Description
Book SynopsisThis book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology.
Table of ContentsForeword, xiii
Preface, xv
Acknowledgements, xix
About the Companion Website, xxi
1 Introduction to R statistical environment, 1
Why R?, 1
Installing R, 2
Interacting with R, 2
Graphical interfaces and integrated development environment (IDE) integration, 3
Scripting and sourcing, 3
The R history and the R environment file, 4
Packages and package repositories, 4
Comprehensive R Archive Network, 5
Bioconductor, 6
Working with data, 7
Basic operations in R, 8
Some basics of graphics in R, 10
Getting help in R, 12
Files for practicing, 13
Study exercises and questions, 14
References, 14
Webliography, 15
2 Simple sequence analysis, 17
Sequence files, 17
FASTA sequence format, 18
GenBank flat file format, 19
Reading sequence files into R, 20
Obtaining sequences from remote databases, 21
Seqinr package, 21
Ape package, 22
Descriptive statistics of nucleotide sequences, 24
Descriptive statistics of proteins, 28
Aligned sequences, 31
Visualization of genes and transcripts in a professional way, 34
Files for practicing, 37
Study exercises and questions, 38
References, 38
Webliography, 39
Packages, 40
3 Annotating gene groups, 41
Enrichment analysis: an overview, 41
Overview of two different methods, 41
Enrichment analysis results, 42
Common aspects of the two different approaches, 43
Overrepresentation analysis, 46
Hypergeometric test using GOstats, 47
ORA analysis using topGO, 48
Enrichment analysis of microarray sets with topGO, 51
Gene set enrichment analysis, 52
GSEA with R, 56
Files for practicing, 61
Study exercises and questions, 61
References, 62
Webliography, 62
Packages, 63
4 Next-generation sequencing: introduction and genomic applications, 65
High-throughput sequencing background, 65
Experimental background, 66
Single-end and paired-end sequencing reads, 67
Assemble reads, 69
How many reads? Depth of coverage, 71
Storing data in files, 72
FASTQ, 72
SAM and BAM files, 76
Variant call format files, 77
General data analysis workflow, 77
Data processing considerations, 78
Quality checking and screening read sequences, 80
Quality checking for one file, 82
Quality inspection for multiple files in a project, 82
Quality filtering of FASTQ files, 83
Handling alignment files and genomic variants, 84
Alignment and variation visualization, 88
Simple handling of VCF files, 89
Genomic applications: low- and medium-depth sequencing, 91
Aneuploidity sequencing and copy number variation identification, 92
SNP identification and validation, 92
Exome sequencing, 93
Genomic region resequencing, 93
Full genome and metagenome sequencing, 94
Files for practicing, 94
Study exercises and questions, 94
References, 95
Webliography, 97
Packages, 97
5 Quantitative transcriptomics: qRT-PCR, 99
Transcriptome, 99
Polymerase chain reaction, 100
Standards for qPCR, 102
R packages, 104
Understanding delta Ct, 104
Calculation of delta Ct, 105
Requirements for real delta Ct calculations, 107
Absolute quantification, 110
Value prediction, the professional way, 114
Relative quantification using the ddCt method, 115
Comparison of two conditions, 116
Comparison of multiple experimental conditions, 118
Quality control with melting curve, 121
Files for practicing, 123
Study exercises and questions, 123
References, 123
Webliography, 124
Packages, 124
6 Advanced transcriptomics: gene expression microarrays, 125
Microarray analysis: probes and samples, 125
Experimental background, 126
Archiving and publishing microarray data, 128
Minimum information standard, 128
Data preprocessing, 128
Accessing data from CEL files, 129
Quality control, 131
Normalization, 132
Differential gene expression, 133
Annotating results, 136
Creating normalized expression set from Illumina data, 138
Automated data access from GEO, 140
Files for practicing, 142
Study exercises and questions, 142
References, 143
Webliography, 144
Packages, 144
7 Next-generation sequencing in transcriptomics: RNA-seq experiments, 145
High-throughput RNA sequencing background, 145
Experimental background, 145
RNA-seq applications, 146
Differential expression with different resolutions, 147
Preparing count tables, 148
Alignment files to read counts, 148
Differential expression in simple comparison, 151
A naive t-test approach, 151
Single factor analysis with edgeR, 153
Differential expression with DESeq, 156
Complex experimental arrangements, 159
Experimental factors and design matrix, 160
GLM with edgeR, 161
GLMs with DESeq, 162
Heatmap visualization, 163
Files for practicing, 164
Study exercises and questions, 164
References, 165
Webliography, 166
Packages, 166
8 Deciphering the regulome: from ChIP to ChIP-seq, 167
Chromatin immunoprecipitation, 167
Experimental background, 168
Fragment analysis, 168
ChIP data in ENCODE, 169
ChIP with tiling microarrays, 169
High-throughput sequencing of ChIP fragments, 176
Connecting annotation to peaks, 181
Analysis of binding site motifs, 182
Files for practicing, 186
Study exercises and questions, 187
References, 187
Webliography, 188
Packages, 189
9 Inferring regulatory and other networks from gene expression data, 191
Gene regulatory networks, 191
Data for gene network inference, 192
Reconstruction of co-expression networks, 193
Gene regulatory network inference focusing of master regulators, 201
Integrated interpretation of genes with GeneAnswers, 207
Files for practicing, 211
Study exercises and questions, 212
References, 213
Packages, 214
10 Analysis of biological networks, 215
A gentle introduction to networks, 215
Networks and their components and features, 215
Random networks, 220
Biological networks, 221
Files for storing network information, 223
Important network metrics in biology, 227
Distance-based measures, 228
Degree and related measures, 230
Vulnerability, 231
Community structure of a network, 234
Graph visualization, 236
Cytoscape, 240
Files for practicing, 241
Study exercises and questions, 241
References, 242
Webliography, 243
Packages, 243
11 Proteomics: mass spectrometry, 245
Mass spectrometry and proteomics: why and how?, 245
File formats for MS data, 246
Accessing the raw data of published studies, 247
Identification of peptides in the samples, 249
Peptide mass fingerprinting, 249
Peptide identification by using MS/MS spectra, 250
Quantitative proteomics, 254
Getting protein-specific annotation, 258
Files for practicing, 259
Study exercises and questions, 259
References, 259
Webliography, 260
Packages, 260
12 Measuring protein abundance with ELISA, 261
Enzyme-linked immunosorbent assays, 261
Accessing ELISA data, 264
Concentration calculation with a standard curve, 264
Preparing reference data, 267
Fitting linear model, 268
Fitting of a logistic model, 269
Concentration calculations by employing models, 270
Comparative calculations using concentrations, 271
Files for practicing, 277
Study exercises and questions, 277
References, 277
Packages, 278
13 Flow cytometry: counting and sorting stained cells, 279
Theoretical aspects of flow cytometry, 279
Experiment types: diagnosis versus discovery, 280
Measurement arrangements, 281
Fluorescent dyes, 281
Tubes versus plates, 285
Instruments, 285
What about data?, 287
Files, 287
Workflows, 288
Data preprocessing, 289
Handling all samples together, 290
Compensation, 292
Quality assurance, 292
Using workflow objects and transformation, 296
Normalization, 298
Cell population identification, 299
Manual gating, 300
Automatic gating, 304
Relating cell populations to external variables, 305
Reporting results, 307
MIFlowCyt, 307
FlowRepository.org, 308
Files for practicing, 308
Study exercises and questions, 309
References, 309
Webliography, 310
Packages, 310
Glossary, 311
Index, 323
