Introduction | Parent-child relations | Bridging to MONDO for disease terminology | Bridging to CL for cell type enumeration | Session information

Introduction | Background on the ivyGlimpse app | Summary information on the underlying data | Additional details | Basic experimental design layout | Tumor-level details | Sub-block-level details | Details on RNA-seq samples | Key RNA-seq subsets | Subsets of design origin | Subsets based on structure | A simple differential expression study | Differential expression by molecular subtype | Classification of structural character | Next steps

Introduction | Rich Gene Queries | Get ALL genes with FULL attributes | Filter by gene_type | Filter by annotation confidence level | Rich Transcript Queries | Exon Queries with Context | CDS and Protein Information | UTRs and Codons | Transcripts by Gene | Region Queries | Performance Tips | Session information

Introduction | Installation | Acquiring ontologies | Make a connection | Make a report | Probe the back end | Use tidy methods | Transformation to ontology_index instances | Background | Searching in label text | Exploring concept properties with 'edge tables' | Generalizing a concept: Ancestors | Working with multiple ontologies | Session information

Introduction | Installation | Demonstration | Sanity check | SummarizedExperiment wrapper; subsetting with GenomicRanges | Session information

Introduction | Given a cell type, what proteins are noted as parts of its plasma membrane? | Given a protein, what cell types are asserted to possess it as a membrane part? | Some details | Session information

Introduction | Scope of package | OWL interface | Methods | Conceptual overview of ontology with cell types | Illustration in a single-cell RNA-seq dataset | Labeling PBMC in the Seurat tutorial | Relationships asserted in the Cell Ontology | Subsetting SingleR resources using ontological mapping | A data.frame mapping from informal to formal terms | Binding formal tags to the HPCA data | Subsetting using the class hierarchy of Cell Ontology | Visually identifying differences between ontology versions for a set of given terms | Disease concept relationships | Related tools | Illustration with a phenotype ontology | References

Introduction | Illustration with Human Phenotype ontology

Introduction | Installation | Attachment and access to documentation | Using tidy methods -- added August 2022 | Getting a recent version of the GWAS catalog | Illustrations: computing | Some visualizations | Basic Manhattan plot | Annotated Manhattan plot | Integrative view of potential genetic determinants | SNP sets and trait sets | SNPs by name | Traits by genomic location | Counting alleles associated with traits | Formal management of trait vocabularies | Diseases: Disease Ontology | Other phenotypic traits: Human Phenotype Ontology | CADD scores | Appendix: Adequacy of location annotation | Acknowledgment | References

Introduction | Basic software design for pogos | Data structures for pharmacogenomics | Interrogating PharmacoDb | Some stored reference data | Cell lines | Compounds | Datasets | Coverage of pharmacogenomic compound names by ChEBI | Relationships among cell lines | Working with specific elements | Cell line identifiers | Bridging to Cell Line Ontology | Vocabularies of anatomy | Bridging to ChEBI | Retrieving dose-response data for cell-line/compound intersections

Introduction | Importing with scanTabix

Introduction | Motivation | Installation | Data | A quick look | Hybrid example | Interactivity | Large disk-based example | HDF5+parquet-backed example: not ready in xenLite | Session information

Introduction | Basic concepts | Module references | Python documentation | Importing data for direct handling by python functions | Dimension reduction with sklearn: illustration with iris dataset | PCA | Incremental PCA | Manual incremental PCA with explicit chunking | Interoperation with HDF5 matrix | How to expand scope of BiocSklearn | Conclusions

Introduction | Examining sample data, again | A graph relating patients to conditions | Adding procedures to the graph | Interactive visualization of the graph | Conclusions | Session information

Overview | Getting acquainted with machine learning via the crabs data | Attaching and checking the data | A simple classifier derived by human reasoning | Prediction via logistic regression | The cross-validation concept | Exploratory multivariate analysis | Scatterplots | Principal components; biplot | Clustering | Supervised learning | RPART | Random forests | Linear discriminants | Neural net | SVM | Learning with expression arrays | Phenotype reduction | Nonspecific filtering | Exploratory work | Classifier construction | Demonstrations | Gene set appraisal | Embedding features selection in cross-validation | Session information

Introduction | Some examples | Making new interfaces | A simple example: ada | Dealing with gbm | Additional features | The MLearn approach to clustering and other forms of unsupervised learning

Introduction | Examining sample data, again | Choosing an approach to FHIR JSON ingestion | Working with a specific type | List-based operations | Processing with BiocFHIR | Direct querying of FHIR JSON | Session information

Introduction | The basic structure of FHIR R4 JSON | Example: a table on Conditions recorded on the patient. | A family of documents | Session information

Introduction | Examining sample data, again | Bundle to data frames | Filtering FHIR elements | The resources extracted from a bundle | Accumulating resources across bundles | Session information

Introduction | Some comments from hl7.org/fhir Executive Summary | FHIR JSON documents in BiocFHIR | Examining sample data | Session information

Introduction | Installation | Setup | Obtaining billing data | Overview | Setting up a request | Output | Drilling down | Using the exploratory app | Session Information

Introduction | Illustration with MAQC data | Illustration with arrays from a clinical trial network | Manual work with the MAQC subset | Intensity contamination in the spikein data | Appendix: Sources and text for statically computed sections with eval set to false

Introduction | Univariate samples | Multivariate samples

Introduction | Illustration with a data.frame: dispersal and analysis | Illustration with geuvStore2

Introduction | Illustrative example of an unpruned VTP | Data on GWAS variants: their associated phenotype, locations, and other characteristics | Data on transcription factor binding sites | Building a VTP network: one edge per phenotype | Appendix: generating the ALT-injected genome image | Session information | Bibliography

Introduction | Import of HapMap LD data | A view of the block structure | Collecting SNPs exhibiting linkage to selected SNP

Introduction | Views of the EFO | Graph operations | Connections to the GWAS catalog

Introduction | A small dataset | Systematic demonstration

Introduction/Executive summary | A challenge: finding expression signatures of anatomic structures or cell types | Discrimination of neuron types: exploratory multivariate analysis | Next steps

Introduction | Illustration | Diversity of field names | Managing tokenized metadata | Querying the corpus | A prototypical app | Further work

Introduction | Basic concepts of transcription factor bioinformatics | Enumerating transcription factors | Classification of transcription factors | Enumerating TF targets | Quantitative predictions of TF binding affinities | Summary | Methods | Implementation | Data resources | Infrastructure for interacting with components of TFutils | Operation: Use cases | Discussion | Acknowledgments | Session Information

Introduction | Overview – the `end user' experience | Caveats – the `R programmer' experience | Conclusion

Introduction | KOgraph | Application to gene filtering | Infrastructure considerations | Session info