Gene Expression Studies using Affymetrix Microarrays

by Gohlmann; Hinrich

• ISBN13: 9781420065152
• ISBN10: 1420065157
• Format: Hardcover
• Copyright: 2009-07-15
• Publisher: Chapman & Hall/

Summary

Gene expression studies merge three disciplines with different historical backgrounds: molecular biology, bioinformatics, and biostatistics. This book tears down the omnipresent language barriers among researchers of these different backgrounds by explaining the entire process of a gene expression study from conception to interpretation. It covers important technical and statistical pitfalls and problems, helping not only to explain concepts outside the domain of researchers, but to provide additional guidance in their field of expertise. The book also describes technical and statistical methods conceptually with illustrative examples, enabling those inexperienced with gene expression studies to grasp the basic principles.

Author Biography

Hinrich Ghlmann and Willem Talloen work at Johnson Johnson Pharmaceutical RD as Principal Scientist and Senior Biostatistician, respectively.

Table of Contents

List of Figures
List of Tables
List of BioBoxes
List of StatsBoxes
Preface
Abbreviations and Terms
Biological question	p. 1
Why gene expression?	p. 1
Biotechnological advancements	p. 1
Biological relevance	p. 1
Research question	p. 6
Correlational vs. experimental research	p. 6
Main types of research questions	p. 8
Comparing two groups	p. 8
Comparing multiple groups	p. 9
Comparing treatment combinations	p. 10
Comparing multiple groups with a reference group	p. 12
Investigating within-subject changes	p. 13
Classifying and predicting samples	p. 14
Affymetrix microarrays	p. 17
Probes	p. 18
Probesets	p. 22
Standard probeset definitions	p. 22
Alternative CDFs	p. 24
Array types	p. 29
Standard expression monitoring arrays	p. 31
Exon arrays	p. 31
Gene arrays	p. 35
Tiling arrays	p. 36
Focused arrays	p. 37
Standard lab processes	p. 38
In vitro transcription assay	p. 38
Whole transcript sense target labeling assay	p. 39
Affymetrix data quality	p. 39
Reproducibility	p. 39
Robustness	p. 40
Sensitivity	p. 40
Running the experiment	p. 41
Biological experiment	p. 41
Biological background	p. 41
Aim/hypothesis	p. 41
Technology platform	p. 41
Expected changes in mRNA levels	p. 43
Sample	p. 44
Selection of appropriate sample/tissue	p. 44
Sample types	p. 45
Sample heterogeneity	p. 50
Gender	p. 53
Time point	p. 53
Dissection artifacts	p. 54
Artifacts due to animal handling	p. 55
RNA quality	p. 57
RNA quantity	p. 62
Pilot experiment	p. 64
Main experiment	p. 65
Control experiment	p. 66
Treatment	p. 66
Blocking	p. 67
Randomization	p. 67
Standardization	p. 67
Matched controls	p. 67
Sample size/replicates/costs	p. 68
Balanced design	p. 68
Control samples	p. 69
Sample pooling	p. 69
Documentation	p. 71
Follow-up experiments	p. 72
Microarray experiment	p. 73
External RNA controls	p. 73
Target synthesis	p. 74
Batch effect	p. 76
Whole genome vs. focused microarrays	p. 76
Data analysis preparation	p. 79
Data preprocessing	p. 79
Probe intensity	p. 79
Log2 transformation	p. 81
Background correction	p. 82
Normalization	p. 83
Summarization	p. 89
PM and MM techniques	p. 90
PM only techniques	p. 93
All in one	p. 95
Detection calls	p. 96
MAS 5.0	p. 97
DABG	p. 97
PANP	p. 97
Standardization	p. 98
Quality control	p. 99
Technical data	p. 99
Pseudo images	p. 99
Evaluating reproducibility	p. 99
Measures for evaluating reproducibility	p. 99
A motivating example	p. 103
Batch effects	p. 106
Batch effect correction	p. 109
Data analysis	p. 113
Why do we need statistics?	p. 113
The need for data interpretation	p. 114
The need for a good experimental design	p. 115
Statistics vs. bioinformatics	p. 115
The curse of high-dimensionality	p. 117
Analysis reproducibility	p. 117
Seek until you find	p. 118
Gene filtering	p. 118
Filtering approaches	p. 120
Intensity of the signal	p. 120
Variation between samples	p. 121
Absent/present calls	p. 121
Informative/non-informative calls	p. 122
Impact of filtering on testing and multiplicity correction	p. 123
Comparison of various filtering approaches	p. 126
Unsupervised data exploration	p. 131
Motivation	p. 132
Batch effects	p. 132
Technical or biological outliers	p. 132
Quality check of phenotypic data	p. 134
Identification of co-regulated genes	p. 134
Clustering	p. 134
Distance and linkage	p. 135
Clustering algorithms	p. 139
Quality check of clustering	p. 147
Multivariate projection methods	p. 148
Types of multivariate projection methods	p. 148
Biplot	p. 152
Detecting differential expression	p. 153
A simple solution for a complex question	p. 153
Test statistic	p. 155
Fold change	p. 155
Types of t-tests	p. 156
From t-statistics to p-values	p. 165
Comparison of methods	p. 168
Linear models	p. 173
Correction for multiple testing	p. 185
The problem of multiple testing	p. 185
Multiplicity correction procedures	p. 186
Comparison of methods	p. 192
Post-hoc comparisons	p. 194
Statistical significance vs. biological relevance	p. 195
Sample size estimation	p. 196
Supervised prediction	p. 197
Classification vs. hypothesis testing	p. 198
Challenges of microarray classification	p. 199
Overfitting	p. 199
The bias-variance trade-off	p. 201
Cross-validation	p. 202
Non-uniqueness of classification solutions	p. 203
Feature selection methods	p. 204
Classification methods	p. 210
Discriminant analysis	p. 210
Nearest neighbor classifier	p. 210
Logistic regression	p. 211
Neural networks	p. 213
Support vector machines	p. 214
Classification trees	p. 215
Ensemble methods	p. 215
PAM	p. 217
Comparison of the methods	p. 217
Complex prediction problems	p. 218
Multiclass problems	p. 218
Prediction of survival	p. 218
Sample sizes	p. 218
Pathway analysis	p. 219
Statistical approaches in pathway analysis	p. 220
Over-representation analysis	p. 220
Functional class scoring	p. 221
Gene set analysis	p. 221
Comparison of the methods	p. 223
Databases	p. 224
Gene ontology	p. 228
KEGG	p. 228
GenMAPP	p. 228
ARED	p. 229
cMAP	p. 229
BioCarta	p. 229
Chromosomal location	p. 229
Other analysis approaches	p. 230
Gene network analysis	p. 230
Meta-analysis	p. 231
Chromosomal location	p. 232
Presentation of results	p. 235
Data visualization	p. 235
Heatmap	p. 235
Intensity plot	p. 238
Gene list plot	p. 239
Venn diagram	p. 242
Scatter plots	p. 243
Volcano plot	p. 243
MA plot	p. 244
Scatter plots for high-dimensional data	p. 246
Histogram	p. 247
Box plot	p. 249
Violin plot	p. 249
Density plot	p. 250
Dendrogram	p. 250
Pathways with gene expression	p. 252
Figures for publication	p. 254
Biological interpretation	p. 255
Important databases	p. 255
Entrez Gene	p. 255
NetAffx	p. 256
OMIM	p. 256
Text mining	p. 256
Data integration	p. 256
Data from multiple molecular screenings	p. 256
Systems biology	p. 257
RTqPCR verification	p. 257
Data publishing	p. 258
ArrayExpress	p. 259
Gene expression omnibus	p. 259
Reproducible research	p. 260
Pharmaceutical R&D	p. 261
The need for early indications	p. 261
Critical path initiative	p. 262
Drug discovery	p. 264
Differences between normal and diseased tissues	p. 264
Disease subclass discovery	p. 265
Identification of molecular targets	p. 265
Profiling for molecular characterization	p. 266
Characterization of a disease model	p. 266
Compound profiling	p. 268
Dose-response treatment	p. 269
Drug development	p. 270
Biomarkers	p. 270
Response signatures	p. 273
Toxigenomics	p. 274
Clinical trials	p. 276
Efficacy markers	p. 276
Signatures for outcome prognosis	p. 276
Using R and Bioconductor	p. 279
R and Bioconductor	p. 280
R and Sweave	p. 281
R and Eclipse	p. 282
Automated array analysis	p. 283
Load packages	p. 283
Gene filtering	p. 284
Unsupervised exploration	p. 284
Testing for differential expression	p. 284
Supervised classification	p. 285
Other software for microarray analysis	p. 286
Future perspectives	p. 289
Co-analyzing different data types	p. 289
The microarrays of the future	p. 290
Next-gen sequencing: the end for microarrays?	p. 292
Bibliography	p. 297
Index	p. 321
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