DNA Methylation Microarrays: Experimental Design and Statistical Analysis

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ISBN: 9781420067279 | 1420067273
Cover: Hardcover
Copyright: 4/24/2008

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Written from the perspective of an analyst who interacts closely with biology colleagues who have only minimal knowledge of statistics and programming, DNA Methylation Microarrays provides a solid understanding of methodological foundations and enables critical thinking for advanced studies. Using graphics to illustrate the results, the book presents examples based on real biological data from a variety of cellular samples with array hybridizations performed in the laboratory. It covers quality control, experimental design, normalization, significant differential methylation, clustering, dependence networks, and online annotations.

Applied Statistics	p. 1
Descriptive statistics	p. 1
Frequency distribution	p. 2
Central tendency and variability	p. 2
Correlation	p. 4
Inferential statistics	p. 6
Probability distribution	p. 6
Central limit theorem and normal distribution	p. 7
Statistical hypothesis testing	p. 7
Two-sample t-test	p. 9
Nonparametric test	p. 9
One-factor ANOVA and F-test	p. 10
Simple linear regression	p. 11
Chi-square test of contingency	p. 13
Statistical power analysis	p. 14
DNA Methylation Microarrays and Quality Control	p. 17
DNA methylation microarrays	p. 18
Workflow of methylome experiment	p. 21
Restriction enzyme-based enrichment	p. 21
Immunoprecipitation-based enrichment	p. 21
Image analysis	p. 23
Visualization of raw data	p. 26
Reproducibility	p. 26
Positive and negative controls by exogenous sequences	p. 32
Intensity fold-change and p-value	p. 32
DNA unmethylation profiling	p. 33
Correlation of intensities between tiling arrays	p. 33
Experimental Design	p. 35
Goals of experiment	p. 36
Class comparison and class prediction	p. 36
Class discovery	p. 36
Reference design	p. 37
Dye swaps	p. 39
Balanced block design	p. 39
Loop design	p. 41
Factorial design	p. 42
Time course experimental design	p. 47
How many samples/arrays are needed?	p. 49
Biological versus technical replicates	p. 49
Statistical power analysis	p. 49
Pooling biological samples	p. 55
Appendix	p. 56
Data Normalization	p. 59
Measure of methylation	p. 59
The need for normalization	p. 61
Strategy for normalization	p. 62
Two-color CpG island microarray normalization	p. 63
Global dependence of log methylation ratios	p. 64
Dependence of log ratios on intensity	p. 65
Dependence of log ratios on print-tips	p. 67
Normalized Cy3- and Cy5-intensities	p. 70
Between-array normalization	p. 71
Oligonucleotide arrays normalization	p. 72
Background correction: PM - MM?	p. 72
Quantile normalization	p. 73
Probeset summarization	p. 75
Normalization using control sequences	p. 76
Appendix	p. 79
Significant Differential Methylation	p. 81
Fold change	p. 81
Linear model for log-ratios or log-intensities	p. 84
Microarrays reference design or oligonucleotide chips	p. 84
Sequence-specific dye effect in two-color microarrays	p. 87
t-test for contrasts	p. 88
F-test for joint contrasts	p. 89
P-value adjustment for multiple testing	p. 92
Bonferroni correction	p. 92
False discovery rate	p. 92
Modified t- and F-test	p. 94
Significant variation within and between groups	p. 95
Within-group variation	p. 95
Between-group variation	p. 96
Significant correlation with a co-variate	p. 97
Permutation test for bisulfite sequence data	p. 100
Euclidean distance	p. 101
Entropy	p. 102
Missing data values	p. 103
Appendix	p. 104
Factorial design	p. 104
Time-course experiments	p. 105
Balanced block design	p. 106
Loop design	p. 107
High-Density Genomic Tiling Arrays	p. 109
Normalization	p. 110
Intra- and interarray normalization	p. 110
Sequence-based probe effects	p. 110
Wilcoxon test in a sliding window	p. 112
Probe score or scan statistic	p. 116
False positive rate	p. 116
Boundaries of methylation regions	p. 118
Multiscale analysis by wavelets	p. 119
Unsupervised segmentation by hidden Markov model	p. 121
Principal component analysis and biplot	p. 125
Cluster Analysis	p. 129
Measure of dissimilarity	p. 129
Dimensionality reduction	p. 130
Hierarchical clustering	p. 133
Bottom-up approach	p. 133
Top-down approach	p. 136
K-means clustering	p. 139
Model-based clustering	p. 141
Quality of clustering	p. 142
Statistically significance of clusters	p. 144
Reproducibility of clusters	p. 146
Repeated measurements	p. 146
Statistical Classification	p. 149
Feature selection	p. 149
Discriminant function	p. 152
Linear discriminant analysis	p. 153
Diagonal linear discriminant analysis	p. 154
K-nearest neighbor	p. 154
Performance assessment	p. 155
Leave-one-out cross validation	p. 156
Receiver operating characteristic analysis	p. 159
Interdependency Network of DNA Methylation	p. 163
Graphs and networks	p. 164
Partial correlation	p. 164
Dependence networks from DNA methylation microarrays	p. 165
Network analysis	p. 168
Distribution of connectivities	p. 169
Active epigenetically regulated loci	p. 169
Correlation of connectivities	p. 170
Modularity	p. 171
Time Series Experiment	p. 179
Regulatory networks from microarray data	p. 181
Dynamic model of regulation	p. 182
A penalized likelihood score for parsimonious model	p. 182
Optimization by genetic algorithms	p. 184
Online Annotations	p. 187
Gene centric resources	p. 187
GenBank: A nucleotide sequence database	p. 187
UniGene: An organized view of transcriptomes	p. 188
RefSeq: Reviews of sequences and annotations	p. 188
PubMed: A bibliographic database of biomedical journals	p. 189
dbSNP: Database for nucleotide sequence variation	p. 190
OMIM: A directory of human genes and genetic disorders	p. 190
Entrez Gene: A Web portal of genes	p. 190
PubMeth: A cancer methylation database	p. 192
Gene Ontology	p. 192
Kyoto Encyclopedia of Genes and Genomes	p. 195
UniProt/Swiss-Prot protein knowledgebase	p. 196
The International HapMap Project	p. 198
UCSC human genome browser	p. 198
Public Microarray Data Repositories	p. 205
Epigenetics Society	p. 205
Microarray Gene Expression Data Society	p. 206
Minimum Information about a Microarray Experiment	p. 206
Public repositories for high-throughput arrays	p. 208
Gene Expression Omnibus at NCBI	p. 208
ArrayExpress at EBI	p. 208
Center for Information Biology Gene Expression data-base at DDBJ	p. 210
Open Source Software for Microarray Data Analysis	p. 211
R: A language and environment for statistical computing and graphics	p. 212
Bioconductor	p. 212
Marray package	p. 215
Affy package	p. 215
Limma package	p. 215
Stats package	p. 215
TilingArray package	p. 217
Ringo package	p. 217
Cluster package	p. 217
Class package	p. 217
GeneNet package	p. 217
Inetwork package	p. 217
GOstats package	p. 218
Annotate package	p. 218
References	p. 219
Index	p. 225
Table of Contents provided by Ingram. All Rights Reserved.

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DNA Methylation Microarrays: Experimental Design and Statistical Analysis

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DNA Methylation Microarrays: Experimental Design and Statistical Analysis

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