Bioconductor case studies:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2008
|
| Schriftenreihe: | Use R
|
| Schlagworte: | |
| Online-Zugang: | Kapitel 2 Einführung/Vorwort Inhaltsverzeichnis |
| Beschreibung: | X, 283 S. Ill., zahlr. graph. Darst. 235 mm x 155 mm |
| ISBN: | 0387772391 9780387772394 |
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Datensatz im Suchindex
| _version_ | 1804137903835578368 |
|---|---|
| adam_text | Titel: Bioconductor case studies
Autor: Hahne, Florian
Jahr: 2008
Contents
Preface v
List of Contributors xi
1 The ALL Dataset 1
F. Hahne and R. Gentleman
1.1 Introduction .......................... 1
1.2 The ALL data......................... 1
1.3 Data subsetting......................... 2
1.4 Nonspecific filtering...................... 3
1.5 BCR/ABL ALL1/AF4 subset ................ 4
2 R and Bioconductor Introduction 5
R. Gentleman, F. Hahne, S. Falcon,
and M. Morgan
2.1 Finding help in R........................ 5
2.2 Working with packages.................... 7
2.3 Some basic R.......................... 8
2.4 Structures for genomic data.................. 11
2.5 Graphics............................. 20
3 Processing Affymetrix Expression Data 25
R. Gentleman and W. Huber
3.1 The input data: CEL files................... 25
3.2 Quality assessment....................... 28
3.3 Preprocessing.......................... 32
3.4 Ranking and filtering probe sets............... 33
3.5 Advanced preprocessing.................... 40
4 Two-Color Arrays 47
Florian Hahne and Wolfgang Huber
4.1 Introduction .......................... 47
4.2 Data import .......................... 48
4.3 Image plots........................... 50
4.4 Normalization.......................... 50
4.5 Differential expression..................... 57
Fold-Changes, Log-Ratios, Background Correction,
Shrinkage Estimation, and Variance Stabilization 63
W. Huber
5.1 Fold-changes and (log-)ratios................. 63
5.2 Background-correction and generalized logarithm..... 65
5.3 Calling VSN .......................... 70
5.4 How does VSN work?..................... 72
5.5 Robust fitting and the most genes not differentially
expressed assumption..................... 74
5.6 Single-color normalization................... 78
5.7 The interpretation of glog-ratios............... 79
5.8 Reference normalization.................... 81
Easy Differential Expression 83
F. Hahne and W. Huber
6.1 Example data.......................... 83
6.2 Nonspecific filtering...................... 84
6.3 Differential expression..................... 85
6.4 Multiple testing correction.................. 87
Differential Expression 89
W. Huber, D. Scholtens, F. Hahne,
and A. von Heydebreck
7.1 Motivation ........................... 89
7.2 Nonspecific filtering...................... 90
7.3 Differential expression..................... 92
7.4 Multiple testing ........................ 94
7.5 Moderated test statistics and the limma package..... 95
7.6 Gene selection by Receiver Operator Characteristic
(ROC).............................. 99
7.7 When power increases..................... 101
Annotation and Metadata 103
W. Huber and F. Hahne
8.1 Our data............................ 103
8.2 Multiple probe sets per gene................. 106
8.3 Categories and overrepresentation.............. 107
8.4 Working with GO....................... 109
8.5 Other annotations available.................. 112
8.6 biomaRt............................ 113
8.7 Database versions of annotation packages.......... 115
9 Supervised Machine Learning 121
R. Gentleman, W. Huber, and V. J. Carey
9.1 Introduction .......................... 121
9.2 The example dataset...................... 123
9.3 Feature selection and standardization............ 124
9.4 Selecting a distance...................... 124
9.5 Machine learning........................ 126
9.6 Cross-validation........................ 129
9.7 Random forests......................... 132
9.8 Multigroup classification ................... 135
10 Unsupervised Machine Learning 137
R. Gentleman and V. J. Carey
10.1 Preliminaries.......................... 137
10.2 Distances............................ 139
10.3 How many clusters?...................... 142
10.4 Hierarchical clustering..................... 144
10.5 Partitioning methods ..................... 146
10.6 Self-organizing maps...................... 148
10.7 Hopach ............................. 151
10.8 Silhouette plots......................... 152
10.9 Exploring transformations .................. 154
10.10 Remarks............................. 157
11 Using Graphs for Interactome Data 159
T. Chiang, S. Falcon, F. Hahne, and W. Huber
11.1 Introduction .......................... 159
11.2 Exploring the protein interaction graph........... 160
11.3 The co-expression graph.................... 162
11.4 Testing the association between physical interaction and
coexpression .......................... 164
11.5 Some harder problems..................... 165
11.6 Reading PSI-25 XML files from IntAct
with the Rintact package .................. 165
12 Graph Layout 173
F. Hahne, W. Huber, and R. Gentleman
12.1 Introduction .......................... 173
12.2 Layout and rendering using Rgraphviz .......... 175
12.3 Directed graphs......................... 180
12.4 Subgraphs............................ 185
12.5 Tooltips and hyperlinks on graphs.............. 187
13 Gene Set Enrichment Analysis 193
R. Gentleman, M. Morgan, and W. Huber
13.1 Introduction .......................... 193
13.2 Data analysis.......................... 196
13.3 Identifying and assessing the effects of overlapping gene
sets................................ 203
14 Hypergeometric Testing Used for Gene Set Enrichment
Analysis 207
S. Falcon and R. Gentleman
14.1 Introduction .......................... 207
14.2 The basic problem....................... 208
14.3 Preprocessing and inputs................... 209
14.4 Outputs and result summarization.............. 215
14.5 The conditional hypergeometric test............. 218
14.6 Other collections of gene sets................. 219
15 Solutions to Exercises 221
2 R and Bioconductor Introduction.............. 221
3 Processing Affymetrix Expression Data........... 226
4 Two-Color Arrays....................... 230
5 Fold-Changes, Log-Ratios, Background Correction,
Shrinkage Estimation, and Variance Stabilization..... 231
6 Easy Differential Expression................. 233
7 Differential Expression..................... 233
8 Annotation and Metadata................... 234
9 Supervised Machine Learning................. 241
10 Unsupervised Machine Learning............... 249
11 Using Graphs for Interactome Data............. 256
12 Graph Layout.......................... 259
13 Gene Set Enrichment Analysis................ 261
14 Hypergeometric Testing Used for Gene Set Enrichment
Analysis............................. 265
References 271
Index 277
|
| adam_txt |
Titel: Bioconductor case studies
Autor: Hahne, Florian
Jahr: 2008
Contents
Preface v
List of Contributors xi
1 The ALL Dataset 1
F. Hahne and R. Gentleman
1.1 Introduction . 1
1.2 The ALL data. 1
1.3 Data subsetting. 2
1.4 Nonspecific filtering. 3
1.5 BCR/ABL ALL1/AF4 subset . 4
2 R and Bioconductor Introduction 5
R. Gentleman, F. Hahne, S. Falcon,
and M. Morgan
2.1 Finding help in R. 5
2.2 Working with packages. 7
2.3 Some basic R. 8
2.4 Structures for genomic data. 11
2.5 Graphics. 20
3 Processing Affymetrix Expression Data 25
R. Gentleman and W. Huber
3.1 The input data: CEL files. 25
3.2 Quality assessment. 28
3.3 Preprocessing. 32
3.4 Ranking and filtering probe sets. 33
3.5 Advanced preprocessing. 40
4 Two-Color Arrays 47
Florian Hahne and Wolfgang Huber
4.1 Introduction . 47
4.2 Data import . 48
4.3 Image plots. 50
4.4 Normalization. 50
4.5 Differential expression. 57
Fold-Changes, Log-Ratios, Background Correction,
Shrinkage Estimation, and Variance Stabilization 63
W. Huber
5.1 Fold-changes and (log-)ratios. 63
5.2 Background-correction and generalized logarithm. 65
5.3 Calling VSN . 70
5.4 How does VSN work?. 72
5.5 Robust fitting and the "most genes not differentially
expressed" assumption. 74
5.6 Single-color normalization. 78
5.7 The interpretation of glog-ratios. 79
5.8 Reference normalization. 81
Easy Differential Expression 83
F. Hahne and W. Huber
6.1 Example data. 83
6.2 Nonspecific filtering. 84
6.3 Differential expression. 85
6.4 Multiple testing correction. 87
Differential Expression 89
W. Huber, D. Scholtens, F. Hahne,
and A. von Heydebreck
7.1 Motivation . 89
7.2 Nonspecific filtering. 90
7.3 Differential expression. 92
7.4 Multiple testing . 94
7.5 Moderated test statistics and the limma package. 95
7.6 Gene selection by Receiver Operator Characteristic
(ROC). 99
7.7 When power increases. 101
Annotation and Metadata 103
W. Huber and F. Hahne
8.1 Our data. 103
8.2 Multiple probe sets per gene. 106
8.3 Categories and overrepresentation. 107
8.4 Working with GO. 109
8.5 Other annotations available. 112
8.6 biomaRt. 113
8.7 Database versions of annotation packages. 115
9 Supervised Machine Learning 121
R. Gentleman, W. Huber, and V. J. Carey
9.1 Introduction . 121
9.2 The example dataset. 123
9.3 Feature selection and standardization. 124
9.4 Selecting a distance. 124
9.5 Machine learning. 126
9.6 Cross-validation. 129
9.7 Random forests. 132
9.8 Multigroup classification . 135
10 Unsupervised Machine Learning 137
R. Gentleman and V. J. Carey
10.1 Preliminaries. 137
10.2 Distances. 139
10.3 How many clusters?. 142
10.4 Hierarchical clustering. 144
10.5 Partitioning methods . 146
10.6 Self-organizing maps. 148
10.7 Hopach . 151
10.8 Silhouette plots. 152
10.9 Exploring transformations . 154
10.10 Remarks. 157
11 Using Graphs for Interactome Data 159
T. Chiang, S. Falcon, F. Hahne, and W. Huber
11.1 Introduction . 159
11.2 Exploring the protein interaction graph. 160
11.3 The co-expression graph. 162
11.4 Testing the association between physical interaction and
coexpression . 164
11.5 Some harder problems. 165
11.6 Reading PSI-25 XML files from IntAct
with the Rintact package . 165
12 Graph Layout 173
F. Hahne, W. Huber, and R. Gentleman
12.1 Introduction . 173
12.2 Layout and rendering using Rgraphviz . 175
12.3 Directed graphs. 180
12.4 Subgraphs. 185
12.5 Tooltips and hyperlinks on graphs. 187
13 Gene Set Enrichment Analysis 193
R. Gentleman, M. Morgan, and W. Huber
13.1 Introduction . 193
13.2 Data analysis. 196
13.3 Identifying and assessing the effects of overlapping gene
sets. 203
14 Hypergeometric Testing Used for Gene Set Enrichment
Analysis 207
S. Falcon and R. Gentleman
14.1 Introduction . 207
14.2 The basic problem. 208
14.3 Preprocessing and inputs. 209
14.4 Outputs and result summarization. 215
14.5 The conditional hypergeometric test. 218
14.6 Other collections of gene sets. 219
15 Solutions to Exercises 221
2 R and Bioconductor Introduction. 221
3 Processing Affymetrix Expression Data. 226
4 Two-Color Arrays. 230
5 Fold-Changes, Log-Ratios, Background Correction,
Shrinkage Estimation, and Variance Stabilization. 231
6 Easy Differential Expression. 233
7 Differential Expression. 233
8 Annotation and Metadata. 234
9 Supervised Machine Learning. 241
10 Unsupervised Machine Learning. 249
11 Using Graphs for Interactome Data. 256
12 Graph Layout. 259
13 Gene Set Enrichment Analysis. 261
14 Hypergeometric Testing Used for Gene Set Enrichment
Analysis. 265
References 271
Index 277 |
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| series2 | Use R |
| spelling | Bioconductor case studies Florian Hahne ... New York, NY Springer 2008 X, 283 S. Ill., zahlr. graph. Darst. 235 mm x 155 mm txt rdacontent n rdamedia nc rdacarrier Use R Bioconductor (Computer file) Bioinformatics R (Computer program language) Bioinformatik (DE-588)4611085-9 gnd rswk-swf Molekulare Bioinformatik (DE-588)4531334-9 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content R Programm (DE-588)4705956-4 s Bioinformatik (DE-588)4611085-9 s DE-604 Molekulare Bioinformatik (DE-588)4531334-9 s Hahne, Florian Sonstige (DE-588)13576968X oth Erscheint auch als Online-Ausgabe 978-0-387-77240-0 text/pdf http://swbplus.bsz-bw.de/bsz287671259kap.htm Kapitel 2 text/pdf http://swbplus.bsz-bw.de/bsz287671259vor.htm Einführung/Vorwort HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016660843&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bioconductor case studies Bioconductor (Computer file) Bioinformatics R (Computer program language) Bioinformatik (DE-588)4611085-9 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4611085-9 (DE-588)4531334-9 (DE-588)4705956-4 (DE-588)4123623-3 |
| title | Bioconductor case studies |
| title_auth | Bioconductor case studies |
| title_exact_search | Bioconductor case studies |
| title_exact_search_txtP | Bioconductor case studies |
| title_full | Bioconductor case studies Florian Hahne ... |
| title_fullStr | Bioconductor case studies Florian Hahne ... |
| title_full_unstemmed | Bioconductor case studies Florian Hahne ... |
| title_short | Bioconductor case studies |
| title_sort | bioconductor case studies |
| topic | Bioconductor (Computer file) Bioinformatics R (Computer program language) Bioinformatik (DE-588)4611085-9 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Bioconductor (Computer file) Bioinformatics R (Computer program language) Bioinformatik Molekulare Bioinformatik R Programm Lehrbuch |
| url | http://swbplus.bsz-bw.de/bsz287671259kap.htm http://swbplus.bsz-bw.de/bsz287671259vor.htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016660843&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hahneflorian bioconductorcasestudies |