Contents - Data Analysis Tools for DNA Microarrays | Intelligent Systems and Bioinformatics Laboratory

Data Analysis Tools for DNA Microarrays

Sorin Draghici

Publisher: Chapman & Hall/CRC Press.
ISBN: 1584883154
Order from Chapman & Hall /CRC Press.
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Contents

Companion CD Contents

Table of Contents

Elementary statistics


0.0.1	Audience and prerequisites	viii
0.0.2	Aims and contents	viii

1	Introduction	3
1.1	Bioinformatics . an emerging discipline	3
1.2	The building blocks of genomic information	5
1.3	Expression of genetic information	9
1.4	The need for microarrays	13
1.5	Summary	14

2	Microarrays	15
2.1	Microarrays . tools for gene expression analysis	15
2.2	Fabrication of microarrays	16
2.2.1	cDNA microarrays	17
2.2.2	In situ synthesis	17
2.2.3	A brief comparison of cDNA and oligonucleotide technologies	22
2.3	Applications of microarrays	22
2.4	Challenges in using microarrays in gene expression studies	23
2.5	Sources of variability	28
2.6	Summary	32

3	Image processing	33
3.1	Introduction	33
3.2	Basic elements of digital imaging	33
3.3	Microarray image processing	38
3.4	Image processing of cDNA microarrays	42
3.4.1	Spot nding	42
3.4.2	Image segmentation	43
3.4.3	Quantication	50
3.4.4	Spot quality assessment	53
3.5	Image processing of Affymetrix arrays	55
3.6	Summary	58

4	Elements of statistics	61
4.1	Introduction	61
4.2	Some basic terms	62
4.3	Elementary statistics	64
4.3.1	Measures of central tendency: mean, mode and median	64
4.3.2	Measures of variability	68
4.3.3	Some interesting data manipulations	70
4.3.4	Covariance and correlation	71
4.4	Probabilities	77
4.4.1	Computing with probabilities	80
4.5	Bayes' theorem	84
4.6	Probability distributions	86
4.6.1	Discrete random variables	87
4.6.2	Binomial distribution	89
4.6.3	Continuous random variables	94
4.6.4	The normal distribution	96
4.6.5	Using a distribution	99
4.7	Central limit theorem	102
4.8	Are replicates useful?	104
4.9	Summary	106
4.10	Solved problems	106
4.11	Exercises	107

5	Statistical hypothesis testing	109
5.1	Introduction	109
5.2	The framework	109
5.3	Hypothesis testing and signicance	112
5.3.1	One-tail testing	113
5.3.2	Two-tail testing	118
5.4	I do not believe God does not exist	120
5.5	An algorithm for hypothesis testing	121
5.6	Errors in hypothesis testing	122
5.7	Summary	126
5.8	Solved problems	126

6	Classical approaches to data analysis	129
6.1	Introduction	129
6.2	Tests involving a single sample	130
6.2.1	Tests involving the mean. The t distribution	130
6.2.2	Choosing the number of replicates	134
6.2.3	Tests involving the variance (σ²). The chi-square distribution	136
6.2.4	Condence intervals for standard deviation	139
6.3	Tests involving two samples	140
6.3.1	Comparing variances. The F distribution	140
6.3.2	Comparing means	144
6.3.3	Condence intervals for the difference of means µ₁ - µ₂	149
6.4	Summary	150
6.5	Exercises	153

7	Analysis of Variance - ANOVA	155
7.1	Introduction	155
7.1.1	Problem denition and model assumptions	155
7.1.2	The .dot. notation	158
7.2	One-way ANOVA	159
7.2.1	One-way Model I ANOVA	159
7.2.2	One-way Model II ANOVA	166
7.3	Two-way ANOVA	169
7.3.1	Randomized complete block design ANOVA	170
7.3.2	Comparison between one-way ANOVA and randomized block design ANOVA	172
7.3.3	Some examples	174
7.3.4	Factorial design two-way ANOVA	178
7.3.5	Data analysis plan for factorial design ANOVA	182
7.3.6	Reference formulae for factorial design ANOVA	183
7.4	Quality control	183
7.5	Summary	186
7.6	Exercises	187

8	Experiment design	189
8.1	The concept of experiment design	189
8.2	Comparing varieties	190
8.3	Improving the production process	192
8.4	Principles of experimental design	193
8.4.1	Replication	194
8.4.2	Randomization	196
8.4.3	Blocking	197
8.5	Guidelines for experimental design	198
8.6	A short synthesis of statistical experiment designs	200
8.6.1	The xed effect design	200
8.6.2	Randomized block design	201
8.6.3	Balanced incomplete block design	201
8.6.4	Latin square design	202
8.6.5	Factorial design	203
8.6.6	Confounding in the factorial design	204
8.7	Some microarray specic experiment designs	205
8.7.1	The Jackson Lab approach	206
8.7.2	Ratios and ip-dye experiments	208
8.7.3	Reference design vs. loop design	210
8.8	Summary	213

9	Multiple comparisons	215
9.1	Introduction	215
9.2	The problem of multiple comparisons	215
9.3	A more precise argument	220
9.4	Corrections for multiple comparisons	222
9.4.1	The Sidak correction	222
9.4.2	The Bonferroni correction	223
9.4.3	Holm's step-wise correction	224
9.4.4	The false discovery rate (FDR)	225
9.4.5	Permutation correction	225
9.4.6	Signicance analysis of microarrays (SAM)	227
9.4.7	On permutations based methods	228
9.5	Summary	229

10	Analysis and visualization tools	231
10.1	Introduction	231
10.2	Box plots	231
10.3	Gene pies	232
10.4	Scatter plots	233
10.4.1	Scatter plot limitations	237
10.4.2	Scatter plot summary	238
10.5	Histograms	239
10.5.1	Histograms summary	244
10.6	Time series	245
10.7	Principal component analysis (PCA)	246
10.7.1	PCA limitations	257
10.7.2	PCA summary	257
10.8	Independent component analysis (ICA)	259
10.9	Summary	260

11	Cluster analysis	263
11.1	Introduction	263
11.2	Distance metric	264
11.2.1	Euclidean distance	265
11.2.2	Manhattan distance	266
11.2.3	Chebychev distance	268
11.2.4	Angle between vectors	268
11.2.5	Correlation distance	269
11.2.6	Squared Euclidean distance	270
11.2.7	Standardized Euclidean distance	270
11.2.8	Mahalanobis distance	272
11.2.9	Minkowski distance	273
11.2.10	When to use what distance	273
11.2.11	A comparison of various distances	275
11.3	Clustering algorithms	276
11.3.1	k-means clustering	281
11.3.2	Hierarchical clustering	288
11.3.3	Kohonen maps or self-organizing feature maps (SOFM)	297
11.4	Summary	305

12	Data pre-processing and normalization	309
12.1	Introduction	309
12.2	General pre-processing techniques	309
12.2.1	The log transform	309
12.2.2	Combining replicates and eliminating outliers	311
12.2.3	Array normalization	313
12.3	Normalization issues specic to cDNA data	318
12.3.1	Background correction	318
12.3.2	Other spot level pre-processing	320
12.3.3	Color normalization	320
12.4	Normalization issues specic to Affymetrix data	329
12.4.1	Background correction	329
12.4.2	Signal calculation	330
12.4.3	Detection calls	334
12.4.4	Relative expression values	335
12.5	Other approaches to the normalization of Affymetrix data	336
12.6	Useful pre-processing and normalization sequences	336
12.7	Summary	338
12.8	Appendix	339
12.8.1	A short primer on logarithms	339

13	Methods for selecting differentially regulated genes	341
13.1	Introduction	341
13.2	Criteria	342
13.3	Fold change	343
13.3.1	Description	343
13.3.2	Characteristics	345
13.4	Unusual ratio	347
13.4.1	Description	347
13.4.2	Characteristics	348
13.5	Hypothesis testing, corrections for multiple comparisons and resampling	349
13.5.1	Description	349
13.5.2	Characteristics	350
13.6	ANOVA	351
13.6.1	Description	351
13.6.2	Characteristics	351
13.7	Noise sampling	352
13.7.1	Description	352
13.7.2	Characteristics	353
13.8	Model based maximum likelihood estimation methods	354
13.8.1	Description	354
13.8.2	Characteristics	357
13.9	Affymetrix comparison calls	358
13.10	Other methods	359
13.11	Summary	360
13.12	Appendix	361
13.12.1	A comparison of the noise sampling method with the full blown ANOVA approach	361

14	Functional analysis and biological interpretation of microarray data	363
14.1	Introduction	363
14.2	The Gene Ontology	364
14.2.1	The need for an ontology	364
14.2.2	What is the Gene Ontology (GO)?	364
14.2.3	What does GO contain?	365
14.2.4	Access to GO	366
14.3	Other related resources	367
14.4	Translating lists of differentially regulated genes into biological knowledge	367
14.4.1	Statistical approaches	369
14.5	Onto-Express	372
14.5.1	Implementation	372
14.5.2	Graphical input interface description	373
14.5.3	Some real data analyses	376
14.5.4	Interpretation of the functional analysis results	381
14.6	Summary	382

15	Focused microarrays . comparison and selection	383
15.1	Introduction	383
15.2	Criteria for array selection	385
15.3	Onto-Compare	385
15.4	Some comparisons	387
15.5	Summary	391

16	Commercial applications	393
16.1	Introduction	393
16.2	Signicance testing among groups using GeneSight	395
16.2.1	Problem description	395
16.2.2	Experiment design	396
16.2.3	Data analysis	396
16.2.4	Conclusion	407
16.3	Statistical analysis of microarray data using S-PLUS and Insightful ArrayAnalyzer	409
16.3.1	Experiment design	410
16.3.2	Data preparation and exploratory data analysis	410
16.3.3	Differential expression analysis	410
16.3.4	Clustering and prediction	411
16.3.5	Analysis summaries, visualization and annotation of results	411
16.3.6	S+ArrayAnalyzer example: Swirl Zebrash experiment	412
16.3.7	Summary	415
16.4	SAS software for genomics	416
16.4.1	SAS research data management	416
16.4.2	SAS microarray solution	418
16.5	Spotre's DecisionSite	421
16.5.1	Introduction	421
16.5.2	Experiment description	421
16.5.3	Microarray data access	422
16.5.4	Data transformation	423
16.5.5	Filtering and visualizing gene expression data	424
16.5.6	Finding gene expression patterns	427
16.5.7	Using clustering and data reduction techniques to isolate group of genes	428
16.5.8	Comparing sample groups	431
16.5.9	Using Portfolio Lists to isolate signicant genes	432
16.5.10	Summary	434
16.6	Summary	436

17	The road ahead	437
17.1	What next?	437
17.2	Molecular diagnosis	437
17.3	Gene regulatory networks	439
17.4	Conclusions	441

References		443

Companion CD Contents
(available with the book)


1	GeneSight (BioDiscovery) - software suite for microarray data analysis.
2	ImaGene (BioDiscovery) - software for Image Processing of microarray slides
3	S-Plus (Insightful) - software suite for statistical analysis
4	ArrayAnalyzer (Insightful) - software suite for microarray data analysis