Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data. 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.
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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.
This course is part of the Data Analysis for Genomics Professional Certificate.
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
What you'll 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
Prerequisites:
Statistical Inference and Modeling for High-throughput Experiments
High-Dimensional Data Analysis
