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E-grāmata: Spectral Analysis of Musical Sounds with Emphasis on the Piano

3.50/5 (4 ratings by Goodreads)
(Engineering Associate (Retired), Corning, Inc.), Commentaries by (Piano Research, Design & Manufacturing Consultant, Fandrich Piano Company)
  • Formāts: 336 pages
  • Izdošanas datums: 13-Nov-2014
  • Izdevniecība: Oxford University Press
  • Valoda: eng
  • ISBN-13: 9780191034435
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Spectral Analysis of Musical Sounds with Emphasis on the PianoPalielināt
  • Formāts: 336 pages
  • Izdošanas datums: 13-Nov-2014
  • Izdevniecība: Oxford University Press
  • Valoda: eng
  • ISBN-13: 9780191034435
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This book addresses the analysis of musical sounds from the viewpoint of someone at the intersection between physicists, engineers, piano technicians, and musicians.

The study is structured into three parts. The reader is introduced to a variety of waves and a variety of ways of presenting, visualizing, and analyzing them in the first part. A tutorial on the tools used throughout the book accompanies this introduction. The mathematics behind the tools is left to the appendices. Part Two provides a graphical survey of the classical areas of acoustics that pertain to musical instruments: vibrating strings, bars, membranes, and plates. Part Three is devoted almost exclusively to the piano. Several two- and three-dimensional graphical tools are introduced to study various characteristics of pianos: individual notes and interactions among them, the missing fundamental, inharmonicity, tuning visualization, the different distribution of harmonic power for the various zones of the piano keyboard, and potential uses for quality control. These techniques are also briefly applied to other musical instruments studied in earlier parts of the book.

For physicists and engineers there are appendices to cover the mathematics lurking beneath the numerous graphs and a brief introduction to MatlabRG which was used to generate these graphs. A website accompanying the book (https://sites.google.com/site/analysisofsoundsandvibrations/) contains:
- Matlab® scripts
- mp3 files of sounds
- references to YouTube videos
- and up-to-date results of recent studies

Recenzijas

I recommend this remarkable book to everyone who wants to know how sounds are generated and how they can be analyzed. I am sure that this book will serve as a standard reference for the spectral analysis of musical sounds over many years * Haye Hinrichsen, Euro Piano *

Part 1 Some Basic Sound Waves and Some Simple Tools
1 An Introduction to Some Elementary Waves
3(19)
1.1 A Simple Sinusoidal Sound Wave
3(10)
1.2 A Built-Up Signal Consisting of the Fundamental and Harmonics
13(6)
1.3 A Square Wave
19(2)
1.4 Exercise
21(1)
2 The Basic Tools of Spectral Analysis
22(40)
2.1 A Graphical Attempt to Approximate the Square Wave
23(2)
2.2 A Sum of Sine Waves as an Approximation to a Square Wave
25(3)
2.3 The Folding Frequency and the Nyquist Interval
28(1)
2.4 The Frequency Grid in the Line Spectrum Plot
29(2)
2.5 Aliasing
31(4)
2.6 A Sine Wave with Zero Frequency
35(1)
2.7 A Finer Grid in the Frequency Domain
35(1)
2.8 A Noisy Example-The Mystery Wave
36(1)
2.9 Alternative Ways to Plot a Wave's Intensity in the Frequency Domain
37(6)
2.10 The Cumulative Line Spectrum and White Noise
43(8)
2.11 The Spectral Centroid
51(3)
2.12 The Autocorrelation
54(6)
2.13 Exercise
60(2)
3 Analysis of Several Common Musical Sounds
62(25)
3.1 The Tuning Fork
62(3)
3.2 The Pitch Pipe, a Hum, and a "Laah"
65(4)
3.3 A Door Chime, a Fire Bell, and Clanging Railroad Bars
69(4)
3.4 A Didjeribone®
73(1)
3.5 A Flute
74(1)
3.6 A Cornet
75(1)
3.7 A Trombone
76(1)
3.8 A Bassoon
77(1)
3.9 Drums and Cymbals
77(4)
3.10 Blowing over the Top of a Beer Bottle-The Helmholtz Resonator
81(2)
3.11 A Train Whistle
83(1)
3.12 Summary
84(2)
3.13 Exercises
86(1)
4 Harmonics in Musical Sounds
87(18)
4.1 Harmonics Associated with a Piano Note
87(4)
4.2 A Tube Closed at One End and Open at the Other
91(2)
4.3 A Tube Open at Both Ends
93(1)
4.4 Another Look at the Cornet, Trombone, and Flute
94(1)
4.5 A Triplet of Simulation Experiments
95(5)
4.6 A Crude Example of Harmonics
100(1)
4.7 Summary
101(1)
4.8 Exercises
101(4)
Part 2 A Visual Analysis of Vibrating Objects (Strings, Membranes, Bars, Plates)
5 The Vibrating String
105(50)
5.0 Basic Parameters of the Flexible String
105(2)
5.1 Plucking the Idealized Flexible String with Fixed End Points at 1/9th of its Length
107(11)
5.2 Experiments on Single Piano Strings
118(6)
5.3 Two Stringed Musical Instruments
124(1)
5.4 Plucking at the Center of the Idealized Flexible String
125(3)
5.5 A Symmetrical Initial Profile
128(2)
5.6 Adding a Soundboard at One End of the String
130(5)
5.7 Forcing a Flexible String at One End
135(3)
5.8 The Effect of a Hammer Striking the String
138(6)
5.9 The Effect of String Stiffness in the Response to a Hammer Strike
144(1)
5.10 The Force Generated on the Fixed End Connection by Struck Flexible and Stiff Strings
145(5)
5.11 Longitudinal Waves
150(3)
5.12 Summary
153(1)
5.13 Exercises
154(1)
6 The Vibrating Bar
155(10)
6.1 Basic Parameters of the Vibrating Bar
155(2)
6.2 A Simulation of a Vibrating Bar
157(3)
6.3 Averaging the Vertical Displacements
160(3)
6.4 An Example: A Toy Xylophone
163(1)
6.5 Summary
164(1)
6.6 Exercises
164(1)
7 The Vibrating Membrane
165(10)
7.1 Basic Parameters of the Vibrating Membrane
165(1)
7.2 Response to a Center Displacement
165(5)
7.3 Response to a Cone-Shaped Initial Condition
170(1)
7.4 Response to an Off-Center Displacement
170(3)
7.5 Summary
173(1)
7.6 Exercise
174(1)
8 The Vibrating Plate
175(20)
8.1 Basic Parameters of the Vibrating Plate
175(1)
8.2 Circular Plate-Gaussian Initial Condition
175(2)
8.3 Circular Plate-Cone Initial Condition
177(3)
8.4 Square Plate-Gaussian Initial Condition
180(3)
8.5 Triangular Plate-Gaussian Initial Condition
183(1)
8.6 Coupling a Vibrating String with a Vibrating Plate
184(5)
8.7 Knabe Soundboard Experiment
189(1)
8.8 Summary of
Chapter Eight and Part Two
190(5)
Part 3 The Piano
9 An Introduction to Pianos
195(8)
9.1 The Cast of Pianos
195(1)
9.2 The Six-Pack
196(1)
9.3 Naming Convention and the Equal Temperament System
197(1)
9.4 A Different Measure of Frequency: Cents and Semitones
197(3)
9.5 Different Zones in the Stringing Arrangement
200(2)
9.6 Summary
202(1)
10 Some Individual Piano Notes and Their Interactions
203(11)
10.1 Aspects of Three Piano Notes: Steinway C3, Knabe C4, Knabe C8
203(6)
10.2 A Chromatic Progression
209(1)
10.3 Interaction between Piano Notes
210(1)
10.4 Effect of the Damper
211(2)
10.5 Summary
213(1)
11 The Missing Fundamental
214(16)
11.1 Empirical Evidence-The Low Notes
214(3)
11.2 The Appearance of the Fundamental in the Piano's Higher Octaves
217(4)
11.3 Some Support Based on Videos and Calculations
221(5)
11.4 Changing the Sound of a Note by Frequency Domain Filtering
226(2)
11.5 Summary
228(1)
11.6 Exercises
229(1)
12 Octave Stretching, Inharmonicity, and the Railsback Curve
230(12)
12.1 Inharmonicity or Octave Stretching
230(3)
12.2 Inharmonicity of the C1 Note for Several Pianos
233(1)
12.3 Inharmonicity of the Strings from
Chapter Five
234(1)
12.4 The Inharmonicity Coefficient for the Knabe Piano
234(3)
12.5 The Inharmonicity Coefficients for the Six Pianos
237(1)
12.6 The Difference between the Fundamental and First Partial Frequencies
237(2)
12.7 The Railsback Curve
239(2)
12.8 Summary
241(1)
13 Beating, Unisons, and Tuning
242(11)
13.1 Beating
242(1)
13.2 Using Spectral Analysis to Follow a Tuning Exercise
242(3)
13.3 Limits of Frequency Detectability
245(1)
13.4 A Spectral Analysis of Intervallic Tuning
246(5)
13.5 Summary
251(1)
13.6 Exercises
252(1)
14 Two-Dimensional Graphical Metrics
253(8)
14.1 A Variety of Spectral Centroids
253(2)
14.2 Bimodal Comparisons of American and European Pianos
255(3)
14.3 The Effect of Strike Force
258(2)
14.4 Summary
260(1)
14.5 Exercise
260(1)
15 Three-Dimensional Graphical Metrics
261(17)
15.1 The Time-Keys-Amplitude Envelope Map
261(3)
15.2 The Line Spectrum Map
264(2)
15.3 Cumulative Line Spectrum Maps
266(10)
15.4 Summary
276(1)
15.5 Exercises
277(1)
16 An Investigation into Hammer Knock and Key Striking
278(13)
16.1 A Manual Keystroke with a Microphone as a Sensor
278(3)
16.2 A Manual Keystroke with a Korg Contact Microphone as a Sensor
281(1)
16.3 A Manual Keystroke with the Schatten Soundboard Transducer
282(1)
16.4 Looking at the Early Part of the Wave
282(3)
16.5 Manual Striking versus the Dropping Rod Strike
285(1)
16.6 Analysis of Hammer Knock for Knabe C5 and A7
286(4)
16.7 Summary
290(1)
17 Evaluation of a Wapin Bridge Conversion
291(6)
17.1 Spectral Centroid Method
291(2)
17.2 Cumulative Line Spectrum Map Method
293(3)
17.3 Summary
296(1)
18 Similarities between Pianos and Repeatability of Data-Gathering Methods
297(10)
18.1 Comparing Two Steinway Model D's
297(2)
18.2 Repeatability of Data-Gathering Method
299(7)
18.3 Summary
306(1)
19 Two Metrics Applied to Other Instruments
307(5)
19.1 Trombone
307(1)
19.2 Cornet
307(1)
19.3 Bassoon
308(1)
19.4 Flute
309(2)
19.5 Summary
311(1)
20 Use of the Metrics in Production and Development
312(4)
20.1 Limit Samples
312(1)
20.2 Grandfathers or Exemplars in Quality Control
313(1)
20.3 Some Soapbox Comments
313(1)
20.4 Summary of Part Three
314(2)
21 Defining and Understanding Piano Tone (Contributed by Delwin D Fandrich)
316(11)
21.1 A Designer's Perspective
316(1)
21.2 The Value of Signal Analysis
317(5)
21.3 The Cumulative Line Spectrum Map
322(3)
21.4 Autocorrelation
325(1)
21.5 Conclusion
326(1)
Appendix 1 Mathematical Basis for Part One 327(21)
The Continuous Fourier Transform (FT)
327(1)
Convolution
328(1)
The Discrete Fourier Transform
328(2)
The Fast Fourier Transform
330(1)
The Fine Grid DFT or Fine Grid Line Spectrum
330(1)
Padding
331(1)
Interpretation via Convolution and Windows
332(6)
The Fourier Series
338(1)
Snapshots of the Line Spectrum
339(1)
Calculation of the Spectral Centroid
340(2)
The Autocorrelation
342(1)
A Fine Grid Estimator of the Autocorrelation
343(3)
MP3 and WAV Files
346(2)
Appendix 2 Mathematical Basis for Part Two 348(13)
The Vibrating String
348(6)
The Stiff Vibrating String
354(1)
The Vibrating Bar
355(1)
The Vibrating Membrane
356(1)
The Vibrating Circular Plate
357(1)
The Vibrating Rectangular Plate
358(1)
Adding the String and the Hammer to the Plate
358(1)
Elasticity and Stiffness
358(2)
Summary
360(1)
Appendix 3 Mathematical Basis for Part Three 361(4)
Conversion of Hertz to Cents
361(1)
Cumulative Sum of a Difference
362(2)
Azimuth and Elevation in Three-Dimensional Plots
364(1)
Appendix 4 Experimental Setup 365(4)
Approach 1
366(1)
Approach 2
367(1)
Approach 3
368(1)
Approach 4
368(1)
Appendix 5 A Brief Exposure to Matlab® and Octave 369(6)
Matlab
369(3)
Octave
372(3)
References 375(2)
Index 377
9780470624562
Preface xvii
Contributors xix
1 Introduction
1(10)
Steven A. Haney
1.1 The Beginning of High Content Screening,
1(3)
1.2 Six Skill Sets Essential for Running HCS Experiments,
4(3)
1.2.1 Biology,
5(1)
1.2.2 Microscopy,
5(1)
1.2.3 HCS Instrumentation (Platform Manager),
5(1)
1.2.4 Image Analysis,
6(1)
1.2.5 Statistical Analysis,
6(1)
1.2.6 Information Technology Support,
7(1)
1.3 Integrating Skill Sets into a Team,
7(1)
1.4 A Few Words on Experimental Design,
8(1)
1.5 Conclusions,
9(1)
Key Points,
9(1)
Further Reading,
10(1)
References,
10(1)
Section I First Principles 11(52)
2 Fluorescence and Cell Labeling
13(20)
Anthony Davies
Steven A. Haney
2.1 Introduction,
13(1)
2.2 Anatomy of Fluorescent Probes, Labels, and Dyes,
14(1)
2.3 Stokes' Shift and Biological Fluorophores,
15(1)
2.4 Fluorophore Properties,
16(2)
2.4.1 The Extinction Coefficient (Efficiency of Absorption),
17(1)
2.4.2 Quantum Yield,
17(1)
2.4.3 Fluorescence Lifetime,
17(1)
2.4.4 Loss of Signal (Fading or Signal Degradation),
18(1)
2.5 Localization of Fluorophores Within Cells,
18(8)
2.5.1 Nuclear Stains,
18(2)
2.5.2 Fluorescent Proteins,
20(3)
2.5.3 Localization Agents,
23(1)
2.5.4 Issues that Affect Fluorescent Reagent Choice,
24(2)
2.6 Multiplexing Fluorescent Reagents,
26(1)
2.7 Specialized Imaging Applications Derived from Complex Properties of Fluorescence,
27(3)
2.7.1 F6rster Resonance Energy Transfer,
28(2)
2.7.2 Fluorescence Lifetime Imaging/Forster Resonance Energy Transfer,
30(1)
2.8 Conclusions,
30(1)
Key Points,
31(1)
Further Reading,
31(1)
References,
31(2)
3 Microscopy Fundamentals
33(14)
Steven A. Haney
Anthony Davies
Douglas Bowman
3.1 Introducing HCS Hardware,
33(4)
3.1.1 The HCS Imager and the Microscope,
33(1)
3.1.2 Common uses of HCS that Require Specific Hardware Adaptations,
34(3)
3.2 Deconstructing Light Microscopy,
37(6)
3.2.1 The Light Source(s),
37(1)
3.2.2 The Filter Cube,
38(2)
3.2.3 The Objective,
40(2)
3.2.4 The Camera,
42(1)
3.3 Using the Imager to Collect Data,
43(2)
3.4 Conclusions,
45(1)
Key Points,
45(1)
Further Reading,
46(1)
References,
46(1)
4 Image Processing
47(16)
John Bradley
Douglas Bowman
Anjit Chakravarty
4.1 Overview of Image Processing and Image Analysis in HCS,
47(1)
4.2 What is a Digital Image?,
48(1)
4.3 "Addressing" Pixel Values in Image Analysis Algorithms,
48(1)
4.4 Image Analysis Workflow,
49(11)
4.4.1 Step 1: Image Preprocessing,
50(2)
4.4.2 Step 2: Image Thresholding and Segmentation,
52(4)
4.4.3 Step 3: Calculation of Image Features,
56(2)
4.4.4 Step 4: Collation and Summary of Features,
58(1)
4.4.5 Step 5: Data Export and Feature Data,
59(1)
4.5 Conclusions,
60(1)
Key Points,
60(1)
Further Reading,
60(1)
References,
60(3)
Section II Getting Started 63(50)
5 A General Guide to Selecting and Setting Up a High Content Imaging Platform
65(16)
Craig Furman
Douglas Bowman
Anthony Davies
Caroline Shamu
Steven A. Haney
5.1 Determining Expectations of the HCS System,
65(1)
5.2 Establishing an HC Platform Acquisition Team,
66(1)
5.2.1 The Platform Manager,
66(1)
5.2.2 The Department or Research Head,
67(1)
5.2.3 Facilities Management/Lab Operations,
67(1)
5.2.4 Local and Institutional IT Personnel,
67(1)
5.3 Basic Hardware Decisions,
67(5)
5.3.1 Consider the Needs of the Users and the Lab Setting,
67(1)
5.3.2 Instrumentation Options,
68(4)
5.4 Data Generation, Analysis, and Retention,
72(1)
5.4.1 Image Acquisition Software,
72(1)
5.4.2 Data Storage,
72(1)
5.4.3 Image Analysis Software,
73(1)
5.4.4 System Configuration,
73(1)
5.5 Installation,
73(2)
5.5.1 Overview,
73(1)
5.5.2 Ownership of Technical Issues,
74(1)
5.6 Managing the System,
75(2)
5.6.1 System Maintenance,
75(1)
5.6.2 New User Training,
76(1)
5.6.3 Scheduling Time on the System,
76(1)
5.6.4 Billing,
76(1)
5.7 Setting Up Workflows for Researchers,
77(1)
5.7.1 Introducing Scientists to HCS and the Imager,
77(1)
5.7.2 Superusers,
77(1)
5.7.3 Initial Experiments and Assay Development,
78(1)
5.8 Conclusions,
78(1)
Key Points,
79(1)
Further Reading,
79(2)
6 Informatics Considerations
81(22)
Jay Copeland
Caroline Shamu
6.1 Informatics Infrastructure for High Content Screening,
81(5)
6.1.1 The Scope of the Data Management Challenge,
81(1)
6.1.2 Do-It-Yourself Data Storage Solutions,
82(1)
6.1.3 Working with Central IT Departments,
83(3)
6.2 Using Databases to Store HCS Data,
86(3)
6.2.1 Introduction,
86(1)
6.2.2 Types of Data,
86(2)
6.2.3 Databases,
88(1)
6.2.4 Basic Features of an HCS Database,
89(1)
6.3 Mechanics of an Informatics Solution,
89(6)
6.3.1 Introduction,
89(1)
6.3.2 Data Life Cycle Management,
90(5)
6.4 Developing Image Analysis Pipelines: Data Management Considerations,
95(4)
6.4.1 Using Commercial Image Analysis Software,
95(1)
6.4.2 Using Custom Image Analysis Pipelines,
96(1)
6.4.3 Data Duplication and Uncontrolled Data Growth,
96(1)
6.4.4 Metadata Loss,
96(1)
6.4.5 Data Movement, Network Bandwidth Limitations, and the Challenges of Moving Large Datasets,
97(1)
6.4.6 Problems with Handling Very Large Numbers of Files,
97(1)
6.4.7 Parallel Data Processing,
97(1)
6.4.8 Workflow Documentation and Automation,
98(1)
6.4.9 Software Development and Maintenance: Managing Software Development Projects,
98(1)
6.4.10 Software Sharing, User Training,
99(1)
6.4.11 Image Repositories,
99(1)
6.5 Compliance With Emerging Data Standards,
99(2)
6.6 Conclusions,
101(1)
Key Points,
102(1)
Further Reading,
102(1)
References,
102(1)
7 Basic High Content Assay Development
103(10)
Steven A. Haney
Douglas Bowman
7.1 Introduction,
103(1)
7.2 Initial Technical Considerations for Developing a High Content Assay,
103(4)
7.2.1 Plate Type,
103(1)
7.2.2 Choice and Use of Staining Reagents and of Positive and Negative Controls,
104(1)
7.2.3 Plate Layout,
104(1)
7.2.4 Replicates,
105(1)
7.2.5 Cell Plating Density,
105(2)
7.3 A Simple Protocol to Fix and Stain Cells,
107(2)
7.3.1 Washing Cells,
108(1)
7.3.2 Fixing Cells,
108(1)
7.3.3 Permeabilization,
108(1)
7.3.4 Blocking,
108(1)
7.3.5 Postblocking Washes,
108(1)
7.3.6 Primary Antibody Application,
109(1)
7.3.7 Postprimary Antibody Washes,
109(1)
7.3.8 Secondary Antibodies,
109(1)
7.3.9 Postsecondary Washes,
109(1)
7.4 Image Capture and Examining Images,
109(2)
7.4.1 Resolution, Magnification, and Image Exposure,
110(1)
7.4.2 Number of Cells to Acquire for the Image Analysis Phase,
110(1)
7.4.3 Performance of Positive and Negative Controls,
111(1)
7.5 Conclusions,
111(1)
Key Points,
112(1)
Further Reading,
112(1)
Reference,
112(1)
Section III Analyzing Data 113(52)
8 Designing Metrics for High Content Assays
115(16)
Arijit Chakravarty
Steven A. Haney
Douglas Bowman
8.1 Introduction: Features, Metrics, Results,
115(1)
8.2 Looking at Features,
116(4)
8.3 Metrics and Results: The Metric is the Message,
120(1)
8.4 Types of High Content Assays and Their Metrics,
121(5)
8.4.1 Intensity,
122(1)
8.4.2 Area Above Threshold,
123(1)
8.4.3 Spot Counting, Including Nuclei or Cell Counting,
123(1)
8.4.4 Translocation,
124(1)
8.4.5 Morphology,
125(1)
8.5 Metrics to Results: Putting it all Together,
126(2)
8.5.1 Basic Assay Measurements,
127(1)
8.5.2 Use of Multiple Independent Measurements to Assess a Perturbation,
127(1)
8.5.3 Integrating Multiple Features to Measure a Phenotypic Response,
128(1)
8.6 Conclusions,
128(1)
Key Points,
128(1)
Further Reading,
129(1)
References,
129(2)
9 Analyzing Well-Level Data
131(14)
Steven A. Haney
9.1 Introduction,
131(1)
9.2 Reviewing Data,
132(2)
9.3 Plate and Control Normalizations of Data,
134(1)
9.3.1 Ratio or Percent of Control,
134(1)
9.3.2 z-Score or Robust z-score,
134(1)
9.3.3 B-score,
135(1)
9.3.4 Mixed Effects Models,
135(1)
9.4 Calculation of Assay Statistics,
135(3)
9.4.1 Signal to Background Ratio (S/B),
136(1)
9.4.2 Signal to Noise Ratio (S/N),
137(1)
9.4.3 Z' Statistic,
137(1)
9.4.4 V Factor,
137(1)
9.4.5 Strictly Standardized Mean Difference,
137(1)
9.5 Data Analysis: Hit Selection,
138(1)
9.5.1 Rank Order,
138(1)
9.5.2 Mean +/- k SD,
138(1)
9.5.3 Median +/- k MAD,
138(1)
9.5.4 SSMD,
139(1)
9.5.5 t-Test,
139(1)
9.6 IC 50 Determinations,
139(4)
9.6.1 Overview,
139(2)
9.6.2 Challenges of Fitting Data to a Standard Dose-Response Curve and Potential Biological Insights from Imaging Data,
141(2)
9.7 Conclusions,
143(1)
Key Points,
143(1)
Further Reading,
143(1)
References,
144(1)
10 Analyzing Cell-Level Data
145(20)
Steven A. Haney
Lin Guey
Arijit Chakravarty
10.1 Introduction,
145(1)
10.2 Understanding General Statistical Terms and Concepts,
146(3)
10.2.1 Normal and Non-normal Distributions,
146(2)
10.2.2 Normalizing or Transforming Data,
148(1)
10.2.3 Robust Statistics,
148(1)
10.2.4 Parametric and Nonparametric Statistics,
148(1)
10.3 Examining Data,
149(6)
10.3.1 Descriptive Statistics,
149(3)
10.3.2 Data Visualization,
152(1)
10.3.3 Transformation of Data,
153(2)
10.4 Developing a Data Analysis Plan,
155(3)
10.4.1 Review the Summary Statistics,
156(1)
10.4.2 Determine the Distribution of the Data,
156(1)
10.4.3 Consider Transforming Non-normal Data,
157(1)
10.5 Cell-Level Data Analysis: Comparing Distributions Through Inferential Statistics,
158(1)
10.6 Analyzing Normal (or Transformed) Data,
159(1)
10.6.1 The t-Test,
159(1)
10.6.2 ANOVA Tests,
159(1)
10.7 Analyzing Non-Normal Data,
160(2)
10.7.1 Wilcoxon's Rank Sum,
160(1)
10.7.2 Kruskal-Wallis ANOVA,
160(1)
10.7.3 The Kolmogrov-Snairnoff (KS) Statistic,
161(1)
10.7.4 Bootstrapping,
161(1)
10.8 When to Call For Help,
162(1)
10.9 Conclusions,
162(1)
Key Points,
162(1)
Further Reading,
163(1)
References,
163(2)
Section IV Advanced Work 165(44)
11 Designing Robust Assays
167(14)
Arijit Chakravarty
Douglas Bowman
Anthony Davies
Steven A. Haney
Caroline Shamu
11.1 Introduction,
167(1)
11.2 Common Technical Issues in High Content Assays,
167(5)
11.2.1 At the Bench,
168(2)
11.2.2 During Image Analysis,
170(2)
11.3 Designing Assays to Minimize Trouble,
172(5)
11.3.1 Choosing the Right Antibodies,
172(1)
11.3.2 Optimizing Your Antibodies,
173(1)
11.3.3 Preparation of Samples and Effects on Fluorescence,
174(1)
11.3.4 Planning Ahead with Image Analysis,
175(2)
11.4 Looking for Trouble: Building in Quality Control,
177(2)
11.4.1 Using Controls for QC,
177(1)
11.4.2 Uniformity Plates,
177(1)
11.4.3 Monitoring Assay Statistics,
178(1)
11.4.4 Monitoring Meta-data,
178(1)
11.4.5 Visually Inspect Images via Montages or Random Sampling,
178(1)
11.4.6 Lock down Standard Operating Procedures (SOPs),
178(1)
11.5 Conclusions,
179(1)
Key Points,
180(1)
Further Reading,
180(1)
References,
180(1)
12 Automation and Screening
181(14)
John Donovan
Arijit Chakravarty
Anthony Davies
Steven A. Haney
Douglas Bowman
John Ringeling
Ben Knight
12.1 Introduction,
181(1)
12.2 Some Preliminary Considerations,
181(2)
12.2.1 Assay or Screen?,
181(1)
12.2.2 To Automate or Not?,
182(1)
12.3 Laboratory Options,
183(3)
12.3.1 Workstation versus Fully Automated Systems,
183(1)
12.3.2 Liquid Handler/Reagent Dispenser/Plate Washer Requirements,
184(1)
12.3.3 Barcode Reading Requirements,
184(1)
12.3.4 Vendor Selection Issues,
185(1)
12.3.5 Highly Customized versus More General Systems and Software,
186(1)
12.3.6 Managing Expectations About Automation,
186(1)
12.4 The Automated HCS Laboratory,
186(6)
12.4.1 Setting Up the Automated Laboratory,
186(3)
12.4.2 Connecting the Components,
189(1)
12.4.3 Reagent Considerations,
190(1)
12.4.4 Planning Ahead with Informatics,
190(1)
12.4.5 Designing an Automation-Friendly Dose-Response Assay,
191(1)
12.5 Conclusions,
192(1)
Key Points,
192(1)
Further Reading,
193(2)
13 High Content Analysis for Tissue Samples
195(14)
Kristine Burke
Vaishali Shinde
Alice McDonald
Douglas Bowman
Arijit Chakravarty
13.1 Introduction,
195(1)
13.2 Design Choices in Setting Up a High Content Assay in Tissue,
196(3)
13.2.1 IF or IHC? When and Why,
196(1)
13.2.2 Frozen Sections or Paraffin?,
197(1)
13.2.3 Primary and Secondary Antibody Choices,
198(1)
13.3 System Configuration: Aspects Unique to Tissue-Based HCS,
199(4)
13.3.1 Optical Configuration,
200(1)
13.3.2 Digital Camera,
200(1)
13.3.3 Stage Accessories,
200(1)
13.3.4 Software,
201(1)
13.3.5 Whole Slide Imaging System,
201(1)
13.3.6 Data Management,
202(1)
13.4 Data Analysis,
203(4)
13.5 Conclusions,
207(1)
Key Points,
207(1)
Further Reading,
207(1)
References,
208(1)
Section V High Content Analytics 209(38)
14 Factoring and Clustering High Content Data
211(20)
Steven A. Haney
14.1 Introduction,
211(1)
14.2 Common Unsupervised Learning Methods,
212(6)
14.2.1 Principal Components Analysis,
212(2)
14.2.2 Factor Analysis,
214(1)
14.2.3 k-Means Clustering,
215(1)
14.2.4 Self-Organizing Maps,
215(1)
14.2.5 Hierarchical Clustering,
216(1)
14.2.6 Emerging Clustering Methods: Density and Grid-Based Clustering,
216(2)
14.3 Preparing for an Unsupervised Learning Study,
218(10)
14.3.1 Develop a Provisional Hypothesis,
218(1)
14.3.2 Select Your Cell Labels Carefully,
219(1)
14.3.3 Establish Treatment Conditions,
219(1)
14.3.4 Collect and Analyze Images,
220(1)
14.3.5 Prepare the Data: Feature Selection,
220(2)
14.3.6 Scale the Data,
222(1)
14.3.7 Generate the Similarity or Difference Matrices,
222(1)
14.3.8 Analyze the Data,
223(1)
14.3.9 Perform Quality Assessments,
223(2)
14.3.10 Generalize the Results,
225(3)
14.4 Conclusions,
228(1)
Key Points,
228(1)
Further Reading,
228(1)
References,
229(2)
15 Supervised Machine Learning
231(18)
Jeff Palmer
Arijit Chakravarty
15.1 Introduction,
231(1)
15.2 Foundational Concepts,
232(2)
15.3 Choosing a Machine Learning Algorithm,
234(9)
15.3.1 Two Common Supervised Learning Algorithms,
236(2)
15.3.2 More Supervised Learning Algorithms,
238(5)
15.4 When Do You Need Machine Learning, and How Do You Use IT?,
243(1)
15.5 Conclusions,
244(1)
Key Points,
244(1)
Further Reading,
244(3)
Appendix A Websites and Additional Information on Instruments, Reagents, and Instruction 247(2)
Appendix B A Few Words About One Letter: Using R to Quickly Analyze HCS Data 249(16)
Steven A. Haney
B.1 Introduction,
249(1)
B.2 Setting Up R,
250(3)
B.2.1 Locate the R Website and Installation Server,
250(1)
B.2.2 Download R and Install,
250(1)
B.2.3 Prepare the Data File,
251(1)
B.2.4 Launch R and Load Your Data,
251(2)
B.3 Analyzing Data in R,
253(8)
B.3.1 Summary Statistics,
253(6)
B.3.2 Drawing Q-Q Plots, Histograms and Density Plots,
259(2)
B.3.3 Arrays of Graphs,
261(1)
B.3.4 Exporting and Saving Results,
261(1)
B.4 Where to Go Next,
261(2)
Further Reading,
263(2)
Appendix C Hypothesis Testing for High Content Data: A Refresher 265(21)
Lin Guev
Arijit Chakravarty
C.1 Introduction,
265(1)
C.2 Defining Simple Hypothesis Testing,
266(3)
C.2.1 The Standard Error and the Confidence Interval,
266(1)
C.2.2 The Null Hypothesis and Fundamentals of Statistical Hypothesis Testing,
267(2)
C.2.3 Inferential Statistics and High Content Data,
269(1)
C.3 Simple Statistical Tests to Compare Two Groups,
269(7)
C.3.1 The t-test,
270(2)
C.3.2 Wilcoxon Rank Sum Test,
272(1)
C.3.3 Kolmogorov-Smirnov Test,
273(1)
C.3.4 Multiple Testing,
274(2)
C.4 Statistical Tests on Groups of Samples,
276(4)
C.4.1 The Foundation of Analysis of Variance,
276(1)
C.4.2 Assumptions and Alternative One-way ANOVAs,
277(2)
C.4.3 Post Hoc Multiple Comparisons of Means,
279(1)
C.5 Introduction to Regression Models,
280(5)
C.5.1 Linear Regression,
281(2)
C.5.2 Multiple Linear Regression,
283(1)
C.5.3 Logistic Regression,
284(1)
C.6 Conclusions,
285(1)
Key Concepts, 286(1)
Further Reading, 286(1)
Glossary 287(8)
Tutorial 295(28)
Index 323
David M. Koenig was born in Columbus, Ohio and lived in the nearby town of Grove City until age 17. He attended Bates College and the University of Chicago where he graduated with a B.S. in Chemistry. After obtaining a M.S. in Chemical Engineering at the University of Connecticut he received a Ph.D. in chemical engineering from The Ohio State University. He worked at Corning, Inc. for 27 years in the area of process control and analysis and wrote a book on that subject in 1991. After retirement he wrote a book on process control and then got interested in applying the analysis tools to the piano which led to this book.

Delwin D. Fandrich is Piano Research, Design and Manufacturing Consultant at Fandrich Piano Company.
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