DNA methylation microarrays: experimental design and statistical analysis
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Fla.
CRC Press
2008
|
| Series: | Chapman & Hall/CRC biostatistics series
26 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXVI, 230 S. Ill., graph. Darst. 1 CD-ROM |
| ISBN: | 9781420067279 |
Staff View
MARC
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| 035 | |a (OCoLC)187548701 | ||
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| 100 | 1 | |a Wang, Sun-Chong |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a DNA methylation microarrays |b experimental design and statistical analysis |c Sun-Chong Wang ; Arturas Petronis |
| 264 | 1 | |a Boca Raton, Fla. |b CRC Press |c 2008 | |
| 300 | |a XXVI, 230 S. |b Ill., graph. Darst. |e 1 CD-ROM | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Chapman & Hall/CRC biostatistics series |v 26 | |
| 650 | 4 | |a DNA Methylation | |
| 650 | 4 | |a DNA microarrays |x Research |x Methodology | |
| 650 | 4 | |a DNA microarrays |x Statistical methods | |
| 650 | 4 | |a DNA |x Methylation |x Research |x Methodology | |
| 650 | 4 | |a DNA |x Methylation |x Statistical methods | |
| 650 | 4 | |a Oligonucleotide Array Sequence Analysis |x statistics & numerical data | |
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Record in the Search Index
| _version_ | 1804137962294738944 |
|---|---|
| adam_text | Contents
1 Applied
Statistics I
1.1
Descriptive statistics
....................... 1
1.1.1
Frequency distribution
.................. 2
1.1.2
Central tendency and variability
............ 2
1.1.3
Correlation
........................ 4
1.2
Inferential statistics
....................... 6
1.2.1
Probability distribution
................. 6
1.2.2
Central limit theorem and normal distribution
.... 7
1.2.3
Statistical hypothesis testing
.............. 7
1.2.4
Two-sample i-test
.................... 9
1.2.5
Nonparametric test
.................... 9
1.2.6
One-factor ANOVA and F-test
............. 10
1.2.7
Simple linear regression
................. 11
1.2.8
Chi-square test of contingency
............. 13
1.2.9
Statistical power analysis
................ 14
2 DNA Methylation
Microarrays and Quality Control
17
2.1 DNA
methylation microarrays
................. 18
2.2
Workflow of methylome experiment
.............. 21
2.2.1
Restriction enzyme-based enrichment
......... 21
2.2.2
Immunoprecipitation-based enrichment
........ 21
2.3
Image analysis
.......................... 23
2.4
Visualization of raw data
.................... 26
2.5
Reproducibility
......................... 26
2.5.1
Positive and negative controls by exogenous sequences
32
2.5.2
Intensity fold-change and p-value
............ 32
2.5.3 DNA
unmethylation profiling
.............. 33
2.5.4
Correlation of intensities between tiling arrays
.... 33
3
Experimental Design
35
3.1
Goals of experiment.
....................... 36
3.1.1
Class comparison and class prediction
......... 36
3.1.2
Class discovery
...................... 36
3.2
Reference design
......................... 37
3.2.1
Dye swaps
......................... 39
3.3
Balanced block design
...................... 39
3.4
Loop design
........................... 41
ix
3.5
Factorial
design
......................... 42
3.6
Time course experimental design
................ 47
3.7
How many samples/arrays are needed?
............ 49
3.7.1
Biological versus technical replicates
.......... 49
3.7.2
Statistical power analysis
................ 49
3.7.3
Pooling biological samples
................ 55
3.8
Appendix
............................. 56
Data Normalization
59
4.1
Measure of methylation
..................... 59
4.2
The need for normalization
................... 61
4.3
Strategy for normalization
................... 62
4.4
Two-color CpG island microarray normalization
....... 63
4.4.1
Global dependence of log methylation ratios
...... 64
4.4.2
Dependence of log ratios on intensity
.......... 65
4.4.3
Dependence of log ratios on print-tips
......... 67
4.4.4
Normalized
СуЗ-
and CyS-intensities
.......... 70
4.4.5
Between-array normalization
.............. 71
4.5
Oligonucleotide arrays normalization
............. 72
4.5.1
Background correction: PM
-
MM?
.......... 72
4.5.2
Quantile normalization
.................. 73
4.5.3 Probeset
summarization
................. 75
4.6
Normalization using control sequences
............. 76
4.7
Appendix
............................. 79
Significant Differential Methylation
81
5.1
Fold change
........................... 81
5.2
Linear model for log-ratios or log-intensities
......... 84
5.2.1
Microarrays reference design or oligonucleotide chips
. 84
5.2.2
Sequence-specific dye effect in two-color microarrays
. 87
5.3
i-test for contrasts
........................ 88
5.4
F-test for joint contrasts
.................... 89
5.5
P-value adjustment for multiple testing
............ 92
5.5.1
Bouferroni correction
................... 92
5.5.2
False discovery rate
................... 92
5.6
Modified t- and
F-test
..................... 94
5.7
Significant, variation within and between groups
....... 95
5.7.1
Within-group rariation
................. 95
5.7.2
Between-group variation
................. 96
5.8
Significant correlation with a co-variate
............ 97
5.9
Permutation test for bisulfite sequence data
.......... 100
5.9.1
Euclidean distance
.................... 101
5.9.2
Entropy
.......................... 102
5.10
Missing data values
....................... 103
5.11
Appendix
............................. 104
5.11.1
Factorial
design
...................... 104
5.11.2
Time-course experiments
................ 105
5.11.3
Balanced block design
.................. 106
5.11.4
Loop design
........................ 107
High-Density Genomic Tiling Arrays
109
6.1
Normalization
.......................... 110
6.1.1
Intra-
and interarray normalization
........... 110
6.1.2
Sequence-based probe effects
.............. 110
6.2
Wilcoxon test in a sliding window
............... 112
6.2.1
Probe score or scan statistic
............... 116
6.2.2
False positive rate
.................... 116
6.3
Boundaries of methylation regions
............... 118
6.4
Multiscale analysis by wavelets
................. 119
6.5
Unsupervised segmentation by hidden Markov model
.... 121
6.6
Principal component analysis and biplot
........... 125
Cluster Analysis
129
7.1
Measure of dissimilarity
..................... 129
7.2
Dimensionality reduction
.................... 130
7.3
Hierarchical clustering
...................... 133
7.3.1
Bottom-up approach
................... 133
7.3.2
Top-down approach
................... 136
7.4
K-means clustering
....................... 139
7.5
Model-based clustering
..................... 141
7.6
Quality of clustering
....................... 142
7.7
Statistically significance of clusters
............... 144
7.8
Reproducibility of clusters
................... 146
7.9
Repeated measurements
..................... 146
Statistical Classification
149
8.1
Feature selection
......................... 149
8.2
Discriminant function
...................... 152
8.2.1
Linear discriminant analysis
............... 153
8.2.2
Diagonal linear discriminant analysis
.......... 154
8.3
K-nearest neighbor
....................... 154
8.4
Performance assessment
..................... 155
8.4.1
Leave-one-out cross validation
.............. 156
8.4.2
Receiver operating characteristic analysis
....... 159
Interdependency Network of
DNA
Methylation
163
9.1
Graphs and networks
...................... 164
9.2
Partial correlation
........................ 164
9.3
Dependence networks from
DNA
methylation microarrays
. . 165
9.4
Network analysis
......................... 168
Xli
9.4.1 Distribution
of
Connectivities.............. 169
9.4.2 Active epigenetically
regulated loci...........
169
9.4.3
Correlation of connectivities
............... 170
9.4.4
Modularity
........................ 171
10
Time Series Experiment
179
10.1
Regulatory networks from microarray data
.......... 181
10.2
Dynamic model of regulation
.................. 182
10.3
A penalized likelihood score for parsimonious model
..... 182
10.4
Optimization by genetic algorithms
.............. 184
11
Online Annotations
187
11.1
Gene centric resources
...................... 187
11.1.1
GenBank: A nucleotide sequence database
....... 187
11.1.2
UniGene: An organized view of
transcriptomes
.... 188
11.1.3
RefSeq: Reviews of sequences and annotations
.... 188
11.1.4
PubMed: A bibliographic database of
biomedical
jour¬
nals
............................ 189
11.1.5
dbSNP: Database for nucleotide sequence variation
. . 190
11.1.6
OMIM: A directory of human genes and genetic disor¬
ders
............................ 190
11.1.7
Entrez
Gene: A Web portal of genes
.......... 190
11.2
PubMeth: A cancer methylation database
........... 192
11.3
Gene Ontology
.......................... 192
11.4
Kyoto Encyclopedia of Genes and Genomes
.......... 195
11.5
UniProt/Swiss-Prot protein knowledgebase
.......... 196
11.6
The International HapMap Project
.............. 198
11.7
UCSC human genome browser
................. 198
12
Public Microarray Data Repositories
205
12.1
Epigenetics Society
....................... 205
12.2
Microarray Gene Expression Data Society
........... 206
12.3
Minimum Information about a Microarray Experiment
. . . 206
12.4
Public repositories for high-throughput arrays
........ 208
12.4.1
Gene Expression Omnibus at NCBI
.......... 208
12.4.2
ArrayExpress at
EBI
................... 208
12.4.3
Center for Information Biology Gene Expression data¬
base at DDBJ
....................... 210
13
Open Source Software for Microarray Data Analysis
211
13.1
R: A language and environment for statistical computing and
graphics
.............................. 212
13.2
Bioconductor
........................... 212
13.2.1
marray package
...................... 215
13.2.2
affy package
....................... 215
ХНІ
13.2.3
limma
package
...................... 215
13.2.4
stats
package
...................... 215
13.2.5
tilingArray
package
.................. 217
13.2.6
Ringo
package
...................... 217
13.2.7
duster
package
..................... 217
13.2.8
class package
...................... 217
13.2.9
GeneNet package
..................... 217
13.2.10
inetwork package
.................... 217
13.2.11
GOstats package
..................... 218
13.2.12annotate package
.................... 218
References
219
Index
225
|
| adam_txt |
Contents
1 Applied
Statistics I
1.1
Descriptive statistics
. 1
1.1.1
Frequency distribution
. 2
1.1.2
Central tendency and variability
. 2
1.1.3
Correlation
. 4
1.2
Inferential statistics
. 6
1.2.1
Probability distribution
. 6
1.2.2
Central limit theorem and normal distribution
. 7
1.2.3
Statistical hypothesis testing
. 7
1.2.4
Two-sample i-test
. 9
1.2.5
Nonparametric test
. 9
1.2.6
One-factor ANOVA and F-test
. 10
1.2.7
Simple linear regression
. 11
1.2.8
Chi-square test of contingency
. 13
1.2.9
Statistical power analysis
. 14
2 DNA Methylation
Microarrays and Quality Control
17
2.1 DNA
methylation microarrays
. 18
2.2
Workflow of methylome experiment
. 21
2.2.1
Restriction enzyme-based enrichment
. 21
2.2.2
Immunoprecipitation-based enrichment
. 21
2.3
Image analysis
. 23
2.4
Visualization of raw data
. 26
2.5
Reproducibility
. 26
2.5.1
Positive and negative controls by exogenous sequences
32
2.5.2
Intensity fold-change and p-value
. 32
2.5.3 DNA
unmethylation profiling
. 33
2.5.4
Correlation of intensities between tiling arrays
. 33
3
Experimental Design
35
3.1
Goals of experiment.
. 36
3.1.1
Class comparison and class prediction
. 36
3.1.2
Class discovery
. 36
3.2
Reference design
. 37
3.2.1
Dye swaps
. 39
3.3
Balanced block design
. 39
3.4
Loop design
. 41
ix
3.5
Factorial
design
. 42
3.6
Time course experimental design
. 47
3.7
How many samples/arrays are needed?
. 49
3.7.1
Biological versus technical replicates
. 49
3.7.2
Statistical power analysis
. 49
3.7.3
Pooling biological samples
. 55
3.8
Appendix
. 56
Data Normalization
59
4.1
Measure of methylation
. 59
4.2
The need for normalization
. 61
4.3
Strategy for normalization
. 62
4.4
Two-color CpG island microarray normalization
. 63
4.4.1
Global dependence of log methylation ratios
. 64
4.4.2
Dependence of log ratios on intensity
. 65
4.4.3
Dependence of log ratios on print-tips
. 67
4.4.4
Normalized
СуЗ-
and CyS-intensities
. 70
4.4.5
Between-array normalization
. 71
4.5
Oligonucleotide arrays normalization
. 72
4.5.1
Background correction: PM
-
MM?
. 72
4.5.2
Quantile normalization
. 73
4.5.3 Probeset
summarization
. 75
4.6
Normalization using control sequences
. 76
4.7
Appendix
. 79
Significant Differential Methylation
81
5.1
Fold change
. 81
5.2
Linear model for log-ratios or log-intensities
. 84
5.2.1
Microarrays reference design or oligonucleotide chips
. 84
5.2.2
Sequence-specific dye effect in two-color microarrays
. 87
5.3
i-test for contrasts
. 88
5.4
F-test for joint contrasts
. 89
5.5
P-value adjustment for multiple testing
. 92
5.5.1
Bouferroni correction
. 92
5.5.2
False discovery rate
. 92
5.6
Modified t- and
F-test
. 94
5.7
Significant, variation within and between groups
. 95
5.7.1
Within-group \rariation
. 95
5.7.2
Between-group variation
. 96
5.8
Significant correlation with a co-variate
. 97
5.9
Permutation test for bisulfite sequence data
. 100
5.9.1
Euclidean distance
. 101
5.9.2
Entropy
. 102
5.10
Missing data values
. 103
5.11
Appendix
. 104
5.11.1
Factorial
design
. 104
5.11.2
Time-course experiments
. 105
5.11.3
Balanced block design
. 106
5.11.4
Loop design
. 107
High-Density Genomic Tiling Arrays
109
6.1
Normalization
. 110
6.1.1
Intra-
and interarray normalization
. 110
6.1.2
Sequence-based probe effects
. 110
6.2
Wilcoxon test in a sliding window
. 112
6.2.1
Probe score or scan statistic
. 116
6.2.2
False positive rate
. 116
6.3
Boundaries of methylation regions
. 118
6.4
Multiscale analysis by wavelets
. 119
6.5
Unsupervised segmentation by hidden Markov model
. 121
6.6
Principal component analysis and biplot
. 125
Cluster Analysis
129
7.1
Measure of dissimilarity
. 129
7.2
Dimensionality reduction
. 130
7.3
Hierarchical clustering
. 133
7.3.1
Bottom-up approach
. 133
7.3.2
Top-down approach
. 136
7.4
K-means clustering
. 139
7.5
Model-based clustering
. 141
7.6
Quality of clustering
. 142
7.7
Statistically significance of clusters
. 144
7.8
Reproducibility of clusters
. 146
7.9
Repeated measurements
. 146
Statistical Classification
149
8.1
Feature selection
. 149
8.2
Discriminant function
. 152
8.2.1
Linear discriminant analysis
. 153
8.2.2
Diagonal linear discriminant analysis
. 154
8.3
K-nearest neighbor
. 154
8.4
Performance assessment
. 155
8.4.1
Leave-one-out cross validation
. 156
8.4.2
Receiver operating characteristic analysis
. 159
Interdependency Network of
DNA
Methylation
163
9.1
Graphs and networks
. 164
9.2
Partial correlation
. 164
9.3
Dependence networks from
DNA
methylation microarrays
. . 165
9.4
Network analysis
. 168
Xli
9.4.1 Distribution
of
Connectivities. 169
9.4.2 Active epigenetically
regulated loci.
169
9.4.3
Correlation of connectivities
. 170
9.4.4
Modularity
. 171
10
Time Series Experiment
179
10.1
Regulatory networks from microarray data
. 181
10.2
Dynamic model of regulation
. 182
10.3
A penalized likelihood score for parsimonious model
. 182
10.4
Optimization by genetic algorithms
. 184
11
Online Annotations
187
11.1
Gene centric resources
. 187
11.1.1
GenBank: A nucleotide sequence database
. 187
11.1.2
UniGene: An organized view of
transcriptomes
. 188
11.1.3
RefSeq: Reviews of sequences and annotations
. 188
11.1.4
PubMed: A bibliographic database of
biomedical
jour¬
nals
. 189
11.1.5
dbSNP: Database for nucleotide sequence variation
. . 190
11.1.6
OMIM: A directory of human genes and genetic disor¬
ders
. 190
11.1.7
Entrez
Gene: A Web portal of genes
. 190
11.2
PubMeth: A cancer methylation database
. 192
11.3
Gene Ontology
. 192
11.4
Kyoto Encyclopedia of Genes and Genomes
. 195
11.5
UniProt/Swiss-Prot protein knowledgebase
. 196
11.6
The International HapMap Project
. 198
11.7
UCSC human genome browser
. 198
12
Public Microarray Data Repositories
205
12.1
Epigenetics Society
. 205
12.2
Microarray Gene Expression Data Society
. 206
12.3
Minimum Information about a Microarray Experiment
. . . 206
12.4
Public repositories for high-throughput arrays
. 208
12.4.1
Gene Expression Omnibus at NCBI
. 208
12.4.2
ArrayExpress at
EBI
. 208
12.4.3
Center for Information Biology Gene Expression data¬
base at DDBJ
. 210
13
Open Source Software for Microarray Data Analysis
211
13.1
R: A language and environment for statistical computing and
graphics
. 212
13.2
Bioconductor
. 212
13.2.1
marray package
. 215
13.2.2
affy package
. 215
ХНІ
13.2.3
limma
package
. 215
13.2.4
stats
package
. 215
13.2.5
tilingArray
package
. 217
13.2.6
Ringo
package
. 217
13.2.7
duster
package
. 217
13.2.8
class package
. 217
13.2.9
GeneNet package
. 217
13.2.10
inetwork package
. 217
13.2.11
GOstats package
. 218
13.2.12annotate package
. 218
References
219
Index
225 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Wang, Sun-Chong Petronis, Arturas |
| author_facet | Wang, Sun-Chong Petronis, Arturas |
| author_role | aut aut |
| author_sort | Wang, Sun-Chong |
| author_variant | s c w scw a p ap |
| building | Verbundindex |
| bvnumber | BV035031583 |
| callnumber-first | Q - Science |
| callnumber-label | QP624 |
| callnumber-raw | QP624.5.D726 |
| callnumber-search | QP624.5.D726 |
| callnumber-sort | QP 3624.5 D726 |
| callnumber-subject | QP - Physiology |
| classification_rvk | WD 5360 |
| ctrlnum | (OCoLC)187548701 (DE-599)BVBBV035031583 |
| dewey-full | 572.8/636 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 572 - Biochemistry |
| dewey-raw | 572.8/636 |
| dewey-search | 572.8/636 |
| dewey-sort | 3572.8 3636 |
| dewey-tens | 570 - Biology |
| discipline | Biologie |
| discipline_str_mv | Biologie |
| format | Book |
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| id | DE-604.BV035031583 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:50:00Z |
| indexdate | 2024-07-09T21:20:37Z |
| institution | BVB |
| isbn | 9781420067279 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016700562 |
| oclc_num | 187548701 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-19 DE-BY-UBM |
| owner_facet | DE-355 DE-BY-UBR DE-19 DE-BY-UBM |
| physical | XXVI, 230 S. Ill., graph. Darst. 1 CD-ROM |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | CRC Press |
| record_format | marc |
| series | Chapman & Hall/CRC biostatistics series |
| series2 | Chapman & Hall/CRC biostatistics series |
| spelling | Wang, Sun-Chong Verfasser aut DNA methylation microarrays experimental design and statistical analysis Sun-Chong Wang ; Arturas Petronis Boca Raton, Fla. CRC Press 2008 XXVI, 230 S. Ill., graph. Darst. 1 CD-ROM txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC biostatistics series 26 DNA Methylation DNA microarrays Research Methodology DNA microarrays Statistical methods DNA Methylation Research Methodology DNA Methylation Statistical methods Oligonucleotide Array Sequence Analysis statistics & numerical data Microarray (DE-588)4544227-7 gnd rswk-swf DNS (DE-588)4070512-2 gnd rswk-swf Methylierung (DE-588)4123751-1 gnd rswk-swf DNS (DE-588)4070512-2 s Methylierung (DE-588)4123751-1 s Microarray (DE-588)4544227-7 s b DE-604 Petronis, Arturas Verfasser aut Chapman & Hall/CRC biostatistics series 26 (DE-604)BV023097394 26 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016700562&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Wang, Sun-Chong Petronis, Arturas DNA methylation microarrays experimental design and statistical analysis Chapman & Hall/CRC biostatistics series DNA Methylation DNA microarrays Research Methodology DNA microarrays Statistical methods DNA Methylation Research Methodology DNA Methylation Statistical methods Oligonucleotide Array Sequence Analysis statistics & numerical data Microarray (DE-588)4544227-7 gnd DNS (DE-588)4070512-2 gnd Methylierung (DE-588)4123751-1 gnd |
| subject_GND | (DE-588)4544227-7 (DE-588)4070512-2 (DE-588)4123751-1 |
| title | DNA methylation microarrays experimental design and statistical analysis |
| title_auth | DNA methylation microarrays experimental design and statistical analysis |
| title_exact_search | DNA methylation microarrays experimental design and statistical analysis |
| title_exact_search_txtP | DNA methylation microarrays experimental design and statistical analysis |
| title_full | DNA methylation microarrays experimental design and statistical analysis Sun-Chong Wang ; Arturas Petronis |
| title_fullStr | DNA methylation microarrays experimental design and statistical analysis Sun-Chong Wang ; Arturas Petronis |
| title_full_unstemmed | DNA methylation microarrays experimental design and statistical analysis Sun-Chong Wang ; Arturas Petronis |
| title_short | DNA methylation microarrays |
| title_sort | dna methylation microarrays experimental design and statistical analysis |
| title_sub | experimental design and statistical analysis |
| topic | DNA Methylation DNA microarrays Research Methodology DNA microarrays Statistical methods DNA Methylation Research Methodology DNA Methylation Statistical methods Oligonucleotide Array Sequence Analysis statistics & numerical data Microarray (DE-588)4544227-7 gnd DNS (DE-588)4070512-2 gnd Methylierung (DE-588)4123751-1 gnd |
| topic_facet | DNA Methylation DNA microarrays Research Methodology DNA microarrays Statistical methods DNA Methylation Research Methodology DNA Methylation Statistical methods Oligonucleotide Array Sequence Analysis statistics & numerical data Microarray DNS Methylierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016700562&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023097394 |
| work_keys_str_mv | AT wangsunchong dnamethylationmicroarraysexperimentaldesignandstatisticalanalysis AT petronisarturas dnamethylationmicroarraysexperimentaldesignandstatisticalanalysis |