Introduction | Applicability and Scope | Key functions | Reading and accessing CNV data | Input data format | Representation as a GRangesList | Representation as a RaggedExperiment | Summarizing individual CNV calls across a population | Trimming low-density areas | Reciprocal overlap | Identifying recurrent regions | Overlap analysis of CNVs with functional genomic regions | Finding and illustrating overlaps | Overlap permutation test | CNV-expression association analysis | Application to individual CNV and RNA-seq assays | Application to TCGA data stored in a MultiAssayExperiment | CNV-phenotype association analysis | Setting up a CNV-GWAS | Running a CNV-GWAS | Producing a GDS file in advance | Using relative signal intensities | Session info | References

Setup | Installation | Data resources | General | BioPlex PPIs | ID mapping | Data structures for BioPlex PPIs | PFAM domains | CORUM complexes | Data structures for CORUM complexes | Transcriptome data | HEK293T cells | GSE122425 | HCT116 cells | Cancer Cell Line Encyclopedia (CCLE) | Klijn et al., 2015 | Splicing data | Proteome data | CCLE | Relative protein expression data from BioPlex3.0 | Caching | SessionInfo

Setup | Check: identify CORUM complexes that have a subunit of interest | Check: extract BioPlex PPIs for a CORUM complex | Check: identify interacting domains for a PFAM domain of interest | Check: expressed genes are showing up as prey (293T cells) | Check: is there a relationship between prey frequency and prey expression level? | Check: differential protein expression (HEK293 vs. HCT116) | SessionInfo

BugSigDB: a comprehensive database of published microbial signatures | Obtaining published microbial signatures from BugSigDB | Extracting microbe signatures | Writing microbe signatures to file in GMT format | Displaying BugSigDB signature and taxon pages | Ontology-based queries for experimental factors and body sites | Session info

Introduction | Reading expression data from file | Types of expression data | Microarray data | RNA-seq data | Normalization | Differential expression | ID mapping | Enrichment analysis | Obtaining gene sets | Set-based enrichment analysis | Guidelines for input and method selection | Result exploration | Network-based enrichment analysis | User-defined enrichment methods | Combining results | Putting it all together | Advanced: configuration parameters | For non-R users: command line invocation | A primer on terminology and statistical theory | Where does it all come from? | Gene sets, pathways & regulatory networks | Resources | Gene set analysis vs. gene set enrichment analysis | Underlying null: competitive vs. self-contained | Generations: ora, fcs & topology-based | Frequently asked questions | References

Purpose of the package | Setup | Expression data sources | Microarray compendium | RNA-seq compendium | User-defined data compendium | Differential expression | Enrichment analysis | User-defined enrichment methods | Benchmarking | Runtime | Fraction of significant gene sets | Phenotype relevance | MalaCards disease relevance rankings | Mapping between dataset ID and disease code | Relevance score of a gene set ranking | Random relevance score distribution | Theoretical optimum | Cross-dataset relevance score distribution | User-defined relevance rankings | Advanced | Caching | Parallel computation