Advanced Bioconductor: Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.
About this course
In this course, you will begin with approaches to the visualization of genome-scale data, and provide tools to build interactive graphical interfaces to speed discovery and interpretation. Using Knitr and Rmarkdown as basic authoring tools, the concept of reproducible research is developed, and the concept of an executable document is presented. In this framework reports are linked tightly to the underlying data and code, enhancing reproducibility and extensibility of completed analyses, then you study out-of-memory approaches to the analysis of very large data resources, using relational databases or HDF5 as “back ends” with familiar R interfaces. Multiomic data integration is illustrated using a curated version of The Cancer Genome Atlas. Finally, they explored cloud-resident resources developed for the Encyclopedia of DNA Elements (the ENCODE project). These address transcription factor binding, ATAC-seq, and RNA-seq with CRISPR interference.
Given the diversity in the educational background of our students, they have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course, they will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.
These courses makeup two Professional Certificates and are self-paced:
- PH525.1x: Statistics and R for the Life Sciences
- PH525.2x: Introduction to Linear Models and Matrix Algebra
- PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
- PH525.4x: High-Dimensional Data Analysis
- PH525.5x: Introduction to Bioconductor
- PH525.6x: Case Studies in Functional Genomics
- PH525.7x: Advanced Bioconductor
- This class was supported in part by NIH grant R25GM114818.
HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning.
What you will learn from Advanced Bioconductor Course?
- Static and interactive visualization of genomic data.
- Reproducible analysis methods.
- Memory-sparing representations of genomic assays.
- Working with multi-omic experiments in cancer.
- Targeted interrogation of cloud-scale genomic archives.
- PH525.3x, PH525.4x
Introduction and Resources
Section 1. Advanced Bioconductor: Visualization of genome-scale data
Section 2. Advanced Bioconductor: Reproducible analysis and building packages
Section 3. Advanced Bioconductor: Using R with external data
Section 4. Advanced Bioconductor: Multi-omic data integration
Section 5. Advanced Bioconductor: Analyzing cloud-scale ENCODE data
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- Harvard University
- Online Course
- 1-4 Weeks
- Free Course (Affordable Certificate)
- Bioinformatics Data Analysis Genomics Research methodology