Introduction | Getting started | Some use cases for quantiseqr | Use case 1: Metastatic melanoma patients (Racle et al 2017) | Use case 2: PBMCs from GSE107572 (Finotello et al 2019) | Use case 3: Expression changes in melanoma patients on vs. pre kinase-inhibitor treatment - GSE75299 (Song et al 2017) | Use case 4: Running on simulated data for validation | FAQs | Session Info | References

Introduction | Installation | Demonstrating the usage of iSEEid | Setting up example data | Launching the app | The SampleIdentificationCenter panel | How to perform annotation within iSEEid | Customizing the initial state for efficient annotation | Session info | Bibliography

Introduction | Installation | Example dataset | KEGGemUP at a glance | Building KEGG pathway graphs | Rendering KEGG pathway graphs | Mapping values onto KEGG pathway graphs | Focusing on subsets of a pathway graph | Working further with KEGG graphs | Caching information with KEGGemUP | Related methods & packages | FAQs | Session info | References

Introduction | Installation | Usage | Example - single-cell RNA-seq clustering benchmark | Programmatic interface | Exporting data from the app | Additional examples | Session info

Overview | What's New in Version 1.6.0 | Server Mode for JSON Uploads | JSON Serialization | Browser localStorage Caching | Cache Versioning | URL Parameters for Programmatic Loading | Server Mode Basics | Starting a Server | What Happens When You Start Server Mode | User Interface | Creating JSON Files | From Data Frames | From Existing SummarizedExperiment | Reading JSON Files | Two Loading Methods | Load from URL (jsonUrl) | Load from File Path (jsonFile) | Browser Cache Management | How Caching Works | Manual Cache Control | Clear Cache Button | Cache Versioning (Administrators) | Setting Up Versioning | Version String Format | User Experience | Session info

Introduction | Installation | Quick start guide | Data preparation | Performing the enrichment tests | Analysis of results | The gene universe and the set of interesting genes | The topGOdata object | Custom annotations | Predefined list of interesting genes | Using the genes score | Filtering and missing GO annotations | Working with the topGOdata object | Running the enrichment tests | Defining and running the test | The groupStats classes | Performing the test | The adjustment of $p$-values | runTest: a high-level interface for testing | Interpretation and visualization of results | The topGOresult object | Summarising the results | Analysing individual GOs | Visualising the GO structure | Session Information | References

Introduction | Getting started | All set! | Using the GeneTonic functions | Overview functions: genes and gene sets | Summary representations | Reporting | Comparison between sets | Miscellaneous functions | Additional Information | FAQs | Session Info | References

Introduction | Getting started | All set! | Using the GeneTonic functions | Overview functions: genes and gene sets | Summary representations | Reporting | Comparison between sets | Miscellaneous functions | Additional Information | FAQs | Session Info | References

Getting started | Introduction | Citation info | Using the application | Launching r Biocpkg("ideal") locally | Accessing the public instance of r Biocpkg("ideal") | Deploying to a Shiny Server | Getting to know the user interface and the functionality | The controls sidebar | App settings | Plot export settings | Quick viewer | First steps help | The task menu | The main app panels | Welcome! | Data Setup | Counts Overview | Extract Results | Summary Plots | Gene Finder | Functional Analysis | Signatures Explorer | Report Editor | About | Running r Biocpkg("ideal") on an exemplary data set | Coming from edgeR/limma-voom | Functions exported by the package for standalone usage | deseqresult2DEgenes and deseqresult2tbl | ggplotCounts | goseqTable | plot_ma | plot_volcano | sepguesser | Creating and sharing output objects | Expanding the analysis after ideal | Enhancing ideal | Further development | Session Info

Getting started | Introduction | Citation info | Using the application | Launching r Biocpkg("ideal") locally | Accessing the public instance of r Biocpkg("ideal") | Deploying to a Shiny Server | Getting to know the user interface and the functionality | The controls sidebar | App settings | Plot export settings | Quick viewer | First steps help | The task menu | The main app panels | Welcome! | Data Setup | Counts Overview | Extract Results | Summary Plots | Gene Finder | Functional Analysis | Signatures Explorer | Report Editor | About | Running r Biocpkg("ideal") on an exemplary data set | Coming from edgeR/limma-voom | Functions exported by the package for standalone usage | deseqresult2DEgenes and deseqresult2tbl | ggplotCounts | goseqTable | plot_ma | plot_volcano | sepguesser | Creating and sharing output objects | Expanding the analysis after ideal | Enhancing ideal | Further development | Session Info

Getting started | Introduction | Citation info | Launching the application | How to provide your input data in r Biocpkg("pcaExplorer") | Up and running with r Biocpkg("pcaExplorer") | The controls sidebar | App settings | Plot export settings | The task menu | The app panels | Data Upload | Instructions | Counts Table | Data Overview | Samples View | Genes View | GeneFinder | PCA2GO | More on the pca2go parameter | Multifactor Exploration | Report Editor | About | Running pcaExplorer on published datasets | Running pcaExplorer on synthetic datasets | Functions exported by the package for standalone usage | pcaplot | pcaplot3d | pcascree | correlatePCs and plotPCcorrs | hi_loadings | genespca | topGOtable | pca2go | limmaquickpca2go | makeExampleDESeqDataSet_multifac | distro_expr | geneprofiler | get_annotation and get_annotation_orgdb | pair_corr | Further development | Session info

Getting started | Introduction | Citation info | Launching the application | How to provide your input data in r Biocpkg("pcaExplorer") | Up and running with r Biocpkg("pcaExplorer") | The controls sidebar | App settings | Plot export settings | The task menu | The app panels | Data Upload | Instructions | Counts Table | Data Overview | Samples View | Genes View | GeneFinder | PCA2GO | More on the pca2go parameter | Multifactor Exploration | Report Editor | About | Running pcaExplorer on published datasets | Running pcaExplorer on synthetic datasets | Functions exported by the package for standalone usage | pcaplot | pcaplot3d | pcascree | correlatePCs and plotPCcorrs | hi_loadings | genespca | topGOtable | pca2go | limmaquickpca2go | makeExampleDESeqDataSet_multifac | distro_expr | geneprofiler | get_annotation and get_annotation_orgdb | pair_corr | Further development | Session info

Introduction | Required input | Demonstrating mosdef on the macrophage data | Installation | Getting started | Load the data | Generating enrichment results with a unified API | mosdef and topGO | mosdef and goseq | mosdef and clusterProfiler | Alternative ways to run enrichment analyses, within mosdef | Plotting expression values in the context of DE | Individual genes - gene_plot() | All genes at once - Volcano plots | All genes at once - MA plots | Beautifying and enhancing analysis reports | More information on features/genes | More information on GO terms | Session Info | References

Setup | Start exploring - the beauty of interactivity | When you're done - the power of reproducibility | Session Info

Setup | Start exploring - the beauty of interactivity | When you're done - the power of reproducibility | Session Info

Introducing the HCAData package | Installing HCAData | Loading HCAData and the Human Cell Atlas data | Explore data with iSEE | Processing a subset of the HCA bone marrow data | Exploring the dataset with iSEE | Saving the processed object | Session info

Introduction | Why r Biocpkg("flowcatchR")? | Purpose of this document | Getting started | Installation | Getting help | Citing r Biocpkg("flowcatchR") | Processing overview | Image acquisition | Particle tracking | Trajectory analysis | Interactive tools for a user-friendly workflow solution | The shinyFlow Shiny Application | r Biocpkg("flowcatchR") in Jupyter notebooks | r Biocpkg("flowcatchR") in Docker containers | Supplementary information | Acknowledgements | Session info | References