Bioinformatics and computational biology solutions using R and bioconductor (New York, 2005). - ОГЛАВЛЕНИЕ / CONTENTS
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ОбложкаBioinformatics and computational biology solutions using R and bioconductor / ed. by R.Gentleman, V.J.Carey. - New York: Springer Science+Business Media, 2005. - xix, 473 p.: ill. - (Statistics for biology and health). - Bibliogr.: p.445-463. - Ind.: p.465-473. - Пер. загл.: Решения биоинформатики и компьютерной биологии с применением R и биоиндикатора. - ISBN 978-0387-25146-2
 

Место хранения: 02 | Отделение ГПНТБ СО РАН | Новосибирск

Оглавление / Contents
 
I   Preprocessing data from genomic experiments ................. 1

1    Preprocessing Overview ..................................... 3
     W. Huber, R.A. Irizarry, and R. Gentleman
1.1  Introduction ............................................... 3
1.2  Tasks ...................................................... 4
     1.2.1  Prerequisites ....................................... 5
     1.2.2  Stepwise and integrated approaches .................. 5
1.3  Data structures ............................................ 6
     1.3.1  Data sources ........................................ 6
     1.3.2  Facilities in R and Bioconductor .................... 7
1.4  Statistical background ..................................... 8
     1.4.1  An error model ...................................... 9
     1.4.2  The variance-bias trade-off ........................ 11
     1.4.3  Sensitivity and specificity of probes .............. 11
1.5  Conclusion ................................................ 12

2    Preprocessing High-density Oligonucleotide Arrays ......... 13
     B.M. Bolstad, R.A. Irizarry, L. Gautier, and Z. Wu
2.1  Introduction .............................................. 13
2.2  Importing and accessing probe-level data .................. 15
     2.2.1  Importing .......................................... 15
     2.2.2  Examining probe-level data ......................... 15
2.3  Background adjustment and normalization ................... 18
     2.3.1  Background adjustment .............................. 18
     2.3.2  Normalization ...................................... 20
     2.3.3  vsn ................................................ 24
2.4  Summarization ............................................. 25
     2.4.1  expresso ........................................... 25
     2.4.2  threestep .......................................... 26
     2.4.3  RMA ................................................ 27
     2.4.4  GCRMA .............................................. 27
     2.4.5  affypdnn ........................................... 28
2.5  Assessing preprocessing methods ........................... 29
     2.5.1  Carrying out the assessment ........................ 30
2.6  Conclusion ................................................ 32

3    Quality Assessment of Affymetrix GeneChip Data ............ 33
     B.M. Bolstad, F. Collin, J. Brettschneider, К. Simpson,
     L. Cope, R.A. Irizarry, and T.P. Speed
3.1  Introduction .............................................. 33
3.2  Exploratory data analysis ................................. 34
     3.2.1   Multi-array approaches ............................ 35
3.3  Affymetrix quality assessment metrics ..................... 37
3.4  RNA degradation ........................................... 38
3.5  Probe level models ........................................ 41
     3.5.1  Quality diagnostics using PLM ...................... 42
3.6  Conclusion ................................................ 47

4    Preprocessing Two-Color Spotted Arrays .................... 49
     Y.H. Yang and A.C. Paquet
4.1  Introduction .............................................. 49
4.2  Two-color spotted microarrays ............................. 50
     4.2.1   Illustrative data ................................. 50
4.3  Importing and accessing probe-level data .................. 51
     4.3.1  Importing .......................................... 51
     4.3.2  Reading target information ......................... 52
     4.3.3  Reading probe-related information .................. 53
     4.3.4  Reading probe and background intensities ........... 54
     4.3.5  Data structure: the marrayRaw class ................ 54
     4.3.6  Accessing the data ................................. 56
     4.3.7  Subsetting ......................................... 56
4.4  Quality assessment ........................................ 57
     4.4.1  Diagnostic plots ................................... 57
     4.4.2  Spatial plots of spot statistics - image ........... 59
     4.4.3  Boxplots of spot statistics - boxplot .............. 60
     4.4.4  Scatter-plots of spot statistics - plot ............ 61
4.5  Normalization ............................................. 62
     4.5.1  Two-channel normalization .......................... 63
     4.5.2  Separate-channel normalization ..................... 64
     4.6  Case study ........................................... 67

5    Cell-Based Assays ......................................... 71
     W. Huber and F. Hahne
5.1  Scope ..................................................... 71
5.2  Experimental technologies ................................. 71
     5.2.1  Expression assays .................................. 72
     5.2.2  Loss of function assays ............................ 72
     5.2.3  Monitoring the response ............................ 72
5.3  Reading data .............................................. 73
     5.3.1  Plate reader data .................................. 74
     5.3.2  Further directions in normalization ................ 76
     5.3.3  FCS format ......................................... 77
5.4  Quality assessment and visualization ...................... 79
     5.4.1  Visualization at the level of individual cells ..... 79
     5.4.2  Visualization at the level of microtiter plates .... 82
     5.4.3  Brushing with Rggobi ............................... 83
5.5  Detection of effectors .................................... 85
     5.5.1  Discrete Response .................................. 85
     5.5.2  Continuous response ................................ 88
     5.5.3  Outlook ............................................ 90

6    SELDI-TOF Mass Spectrometry Protein Data .................. 91
     X. Li, R. Gentleman, X. Lu, Q. Shi, J.D. Iglehart,
     L. Harris, and A. Miron
6.1  Introduction .............................................. 91
6.2  Baseline subtraction ...................................... 93
6.3  Peak detection ............................................ 95
6.4  Processing a set of calibration spectra ................... 96
     6.4.1  Apply baseline subtraction to a set of spectra ..... 98
     6.4.2  Normalize spectra .................................. 99
     6.4.3  Cutoff selection .................................. 100
     6.4.4  Identify peaks .................................... 101
     6.4.5  Quality assessment ................................ 101
     6.4.6  Get proto-biomarkers .............................. 102
6.5  An example ............................................... 105
6.6  Conclusion ............................................... 108

II   Meta-data: biological annotation and visualiza-
     tion ..................................................... 111

7    Meta-data Resources and Tools in Bioconductor ............ 113
     R. Gentleman, V.J. Carey, and J. Zhang
7.1  Introduction ............................................. 113
7.2  External annotation resources ............................ 115
7.3  Bioconductor annotation concepts: curated persistent
     packages and Web services ................................ 116
     7.3.1  Annotating a platform: HG-U95Av2 .................. 117
     7.3.2  An Example ........................................ 118
     7.3.3  Annotating a genome ............................... 119
7.4  The annotate package ..................................... 119
7.5  Software tools for working with Gene Ontology (GO) ....... 120
     7.5.1  Basics of working with the GO package ............. 121
     7.5.2  Navigating the hierarchy .......................... 122
     7.5.3  Searching for terms ............................... 122
     7.5.4  Annotation of GO terms to LocusLink sequences:
            evidence codes .................................... 123
     7.5.5  The GO graph associated with a term ............... 125
7.6  Pathway annotation packages: KEGG and cMAP ............... 125
     7.6.1  KEGG .............................................. 126
     7.6.2  cMAP .............................................. 127
     7.6.3  A Case Study ...................................... 129
7.7  Cross-organism annotation: the homology packages ......... 130
7.8  Annotation from other sources ............................ 132
7.9  Discussion ............................................... 133

8    Querying On-line Resources ............................... 135
     V.J. Carey, D. Temple Lang, J. Gentry, J. Zhang, and
     R. Gentleman
8.1  The Tools ................................................ 135
     8.1.1  Entrez ............................................ 137
     8.1.2  Entrez examples ................................... 137
8.2  PubMed ................................................... 138
     8.2.1  Accessing PubMed information ...................... 139
     8.2.2  Generating HTML output for your abstracts ......... 141
8.3  KEGG via SOAP ............................................ 142
8.4  Getting gene sequence information ........................ 144
8.5  Conclusion ............................................... 145

9    Interactive Outputs ...................................... 147
     C.A. Smith, W. Huber, and R. Gentleman
9.1  Introduction ............................................. 147
9.2  A simple approach ........................................ 148
9.3  Using the annaffy package ................................ 149
9.4  Linking to On-line Databases ............................. 152
9.5  Building HTML pages ...................................... 153
     9.5.1  Limiting the results .............................. 153
     9.5.2  Annotating the probes ............................. 154
     9.5.3  Adding other data ................................. 155
9.6  Graphical displays with drill-down functionality ......... 156
     9.6.1  HTML image maps ................................... 157
     9.6.2  Scalable Vector Graphics (SVG) .................... 158
9.7  Searching Meta-data ...................................... 159
     9.7.1  Text searching .................................... 159
9.8  Concluding Remarks ....................................... 160

10   Visualizing Data ......................................... 161
     W. Huber, X. Li, and R. Gentleman
10.1 Introduction ............................................. 161
10.2 Practicalities ........................................... 162
10.3 High-volume scatterplots ................................. 163
     10.3.1 A note on performance ............................. 164
10.4 Heatmaps ................................................. 166
     10.4.1 Heatmaps of residuals ............................. 168
10.5 Visualizing distances .................................... 170
     10.5.1 Multidimensional scaling .......................... 173
10.6 Plotting along genomic coordinates ....................... 174
     10.6.1 Cumulative Expression ............................. 178
10.7 Conclusion ............................................... 179

III  Statistical analysis for genomic experiments ............. 181

11   Analysis Overview ........................................ 183
     V.J. Carey and R. Gentleman
11.1 Introduction and road map ................................ 183
     11.1.1 Distance concepts ................................. 184
     11.1.2 Differential expression ........................... 184
     11.1.3 Cluster analysis .................................. 184
     11.1.4 Machine learning .................................. 184
     11.1.5 Multiple comparisons .............................. 185
     11.1.6 Workflow support .................................. 185
11.2 Absolute and relative expression measures ................ 185

12   Distance Measures in DNA Microarray Data Analysis ........ 189
     R. Gentleman, B. Ding, S. Dudoit, and J. Ibrahim
12.1 Introduction ............................................. 189
12.2 Distances ................................................ 191
     12.2.1 Definitions ....................................... 191
     12.2.2 Distances between points .......................... 192
     12.2.3 Distances between distributions ................... 195
     12.2.4 Experiment-specific distances between genes ....... 198
12.3 Microarray data .......................................... 199
     12.3.1 Distances and standardization ..................... 199
12.4 Examples ................................................. 201
     12.4.1 A co-citation example ............................. 203
     12.4.2 Adjacency ......................................... 207
12.5 Discussion ............................................... 208

13    Cluster Analysis of Genomic Data ........................ 209
      K.S. Pollard and M.J. van der Laan
13.1 Introduction ............................................. 209
13.2 Methods .................................................. 210
     13.2.1 Overview of clustering algorithms ................. 210
     13.2.2 Ingredients of a clustering algorithm ............. 211
     13.2.3 Building sequences of clustering results .......... 211
     13.2.4 Visualizing clustering results .................... 214
     13.2.5 Statistical issues in clus'tering ................. 215
     13.2.6 Bootstrapping a cluster analysis .................. 216
     13.2.7 Number of clusters ................................ 217
13.3 Application: renal cell cancer ........................... 222
     13.3.1 Gene selection .................................... 222
     13.3.2 HOPACH clustering of genes ........................ 223
     13.3.3 Comparison with PAM ............................... 224
     13.3.4 Bootstrap resampling .............................. 224
     13.3.5 HOPACH clustering of arrays ....................... 224
     13.3.6 Output files ...................................... 226
13.4 Conclusion ............................................... 228

14   Analysis of Differential Gene Expression Studies ......... 229
     D. Scholtens and A. von Heydebreck
14.1 Introduction ............................................. 229
14.2 Differential expression analysis ......................... 230
     14.2.1 Example: ALL data ................................. 232
     14.2.2 Example: Kidney cancer data ....................... 236
14.3 Multifactor experiments .................................. 239
     14.3.1 Example: Estrogen data ............................ 241
     14.4 Conclusion .......................................... 248

15   Multiple Testing Procedures: the multtest Package and
     Applications to Genomics ................................. 249
     K.S. Pollard, S. Dudoit, and M.J. van der Laan
15.1 Introduction ............................................. 249
15.2 Multiple hypothesis testing methodology .................. 250
     15.2.1 Multiple hypothesis testing framework ............. 250
     15.2.2 Test statistics null distribution ................. 255
     15.2.3 Single-step procedures for controlling general
            Type I error rates 6{FVri) ........................ 256
     15.2.4 Step-down procedures for controlling the
            family-wise error rate ............................ 257
     15.2.5 Augmentation multiple testing procedures for
            controlling tail probability error rates .......... 258
15.3 Software implementation: R multtest package .............. 259
     15.3.1 Resampling-based multiple testing procedures:
            MTP function ...................................... 260
     15.3.2 Numerical and graphical summaries ................. 262
15.4 Applications: ALL microarray data set .................... 262
     15.4.1 ALL data package and initial gene filtering ....... 262
     15.4.2 Association of expression measures and tumor
            cellular subtype: Two-sample ^-statistics ......... 263
     15.4.3 Augmentation procedures ........................... 265
     15.4.4 Association of expression measures and tumor
            molecular subtype: Multi-sample F-statistics ...... 266
     15.4.5 Association of expression measures and time to
            relapse: Cox t-statistics ......................... 268
15.5  Discussion .............................................. 270

16   Machine Learning Concepts and Tools for Statistical
     Genomics ................................................. 273
     V.J. Carey
16.1 Introduction   273
16.2 Illustration: Two continuous features; decision regions .. 274
16.3 Methodological issues .................................... 276
     16.3.1 Families of learning methods ...................... 276
     16.3.2 Model assessment .................................. 281
     16.3.3 Metatheorems on learner and feature selection ..... 283
     16.3.4 Computing interfaces .............................. 284
16.4 Applications ............................................. 285
     16.4.1 Exploring and comparing classifiers with the ALL
            data .............................................. 285
     16.4.2 Neural net initialization, convergence, and
            tuning ............................................ 287
     16.4.3 Other methods ..................................... 287
     16.4.4 Structured cross-validation support ............... 288
     16.4.5 Assessing variable importance ..................... 289
     16.4.6 Expression density diagnostics .................... 289
16.5 Conclusions .............................................. 291

17   Ensemble Methods of Computational Inference .............. 293
     T. Hothorn, and Bühlmann
17.1 Introduction ............................................. 293
17.2 Bagging and random forests ............................... 295
17.3 Boosting ................................................. 296
17.4 Multiclass problems ...................................... 298
17.5 Evaluation ............................................... 298
17.6 Applications: tumor prediction ........................... 300
     17.6.1 Acute lymphoblastic leukemia ...................... 300
     17.6.2 Renal cell cancer ................................. 303
17.7 Applications: Survival analysis .......................... 307
17.8 Conclusion ............................................... 310

18   Browser-based Affymetrix Analysis and Annotation ......... 313
     С.A. Smith
18.1 Introduction ............................................. 313
     18.1,1 Key user interface features ....................... 314
18.2 Deploying webbioc ........................................ 315
     18.2.1 System requirements ............................... 315
     18.2.2 Installation ...................................... 315
     18.2.3 Configuration ..................................... 316
18.3 Using webbioc ............................................ 317
     18.3.1 Data Preprocessing ................................ 317
     18.3.2 Differential expression multiple testing .......... 318
     18.3.3 Linked annotation meta-data ....................... 320
     18.3.4 Retrieving results ................................ 321
18.4 Extending webbioc ........................................ 322
     18.4.1 Architectural overview ............................ 322
     18.4.2 Creating a new module ............................. 324
18.5 Conclusion ............................................... 326

IV   Graphs and networks ...................................... 327

19   Introduction and Motivating Examples ..................... 329
     R. Gentleman, W. Huber, and V.J. Carey
19.1 Introduction ............................................. 329
19.2 Practicalities ........................................... 330
     19.2.1 Representation .................................... 330
     19.2.2 Algorithms ........................................ 330
     19.2.3 Data Analysis ..................................... 331
19.3 Motivating examples ...................................... 331
     19.3.1 Biomolecular Pathways ............................. 331
     19.3.2 Gene ontology: A graph of concept-terms ........... 333
     19.3.3 Graphs induced by literature references and
            citations ......................................... 334
19.4 Discussion ............................................... 336

20   Graphs ................................................... 337
     W. Huber, R. Gentleman, and V.J. Carey
20.1 Overview ................................................. 337
20.2 Definitions .............................................. 338
     20.2.1 Special types of graphs ........................... 341
     20.2.2 Random graphs ..................................... 343
     20.2.3 Node and edge labeling ............................ 344
     20.2.4 Searching and related algorithms .................. 344
20.3 Cohesive subgroups ....................................... 344
20.4 Distances ................................................ 346

21   Bioconductor Software for Graphs ......................... 347
     V.J. Carey, R. Gentleman, W. Huber, J. Centry
21.1 Introduction ............................................. 347
21.2 The graph package ........................................ 348
     21.2.1 Getting started ................................... 349
     21.2.2 Random graphs ..................................... 352
21.3 The RBGL package ......................................... 352
     21.3.1 Connected graphs .................................. 355
     21.3.2 Paths and related concepts ........................ 357
     21.3.3 RBGL summary ...................................... 360
21.4 Drawing graphs ........................................... 360
     21.4.1 Global attributes ................................. 363
     21.4.2 Node and edge attributes .......................... 363
     21.4.3 The function agopen and the Ragraph class ......... 365
     21.4.4 User-defined drawing functions .................... 366
     21.4.5 Image maps on graphs .............................. 368

22   Case Studies Using Graphs on Biological Data ............. 369
     R. Gentleman, D. Scholtens, B. Ding, V.J. Carey, and
     W. Huber
22.1 Introduction ............................................. 369
22.2 Comparing the transcriptome and the interactome .......... 370
     22.2.1 Testing associations .............................. 371
     22.2.2 Data analysis ..................................... 373
22.3 Using GO ................................................. 374
     22.3.1 Finding interesting GO terms ...................... 375
22.4 Literature co-citation ................................... 378
     22.4.1 Statistical development ........................... 380
     22.4.2 Comparisons of interest ........................... 382
     22.4.3 Examples .......................................... 382
22.5 Pathways ................................................. 387
     22.5.1 The graph structure of pathways ................... 388
     22.5.2 Relating expression data to pathways .............. 390
22.6 Concluding remarks ....................................... 393

V    Case studies ............................................. 395

23   Limma: Linear Models for Microarray Data ................. 397
     G.K. Smyth
23.1 Introduction ............................................. 397
23.2 Data representations ..................................... 398
23.3 Linear models ............................................ 399
23.4 Simple comparisons ....................................... 400
23.5 Technical Replication .................................... 403
23.6 Within-array replicate spots ............................. 406
23.7 Two groups ............................................... 407
23.8 Several groups ........................................... 409
23.9 Direct two-color designs ................................. 411
23.10 Factorial designs ....................................... 412
23.11 Time course experiments ................................. 414
23.12 Statistics for differential expression .................. 415
23.13 Fitted model objects .................................... 417
23.14 Preprocessing considerations ............................ 418
23.15 Conclusion .............................................. 420

24   Classification with Gene Expression Data ................. 421
     M. Dettling
24.1 Introduction ............................................. 421
24.2 Reading and customizing the data ......................... 422
24.3 Training and validating classifiers ...................... 423
24.4 Multiple random divisions ................................ 426
24.5 Classification of test data .............................. 428
24.6 Conclusion ............................................... 429

25   From CEL Files to Annotated Lists of Interesting Genes ... 431
     R.A. Irizarry
25.1 Introduction ............................................. 431
25.2 Reading CEL files ........................................ 432
25.3 Preprocessing ............................................ 432
25.4 Ranking and filtering genes .............................. 433
     25.4.1 Summary statistics and tests for ranking .......... 434
     25.4.2 Selecting cutoffs ................................. 437
     25.4.3 Comparison ........................................ 437
25.5 Annotation ............................................... 438
     25.5.1 PubMed abstracts .................................. 439
     25.5.2 Generating reports ................................ 441
25.6 Conclusion ............................................... 442

A    Details on selected resources ............................ 443
A.l  Data sets ................................................ 443
     A.l.l  ALL ............................................... 443
     A.1.2  Renal cell cancer ................................. 443
     A.1.3  Estrogen receptor stimulation ..................... 443
A.2  URLs for projects mentioned .............................. 444

References .................................................... 445

Index ......................................................... 465


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