Advanced Bioconductor

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In this course, we begin with approaches to 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. We 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, we explore 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 educational background of our students we 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 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 make up two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

• 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

Genomics 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.

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What you will learn

• Static and interactive visualization of genomic data
• Reproducible analysis methods
• Memory-sparing representations of genomic assays
• Working with multiomic experiments in cancer
• Targeted interrogation of cloud-scale genomic archives

Skills you learn

• Life Sciences
• Data Analysis
• Functional Genomics
• Rmarkdown
• Data Warehousing
• Statistics
• Relational Databases
• Biology
• R (Programming Language)
• Ribonucleic Acid Sequencing
• Data Architecture
• Data Integration
• Matrix Algebra
• Software Engineering
• Hierarchical Data Format
• Bioconductor (Bioinformatics Software)
• Graphical User Interface
• Transcription Factors
• Statistical Inference
• Linear Model

Auto Summary

"Advanced Bioconductor" is an expert-level course offered by edX, tailored for IT and Computer Science professionals. This comprehensive program delves into sophisticated techniques for genomic visualization, ensuring reproducible analysis, and understanding the architecture and exploration of extensive cloud-scale genomic data generated by various consortia. Over a span of 44 hours, learners will engage deeply with the material, gaining valuable skills pertinent to advanced genomic studies. The course is available through both Professional and Starter subscription plans, catering to those seeking to elevate their expertise in genomic data analysis and visualization. Ideal for seasoned professionals in the field, this course promises to enhance your ability to handle complex genomic datasets efficiently.