for relative quantification
5.12 Analysis for differentially abundant proteins
5.13 Normalization of data
5.14 Exercises
5.15 Bibliographic notes
6 Statistical Normalization 82
6.1 Some illustrative examples
6.2 Non-normally distributed populations
6.2.1 Skewed distributions
6.2.2 Measures of skewness
6.2.3 Steepness of the peak - kurtosis
6.3 Testing for normality .
6.3.1 Normal probability plot
6.3.2 Some test statistics for normality testing .
6.4 Outliers
6.4.1 Test statistics for the identification of a single outlier
6.4.2 Testing for more than one outlier
6.4.3 Robust statistics for mean and standard deviation
6.4.4 Outliers in regression
6.5 Variance inequality
6.6 Normalization and logarithmic transformation
6.6.1 The logarithmic function
6.6.2 Choosing the base to use
6.6.3 Logarithmic normalization of peptide/protein ratios
6.6.4 Pitfalls of logarithmic transformations
6.6.5 Variance stabilization by logarithmic transformation
6.6.6 Logarithmic scale for presentation
6.7 Exercises
6.8 Bibliographic notes
7 Experimental Normalization
7.1 Sources of variation and level of normalization
7.2 Spectral normalization
7.2.1 Scale based normalization
7.2.2 Rank based normalization
7.2.3 Combining scale based and rank based normalization
7.2.4 Reproducibility of the normalization methods
7.3 Normalization at the peptide and protein level
7.4 Normalizing using sum, mean and median
7.5 MA-plot for normalization .
7.5.1 Global intensity normalization
7.5.2 Linear regression normalization
7.6 Local regression normalization - LOWESS
7.7 Quantile normalization
7.8 Overfitting
7.9 Exercises
7.10 Bibliographic notes
8 Statistical Analysis
8.1 Use of replicates for statistical analysis
8.2 Using a set of proteins for statistical analysis
8.2.1 Z-variable
8.2.2 G-statistic
8.2.3 Fisher-Irwin exact test
8.3 Missing values
8.3.1 Reasons for missing values
8.3.2 Handling missing values
8.4 Prediction and hypothesis testing
8.4.1 Prediction errors
8.4.2 Hypothesis testing
8.5 Statistical significance for multiple testing .
8.5.1 False positive rate control
8.5.2 False discovery rate control
8.6 Exercises
8.7 Bibliographic notes
9 Label-based Quantification
9.1 Labeling techniques for label-based quantification
9.2 Label requirements
9.3 Labels and labeling properties
9.3.1 Quantification level
9.3.2 Label incorporation
9.3.3 Incorporation level
9.3.4 Number of compared samples
9.3.5 Common labels
9.4 Experimental requirements
9.5 Recognizing corresponding peptide variants
9.5.1 Recognizing peptide variants in MS spectra
9.5.2 Recognizing peptide variants in MS/MS
Spectra
9.6 Reference-free vs. reference-based
9.6.1 Reference-free quantification
9.6.2 Reference-based quantification
9.7 Labeling considerations
9.8 Exercises
9.9 Bibliographic notes
10 Reporter-based MS/MS Quantification
10.1 Isobaric labels
10.2 iTRAQ
10.2.1 Fragmentation
10.2.2 Reporter ion intensities
10.2.3 iTRAQ 8-plex
10.3 TMT - Tandem Mass Tag
10.4 Reporter-based quantification runs
10.5 Identification and quantification
10.6 Peptide table
10.7 Reporter-based quantification experiments
10.7.1 Normalization across LC-MS/MS runs - use of a reference
sample
10.7.2 Normalizing within an LC-MS/MS run
10.7.3 From reporter intensities to protein abundances
10.7.4 Finding differentially abundant proteins
10.7.5 Distributing the replicates on the quantification runs
10.7.6 Protocols
10.8 Exercises
10.9 Bibliographic notes =
11 Fragment-based MS/MS Quantification
11.1 The label masses
11.2 Identification
11.3 Peptide and protein quantification
11.4 Exercises .
11.5 Bibliographic notes
12 Label-based Quantification by MS-spectra
12.1 Different labeling techniques
12.1.1 Metabolic labeling - SILAC
12.1.2 Chemical labeling
12.1.3 Enzymatic labeling - 18O
12.2 Experimental setup
12.3 MaxQuant as a model
12.3.1 HL-pairs .
12.3.2 Reliability of HL-pairs
12.3.3 Reliable protein results
12.4 The MaxQuant procedure
12.4.1 Recognize HL-pairs
12.4.2 Estimate HL-ratios
12.4.3 Identify HL-pairs by database search
12.4.4 Infer protein data
12.5 Exercises
12.6 Bibliographic notes
13 Label-free Quantification by MS spectra
13.1 An ideal case - two protein samples
13.2 The real world
13.2.1 Multiple samples
13.3 Experimental setup
13.4 Features
13.5 The quantification process
13.6 Feature detection
13.7 Pairwise retention time corrections
13.7.1 Determining potentially corresponding features
13.7.2 Linear corrections
13.7.3 Nonlinear corrections
13.8 Approaches for feature-tuple detection
13.9 Pairwise alignment
13.9.1 Finding an optimal alignment
13.10Using a reference run for alignment
13.11Complete pairwise alignment
13.12Hierarchical progressive alignment
13.12.1Measuring the similarity or the distance of two runs
13.12.2Constructing static guide trees
13.12.3Constructing dynamic guide trees
13.12.4Aligning subalignments
13.12.5SuperHirn
13.13Simultaneous iterative alignment
13.13.1Constructing the initial alignment in XCMS .
13.13.2Changing the initial alignment
13.14The end result and further analysis
13.15Exercises
13.16Bibliographic notes
14 Label-free Quantification by MS/MS spectra
14.1 Abundance measurements
14.2 Normalization
14.3 Proposed methods
14.4 Methods for single abundance calculation
14.4.1 emPAI
14.4.2 PMSS
14.4.3 SI
14.5 Methods for relative abundance calculation
14.6 Comparing methods
14.6.1 An analysis by Griffin
14.6.2 An analysis by Colaert
14.7 Improving the reliability of spectral count quantification
14.8 Handling shared peptides
14.9 Statistical analysis
14.10Exercises
14.11Bibliographic notes
15 Targeted Quantification - Selected Reaction Monitoring
15.1 Selected Reaction Monitoring - the concept
15.2 A suitable instrument
15.3 The LC-MS/MS run
15.3.1 Sensitivity and accuracy
15.4 Label-free and label-based quantification
15.4.1 Label-free SRM-based quantification
15.4.2 Label-based SRM-based quantification
15.5 Requirements for SRM transitions
15.5.1 Requirements for the peptides
15.5.2 Requirements for the fragment ions
15.6 Finding optimal transitions
15.7 Validating transitions
15.7.1 Testing linearity
15.7.2 Determining retention time
15.7.3 Limit of detection/quantification
15.7.4 Dealing with low abundant proteins
15.7.5 Checking for interference
15.8 Assay development
15.9 Exercises
15.10Bibliographic notes
16 Absolute Quantification
16.1 Performing absolute quantification
16.1.1 Linear dependency between the calculated and the real
abundances
16.2 Label-based absolute quantification
16.2.1 Stable isotope-labeled peptide standards
16.2.2 Stable isotope-labeled concatenated peptide standards
16.2.3 Stable isotope-labeled intact protein standards
16.3 Label-free absolute quantification
16.3.1 Quantification by MS spectra
16.3.2 Quantification by the number of MS/MS spectra
16.4 Exercises
16.5 Bibliographic notes
17 Quantification of Posttranslational Modifications
17.1 PTM and mass spectrometry
17.2 Modification degree
17.3 Absolute modification degree
17.3.1 Reversing the modification
17.3.2 Use of two standards
17.3.3 Label-free modification degree analysis
17.4 Relative modification degree
17.5 Discovery-based modification stoichiometry
17.5.1 Separate LC-MS/MS experiments for modified and unmodified
peptides
17.5.2 Common LC-MS/MS experiment for modified and unmodified
peptides
17.5.3 Reliable results and significant differences
17.6 Exercises
17.7 Bibliographic notes
18 Biomarkers
18.1 Evaluation of potential biomarkers
18.1.1 Taking disease prevalence into account
18.2 Evaluating threshold values for biomarkers
18.3 Exercises
18.4 Bibliographic notes
19 Standards and Databases
19.1 Standard data formats for (quantitative) proteomics
19.1.1 Controlled vocabularies (CVs)
19.1.2 Benefits of using CV terms to annotate metadata
19.1.3 A standard for quantitative proteomics data .
19.1.4 HUPO PSI
19.2 Databases for proteomics data
19.3 Bibliographic notes
20 Appendix A: Statistics
20.1 Samples, populations and statistics
20.2 Population parameter estimation
20.2.1 Estimating the mean of a population
20.3 Hypothesis testing
20.3.1 Two types of errors
20.4 Performing the test - test statistics and p-values
20.4.1 Parametric test statistics
20.4.2 Nonparametric test statistics
20.4.3 Confidence intervals and hypothesis testing .
20.5 Comparing means of populations
20.5.1 Analyzing the mean of a single population
20.5.2 Comparing the means from two populations
20.5.3 Comparing means of paired populations
20.5.4 Multiple populations
20.5.5 Multiple testing
20.6 Comparing variances
20.6.1 Testing the variance of a single population
20.6.2 Testing the variances of two populations
20.7 Percentiles and quantiles
20.7.1 A straightforward method for estimating the percentiles
20.7.2 Quantiles
20.7.3 Box plots
20.8 Correlation
20.8.1 Pearson’s product-moment correlation-coefficient
20.8.2 Spearman’s rank correlation coefficient
20.9 Regression analysis
20.9.1 Regression line
20.9.2 Relation between Pearson’s correlation coefficient and
the regression parameters
20.10Types of values and variables
21 Appendix B: Clustering and Discriminant Analysis
21.1 Clustering
21.1.1 Distances and similarities
21.1.2 Distance measures
21.1.3 Similarity measures
21.1.4 Distances between an object and a class
21.1.5 Distances between two classes
21.1.6 Missing data .
21.1.7 Clustering approaches
21.1.8 Sequential clustering
21.1.9 Hierarchical clustering
21.2 Discriminant analysis
21.2.1 Stepwise feature selection
21.2.2 Linear discriminant analysis using original features
21.2.3 Canonical discriminant analysis
21.3 Bibliographic notes
Bibliography
Index
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