Bioconductor (https://bioconductor.org) is a flexible, widely used, and respected collection of R packages for the statistical analysis and comprehension of many common types (bulk and single-cell sequencing, methylation and other microarrays, flow cytometry, ...) of high-throughput genomic data. Learning to use Bioconductor's core infrastructure, domain-specific analysis packages, and annotation resources can pose significant challenges, both to those embarking on their first significant bioinformatic analysis and to those encountering the Bioconductor ecosystem after developing considerable skill in other programming paradigms.
This workshop introduces strategies for effectively training users new to Bioconductor. Topics covered include the following
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Assessing abilities: where do we begin? (9:00 - 9:45)
Introduction. Course objectives. Course content. Biology - statistics - computation skills. Experience with R. R refresher. Atomic vectors. Functions. Classes and methods. Help. Packages.
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Engaging new and experienced users (9:45 - 10:30)
Data input and manipulation in base R. Tidy data. Bioconductor. Bioinformatic data types and input. Genomic ranges.
BREAK (10:30 - 10:45)
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Practical activities with meaningful feedback (10:45 - 11:30)
Bioconductor 'TxDb' and 'org' packages. Annotating ChIP-seq peaks. Scripts and functions.
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Group activity I (11:30 - 12:15)
LUNCH (12:15 - 1:15)
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Managing cognitive load (1:15 - 2:00)
RNA-seq differential expression. Count matrix. Experimental design. 'Top table'.
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Matching expectations and learning objectives (2:15 - 3:00)
Enabling independent learning. Package discovery and assessment.
BREAK (3:00 - 3:15)
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Effective instruction and resource creation (3:15 - 4:00)
Configured servers versus user laptops. Using RStudio. Markdown vignettes. Course packages. Jupyter notebooks. Best practices, literate programming, version control.
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Group activity II (4:00 - 4:45)
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What have we learned? (4:45 - 5:00)