Registration and Call for Abstracts Open for Bioc2024

Courses & Conferences

Bioconductor provides training in computational and statistical methods for the analysis of genomic data. You are welcome to use material from previous courses. However, you may not include these in separately published works (articles, books, websites). When using all or parts of the Bioconductor course materials (slides, vignettes, scripts) please cite the authors and refer your audience to the Bioconductor website.

Upcoming events are advertised 6 to 8 weeks in advance.

Keyword Title Course Materials Date Bioc/R Version
Course Bioconductor Asia 2023 Symposium, Various BiocAsia2023 2023‑10‑15 3.18/4.3
Course European Bioconductor Conference 2023, Various EuroBioc2023 2023‑09‑20 3.17/4.3
Course Bioc2023, Various Bioc2023 videos 2023‑08‑02 3.17/4.3
Course CSAMA: Statistical Data Analysis for Genome Scale Biology, Various CSAMA2023 material 2023‑06‑11 3.17/4.3
Course BiocAsia2022 Conference, Various BiocAsia2022 videos 2022‑12‑02 3.16/4.2
Course EuroBioc2022 Conference, Various EuroBioc2022 videos 2022‑09‑14 3.16/4.2
Course CSAMA: Statistical Data Analysis for Genome Scale Biology, Various CSAMA2022 html 2022‑06‑19 3.16/4.2
Talk Visualization and analysis of highly multiplexed imaging data, Nils Eling SpatialSeminar slides, video 2022‑03‑28 3.15/4.2
Talk Analysing Spatially Resolved Transcriptomics Data with Bioconductor, Helena Crowell SpatialSeminar slides, video 2022‑01‑31 3.15/4.2
Talk Spatial Transcriptomics Technologies and Analysis Tools, Dario Righelli SpatialSeminar slides, video 2022‑01‑31 3.15/4.2
Talk The R-universe Build Infrastructure, Jeroen Ooms BiocDevelForum slides, video 2021‑11‑18 3.15/4.2
Talk Translating R package documentation, Martin Morgan BiocDevelForum slides, video 2021‑06‑17 3.14/4.1
Talk HCA / Bioconductor Seed Network Symposium, Various HCA video 2021‑06‑15 3.13/4.1
Workshop Week 7: Participant stories, Various AnVILpopup material, video 2021‑06‑14 3.13/4.1
Workshop Week 6: Reproducible research with AnVILPublish, Martin Morgan AnVILpopup material, video 2021‑06‑07 3.13/4.1
Talk Discussion of changes in R-4.1, Martin Morgan, Mike Smith BiocDevelForum video 2021‑05‑27 3.13/4.1
Workshop Week 5: Using AnVIL for teaching R / Bioconductor, Levi Waldron AnVILpopup material, video 2021‑05‑24 3.13/4.1
Workshop Week 4: Single-cell RNASeq with ‘Orchestrating Single Cell Analysis’ in R / Bioconductor, Vince Carey AnVILpopup material, video 2021‑05‑17 3.13/4.1
Workshop Week 3: Running a Workflow, Martin Morgan, Kayla Interdonato AnVILpopup material, video 2021‑05‑10 3.13/4.1
Workshop Week 2: The R / Bioconductor AnVIL package, Martin Morgan, Nitesh Turaga AnVILpopup material, video 2021‑05‑03 3.13/4.1
Workshop Week 1: Using R / Bioconductor in AnVIL, Martin Morgan AnVILpopup material, video 2021‑04‑26 3.13/4.1
Talk Release schedule discussion, Various BiocDevelForum video 2021‑04‑15 3.13/4.1
Talk Updates to default caching location, Lori Shepherd BiocDevelForum slides, video 2021‑04‑15 3.13/4.1
Talk R7 for Bioconductor, Michael Lawrence BiocDevelForum slides, video 2021‑03‑18 3.13/4.1
Talk Testing R Packages, Dirk Eddelbuettel BiocDevelForum slides, video 2021‑02‑25 3.13/4.1
Talk Bioconductor - Microsoft Genomics, Jass Bagga, Erdal Cosgun BiocDevelForum slides, video 2021‑01‑21 3.13/4.1
Talk Say Hello to ALTREP, Jiefei Wang BiocDevelForum slides, video 2020‑12‑10 3.13/4.1
Talk Alternate Representations of R Objects or ALTREP, Gabe Becker BiocDevelForum slides, github, video 2020‑11‑19 3.13/4.1
Talk Presentation on sparseMatrixStats, Constantin Ahlmann-Eltze BiocDevelForum slides, video 2020‑10‑22 3.12/4
Talk Presentation on AnVIL, Martin Morgan BiocDevelForum video 2020‑09‑24 3.12/4
Talk Q&A about upcoming release, Lori Shepherd BiocDevelForum video 2020‑09‑24 3.12/4
Talk Browsing and searching the Bioconductor codebase, Mike Smith BiocDevelForum slides, video 2020‑08‑20 3.12/4
Talk Compiling the OSCA book on the Bioconductor build system, Aaron Lun BiocDevelForum video 2020‑08‑20 3.12/4
Talk New builder update, Hervé Pagès BiocDevelForum video 2020‑08‑20 3.12/4
Conference Where Software and Biology Connect, Various BioC2020 Talks & Workshops 2020‑07‑27 3.12/4
Talk Challenges and opportunities, Kasper Hansen HCA slides, video 2020‑07‑20 3.12/4
Talk Data access and representation, Martin Morgan, Daniel van Twisk, Marcel Ramos HCA slides, video 2020‑07‑20 3.12/4
Talk Introduction, Martin Morgan HCA slides, video 2020‑07‑20 3.12/4
Talk Methods for emerging data, Greg Finak, Matt Ritchie HCA slides, video 2020‑07‑20 3.12/4
Talk Methods for scalable, performant analysis, Davide Risso, Aedin Culhane HCA slides, video 2020‑07‑20 3.12/4
Talk Outreach, Stephanie Hicks HCA slides, video 2020‑07‑20 3.12/4
Talk Continuous integration with Github Actions, Sean Davis BiocDevelForum github, video 2020‑07‑16 3.12/4
Talk BiocCheck-a-thon check in, Various BiocDevelForum video 2020‑05‑21 3.12/4
Talk Discussion about unit testing, Various BiocDevelForum video 2020‑04‑16 3.11/4
Talk Writing our first Bioconductor package as members of the CDSB community, Joselyn Chávez, Carmina Barberena Jonas, Emiliano Sotelo BiocDevelForum pdf, video 2020‑04‑16 3.11/4
Talk Bioc vs. CRAN build systems, Various BiocDevelForum video 2020‑03‑19 3.11/4
Talk Bioconductor and R 4.0, Lori Shepherd BiocDevelForum pdf, video 2020‑03‑19 3.11/4
Talk Quantifying and lightening the package dependency burden, Robert Castelo BiocDevelForum pdf, video 2020‑02‑20 3.11/4
Talk Update: Bioconductor Images, Nitesh Turaga BiocDevelForum pdf, video 2020‑02‑20 3.11/4
Talk Windows and Rtools 4.0, Mike Smith BiocDevelForum pdf, video 2020‑01‑23 3.11/4
Workshop R packages for communicating reproducible research, Martin Morgan R-IISA html; Rmd; github 2019‑12‑26 3.1/3.6.1
Talk Adding additional compression filters to rhdf5, Mike Smith BiocDevelForum pdf, video 2019‑12‑19 3.1/3.6
Talk Update from EuroBioc2019 user/developer sessions, Various authors BiocDevelForum pdf, video 2019‑12‑19 3.1/3.6
Talk Bioconductor updates and directions, Martin Morgan BiocEurope slides 2019‑12‑09 3.1/3.6.1
Talk Bioconductor updates, Martin Morgan BiocAsia slides 2019‑12‑05 3.1/3.6.1
Talk How to advance science using Bioconductor, Martin Morgan BiocAsia slides 2019‑12‑05 3.1/3.6.1
Workshop R and Bioconductor for Genomic Analysis, Martin Morgan BiocAsia Rmd; html 2019‑12‑05 3.1/3.6.1
Talk Bioconductor and R 4.0, Lori Shepherd, Hervé Pagès BiocDevelForum pdf, video 2019‑11‑21 3.11/4
Talk Improving findability of BioC packages, Steffen Neumann BiocDevelForum pdf, video 2019‑11‑21 3.1/3.6
Workshop Cancer Immuno-Oncology: Bioconductor and beyond, Martin Morgan CMCM Rmd, html 2019‑11‑18 3.1/3.6
Talk Discussion: Guidelines for submitting data to ExperimentHub, Stephanie Hicks BiocDevelForum pdf, video 2019‑10‑17 3.9/3.6
Workflow Orchestrating Single-Cell Analysis with Bioconductor, Various authors OSCA online book 2019‑10‑10 3.9/3.6
Talk HCA Data Access, Daniel van Twisk HCA pptx 2019‑10‑03 3.9/3.6
Talk Introduction to DataFrames and the impact of recent changes, Hervé Pagès BiocDevelForum pdf, video 2019‑09‑19 3.9/3.6
Talk Pitfalls and best practices for serializing Bioconductor objects, Lori Shepherd BiocDevelForum pdf, video 2019‑09‑19 3.9/3.6
Talk Surprises from scalable container and cloud-based R / Bioconductor deployments, Martin Morgan DSC slides 2019‑09‑18 3.9/3.6
Talk Presentation/discussion on biomaRt, recent issues & future plans, Mike Smith BiocDevelForum pdf, video 2019‑08‑15 3.9/3.6
Talk Single cell updates, Aaron Lun BiocDevelForum pptx, video 2019‑08‑15 3.9/3.6
Course CSAMA: Statistical Analysis for Genome Scale Biology, various authors, Various CSAMA2019 html 2019‑07‑22 3.9/3.6
Big data Evening session: Efficient R, Martin Morgan CSAMA2019 html, Rmd 2019‑07‑22 3.9/3.6
introduction Lab 1: Introduction to R and Bioconductor, Martin Morgan CSAMA2019 html 2019‑07‑22 3.9/3.6
Big data Lab 9-1: Efficient and Parallel Evaluation, Martin Morgan CSAMA2019 html, Rmd 2019‑07‑22 3.9/3.6
GSEA Lecture 19: Gene Set Enrichment Analysis, Martin Morgan CSAMA2019 pdf; example html, Rmd 2019‑07‑22 3.9/3.6
introduction Lecture 1: Introduction to R and Bioconductor, Martin Morgan CSAMA2019 html 2019‑07‑22 3.9/3.6
Big data Lecture 20-1: Working with Large Data, Martin Morgan CSAMA2019 html, Rmd 2019‑07‑22 3.9/3.6
Keynote How Bioconductor advances science and contributes to R, Martin Morgan useR! 2019 slides, video 2019‑07‑12 3.9/3.6
Intro 01: Introduction to R and Bioconductor, Martin Morgan BSS2019 html, R, Rmd 2019‑07‑01 3.9/3.6
Intro 02: Practical: R / Bioconductor and Reproducible Research, Martin Morgan BSS2019 html, R, Rmd 2019‑07‑01 3.9/3.6
Intro 03: Core approaches in Bioconductor, Martin Morgan BSS2019 html, R, Rmd 2019‑07‑01 3.9/3.6
Intro 04: Practical: Organizing data with SummarizedExperiment, Martin Morgan BSS2019 html, R, Rmd 2019‑07‑01 3.9/3.6
Intro 05: Bioconductor Annotation Resources, Martin Morgan BSS2019 html, R, Rmd 2019‑07‑01 3.9/3.6
Intro 06: Gene Set Enrichment – Introduction, Martin Morgan BSS2019 pdf, Rnw 2019‑07‑01 3.9/3.6
Intro 06: Gene Set Enrichment Analysis, Martin Morgan BSS2019 html, R, Rmd 2019‑07‑01 3.9/3.6
Conference Workshops, Various BioC2019 workshops 2019‑06‑26 3.9/3.6
Talk R / Bioconductor for open-source analysis and comprehension of high-throughput genomic data, Martin Morgan BioME-2019 slides 2019‑04‑15 3.9/3.6
Workshop Bioconductor for Everyone: Exploring, Analyzing and Visualizing Large Data Sets in R, Martin Morgan CDSE-2019 Rmd, html 2019‑04‑11 3.9/3.6
Workshop Developing robust and efficient code, Martin Morgan NURDS html 2019‑04‑04 3.9/3.6
Talk Benchmarking on-disk file formats for single-cell data, Raphael Gottardo, Mike Jiang HCA slides, video 2019‑02‑20 3.9/3.6
Talk Developments in rhdf5, Mike Smith HCA slides, video 2019‑02‑20 3.9/3.6
Talk HCABrowser for discovery and access, Daniel van Twisk HCA video 2019‑02‑20 3.9/3.6
Talk HCAMatrixBrowser for retrieving count matrices, Marcel Ramos HCA video 2019‑02‑20 3.9/3.6
Talk New Bioconductor packages for HCA Analysis, Aaron Lun HCA slides, video 2019‑02‑20 3.9/3.6
Talk Ontology issues, Vincent Carey HCA slides, shiny, video 2019‑02‑20 3.9/3.6
Talk Orchestrating single cell analysis with Bioconductor, Stephanie Hicks HCA slides, video 2019‑02‑20 3.9/3.6
Talk State of PCA, Kasper Hansen HCA slides, video 2019‑02‑20 3.9/3.6
Talk Work in Progress: Bioconductor and the HCA, Martin Morgan HCA slides, video 2019‑02‑20 3.9/3.6
Talk mbkmeans for clustering data with lots of samples, Davide Risso HCA slides, video 2019‑02‑20 3.9/3.6
Introduction 100: R and Bioconductor for everyone: an introduction, Martin Morgan, Lori Shepherd BioC2018 html 2018‑07‑25 3.8/3.5
Introduction 101: Introduction to Bioconductor annotation resources, James MacDonald, Lori Shepherd BioC2018 html 2018‑07‑25 3.8/3.5
Genomic Ranges 102: Solving common bioinformatic challenges using GenomicRanges, Michael Lawrence BioC2018 html 2018‑07‑25 3.8/3.5
Annotation 103: Public data resources and Bioconductor, Levi Waldron, Benjamin Haibe-Kains, Sean Davis BioC2018 html 2018‑07‑25 3.8/3.5
RNAseq 200: RNA-seq analysis is easy as 1-2-3 with limma, Glimma, and edgeR, Charity Law BioC2018 html 2018‑07‑25 3.8/3.5
RNAseq 201: RNA-seq data analysis with DESeq2, Michael Love, Simon Anders, Wolfgang Huber BioC2018 html 2018‑07‑25 3.8/3.5
Single-cell 202: Analysis of single-cell RNA-seq data: Dimensionality reduction, clustering, and lineage inference, Diya Das, Kelly Street, Davide Risso BioC2018 html 2018‑07‑25 3.8/3.5
Enrichment 210: Functional enrichment analysis of high-throughput omics data, Ludwig Geistlinger, Levi Waldron BioC2018 html 2018‑07‑25 3.8/3.5
Omics 220: Workflow for multi-omics analysis with MultiAssayExperiment, Marcel Ramos, Ludwing Geistlinger, Levi Waldron BioC2018 html 2018‑07‑25 3.8/3.5
Network analysis 230: Cytoscape automation in R using Rcy3, Ruth Isserlin, Brendan Innes, Jeff Wong, Gary Bader BioC2018 html 2018‑07‑25 3.8/3.5
Genomic data 240: Fluent genomic data analysis with plyranges, Stuart Lee, Michael Lawrence BioC2018 html 2018‑07‑25 3.8/3.5
Genomic data 250: Working with genomic data in R with the DECIPHER package, Nicholas Cooley BioC2018 html 2018‑07‑25 3.8/3.5
Biomarker 260: Biomarker discovery from large pharmacogenomics datasets, Zhaleh Safikhani, Petr Smirnov, Benjamin Haibe-Kains BioC2018 html 2018‑07‑25 3.8/3.5
Large data 500: Effectively using the DelayedArray framework to support the analysis of large datasets, Pete Hickey BioC2018 html 2018‑07‑25 3.8/3.5
Packages 510: Maintaining your Bioconductor package, Nitesh Turaga BioC2018 html 2018‑07‑25 3.8/3.5
Conference BioC 2018: Where Software and Biology Connect, Various authors BioC2018 book 2018‑07‑25 3.8/3.5
Talk Conducting Genomic Symphonies with Bioconductor, Michael Lawrence BioInfoSummer17 pdf, zip 2017‑12‑4 3.6/3.4
Conference Day 1 European Bioconductor Meeting 2017, Various authors EuroBioc2017 video 2017‑12‑5 3.6/3.4
Conference Day 2 European Bioconductor Meeting 2017, Various authors EuroBioc2017 video 2017‑12‑5 3.6/3.4
Talk File Management: BiocFileCache, AnnotationHub, ExperimentHub, Lori Shepherd EuroBioc2017 slides 2017‑12‑5 3.6/3.4
Talk The Bioconductor Project: Current Status, Martin Morgan EuroBioc2017 pdf, github 2017‑12‑5 3.6/3.4
Talk The Bioconductor Project: Current Status, Martin Morgan BiocAsia2017 pdf, github 2017‑11‑17 3.6/3.4
Devel Bioconductor Masterclass: Package Development, Martin Morgan BiocAsia2017 html, R, Rmd, github 2017‑11‑16 3.6/3.4
Intro Bioconductor Masterclass: Bioconductor Essentials, Martin Morgan BiocAsia2017 html, R, Rmd, github 2017‑11‑16 3.6/3.4
Intro R and Bioconductor for Genomic Analysis, Martin Morgan OSU Using R html, Rmd; Data Input and Manipulation html, Rmd; Statistics and Graphics html, Rmd; Introduction to Bioconductor html, Rmd; Bioconductor Building Blocks html, Rmd; An RNA-seq Work Flow html, Rmd; Appendix: Quantifying Gene Expression Using salmon html, Rmd 2017‑09‑11 3.6/3.4
Genomic Ranges A Journey of Discovery through the GenomicRanges Infrastructure, Michael Lawrence BioC2017 pdf slides, R, Github 2017‑07‑26 3.6/3.4
Conference BioC2017: Where Software and Biology Connect, Various authors BioC2017 Course material 2017‑07‑26 3.6/3.4
Cloud Cloud and other creative approaches to working with big data, Vincent Carey, Sean Davis BioC2017 Workshop Github 2017‑07‑26 3.6/3.4
RNA Design and evaluate guide RNAs for CRISPR-Cas9 genome-editing using CRISPRseek and GUIDEseq, Lihua Julie Zhu BioC2017 pdf slides, Github
CRISPRdemo: html, R, Rmd
GUIDEseqdemo: html, R, Rmd
2017‑07‑26 3.6/3.4
Best Practices Developer best practices, Martin Morgan, Kasper Hansen BioC2017 Unit tests, Efficiency, Query Web Resource, Coding Style, Common Bioconductor Classes and Import 2017‑07‑26 3.6/3.4
Workflows Differential Gene Expression analysis using R and Bioconductor, Radhika Khetani, Meeta Mistry, Mary Piper BioC2017 Workflow 2017‑07‑26 3.6/3.4
Enrichment Ensembl gene set enrichment analysis with EGSEA, Matt Ritchie BioC2017 EGSEA workflow: html, R, Rmd
Github
2017‑07‑26 3.6/3.4
Enrichment Functional enrichment analysis of high-throughput omics data in Bioconductor, Ludwig Geistlinger, Levi Waldron BioC2017 pdf slides, Github
enrichOmics: html, R, Rmd
2017‑07‑26 3.6/3.4
Git Git with the program: new Bioconductor version control, Nitesh Turaga BioC2017 pdf slides 2017‑07‑26 3.6/3.4
ChIP-seq Integrative analysis and visualization of ChIP-seq data using ChIPpeakAnno , GeneNetworkBuilder, and TrackViewer, Jianhong Ou, Lihua Julia Zhu, Jun Yu BioC2017 pdf slides, Github
Workflow: html, R, Rmd
ChIPseq_stepBystep: html, R, Rmd
2017‑07‑26 3.6/3.4
Introduction Integrative analysis workshop with TCGAbiolinks and ELMER, Tiago Chedraoui Silva, Houtan Noushmehr, Benjamin Berman BioC2017 Analysis: html, R, Rmd
AnalysisGui: html, R, Rmd
Data: html, R, Rmd
DataGui: html, R, Rmd
Github, Workshop Link
2017‑07‑26 3.6/3.4
Visualization Interactive visualization and data analysis with epiviz web components, Jayaram Kancherla, Hector Corrada Bravo, Brian Gottfried BioC2017 data preprocessing: html, R, Rmd
minfi: html, R, Rmd
Bioc2017 Addendum: html, R, Rmd
Github
2017‑07‑26 3.6/3.4
Introduction Introduction to R and Bioconductor, Lori Shepherd BioC2017 Intro course: Github 2017‑07‑26 3.6/3.4
Packages Introduction to new package development and submission, Lori Shepherd BioC2017 Make A Package: html, Github 2017‑07‑26 3.6/3.4
RNAseq Learn to leverage 70,000 human RNA-seq samples for your projects, Leonardo Collado Torres BioC2017 pdf slides, Github
recount workshop: html, R, Rmd
2017‑07‑26 3.6/3.4
Microbiome Microbiome Data Analysis, Levi Waldron, Susan Holmes, Pau J. McMurdie, Edoardo Pasolli, Joe Paulson, Lucas Schiffer, Justin Wagner BioC2017 MicrobiomeWorkshop: html, R, Rmd
MicrobiomeWorkshop II: html, R, Rmd
Github
2017‑07‑26 3.6/3.4
Omics Multi-omics data representation and analysis with MultiAssayExperiment, Marcel Ramos, Levi Waldron BioC2017 MAE lab: html, R, Rmd
Github
2017‑07‑26 3.6/3.4
Annotation Understanding Bioconductor Annotation Packages, James MacDonald BioC2017 Workshop: html, R, Rmd
Github
2017‑07‑26 3.6/3.4
Annotation Variant Annotation Workshop with FunciVAR, StateHub, and MotifBreakR, Dennis J. Hazelett, Simon G Coetzee BioC2017 Workshop Link: html 2017‑07‑26 3.6/3.4
Large Data Working with large arrays: the DelayedArray package, Hervé Pagès BioC2017 Working with large arrays: pdf slides, R, Rnw 2017‑07‑26 3.6/3.4
Single-cell Bioconductor workflow for single-cell RNA-seq data analysis: dimensionality reduction, clustering, and pseudotime ordering, Fanny Perraudeau, Kelly Street, Davide Risso, Sandrine Dudoit, Elizabeth Purdom BioC2017 pdf slides, Github
Workshop: html, R, Rmd
2017‑07‑26 3.6/3.4
Workflows CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets, Malgorzata Nowicka, Lukas M. Weber, Mark D. Robinson BioC2017 Workshop: html, Rmd
pdf, Github, Workflow
2017‑07‑26 3.6/3.4
Advanced Advanced R and Bioconductor, Martin Morgan, Hervé Pagès U. Zurich Inside the mind of R, R data and algorithms, S4 classes and methods, Doing R better 2017‑06‑19 3.6/3.4
Introduction CSAMA: Statistical Analysis for Genome Scale Biology, Various authors CSAMA2017 Course material 2017‑06‑11 3.5/3.4
Introduction Introduction to R and Bioconductor, Martin Morgan OMRF Install, Intro to R, Data input and manipulation, Statistics, Organization and Graphics, Intro to Bioconductor, Key classes and methods, An RNA-seq work flow, A ChIP-seq workflow 2017‑05‑08 3.5/3.4
Talk Good software: simple, tidy, rich, Martin Morgan Meetup pdf 2017‑04‑20 3.5/3.4
Introduction Introduction to R and Bioconductor, Martin Morgan, Lori Shepherd Moffitt Install, Intro to R, Data input and manipulation, Statistics, Graphics, Intro to Bioconductor, Key classes and methods, An RNA-seq work flow, Next steps 2017‑03‑02 3.4/3.3
Packages Software development in R and Bioconductor, Martin Morgan UIdaho Packages & version control 2017‑01‑31 3.4/3.3
Overview R / Bioconductor for ‘Omics Analysis (U. Idaho), Martin Morgan pdf (slides) 2017‑01‑30 3.4/3.3
Introduction Introduction to R, Martin Morgan RPCI RIntro Installation, Using R, Input and manipulation, Statistics, Workflows and visualization 2017‑01‑09 3.4/3.3
Overview Bioconductor for ‘Omics Analysis (University of Rochester Medical Center), Martin Morgan pdf (slides) 2016‑12‑01 3.4/3.3
RNAseq An RNA-seq work flow, Martin Morgan Technion-BKU html, R, Rmd 2016‑11‑20 3.4/3.3
Annotation Annotation, Communication, and Performance, Martin Morgan Technion-BKU html, R, Rmd 2016‑11‑20 3.4/3.3
Introduction Introduction to Bioconductor, Martin Morgan Technion-BKU html, R, Rmd 2016‑11‑20 3.4/3.3
Status The Bioconductor Project: Current Status, Martin Morgan BiocAsia2016 pdf (slides) 2016‑11‑04 3.4/3.3
Introduction Bioconductor for Genomic Analysis, Martin Morgan BiocAsia2016 html, R, Rmd, github 2016‑11‑03 3.4/3.3
Overview R / Bioconductor for ‘Omics Analysis (CMRI, Sydney), Martin Morgan pdf (slides) 2016‑10‑31 3.4/3.3
Introduction Reproducible Research in R / Bioconductor (CMRI, Sydney), Martin Morgan html, R, Rmd, github 2016‑10‑31 3.4/3.3
Annotation Annotating genes, genomes, and variants, Martin Morgan CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Performance Benchmarking out-of-memory strategies, Vincent J. Carey CSAMA2016 pdf 2016‑07‑10 3.4/3.3
ChIP-seq ChIP-Seq analysis basics, Alejandro Reyes; Mike Smith CSAMA2016 Rmd html Rpackage 2016‑07‑10 3.4/3.3
Statistics Clustering, classification, and regression with genomic examples, Vincent J. Carey CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Genomic Ranges Computing with sequences and ranges, Martin Morgan CSAMA2016 pdf 2016‑07‑10 3.4/3.3
RNAseq End-to-end RNA-Seq workflow, Simon Anders; Michael Love; Charlotte Soneson CSAMA2016 Rmd html pdf 2016‑07‑10 3.4/3.3
Experimental design Experimental design, Charlotte Soneson CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Gene set enrichment Gene set enrichment - Introduction, Martin Morgan CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Gene set enrichment Gene-sets and correlation, Michael Love CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Graphics Graphics, Wolfgang Huber CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Sequencing High-throughput sequencing and using short-read aligners, Simon Anders CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Statistics Hypothesis testing, Wolfgang Huber CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Statistics Independent Hypothesis Weighting, Wolfgang Huber CSAMA2016 Rmd html 2016‑07‑10 3.4/3.3
Introduction Introduction to R and Bioconductor, Martin Morgan CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Introduction Introduction to R and Bioconductor, Martin Morgan CSAMA2016 html data Rmd 2016‑07‑10 3.4/3.3
Statistics Introduction to linear models, Levi Waldron CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Introduction Learn to love the data frame, Jenny Bryan CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Performance Machine Learning, Parallelization and performance, Martin Morgan; Vincent J. Carey CSAMA2016 Efficient and parallel code html
Machine Learning html Rmd pdf Code
2016‑07‑10 3.4/3.3
Statistics Meta-analysis, Levi Waldron CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Microbiome Microbial genomics, Charlotte Soneson CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Statistics Multiple testing, Wolfgang Huber CSAMA2016 pdf 2016‑07‑10 3.4/3.3
RNAseq New RNA-seq workflows, Charlotte Soneson CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Performance Performance and parallel evaluation, Martin Morgan CSAMA2016 pdf 2016‑07‑10 3.4/3.3
RNAseq RNA-seq data analysis and differential expression, Michael Love CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Git Reproducible research and R authoring with markdown and knitr, Jenny Bryan CSAMA2016 github 2016‑07‑10 3.4/3.3
Statistics Resampling methods, Levi Waldron CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Statistics Robust statistics, Michael Love CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Git Use of Git and GitHub with R, RStudio, and R Markdown, Jenny Bryan CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Intermediate What should you do next?, Jenny Bryan CSAMA2016 pdf 2016‑07‑10 3.4/3.3
Graphics R Graphics, Wolfgang Huber CSAMA2016 Rmd pdf 2016‑07‑10 3.4/3.3
Methylation Analysing DNA methylation data with Bioconductor, Peter Hickey, Kasper Hansen BioC2016 HTML R Rmd github 2016‑06‑25 3.4/3.3
RNASeq Analysis of single-cell RNA-seq data with R and Bioconductor, Davide Risso, Kelly Street, Michael Cole BioC2016 Cluster: html R Rmd
Scone: html R Rmd
Slingshot: html R Rmd
github
2016‑06‑25 3.4/3.3
RNASeq Analyzing splice events from RNA-seq data with SGSeq, Leonard Goldstein BioC2016 HTML R Rmd
github package
2016‑06‑25 3.4/3.3
Annotation Annotating high throughput data using Bioconductor resources, James MacDonald BioC2016 HTML R Rmd github 2016‑06‑25 3.4/3.3
workflows Building and running automated NGS analysis workflows, Thomas Girke BioC2016 SystemPipeR Intro: html R Rmd
SystemPipeRdata: html R Rmd
github
2016‑06‑25 3.4/3.3
Introduction Getting to know R and Bioconductor, Valerie Obenchain, Lori Shepherd BioC2016 Lecture Overview:
html R Rmd
github
2016‑06‑25 3.4/3.3
Genomic Ranges Hello Ranges: An Introduction to Analyzing Genomic Ranges in R, Michael Lawrence BioC2016 PDF R Rmd github 2016‑06‑25 3.4/3.3
Visualization Interactive visualization with epiviz, Héctor Corrada Bravo, Jayaram kancherla, Justin Wagner BioC2016 Preprocessing: html R Rmd
Minifi: html R Rmd
github
2016‑06‑25 3.4/3.3
Statistics Introduction to Bayesian Inference using Stan with Applications to Cancer Genomics, Jacqueline Buros BioC2016 Applied Survival Models: html R Rmd
Survival Model Example: html R Rmd
github
2016‑06‑25 3.4/3.3
Introduction Introduction to ImmuneSpaceR, Renan Sauteraud, Lev Dashevskiy, Greg Finak, Raphael Gottardo BioC2016 Content: html R Rmd
Example: html R Rmd
github
2016‑06‑25 3.4/3.3
RNASeq Low-level exploratory data analysis and methods development for RNA-seq, Michael Love BioC2016 HTML R Rmd github 2016‑06‑25 3.4/3.3
Introductoin Making your packages accessible to non-programmer collaborators using the VisRseq platform, Hamid Younesy, Torsten Möller, Mohammad M. Karimi BioC2016 Slides PDF R Rmd
Software github
2016‑06‑25 3.4/3.3
Sequence Analysis Managing big biological sequence data with Biostrings and DECIPHER, Erik Wright BioC2016 Slides html R Rmd github 2016‑06‑25 3.4/3.3
Gene set enrichment Single Cell Differential Expression and Gene Set Enrichment with MAST, Andrew McDavid, Raphael Gottardo, Greg Finak BioC2016 Slides PDF R Rmd
MAIT Analysis github
2016‑06‑25 3.4/3.3
Omics The MultiAssayExperiment class for analysis of multi-omics experiments, Levi Waldron, Marcel Ramos, Vince Carey, Kasper Hansen, Martin Morgan BioC2016 HTML R Rmd github 2016‑06‑25 3.4/3.3
Cloud Wrapping Your R tools to Analyze National-Scale Cancer Genomics in the Cloud, Tengfei Yin, Nan Xiao BioC2016 api: html R Rmd
apps: html R Rmd
workflow: html R Rmd
cgc-sparql: html R Rmd
docker: html R Rmd
rstudio: html R Rmd
github
2016‑06‑25 3.4/3.3
Intermediate Writing efficient, scalable code, Martin Morgan BioC2016 HTML R Rmd github 2016‑06‑25 3.4/3.3
Proteomics R / Bioconductor tools for mass spectrometry-based proteomics, Laurent Gatto BioC2016 HTML R Rmd github 2016‑06‑25 3.4/3.3
Introduction China R Conference: Bioconductor for high-throughput genetic data, Martin Morgan China-R PDF 2016‑05‑29 3.3/3.3
Introduction A1: R Intro, Martin Morgan, Lori Shepherd BiocIntroRPCI HTML R Rmd 2016‑05‑16 3.3/3.3
Introduction A2: Input and Manipulation, Martin Morgan, Lori Shepherd BiocIntroRPCI HTML R Rmd 2016‑05‑16 3.3/3.3
Introduction A3: Statistical Analysis, Martin Morgan, Lori Shepherd BiocIntroRPCI HTML R Rmd 2016‑05‑16 3.3/3.3
Introduction A4: Visualization, Martin Morgan, Lori Shepherd BiocIntroRPCI HTML R Rmd 2016‑05‑16 3.3/3.3
Introduction B1: Bioconductor Intro, Martin Morgan, Lori Shepherd BiocIntroRPCI HTML R Rmd 2016‑05‑16 3.3/3.3
Introduction B2: Common Operations, Martin Morgan, Lori Shepherd BiocIntroRPCI HTML R Rmd 2016‑05‑16 3.3/3.3
Introduction B3: RNASeq Workflow, Martin Morgan, Lori Shepherd BiocIntroRPCI HTML R Rmd 2016‑05‑16 3.3/3.3
Introduction B4: Next Steps, Martin Morgan, Lori Shepherd BiocIntroRPCI HTML R Rmd 2016‑05‑16 3.3/3.3
Introduction Introduction to High Throughput DNA Sequence Data Analysis Using R / Bioconductor, Martin Morgan ENAR 2016 Rmd R; github 2016‑04‑08 3.2/3.2
Intro Introduction to Sequence Analysis, R, and Bioconductor: content, Martin Morgan EMBO 2015 html, Rmd, R 2015‑10‑19 3.2/3.2
Intro Introduction to Sequence Analysis, R, and Bioconductor: exercises, Martin Morgan EMBO 2015 html, Rmd R 2015‑10‑19 3.2/3.2
Intro Introduction to Sequence Analysis, R, and Bioconductor: notes, Martin Morgan EMBO 2015 html, Rmd, R; github 2015‑10‑19 3.2/3.2
Annotation Adding Annotation To Your Analysis, Martin Morgan Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
ChIPSeq ChIP-Seq for Understanding Gene Regulation, Martin Morgan Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
Large Data Counting Reads And Working With Large Files, Martin Morgan Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
Introduction Data manipulation, Houtan Noushmehr Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
Microarrays Gene Expression, Houtan Noushmehr Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
Genomic Ranges Genomic Ranges For Genome-Scale Data And Annotation, Martin Morgan Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
Introduction Introduction, Martin Morgan Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
RNASeq RNA-Seq Differential Expression, Martin Morgan Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
RNASeq Supplement 1: RNA-Seq Workflow, Martin Morgan Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
RNASeq Supplement 2: RNA-Seq Statistical Issues, Martin Morgan Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
Data Integration The TCGAbiolinks package, Houtan Noushmehr Uruguay2015 pdf 2015‑10‑05 3.2/3.2
SummarizedExperiment Working with Data: SummarizedExperiment, Martin Morgan Uruguay2015 HTML Rmd R 2015‑10‑05 3.2/3.2
Introduction Introduction: Analysis and Comprehension, Martin Morgan BiocAsia2015 AMI, HTML, Rmd, R 2015‑09‑07 3.2/3.2
Annotation Workshop: Gene, Genome, and Variant Annotation, Martin Morgan BiocAsia2015 AMI, HTML, Rmd, R 2015‑09‑07 3.2/3.2
RNASeq Workshop: RNASeq, Martin Morgan BiocAsia2015 AMI, HTML, Rmd, R 2015‑09‑07 3.2/3.2
Data Representation Workshop: Sequences, Alignments, and Large Data, Martin Morgan BiocAsia2015 AMI, HTML, Rmd, R 2015‑09‑07 3.2/3.2
RNASeq SGSeq and alternative splicing, Leonard Goldstein BioC2015 AMI, HTML, Rmd, R, GitHub 2015‑07‑20 3.2/3.2
RNASeq Advanced Lab: Explore the Data, Sonali Arora BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
RNASeq Advanced RnaSeq, Sonali Arora BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Reproducibility An introduction to Docker and the Bioconductor Docker containers, Dan Tenenbaum BioC2015 AMI, HTML, Rmd, R, GitHub 2015‑07‑20 3.2/3.2
Flow Cytometry Analysis of Designed Experiments Using OpenCyto, Greg Finak BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Annotation Annotation Resources for Bioconductor, Marc Carlson, Sonali Arora BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Annotation AnnotationHub Recipes, Marc Carlson, Sonali Arora BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Workflows Automated NGS workflows with systemPipeR running on clusters or single machines, with a focus on VAR-seq: systemPipeR, Thomas Girke BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Image Analysis Basics of image data and spatial patterns analysis in R, Andrzej Oleś, Wolfgang Huber BioC2015 AMI, HTML, Rmd, R, GitHub 2015‑07‑20 3.2/3.2
ChIPSeq Detecting differential binding in ChIP-seq data with csaw, Aaron Lun BioC2015 AMI, HTML, Rmd, R, GitHub 2015‑07‑20 3.2/3.2
RNASeq Differential expression, manipulation, and visualization of RNA-seq reads, Mike Love BioC2015 AMI, HTML, Rmd, R, GitHub 2015‑07‑20 3.2/3.2
Best Practices FAQ Live! Common questions and expert solutions, delivered in person, James MacDonald BioC2015 AMI, PDF, Rnw, R, GitHub 2015‑07‑20 3.2/3.2
Scalable Computing GoogleGenomics, Nicole Deflaux, Siddhartha Bagaria and Craig Citro BioC2015 AMI, GitHub, readthedocs 2015‑07‑20 3.2/3.2
Introduction Introduction to R, Sonali Arora BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Introduction Introduction to Bioconductor, Sonali Arora BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Introduction Lab: Basic Bioconductor, Sonali Arora BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Intermediate Lab: Intermediate Bioconductor, Sonali Arora BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Workflows Liftr & sbgr, Nan Xiao BioC2015 AMI, PDF, Video 2015‑07‑20 3.2/3.2
Large Data Management and analysis of large genomic data., Valerie Obenchain, Martin Morgan BioC2015 AMI, HTML, Rmd, R, GitHub 2015‑07‑20 3.2/3.2
Methylation Methylation and the analysis of Illumina 450k microarrays, Kasper Hansen BioC2015 AMI, HTML, Rmd, R, GitHub 2015‑07‑20 3.2/3.2
Genomic Ranges Practical introduction to Bioconductor foundational data structures for high throughput sequencing analysis, Hervé Pagès, Michael Lawrence BioC2015 AMI, PDF, R 2015‑07‑20 3.2/3.2
Omics TCGAloading, Levi Waldron, Tim Triche, Aedin Culhane BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Flow Cytometry Visualize cytometry data with ggcyto, Greg Finak BioC2015 AMI, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Omics multiAssayQC, Levi Waldron, Tim Triche, Aedin Culhane BioC2015 AMI, GitHub, HTML, Rmd, R 2015‑07‑20 3.2/3.2
Introduction Bioconductor for high-throughput sequence analysis, Martin Morgan, Sonali Arora useR! 2015 talk, lab; github 2015‑06‑30 3.1/3.2
ChIPSeq Lab: ChIP-seq analysis basics, Aleksandra Pȩkowska, Simon Anders CSAMA 2015 pdf; github 2015‑06‑15 3.1/3.2
RNASeq Lab: End-to-end RNA-Seq workflow, Mike Love, Simon Anders, Wolfgang Huber CSAMA 2015 html; github 2015‑06‑15 3.1/3.2
Visualization Lab: Interactive data visualization with Shiny, Andrzej Oleś CSAMA 2015 html; github 2015‑06‑15 3.1/3.2
Introduction Lab: Introduction to R and Bioconductor, Martin Morgan CSAMA 2015 html; github 2015‑06‑15 3.1/3.2
MachineLearning Lab: Machine Learning and Parallel Computing, Wolfgang Huber, Martin Morgan CSAMA 2015 html; github 2015‑06‑15 3.1/3.2
LargeData Lab: Performance and Parallel Evaluation, Martin Morgan CSAMA 2015 html; github 2015‑06‑15 3.1/3.2
Metagenomics Lab: Phyloseq Basic Usage for Metagenomics, Paul Pyl CSAMA 2015 html; github 2015‑06‑15 3.1/3.2
Variants Lab: Plotting Regions from BAM files directly, Paul Pyl CSAMA 2015 html; github 2015‑06‑15 3.1/3.2
ReproducibleResearch Lab: Reproducible Research and R Authoring with markdown and knitr, Laurent Gatto CSAMA 2015 html; github 2015‑06‑15 3.1/3.2
Technology Lecture: Basics of sequence alignment and aligners, Simon Anders CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
Statistics Lecture: Clustering and classification, Vincent Carey CSAMA 2015 html 2015‑06‑15 3.1/3.2
GenomicRanges Lecture: Computing with sequences and genomic intervals, Martin Morgan CSAMA 2015 pdf, html 2015‑06‑15 3.1/3.2
ChIPSeq Lecture: Epigenetics and ChIP-Seq, Aleksandra Pȩkowska CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
GeneSetEnrichment Lecture: Gene set enrichment analysis, part I, Martin Morgan CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
GeneSetEnrichment Lecture: Gene set enrichment analysis, part II, Michael Love CSAMA 2015 html 2015‑06‑15 3.1/3.2
HiC Lecture: HiC data analysis, Aleksandra Pȩkowska CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
Statistics Lecture: Hypothesis testing and multiple testing, Wolfgang Huber CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
Proteomics Lecture: Introduction to MS based proteomics and Bioconductor infrastructure, Laurent Gatto CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
Introduction Lecture: Introduction to R and Bioconductor, Martin Morgan CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
LargeData Lecture: Large data, performance, and parallelization; large-scale efficient computation with genomic intervals, Martin Morgan CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
RNASeq Lecture: RNA-Seq data analysis and differential expression part I, Simon Anders CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
RNASeq Lecture: RNA-Seq data analysis and differential expression part II, Michael Love CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
RNASeq Lecture: Single-cell RNA-Seq, Alejandro Reyes, Simon Anders CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
Visualization Lecture: Visualization, the grammar of graphics and ggplot2, Wolfgang Huber CSAMA 2015 pdf 2015‑06‑15 3.1/3.2
Annotation Lecture: Working with annotation - genes, genomic features and variants, Martin Morgan CSAMA 2015 pdf, html 2015‑06‑15 3.1/3.2
MOOC Introduction to Bioconductor, Rafael Irizarry, Michael Love, Vincent Carey EdX-PH525.4x html 2015‑03‑30 3.1/3.2
MOOC Statistics and R for the Life Sciences, Rafael Irizarry, Michael Love EdX-PH525.1x html 2015‑01‑19 3.1/3.2
ChIPSeq ChIP-seq with csaw, Martin Morgan SeattleApr2015 html, R, Rmd 2015‑04‑06 3.1/3.2
RNASeq Differential Gene Expression, Martin Morgan SeattleApr2015 html, R, Rmd 2015‑04‑06 3.1/3.2
Gene set enrichment Gene set enrichment, Martin Morgan SeattleApr2015 html, R, Rmd 2015‑04‑06 3.1/3.2
Genomic Ranges Genomic Ranges, Martin Morgan SeattleApr2015 html, R, Rmd 2015‑04‑06 3.1/3.2
Large Data Integrative Data Analysis, Martin Morgan SeattleApr2015 html, R, Rmd 2015‑04‑06 3.1/3.2
Large Data Large Data, Martin Morgan SeattleApr2015 htm, R, Rmd 2015‑04‑06 3.1/3.2
Machine Learning Machine Learning, Sonali Arora SeattleApr2015 html, R, Rmd 2015‑04‑06 3.1/3.2
Methylation Methylation and regulatory work flows with minfi, Martin Morgan SeattleApr2015 html, R, Rmd 2015‑04‑06 3.1/3.2
Intermediate Use R / Bioconductor for Sequence Analysis, Martin Morgan, Sonali Arora SeattleApr2015 package, AMI 2015‑04‑06 3.1/3.2
Genomic Ranges Genomic Ranges, Martin Morgan, Hervé Pagès Use Bioc html, R, Rmd, slides 2015‑02‑04 3.1/3.2
Visualization Interactive visualization, Martin Morgan, Hervé Pagès Use Bioc html, R, Rmd 2015‑02‑04 3.1/3.2
Introduction Introduction to R / Bioconductor, Martin Morgan, Hervé Pagès Use Bioc html, R, Rmd 2015‑02‑04 3.1/3.2
Status Update New packages and functionality, Martin Morgan, Hervé Pagès Use Bioc html, R, Rmd 2015‑02‑04 3.1/3.2
Annotation Package, web, and hub annotation resources, Martin Morgan, Hervé Pagès Use Bioc html, R, Rmd 2015‑02‑04 3.1/3.2
Intermediate Use R / Bioconductor for Sequence Analysis, Martin Morgan, Hervé Pagès Use Bioc package, AMI 2015‑02‑04 3.1/3.2
Large Data Working with large data, Martin Morgan, Hervé Pagès Use Bioc html, R, Rmd 2015‑02‑04 3.1/3.2
Packages From code to packages, Martin Morgan, Hervé Pagès Learn Bioc html, R, Rmd 2015‑02‑02 3.1/3.2
Bioconductor Introduction to Bioconductor, Martin Morgan, Hervé Pagès Learn Bioc html, R, Rmd 2015‑02‑02 3.1/3.2
Sequence Analysis Introduction to Bioconductor for Sequence Analysis, Martin Morgan, Hervé Pagès Learn Bioc html, R, Rmd 2015‑02‑02 3.1/3.2
R Introduction to R, Martin Morgan, Hervé Pagès Learn Bioc html, R, Rmd 2015‑02‑02 3.1/3.2
Reproducibility Introduction to reproducible analysis, Martin Morgan, Hervé Pagès Learn Bioc html, R, Rmd 2015‑02‑02 3.1/3.2
Introduction Learning R / Bioconductor for Sequence Analysis, Martin Morgan, Hervé Pagès Learn Bioc package, AMI 2015‑02‑02 3.1/3.2
Sequence Analysis Overview of common sequencing work flows, Martin Morgan, Hervé Pagès Learn Bioc html, R, Rmd 2015‑02‑02 3.1/3.2
RNASeq RNA-seq overview and work flow, Martin Morgan, Hervé Pagès Learn Bioc slides, lab, R, Rmd 2015‑02‑02 3.1/3.2
Visualization Visualizing genomic data, Martin Morgan, Hervé Pagès Learn Bioc html, R, Rmd 2015‑02‑02 3.1/3.2
Large Data Working with large data, Martin Morgan, Hervé Pagès Learn Bioc html, R, Rmd 2015‑02‑02 3.1/3.2
Introduction R/Bioconductor for Integrative Genomic Analysis, Martin Morgan useR! Lyon slides 2015‑01‑15 3.0/3.1.2
Status Update Bioconductor Project Update, January 2015, Martin Morgan BiocEurope2015 slides 2015‑01‑12 3.1/3.2
Best Practices Google Hangout for New Package Submitters, Marc Carlson NewPackages slides, Video 2014‑12‑10 3.1/3.2
Annotation Annotating Genes, Genomes, and Variants, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Appendix Appendix: Install IGV, Martin Morgan SeattleOct2014 html, R, Rmd hg19_alias.tab 2014‑10‑27 3.0/3.1.1
Workflows Common Sequence Analysis Work Flows, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Copy Number Copy Number, Sonali Arora SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Gene set enrichment Gene set enrichment, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Introduction Introduction, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Bioconductor Introduction to Bioconductor, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
R Introduction to R, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Machine Learning Machine Learning, Sonali Arora SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Packages Organizing Code in Functions, Files, and Packages, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
RNASeq RNA-Seq, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
RNASeq RNA-Seq Lab: Workflow – gene-level exploratory analysis and differential expression, Michael Love et al. SeattleOct2014 html 2014‑10‑27 3.0/3.1.1
Reproducibility Reproducible Research, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Visualization Visualization, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Large Data Working with Large Data, Martin Morgan SeattleOct2014 html, R, Rmd 2014‑10‑27 3.0/3.1.1
Methylation A short methylation analysis using minfi, Martin Morgan Epigenomics html R Rmd 2014‑08‑24 2.14/3.1.1
RNASeq Counting reads for RNA-seq in Bioconductor, Martin Morgan Epigenomics pdf R Rnw 2014‑08‑24 2.14/3.1.1
Epigenomics Introduction to Bioconductor for Epigneomics, Martin Morgan Epigenomics pdf R Rnw 2014‑08‑24 2.14/3.1.1
Sequence Analysis Introduction to Bioconductor for Sequence Analysis, Martin Morgan Epigenomics html R Rmd 2014‑08‑24 2.14/3.1.1
R Introduction to R (slides), Martin Morgan Epigenomics html 2014‑08‑24 2.14/3.1.1
RNASeq Introduction to RNA-Seq data analysis, Benilton Carvalho Epigenomics pdf 2014‑08‑24 2.14/3.1.1
Methylation Introduction to working with methylation arrays (slides), Martin Morgan Epigenomics html 2014‑08‑24 2.14/3.1.1
Data Representation Sequence data represenations in Bioconductor, Martin Morgan Epigenomics html R Rmd 2014‑08‑24 2.14/3.1.1
eQTL eQTL analysis – an approach with Bioconductor, Vincent Carey Epigenomics pdf 2014‑08‑24 2.14/3.1.1
Methylation Analysis of 450k methylation data with the minfi package, Kasper Hansen BioC2014 pdf, Rnw, R 2014‑07‑30 2.14/3.1.1
RNASeq Analysis of RNA-Seq using the DESeq2 package, Michael Love BioC2014 pdf, Rnw, R 2014‑07‑30 2.14/3.1.1
Annotation Bioconductor annotations: using and sharing resources, Marc Carlson BioC2014 workflow, vignette 2014‑07‑30 2.14/3.1.1
RNASeq CRISPRseek: Design of target-specific guide RNAs in CRISPR-Cas9 genome-editing systems, Julie Zhu BioC2014 pdf, html, Rmd, R 2014‑07‑30 2.14/3.1.1
RNASeq Differential gene- and exon-level expression analyses for RNA-seq data using edgeR, voom and featureCounts, Mark Robinson BioC2014 pdf, html, md 2014‑07‑30 2.14/3.1.1
eQTL Genetics of gene expression: computation and integrative prediction, Vincent Carey BioC2014 html, Rmd, R 2014‑07‑30 2.14/3.1.1
Pathway Integrated pathway analysis of multiple omics datasets, Aedin Culhane BioC2014 html, Rmd, R 2014‑07‑30 2.14/3.1.1
Genomic Ranges Learn how to use Bioconductor to perform common tasks on your high-throughput sequencing data, Hervé Pagès BioC2014 pdf 2014‑07‑30 2.14/3.1.1
Meta-analysis Meta-analysis of genomics experiments using Bioconductor, Levi Waldron BioC2014 Rpres, R 2014‑07‑30 2.14/3.1.1
Scalable Computing Parallel Computing with Bioconductor in the Amazon Cloud, Valerie Obenchain BioC2014 pdf, R 2014‑07‑30 2.14/3.1.1
R/Bioconductor R / Bioconductor for everyone, Martin Morgan BioC2014 slides, pdf, R 2014‑07‑30 2.14/3.1.1
Proteomics R / Bioconductor packages for Proteomics, Laurent Gatto BioC2014 html, Rmd, R 2014‑07‑30 2.14/3.1.1
Flow Cytometry Tutorial, Introduction to Flow Cytometry Data Analysis using OpenCyto and Bioconductor, Greg Finak BioC2014 html, Rmd, R 2014‑07‑30 2.14/3.1.1
Variants Variant calling with Bioconductor, Michael Lawrence BioC2014 pdf, R, pkg 2014‑07‑30 2.14/3.1.1
ChIPSeq Visualisation and assessment of ChIP-seq quality using ChIPQC and Diffbind packages, Tom Carroll BioC2014 pdf, R 2014‑07‑30 2.14/3.1.1
Annotation Accessing Annotation Resources, Martin Morgan ISMB2014 pdf, R, R 2014‑07‑15 2.14/3.1.1
RNASeq Analysis of RNA-Seq Data, Mike Love ISMB2014 html 2014‑07‑15 2.14/3.1.1
Scalable Computing Scalable Integrative Bioinformatics with Bioconductor, Vincent Carey ISMB2014 pptx 2014‑07‑15 2.14/3.1.1
R/Bioconductor Trends in Genomic Data Analysis in R, Levi Waldron ISMB2014 pdf 2014‑07‑15 2.14/3.1.1
Annotation Annotations, Martin Morgan useR2014 html, R 2014‑06‑30 2.14/3.1.0
R/Bioconductor Introduction to R / Bioconductor, Martin Morgan useR2014 html, R 2014‑06‑30 2.14/3.1.0
Data Representation Sequence Data Representation, Martin Morgan useR2014 html, R 2014‑06‑30 2.14/3.1.0
RNASeq Work flows : RNA-Seq, Martin Morgan useR2014 html, R 2014‑06‑30 2.14/3.1.0
Annotation Annotations, Martin Morgan SeattleFeb2014 pdf, R, Rnw 2014‑02‑27 2.14/3.1.0
R/Bioconductor Bioconductor - Slides, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Data Representation Genomic Ranges - Slides, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Annotation RNASeq Analysis, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
RNASeq Visualization of Genomic Data, Sonali Arora SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Visualization Working with Annotations, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Data Representation Working with DNA Sequences, Sonali Arora SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Genomic Ranges Working with FASTQ, BAM, and VCF files, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
Genomic Ranges Working with Genomic Ranges, Martin Morgan SeattleFeb2014 pdf, Rnw, R 2014‑02‑27 2.14/3.1.0
R/Bioconductor Working with R, Martin Morgan SeattleFeb2014 pdf, R, Rnw 2014‑02‑27 2.14/3.1.0
Annotation Annotations, Martin Morgan summerx pdf, R, Rnw 2014‑01‑27 2.14/3.1.0
Best Practices Best Practices for Managing R / Bioconductor Scripts, Martin Morgan summerx pdf, Rnw 2014‑01‑27 2.14/3.1.0
R/Bioconductor Bioconductor, Martin Morgan summerx pdf, R, Rnw 2014‑01‑27 2.14/3.1.0
Genomic Ranges Ranges, Hervé Pagès summerx pdf, R, Rnw 2014‑01‑27 2.14/3.1.0
Variants Variants, Martin Morgan summerx pdf, Rnw 2014‑01‑27 2.14/3.1.0
Visualization Visualization., Martin Morgan summerx pdf, R, Rnw 2014‑01‑27 2.14/3.1.0
RNASeq A complete RNA-Seq differential expression workflow, Michael Love, Simon Anders, Wolfgang Huber CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
R/Bioconductor Accessing resources - packages, classes, methods, and efficient code, Martin Morgan CSAMA2014 html, R 2014‑06‑22 2.14/3.1.1
ChIPSeq ChIP-seq Analysis, Martin Morgan CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
Genomic Ranges Computing with genomic ranges, sequences and alignments, Michael Lawrence CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
DNASeq DNA-Seq 1: Variant calling, Michael Lawrence CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
DNASeq DNA-Seq 2: visualisation and quality assessment of variant calls, Paul Theodor Pyl CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 1: t-test and linear model, Robert Gentleman CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 2: multiple testing, false discovery rates, independent filtering, Wolfgang Huber CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 3: Classification and clustering - basic concepts, Unknown CSAMA2014 html 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 4: regularisation & kernels, Wolfgang Huber CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Statistics Elements of statistics 5: experimental design, Simon Anders CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Gene set enrichment Gene set enrichment analysis, Robert Gentleman CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
RNASeq High-throughput sequencing: Alignment and related topic, Simon Anders CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Visualization Image Analysis, Susan Holmes, Wolfgang Huber, Trevor Martin CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Introduction Introduction to R and Bioconductor, Martin Morgan CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Proteomics Proteomics, Laurent Gatto CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
RNASeq RNA-Seq 1: differential expression analysis - GLMs and testing, Simon Anders CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
RNASeq RNA-Seq 3: alternative exon usage, Alejandro Reyes CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Reporting Reporting your analysis - authoring knitr/Rmarkdown, ReportingTools, shiny, Laurent Gatto CSAMA2014 pkg 2014‑06‑22 2.14/3.1.1
Variants Variant tallies, visualisation, HDF5, Paul Theodor Pyl CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
Visualization Visualisation in Statistical Genomics, Vincent Carey CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1
Annotation Working with Ranges infrastructure: annotating and understanding regions, Martin Morgan CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
Annotation Working with gene and genome annotations, Martin Morgan CSAMA2014 pdf, R 2014‑06‑22 2.14/3.1.1
eQTL / molecular-QTL eQTL / molecular-QTL analyses, Vincent Carey CSAMA2014 pdf 2014‑06‑22 2.14/3.1.1

 

Courses by year

Custom workshops

The Bioconductor project can provide customized workshops on statistical methods and software for the analysis of genomic data for different educational and industrial clients. Interested parties should contact Vincent Carey.