Muutke küpsiste eelistusi

E-raamat: Batch Effects and Noise in Microarray Experiments: Sources and Solutions

3.00/5 (2 hinnangut Goodreads-ist)
(Spheromics, Kontiolahti, Finland)
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 106,15 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele
Batch Effects and Noise in Microarray Experiments: Sources and SolutionsSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Researchers, clinicians, laboratory personnel, managers, and others responsible for gene expression studies are the expected readers as like professionals in a wide range of fields explain bias in microarray data, describe sources of technical and biological variation in such experiments and genome-wide associated studies, and suggest how to reduce bias. Many of the statistical methods they provide for reducing bias and alleviating its effects are previously unpublished. Among their topics are microarray platforms and aspects of experimental variation, aspects of technical bias, bioinformatic strategies for cDNA-microarray data processing, adjusting batch effects in microarray experiments with small sample size using empirical Bayes methods, visualizing cross-platform microarray normalization, and standard operating procedures in clinical gene expression biomarker panel development. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)

Batch effects and experimental shift are major sources for noise in a microarray dataset. Their effect on gene expression profiling has been largely ignored until now. This book provides a valuable insight into the nature of batch effects, providing guidance on possible ways of dealing with it and illustrating ways of keeping it to a minimum. Guidance in the design of balanced experiments is provided by leading experts in the field and examples are drawn from real-life examples.
List of Contributors
xiii
Foreword xvii
Preface xix
1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction
1(4)
Andreas Scherer
2 Microarray Platforms and Aspects of Experimental Variation
5(14)
John A Coller Jr
2.1 Introduction
5(1)
2.2 Microarray Platforms
6(3)
2.2.1 Affymetrix
6(1)
2.2.2 Agilent
7(1)
2.2.3 Illumina
7(1)
2.2.4 Nimblegen
8(1)
2.2.5 Spotted Microarrays
8(1)
2.3 Experimental Considerations
9(8)
2.3.1 Experimental Design
9(1)
2.3.2 Sample and RNA Extraction
9(3)
2.3.3 Amplification
12(1)
2.3.4 Labeling
13(1)
2.3.5 Hybridization
13(1)
2.3.6 Washing
14(1)
2.3.7 Scanning
15(1)
2.3.8 Image Analysis and Data Extraction
16(1)
2.3.9 Clinical Diagnosis
17(1)
2.3.10 Interpretation of the Data
17(1)
2.4 Conclusions
17(2)
3 Experimental Design
19(14)
Peter Grass
3.1 Introduction
19(1)
3.2 Principles of Experimental Design
20(4)
3.2.1 Definitions
20(1)
3.2.2 Technical Variation
21(1)
3.2.3 Biological Variation
21(1)
3.2.4 Systematic Variation
22(1)
3.2.5 Population, Random Sample, Experimental and Observational Units
22(1)
3.2.6 Experimental Factors
22(1)
3.2.7 Statistical Errors
23(1)
3.3 Measures to Increase Precision and Accuracy
24(4)
3.3.1 Randomization
25(1)
3.3.2 Blocking
25(1)
3.3.3 Replication
25(1)
3.3.4 Further Measures to Optimize Study Design
26(2)
3.4 Systematic Errors in Microarray Studies
28(2)
3.4.1 Selection Bias
28(1)
3.4.2 Observational Bias
28(1)
3.4.3 Bias at Specimen/Tissue Collection
29(1)
3.4.4 Bias at mRNA Extraction and Hybridization
30(1)
3.5 Conclusion
30(3)
4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies
33(18)
Naomi Altman
4.1 Introduction
33(2)
4.1.1 Batch Effects
35(1)
4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments
35(4)
4.2.1 Using the Linear Model for Design
37(1)
4.2.2 Examples of Design Guided by the Linear Model
37(2)
4.3 Blocks and Batches
39(2)
4.3.1 Complete Block Designs
39(1)
4.3.2 Incomplete Block Designs
39(1)
4.3.3 Multiple Batch Effects
40(1)
4.4 Reducing Batch Effects by Normalization and Statistical Adjustment
41(6)
4.4.1 Between and Within Batch Normalization with Multi-array Methods
43(3)
4.4.2 Statistical Adjustment
46(1)
4.5 Sample Pooling and Sample Splitting
47(2)
4.5.1 Sample Pooling
47(1)
4.5.2 Sample Splitting: Technical Replicates
48(1)
4.6 Pilot Experiments
49(1)
4.7 Conclusions
49(2)
Acknowledgements
50(1)
5 Aspects of Technical Bias
51(10)
Martin Schumacher
Frank Staedtler
Wendell D Jones
Andreas Scherer
5.1 Introduction
51(1)
5.2 Observational Studies
52(8)
5.2.1 Same Protocol, Different Times of Processing
52(1)
5.2.2 Same Protocol, Different Sites (Study 1)
53(2)
5.2.3 Same Protocol, Different Sites (Study 2)
55(2)
5.2.4 Batch Effect Characteristics at the Probe Level
57(3)
5.3 Conclusion
60(1)
6 Bioinformatic Strategies for cDNA-Microarray Data Processing
61(14)
Jessica Fahlen
Mattias Landfors
Eva Freyhult
Max Bylesjo
Johan Trygg
Torgeir R Hvidsten
Patrik Ryden
6.1 Introduction
61(3)
6.1.1 Spike-in Experiments
62(1)
6.1.2 Key Measures - Sensitivity and Bias
63(1)
6.1.3 The IC Curve and MA Plot
63(1)
6.2 Pre-processing
64(7)
6.2.1 Scanning Procedures
65(1)
6.2.2 Background Correction
65(2)
6.2.3 Saturation
67(1)
6.2.4 Normalization
68(2)
6.2.5 Filtering
70(1)
6.3 Downstream Analysis
71(2)
6.3.1 Gene Selection
71(1)
6.3.2 Cluster Analysis
71(2)
6.4 Conclusion
73(2)
7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance
75(12)
Nysia I George
James J Chen
7.1 Introduction
75(3)
7.1.1 Microarray Gene Expression Data
76(1)
7.1.2 Analysis of Variance in Gene Expression Data
77(1)
7.2 Variance Component Analysis across Microarray Platforms
78(1)
7.3 Methodology
78(3)
7.3.1 Data Description
78(1)
7.3.2 Normalization
79(2)
7.3.3 Gene-Specific ANOVA Model
81(1)
7.4 Application: The MAQC Project
81(4)
7.5 Discussion and Conclusion
85(2)
Acknowledgements
85(2)
8 Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set
87(14)
Michael J Boedigheimer
Jeff W Chou
J Christopher Corton
Jennifer Fostel
Raegan O'Lone
P Scott Pine
John Quackenbush
Karol L Thompson
Russell D Wolfinger
8.1 Introduction
87(2)
8.2 Methodology
89(1)
8.3 Results
89(8)
8.3.1 Assessment of Smooth Bias in Baseline Expression Data Sets
89(2)
8.3.2 Relationship between Smooth Bias and Signal Detection
91(1)
8.3.3 Effect of Smooth Bias Correction on Principal Components Analysis
92(2)
8.3.4 Effect of Smooth Bias Correction on Estimates of Attributable Variability
94(1)
8.3.5 Effect of Smooth Bias Correction on Detection of Genes Differentially Expressed by Fasting
95(1)
8.3.6 Effect of Smooth Bias Correction on the Detection of Strain-Selective Gene Expression
96(1)
8.4 Discussion
97(4)
Acknowledgements
99(2)
9 Microarray Gene Expression: The Effects of Varying Certain Measurement Conditions
101(12)
Walter Liggett
Jean Lozach
Anne Bergstrom Lucas
Ron L Peterson
Marc L Salit
Danielle Thierry-Mieg
Jean Thierry-Mieg
Russell D Wolfinger
9.1 Introduction
101(2)
9.2 Input Mass Effect on the Amount of Normalization Applied
103(1)
9.3 Probe-by-Probe Modeling of the Input Mass Effect
103(5)
9.4 Further Evidence of Batch Effects
108(2)
9.5 Conclusions
110(3)
10 Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods
113(18)
W Evan Johnson
Cheng Li
10.1 Introduction
113(2)
10.1.1 Bayesian and Empirical Bayes Applications in Microarrays
114(1)
10.2 Existing Methods for Adjusting Batch Effect
115(2)
10.2.1 Microarray Data Normalization
115(1)
10.2.2 Batch Effect Adjustment Methods for Large Sample Size
115(1)
10.2.3 Model-Based Location and Scale Adjustments
116(1)
10.3 Empirical Bayes Method for Adjusting Batch Effect
117(4)
10.3.1 Parametric Shrinkage Adjustment
117(3)
10.3.2 Empirical Bayes Batch Effect Parameter Estimates using Nonparametric Empirical Priors
120(1)
10.4 Data Examples, Results and Robustness of the Empirical Bayes Method
121(7)
10.4.1 Microarray Data with Batch Effects
121(3)
10.4.2 Results for Data Set 1
124(1)
10.4.3 Results for Data Set 2
124(2)
10.4.4 Robustness of the Empirical Bayes Method
126(1)
10.4.5 Software Implementation
127(1)
10.5 Discussion
128(3)
11 Identical Reference Samples and Empirical Bayes Method for Cross-Batch Gene Expression Analysis
131(10)
Wynn L Walker
Frank R Sharp
11.1 Introduction
131(2)
11.2 Methodology
133(2)
11.2.1 Data Description
133(1)
11.2.2 Empirical Bayes Method for Batch Adjustment
134(1)
11.2.3 Naive t-test Batch Adjustment
135(1)
11.3 Application: Expression Profiling of Blood from Muscular Dystrophy Patients
135(3)
11.3.1 Removal of Cross-Experimental Batch Effects
135(1)
11.3.2 Removal of Within-Experimental Batch Effects
136(1)
11.3.3 Removal of Batch Effects: Empirical Bayes Method versus t-Test Filter
137(1)
11.4 Discussion and Conclusion
138(3)
11.4.1 Methods for Batch Adjustment Within and Across Experiments
138(1)
11.4.2 Bayesian Approach is Well Suited for Modeling Cross-Experimental Batch Effects
139(1)
11.4.3 Implications of Cross-Experimental Batch Corrections for Clinical Studies
139(2)
12 Principal Variance Components Analysis: Estimating Batch Effects in Microarray Gene Expression Data
141(14)
Jianying Li
Pierre R Bushel
Tzu-Ming Chu
Russell D Wolfinger
12.1 Introduction
141(2)
12.2 Methods
143(3)
12.2.1 Principal Components Analysis
143(2)
12.2.2 Variance Components Analysis and Mixed Models
145(1)
12.2.3 Principal Variance Components Analysis
145(1)
12.3 Experimental Data
146(2)
12.3.1 A Transcription Inhibition Study
146(1)
12.3.2 A Lung Cancer Toxicity Study
147(1)
12.3.3 A Hepato-toxicant Toxicity Study
147(1)
12.4 Application of the PVCA Procedure to the Three Example Data Sets
148(5)
12.4.1 PVCA Provides Detailed Estimates of Batch Effects
148(1)
12.4.2 Visualizing the Sources of Batch Effects
149(1)
12.4.3 Selecting the Principal Components in the Modeling
150(3)
12.5 Discussion
153(2)
13 Batch Profile Estimation, Correction, and Scoring
155(12)
Tzu-Ming Chu
Wenjun Bao
Russell S Thomas
Russell D Wolfinger
13.1 Introduction
155(2)
13.2 Mouse Lung Tumorigenicity Data Set with Batch Effects
157(7)
13.2.1 Batch Profile Estimation
159(1)
13.2.2 Batch Profile Correction
160(1)
13.2.3 Batch Profile Scoring
161(1)
13.2.4 Cross-Validation Results
162(2)
13.3 Discussion
164(3)
Acknowledgements
165(2)
14 Visualization of Cross-Platform Microarray Normalization
167(16)
Xuxin Liu
Joel Parker
Cheng Fan
Charles M Perou
J S Marron
14.1 Introduction
167(2)
14.2 Analysis of the NCI 60 Data
169(5)
14.3 Improved Statistical Power
174(4)
14.4 Gene-by-Gene versus Multivariate Views
178(3)
14.5 Conclusion
181(2)
15 Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis
183(8)
Lev Klebanov
Andreas Scherer
15.1 Introduction
183(2)
15.2 Aggregated Expression Intensities
185(1)
15.3 Covariance between Log-Expressions
186(3)
15.4 Conclusion
189(2)
Acknowledgements
190(1)
16 Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies
191(12)
Huixiao Hong
Leming Shi
James C Fuscoe
Federico Goodsaid
Donna Mendrick
Weida Tong
16.1 Introduction
191(1)
16.2 Potential Sources of Spurious Associations
192(4)
16.2.1 Spurious Associations Related to Study Design
194(1)
16.2.2 Spurious Associations Caused in Genotyping Experiments
195(1)
16.2.3 Spurious Associations Caused by Genotype Calling Errors
195(1)
16.3 Batch Effects
196(5)
16.3.1 Batch Effect in Genotyping Experiment
196(1)
16.3.2 Batch Effect in Genotype Calling
197(4)
16.4 Conclusion
201(2)
Disclaimer
201(2)
17 Standard Operating Procedures in Clinical Gene Expression Biomarker Panel Development
203(12)
Khurram Shahzad
Anshu Sinha
Farhana Latif
Mario C Deng
17.1 Introduction
203(1)
17.2 Theoretical Framework
204(1)
17.3 Systems-Biological Concepts in Medicine
204(1)
17.4 General Conceptual Challenges
205(1)
17.5 Strategies for Gene Expression Biomarker Development
205(8)
17.5.1 Phase 1: Clinical Phenotype Consensus Definition
206(1)
17.5.2 Phase 2: Gene Discovery
207(2)
17.5.3 Phase 3: Internal Differential Gene List Confirmation
209(1)
17.5.4 Phase 4: Diagnostic Classifier Development
209(1)
17.5.5 Phase 5: External Clinical Validation
210(1)
17.5.6 Phase 6: Clinical Implementation
211(1)
17.5.7 Phase 7: Post-Clinical Implementation Studies
212(1)
17.6 Conclusions
213(2)
18 Data, Analysis, and Standardization
215(16)
Gabriella Rustici
Andreas Scherer
John Quackenbush
18.1 Introduction
215(1)
18.2 Reporting Standards
216(3)
18.3 Computational Standards: From Microarray to Omic Sciences
219(7)
18.3.1 The Microarray Gene Expression Data Society
219(1)
18.3.2 The Proteomics Standards Initiative
220(1)
18.3.3 The Metabolomics Standards Initiative
220(1)
18.3.4 The Genomic Standards Consortium
220(1)
18.3.5 Systems Biology Initiatives
221(1)
18.3.6 Data Standards in Biopharmaceutical and Clinical Research
221(1)
18.3.7 Standards Integration Initiatives
222(1)
18.3.8 The MIBBI project
223(1)
18.3.9 OBO Foundry
223(1)
18.3.10 FuGE and ISA-TAB
223(3)
18.4 Experimental Standards: Developing Quality Metrics and a Consensus on Data Analysis Methods
226(2)
18.5 Conclusions and Future Perspective
228(3)
References 231(14)
Index 245
Andreas Scherer studied biology in Cologne, Germany, and Freiburg, Germany, and received his Ph.D. for his studies in the fields of genetics, developmental biology, and microbiology. Following a postdoctoral position at UT Southwestern Medical Center in Dallas, TX, he worked for many years in pharmaceutical industry in various positions in the field of experimental and statistical genomics biomarker discovery. In 2007, Andreas Scherer founded Spheromics, a company specialized in analytical and consultancy services in gene expression technologies and biomarker development.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97804706859902e.html
Märksõnad: