{"product_id":"computational-and-statistical-methods-for-protein-quantification-by-mass-spectrometry-hardback-9781119964001","title":"Computational and Statistical Methods for Protein Quantification by Mass Spectrometry (Hardback) 9781119964001","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eComputational and Statistical Methods for Protein Quantification by Mass Spectrometry\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eIngvar Eidhammer (Author), Harald Barsnes (Author), Geir Egil Eide (Author), Lennart Martens (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119964001, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 4 January 2013\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e360 pages\u003cbr\u003e23.9 x 16 x 2.3 cm, 0.576 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e“Computational and Statistical Methods for Protein Quantification by Mass Spectrometry is a book that can be used by undergraduate students in both analytical chemistry and biochemistry, as well as by scientists who are familiar with the field. The book teaches the reader how to perform proteomic analysis by mass spectrometry and how to interpret the large amount of data collected.”  (\u003ci\u003eAnalytical and Bioanalytical Chemistry\u003c\/i\u003e\u003ci\u003e, 10 January 2014)\u003c\/i\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe definitive introduction to data analysis in quantitative proteomics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eComputational and Statistical Methods for Protein Quantification by Mass Spectrometry:\u003c\/i\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eIntroduces 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.\u003c\/li\u003e \u003cli\u003eIs illustrated by a large number of figures and examples as well as numerous exercises.\u003c\/li\u003e \u003cli\u003eProvides both clear and rigorous descriptions of methods and approaches.\u003c\/li\u003e \u003cli\u003eIs thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work.\u003c\/li\u003e \u003cli\u003eFeatures detailed discussions of both wet-lab approaches and statistical and computational methods.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWith 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eTerminology xvii\u003c\/p\u003e \u003cp\u003eAcknowledgements xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The composition of an organism 1\u003c\/p\u003e \u003cp\u003e1.1.1 A simple model of an organism 1\u003c\/p\u003e \u003cp\u003e1.1.2 Composition of cells 3\u003c\/p\u003e \u003cp\u003e1.2 Homeostasis, physiology, and pathology 4\u003c\/p\u003e \u003cp\u003e1.3 Protein synthesis 4\u003c\/p\u003e \u003cp\u003e1.4 Site, sample, state, and environment 4\u003c\/p\u003e \u003cp\u003e1.5 Abundance and expression – protein and proteome profiles 5\u003c\/p\u003e \u003cp\u003e1.5.1 The protein dynamic range 6\u003c\/p\u003e \u003cp\u003e1.6 The importance of exact specification of sites and states 6\u003c\/p\u003e \u003cp\u003e1.6.1 Biological features 7\u003c\/p\u003e \u003cp\u003e1.6.2 Physiological and pathological features 7\u003c\/p\u003e \u003cp\u003e1.6.3 Input features 7\u003c\/p\u003e \u003cp\u003e1.6.4 External features 7\u003c\/p\u003e \u003cp\u003e1.6.5 Activity features 7\u003c\/p\u003e \u003cp\u003e1.6.6 The cell cycle 8\u003c\/p\u003e \u003cp\u003e1.7 Relative and absolute quantification 8\u003c\/p\u003e \u003cp\u003e1.7.1 Relative quantification 8\u003c\/p\u003e \u003cp\u003e1.7.2 Absolute quantification 9\u003c\/p\u003e \u003cp\u003e1.8 In vivo and in vitro experiments 9\u003c\/p\u003e \u003cp\u003e1.9 Goals for quantitative protein experiments 10\u003c\/p\u003e \u003cp\u003e1.10 Exercises 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Correlations of mRNA and protein abundances 12\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Investigating the correlation 12\u003c\/p\u003e \u003cp\u003e2.2 Codon bias 14\u003c\/p\u003e \u003cp\u003e2.3 Main results from experiments 15\u003c\/p\u003e \u003cp\u003e2.4 The ideal case for mRNA-protein comparison 16\u003c\/p\u003e \u003cp\u003e2.5 Exploring correlation across genes 17\u003c\/p\u003e \u003cp\u003e2.6 Exploring correlation within one gene 18\u003c\/p\u003e \u003cp\u003e2.7 Correlation across subsets 18\u003c\/p\u003e \u003cp\u003e2.8 Comparing mRNA and protein abundances across genes from two situations 19\u003c\/p\u003e \u003cp\u003e2.9 Exercises 20\u003c\/p\u003e \u003cp\u003e2.10 Bibliographic notes 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Protein level quantification 22\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Two-dimensional gels 22\u003c\/p\u003e \u003cp\u003e3.1.1 Comparing results from different experiments – DIGE 23\u003c\/p\u003e \u003cp\u003e3.2 Protein arrays 23\u003c\/p\u003e \u003cp\u003e3.2.1 Forward arrays 24\u003c\/p\u003e \u003cp\u003e3.2.2 Reverse arrays 25\u003c\/p\u003e \u003cp\u003e3.2.3 Detection of binding molecules 25\u003c\/p\u003e \u003cp\u003e3.2.4 Analysis of protein array readouts 25\u003c\/p\u003e \u003cp\u003e3.3 Western blotting 25\u003c\/p\u003e \u003cp\u003e3.4 ELISA – Enzyme-Linked Immunosorbent Assay 26\u003c\/p\u003e \u003cp\u003e3.5 Bibliographic notes 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Mass spectrometry and protein identification 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Mass spectrometry 27\u003c\/p\u003e \u003cp\u003e4.1.1 Peptide mass fingerprinting (PMF) 28\u003c\/p\u003e \u003cp\u003e4.1.2 MS\/MS – tandem MS 29\u003c\/p\u003e \u003cp\u003e4.1.3 Mass spectrometers 29\u003c\/p\u003e \u003cp\u003e4.2 Isotope composition of peptides 32\u003c\/p\u003e \u003cp\u003e4.2.1 Predicting the isotope intensity distribution 34\u003c\/p\u003e \u003cp\u003e4.2.2 Estimating the charge 34\u003c\/p\u003e \u003cp\u003e4.2.3 Revealing isotope patterns 34\u003c\/p\u003e \u003cp\u003e4.3 Presenting the intensities – the spectra 36\u003c\/p\u003e \u003cp\u003e4.4 Peak intensity calculation 38\u003c\/p\u003e \u003cp\u003e4.5 Peptide identification by MS\/MS spectra 38\u003c\/p\u003e \u003cp\u003e4.5.1 Spectral comparison 41\u003c\/p\u003e \u003cp\u003e4.5.2 Sequential comparison 41\u003c\/p\u003e \u003cp\u003e4.5.3 Scoring 42\u003c\/p\u003e \u003cp\u003e4.5.4 Statistical significance 42\u003c\/p\u003e \u003cp\u003e4.6 The protein inference problem 42\u003c\/p\u003e \u003cp\u003e4.6.1 Determining maximal explanatory sets 44\u003c\/p\u003e \u003cp\u003e4.6.2 Determining minimal explanatory sets 44\u003c\/p\u003e \u003cp\u003e4.7 False discovery rate for the identifications 44\u003c\/p\u003e \u003cp\u003e4.7.1 Constructing the decoy database 45\u003c\/p\u003e \u003cp\u003e4.7.2 Separate or composite search 46\u003c\/p\u003e \u003cp\u003e4.8 Exercises 46\u003c\/p\u003e \u003cp\u003e4.9 Bibliographic notes 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Protein quantification by mass spectrometry 48\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Situations, protein, and peptide variants 48\u003c\/p\u003e \u003cp\u003e5.1.1 Situation 48\u003c\/p\u003e \u003cp\u003e5.1.2 Protein variants – peptide variants 48\u003c\/p\u003e \u003cp\u003e5.2 Replicates 49\u003c\/p\u003e \u003cp\u003e5.3 Run – experiment – project 50\u003c\/p\u003e \u003cp\u003e5.3.1 LC-MS\/MS run 50\u003c\/p\u003e \u003cp\u003e5.3.2 Quantification run 51\u003c\/p\u003e \u003cp\u003e5.3.3 Quantification experiment 52\u003c\/p\u003e \u003cp\u003e5.3.4 Quantification project 52\u003c\/p\u003e \u003cp\u003e5.3.5 Planning quantification experiments 52\u003c\/p\u003e \u003cp\u003e5.4 Comparing quantification approaches\/methods 54\u003c\/p\u003e \u003cp\u003e5.4.1 Accuracy 54\u003c\/p\u003e \u003cp\u003e5.4.2 Precision 55\u003c\/p\u003e \u003cp\u003e5.4.3 Repeatability and reproducibility 56\u003c\/p\u003e \u003cp\u003e5.4.4 Dynamic range and linear dynamic range 56\u003c\/p\u003e \u003cp\u003e5.4.5 Limit of blank – LOB 56\u003c\/p\u003e \u003cp\u003e5.4.6 Limit of detection – LOD 57\u003c\/p\u003e \u003cp\u003e5.4.7 Limit of quantification – LOQ 57\u003c\/p\u003e \u003cp\u003e5.4.8 Sensitivity 57\u003c\/p\u003e \u003cp\u003e5.4.9 Selectivity 57\u003c\/p\u003e \u003cp\u003e5.5 Classification of approaches for quantification using LC-MS\/MS 57\u003c\/p\u003e \u003cp\u003e5.5.1 Discovery or targeted protein quantification 58\u003c\/p\u003e \u003cp\u003e5.5.2 Label based vs. label free quantification 59\u003c\/p\u003e \u003cp\u003e5.5.3 Abundance determination – ion current vs. peptide identification 60\u003c\/p\u003e \u003cp\u003e5.5.4 Classification 60\u003c\/p\u003e \u003cp\u003e5.6 The peptide (occurrence) space 60\u003c\/p\u003e \u003cp\u003e5.7 Ion chromatograms 62\u003c\/p\u003e \u003cp\u003e5.8 From peptides to protein abundances 62\u003c\/p\u003e \u003cp\u003e5.8.1 Combined single abundance from single abundances 64\u003c\/p\u003e \u003cp\u003e5.8.2 Relative abundance from single abundances 65\u003c\/p\u003e \u003cp\u003e5.8.3 Combined relative abundance from relative abundances 66\u003c\/p\u003e \u003cp\u003e5.9 Protein inference and protein abundance calculation 67\u003c\/p\u003e \u003cp\u003e5.9.1 Use of the peptides in protein abundance calculation 67\u003c\/p\u003e \u003cp\u003e5.9.2 Classifying the proteins 68\u003c\/p\u003e \u003cp\u003e5.9.3 Can shared peptides be used for quantification? 68\u003c\/p\u003e \u003cp\u003e5.10 Peptide tables 70\u003c\/p\u003e \u003cp\u003e5.11 Assumptions for relative quantification 70\u003c\/p\u003e \u003cp\u003e5.12 Analysis for differentially abundant proteins 71\u003c\/p\u003e \u003cp\u003e5.13 Normalization of data 71\u003c\/p\u003e \u003cp\u003e5.14 Exercises 72\u003c\/p\u003e \u003cp\u003e5.15 Bibliographic notes 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Statistical normalization 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Some illustrative examples 75\u003c\/p\u003e \u003cp\u003e6.2 Non-normally distributed populations 76\u003c\/p\u003e \u003cp\u003e6.2.1 Skewed distributions 76\u003c\/p\u003e \u003cp\u003e6.2.2 Measures of skewness 76\u003c\/p\u003e \u003cp\u003e6.2.3 Steepness of the peak – kurtosis 77\u003c\/p\u003e \u003cp\u003e6.3 Testing for normality 78\u003c\/p\u003e \u003cp\u003e6.3.1 Normal probability plot 79\u003c\/p\u003e \u003cp\u003e6.3.2 Some test statistics for normality testing 81\u003c\/p\u003e \u003cp\u003e6.4 Outliers 82\u003c\/p\u003e \u003cp\u003e6.4.1 Test statistics for the identification of a single outlier 83\u003c\/p\u003e \u003cp\u003e6.4.2 Testing for more than one outlier 86\u003c\/p\u003e \u003cp\u003e6.4.3 Robust statistics for mean and standard deviation 88\u003c\/p\u003e \u003cp\u003e6.4.4 Outliers in regression 89\u003c\/p\u003e \u003cp\u003e6.5 Variance inequality 90\u003c\/p\u003e \u003cp\u003e6.6 Normalization and logarithmic transformation 90\u003c\/p\u003e \u003cp\u003e6.6.1 The logarithmic function 90\u003c\/p\u003e \u003cp\u003e6.6.2 Choosing the base 91\u003c\/p\u003e \u003cp\u003e6.6.3 Logarithmic normalization of peptide\/protein ratios 91\u003c\/p\u003e \u003cp\u003e6.6.4 Pitfalls of logarithmic transformations 92\u003c\/p\u003e \u003cp\u003e6.6.5 Variance stabilization by logarithmic transformation 92\u003c\/p\u003e \u003cp\u003e6.6.6 Logarithmic scale for presentation 93\u003c\/p\u003e \u003cp\u003e6.7 Exercises 94\u003c\/p\u003e \u003cp\u003e6.8 Bibliographic notes 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Experimental normalization 96\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Sources of variation and level of normalization 96\u003c\/p\u003e \u003cp\u003e7.2 Spectral normalization 98\u003c\/p\u003e \u003cp\u003e7.2.1 Scale based normalization 99\u003c\/p\u003e \u003cp\u003e7.2.2 Rank based normalization 101\u003c\/p\u003e \u003cp\u003e7.2.3 Combining scale based and rank based normalization 101\u003c\/p\u003e \u003cp\u003e7.2.4 Reproducibility of the normalization methods 102\u003c\/p\u003e \u003cp\u003e7.3 Normalization at the peptide and protein level 103\u003c\/p\u003e \u003cp\u003e7.4 Normalizing using sum, mean, and median 104\u003c\/p\u003e \u003cp\u003e7.5 MA-plot for normalization 104\u003c\/p\u003e \u003cp\u003e7.5.1 Global intensity normalization 105\u003c\/p\u003e \u003cp\u003e7.5.2 Linear regression normalization 106\u003c\/p\u003e \u003cp\u003e7.6 Local regression normalization – LOWESS 106\u003c\/p\u003e \u003cp\u003e7.7 Quantile normalization 107\u003c\/p\u003e \u003cp\u003e7.8 Overfitting 108\u003c\/p\u003e \u003cp\u003e7.9 Exercises 109\u003c\/p\u003e \u003cp\u003e7.10 Bibliographic notes 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Statistical analysis 110\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Use of replicates for statistical analysis 110\u003c\/p\u003e \u003cp\u003e8.2 Using a set of proteins for statistical analysis 111\u003c\/p\u003e \u003cp\u003e8.2.1 Z-variable 111\u003c\/p\u003e \u003cp\u003e8.2.2 G-statistic 112\u003c\/p\u003e \u003cp\u003e8.2.3 Fisher–Irwin exact test 115\u003c\/p\u003e \u003cp\u003e8.3 Missing values 116\u003c\/p\u003e \u003cp\u003e8.3.1 Reasons for missing values 116\u003c\/p\u003e \u003cp\u003e8.3.2 Handling missing values 118\u003c\/p\u003e \u003cp\u003e8.4 Prediction and hypothesis testing 118\u003c\/p\u003e \u003cp\u003e8.4.1 Prediction errors 119\u003c\/p\u003e \u003cp\u003e8.4.2 Hypothesis testing 120\u003c\/p\u003e \u003cp\u003e8.5 Statistical significance for multiple testing 121\u003c\/p\u003e \u003cp\u003e8.5.1 False positive rate control 122\u003c\/p\u003e \u003cp\u003e8.5.2 False discovery rate control 123\u003c\/p\u003e \u003cp\u003e8.6 Exercises 127\u003c\/p\u003e \u003cp\u003e8.7 Bibliographic notes 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Label based quantification 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Labeling techniques for label based quantification 129\u003c\/p\u003e \u003cp\u003e9.2 Label requirements 130\u003c\/p\u003e \u003cp\u003e9.3 Labels and labeling properties 130\u003c\/p\u003e \u003cp\u003e9.3.1 Quantification level 130\u003c\/p\u003e \u003cp\u003e9.3.2 Label incorporation 131\u003c\/p\u003e \u003cp\u003e9.3.3 Incorporation level 131\u003c\/p\u003e \u003cp\u003e9.3.4 Number of compared samples 132\u003c\/p\u003e \u003cp\u003e9.3.5 Common labels 132\u003c\/p\u003e \u003cp\u003e9.4 Experimental requirements 132\u003c\/p\u003e \u003cp\u003e9.5 Recognizing corresponding peptide variants 133\u003c\/p\u003e \u003cp\u003e9.5.1 Recognizing peptide variants in MS spectra 133\u003c\/p\u003e \u003cp\u003e9.5.2 Recognizing peptide variants in MS\/MS spectra 134\u003c\/p\u003e \u003cp\u003e9.6 Reference free vs. reference based 135\u003c\/p\u003e \u003cp\u003e9.6.1 Reference free quantification 135\u003c\/p\u003e \u003cp\u003e9.6.2 Reference based quantification 135\u003c\/p\u003e \u003cp\u003e9.7 Labeling considerations 136\u003c\/p\u003e \u003cp\u003e9.8 Exercises 136\u003c\/p\u003e \u003cp\u003e9.9 Bibliographic notes 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Reporter based MS\/MS quantification 138\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Isobaric labels 138\u003c\/p\u003e \u003cp\u003e10.2 iTRAQ 140\u003c\/p\u003e \u003cp\u003e10.2.1 Fragmentation 141\u003c\/p\u003e \u003cp\u003e10.2.2 Reporter ion intensities 143\u003c\/p\u003e \u003cp\u003e10.2.3 iTRAQ 8-plex 144\u003c\/p\u003e \u003cp\u003e10.3 TMT – Tandem Mass Tag 145\u003c\/p\u003e \u003cp\u003e10.4 Reporter based quantification runs 145\u003c\/p\u003e \u003cp\u003e10.5 Identification and quantification 145\u003c\/p\u003e \u003cp\u003e10.6 Peptide table 147\u003c\/p\u003e \u003cp\u003e10.7 Reporter based quantification experiments 147\u003c\/p\u003e \u003cp\u003e10.7.1 Normalization across LC-MS\/MS runs – use of a reference sample 147\u003c\/p\u003e \u003cp\u003e10.7.2 Normalizing within an LC-MS\/MS run 149\u003c\/p\u003e \u003cp\u003e10.7.3 From reporter intensities to protein abundances 149\u003c\/p\u003e \u003cp\u003e10.7.4 Finding differentially abundant proteins 150\u003c\/p\u003e \u003cp\u003e10.7.5 Distributing the replicates on the quantification runs 151\u003c\/p\u003e \u003cp\u003e10.7.6 Protocols 152\u003c\/p\u003e \u003cp\u003e10.8 Exercises 152\u003c\/p\u003e \u003cp\u003e10.9 Bibliographic notes 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Fragment based MS\/MS quantification 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The label masses 155\u003c\/p\u003e \u003cp\u003e11.2 Identification 157\u003c\/p\u003e \u003cp\u003e11.3 Peptide and protein quantification 158\u003c\/p\u003e \u003cp\u003e11.4 Exercises 158\u003c\/p\u003e \u003cp\u003e11.5 Bibliographic notes 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Label based quantification by MS spectra 160\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Different labeling techniques 160\u003c\/p\u003e \u003cp\u003e12.1.1 Metabolic labeling – SILAC 160\u003c\/p\u003e \u003cp\u003e12.1.2 Chemical labeling 162\u003c\/p\u003e \u003cp\u003e12.1.3 Enzymatic labeling – 18O 165\u003c\/p\u003e \u003cp\u003e12.2 Experimental setup 166\u003c\/p\u003e \u003cp\u003e12.3 MaxQuant as a model 167\u003c\/p\u003e \u003cp\u003e12.3.1 HL-pairs 167\u003c\/p\u003e \u003cp\u003e12.3.2 Reliability of HL-pairs 169\u003c\/p\u003e \u003cp\u003e12.3.3 Reliable protein results 169\u003c\/p\u003e \u003cp\u003e12.4 The MaxQuant procedure 169\u003c\/p\u003e \u003cp\u003e12.4.1 Recognize HL-pairs 169\u003c\/p\u003e \u003cp\u003e12.4.2 Estimate HL-ratios 176\u003c\/p\u003e \u003cp\u003e12.4.3 Identify HL-pairs by database search 177\u003c\/p\u003e \u003cp\u003e12.4.4 Infer protein data 181\u003c\/p\u003e \u003cp\u003e12.5 Exercises 183\u003c\/p\u003e \u003cp\u003e12.6 Bibliographic notes 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Label free quantification by MS spectra 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 An ideal case – two protein samples 185\u003c\/p\u003e \u003cp\u003e13.2 The real world 186\u003c\/p\u003e \u003cp\u003e13.2.1 Multiple samples 187\u003c\/p\u003e \u003cp\u003e13.3 Experimental setup 187\u003c\/p\u003e \u003cp\u003e13.4 Forms 187\u003c\/p\u003e \u003cp\u003e13.5 The quantification process 188\u003c\/p\u003e \u003cp\u003e13.6 Form detection 189\u003c\/p\u003e \u003cp\u003e13.7 Pair-wise retention time correction 191\u003c\/p\u003e \u003cp\u003e13.7.1 Determining potentially corresponding forms 191\u003c\/p\u003e \u003cp\u003e13.7.2 Linear corrections 192\u003c\/p\u003e \u003cp\u003e13.7.3 Nonlinear corrections 192\u003c\/p\u003e \u003cp\u003e13.8 Approaches for form tuple detection 193\u003c\/p\u003e \u003cp\u003e13.9 Pair-wise alignment 193\u003c\/p\u003e \u003cp\u003e13.9.1 Distance between forms 194\u003c\/p\u003e \u003cp\u003e13.9.2 Finding an optimal alignment 195\u003c\/p\u003e \u003cp\u003e13.10 Using a reference run for alignment 196\u003c\/p\u003e \u003cp\u003e13.11 Complete pair-wise alignment 197\u003c\/p\u003e \u003cp\u003e13.12 Hierarchical progressive alignment 197\u003c\/p\u003e \u003cp\u003e13.12.1 Measuring the similarity or the distance of two runs 198\u003c\/p\u003e \u003cp\u003e13.12.2 Constructing static guide trees 198\u003c\/p\u003e \u003cp\u003e13.12.3 Constructing dynamic guide trees 199\u003c\/p\u003e \u003cp\u003e13.12.4 Aligning subalignments 199\u003c\/p\u003e \u003cp\u003e13.12.5 SuperHirn 199\u003c\/p\u003e \u003cp\u003e13.13 Simultaneous iterative alignment 200\u003c\/p\u003e \u003cp\u003e13.13.1 Constructing the initial alignment in XCMS 200\u003c\/p\u003e \u003cp\u003e13.13.2 Changing the initial alignment 201\u003c\/p\u003e \u003cp\u003e13.14 The end result and further analysis 202\u003c\/p\u003e \u003cp\u003e13.15 Exercises 202\u003c\/p\u003e \u003cp\u003e13.16 Bibliographic notes 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Label free quantification by MS\/MS spectra 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Abundance measurements 205\u003c\/p\u003e \u003cp\u003e14.2 Normalization 207\u003c\/p\u003e \u003cp\u003e14.3 Proposed methods 207\u003c\/p\u003e \u003cp\u003e14.4 Methods for single abundance calculation 207\u003c\/p\u003e \u003cp\u003e14.4.1 emPAI 208\u003c\/p\u003e \u003cp\u003e14.4.2 PMSS 208\u003c\/p\u003e \u003cp\u003e14.4.3 NSAF 209\u003c\/p\u003e \u003cp\u003e14.4.4 SI 209\u003c\/p\u003e \u003cp\u003e14.5 Methods for relative abundance calculation 210\u003c\/p\u003e \u003cp\u003e14.5.1 PASC 210\u003c\/p\u003e \u003cp\u003e14.5.2 RIBAR 210\u003c\/p\u003e \u003cp\u003e14.5.3 xRIBAR 211\u003c\/p\u003e \u003cp\u003e14.6 Comparing methods 212\u003c\/p\u003e \u003cp\u003e14.6.1 An analysis by Griffin 212\u003c\/p\u003e \u003cp\u003e14.6.2 An analysis by Colaert 213\u003c\/p\u003e \u003cp\u003e14.7 Improving the reliability of spectral count quantification 213\u003c\/p\u003e \u003cp\u003e14.8 Handling shared peptides 214\u003c\/p\u003e \u003cp\u003e14.9 Statistical analysis 215\u003c\/p\u003e \u003cp\u003e14.10 Exercises 215\u003c\/p\u003e \u003cp\u003e14.11 Bibliographic notes 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Targeted quantification – Selected Reaction Monitoring 218\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Selected Reaction Monitoring – the concept 218\u003c\/p\u003e \u003cp\u003e15.2 A suitable instrument 219\u003c\/p\u003e \u003cp\u003e15.3 The LC-MS\/MS run 220\u003c\/p\u003e \u003cp\u003e15.3.1 Sensitivity and accuracy 222\u003c\/p\u003e \u003cp\u003e15.4 Label free and label based quantification 224\u003c\/p\u003e \u003cp\u003e15.4.1 Label free SRM based quantification 224\u003c\/p\u003e \u003cp\u003e15.4.2 Label based SRM based quantification 225\u003c\/p\u003e \u003cp\u003e15.5 Requirements for SRM transitions 227\u003c\/p\u003e \u003cp\u003e15.5.1 Requirements for the peptides 227\u003c\/p\u003e \u003cp\u003e15.5.2 Requirements for the fragment ions 228\u003c\/p\u003e \u003cp\u003e15.6 Finding optimal transitions 229\u003c\/p\u003e \u003cp\u003e15.7 Validating transitions 230\u003c\/p\u003e \u003cp\u003e15.7.1 Testing linearity 230\u003c\/p\u003e \u003cp\u003e15.7.2 Determining retention time 231\u003c\/p\u003e \u003cp\u003e15.7.3 Limit of detection\/quantification 231\u003c\/p\u003e \u003cp\u003e15.7.4 Dealing with low abundant proteins 231\u003c\/p\u003e \u003cp\u003e15.7.5 Checking for interference 232\u003c\/p\u003e \u003cp\u003e15.8 Assay development 232\u003c\/p\u003e \u003cp\u003e15.9 Exercises 233\u003c\/p\u003e \u003cp\u003e15.10 Bibliographic notes 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Absolute quantification 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Performing absolute quantification 235\u003c\/p\u003e \u003cp\u003e16.1.1 Linear dependency between the calculated and the real abundances 236\u003c\/p\u003e \u003cp\u003e16.2 Label based absolute quantification 236\u003c\/p\u003e \u003cp\u003e16.2.1 Stable isotope-labeled peptide standards 237\u003c\/p\u003e \u003cp\u003e16.2.2 Stable isotope-labeled concatenated peptide standards 238\u003c\/p\u003e \u003cp\u003e16.2.3 Stable isotope-labeled intact protein standards 239\u003c\/p\u003e \u003cp\u003e16.3 Label free absolute quantification 239\u003c\/p\u003e \u003cp\u003e16.3.1 Quantification by MS spectra 239\u003c\/p\u003e \u003cp\u003e16.3.2 Quantification by the number of MS\/MS spectra 241\u003c\/p\u003e \u003cp\u003e16.4 Exercises 242\u003c\/p\u003e \u003cp\u003e16.5 Bibliographic notes 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Quantification of post-translational modifications 244\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 PTM and mass spectrometry 244\u003c\/p\u003e \u003cp\u003e17.2 Modification degree 245\u003c\/p\u003e \u003cp\u003e17.3 Absolute modification degree 246\u003c\/p\u003e \u003cp\u003e17.3.1 Reversing the modification 246\u003c\/p\u003e \u003cp\u003e17.3.2 Use of two standards 248\u003c\/p\u003e \u003cp\u003e17.3.3 Label free modification degree analysis 249\u003c\/p\u003e \u003cp\u003e17.4 Relative modification degree 250\u003c\/p\u003e \u003cp\u003e17.5 Discovery based modification stoichiometry 251\u003c\/p\u003e \u003cp\u003e17.5.1 Separate LC-MS\/MS experiments for modified and unmodified peptides 251\u003c\/p\u003e \u003cp\u003e17.5.2 Common LC-MS\/MS experiment for modified and unmodified peptides 252\u003c\/p\u003e \u003cp\u003e17.5.3 Reliable results and significant differences 252\u003c\/p\u003e \u003cp\u003e17.6 Exercises 253\u003c\/p\u003e \u003cp\u003e17.7 Bibliographic notes 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Biomarkers 254\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Evaluation of potential biomarkers 254\u003c\/p\u003e \u003cp\u003e18.1.1 Taking disease prevalence into account 255\u003c\/p\u003e \u003cp\u003e18.2 Evaluating threshold values for biomarkers 257\u003c\/p\u003e \u003cp\u003e18.3 Exercises 258\u003c\/p\u003e \u003cp\u003e18.4 Bibliographic notes 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Standards and databases 259\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Standard data formats for (quantitative) proteomics 259\u003c\/p\u003e \u003cp\u003e19.1.1 Controlled vocabularies (CVs) 260\u003c\/p\u003e \u003cp\u003e19.1.2 Benefits of using CV terms to annotate metadata 260\u003c\/p\u003e \u003cp\u003e19.1.3 A standard for quantitative proteomics data 261\u003c\/p\u003e \u003cp\u003e19.1.4 HUPO PSI 262\u003c\/p\u003e \u003cp\u003e19.2 Databases for proteomics data 262\u003c\/p\u003e \u003cp\u003e19.3 Bibliographic notes 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Appendix A: Statistics 264\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Samples, populations, and statistics 264\u003c\/p\u003e \u003cp\u003e20.2 Population parameter estimation 265\u003c\/p\u003e \u003cp\u003e20.2.1 Estimating the mean of a population 266\u003c\/p\u003e \u003cp\u003e20.3 Hypothesis testing 267\u003c\/p\u003e \u003cp\u003e20.3.1 Two types of errors 268\u003c\/p\u003e \u003cp\u003e20.4 Performing the test – test statistics and p-values 268\u003c\/p\u003e \u003cp\u003e20.4.1 Parametric test statistics 269\u003c\/p\u003e \u003cp\u003e20.4.2 Nonparametric test statistics 269\u003c\/p\u003e \u003cp\u003e20.4.3 Confidence intervals and hypothesis testing 270\u003c\/p\u003e \u003cp\u003e20.5 Comparing means of populations 271\u003c\/p\u003e \u003cp\u003e20.5.1 Analyzing the mean of a single population 271\u003c\/p\u003e \u003cp\u003e20.5.2 Comparing the means from two populations 272\u003c\/p\u003e \u003cp\u003e20.5.3 Comparing means of paired populations 275\u003c\/p\u003e \u003cp\u003e20.5.4 Multiple populations 275\u003c\/p\u003e \u003cp\u003e20.5.5 Multiple testing 276\u003c\/p\u003e \u003cp\u003e20.6 Comparing variances 276\u003c\/p\u003e \u003cp\u003e20.6.1 Testing the variance of a single population 276\u003c\/p\u003e \u003cp\u003e20.6.2 Testing the variances of two populations 277\u003c\/p\u003e \u003cp\u003e20.7 Percentiles and quantiles 278\u003c\/p\u003e \u003cp\u003e20.7.1 A straightforward method for estimating the percentiles 279\u003c\/p\u003e \u003cp\u003e20.7.2 Quantiles 279\u003c\/p\u003e \u003cp\u003e20.7.3 Box plots 280\u003c\/p\u003e \u003cp\u003e20.8 Correlation 280\u003c\/p\u003e \u003cp\u003e20.8.1 Pearson’s product-moment correlation coefficient 283\u003c\/p\u003e \u003cp\u003e20.8.2 Spearman’s rank correlation coefficient 285\u003c\/p\u003e \u003cp\u003e20.8.3 Correlation line 286\u003c\/p\u003e \u003cp\u003e20.9 Regression analysis 287\u003c\/p\u003e \u003cp\u003e20.9.1 Regression line 288\u003c\/p\u003e \u003cp\u003e20.9.2 Relation between Pearson’s correlation coefficient and the regression parameters 289\u003c\/p\u003e \u003cp\u003e20.10 Types of values and variables 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Appendix B: Clustering and discriminant analysis 292\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Clustering 292\u003c\/p\u003e \u003cp\u003e21.1.1 Distances and similarities 293\u003c\/p\u003e \u003cp\u003e21.1.2 Distance measures 294\u003c\/p\u003e \u003cp\u003e21.1.3 Similarity measures 295\u003c\/p\u003e \u003cp\u003e21.1.4 Distances between an object and a class 295\u003c\/p\u003e \u003cp\u003e21.1.5 Distances between two classes 296\u003c\/p\u003e \u003cp\u003e21.1.6 Missing data 297\u003c\/p\u003e \u003cp\u003e21.1.7 Clustering approaches 297\u003c\/p\u003e \u003cp\u003e21.1.8 Sequential clustering 298\u003c\/p\u003e \u003cp\u003e21.1.9 Hierarchical clustering 300\u003c\/p\u003e \u003cp\u003e21.2 Discriminant analysis 303\u003c\/p\u003e \u003cp\u003e21.2.1 Step-wise feature selection 304\u003c\/p\u003e \u003cp\u003e21.2.2 Linear discriminant analysis using original features 307\u003c\/p\u003e \u003cp\u003e21.2.3 Canonical discriminant analysis 309\u003c\/p\u003e \u003cp\u003e21.3 Bibliographic notes 312\u003c\/p\u003e \u003cp\u003eBibliography 313\u003c\/p\u003e \u003cp\u003eIndex 327\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52173818724632,"sku":"9781119964001","price":68.36,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119964001.jpg?v=1781173321","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/computational-and-statistical-methods-for-protein-quantification-by-mass-spectrometry-hardback-9781119964001","provider":"Freshly Printed Books","version":"1.0","type":"link"}