https://github.com/vjcitn/bosbioctrain

Training materials for Bioconductor courses in Longwood area, Boston MA
https://github.com/vjcitn/bosbioctrain

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Training materials for Bioconductor courses in Longwood area, Boston MA

• Host: GitHub
• URL: https://github.com/vjcitn/bosbioctrain
• Owner: vjcitn
• Created: 2013-09-12T15:22:49.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2013-09-30T16:09:00.000Z (over 11 years ago)
• Last Synced: 2025-01-09T13:46:37.897Z (5 months ago)
• Language: R
• Size: 965 KB
• Stars: 2
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
bosBiocTrain

============

Training materials for Bioconductor courses in Longwood area, Boston MA



THIS COURSE IS CANCELED.  ANOTHER COURSE WILL BE ANNOUNCED.  THESE MATERIALS

WILL CONTINUE TO BE DEVELOPED FOR PUBLIC USE.




CANCELED October 10-11 2013, Fenway Room, Inn at Longwood

CANCELED 432 Longwood Ave Boston MA 02115




CANCELED To register, fill out [this form](utils/form13b.pdf) (you

CANCELED can 'view raw' and it will be downloaded, or just right

CANCELED click and save link)  and send to me as indicated.



In what follows, _italics_ denote Bioconductor packages,

and `sansserif` tokens denote functions or classes

Day 1

=====

 * Lecture 1: Pitfalls of genomic data analysis.

  Complexity, poor design, batch effects.  Vehicles for

  avoiding some of the pitfalls with Bioconductor.  Approaches

  to systematic version control and literate data analysis.

 * [Lab 1: Managing genomic annotation for human and model organisms](AnnotationLab/anno.Rnw.md)

   + [_OrganismDbi_](http://www.bioconductor.org/packages/release/bioc/html/OrganismDbi.html), `select`

   + [_GenomicRanges_](http://www.bioconductor.org/packages/release/bioc/html/GenomicRanges.html) and see [the recent PLoS CompBio paper](http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118)

 * [Lab 2: Managing and using experimental archives](ArchiveLab/archive.Rnw.md)

   + General principle: `X[G, S]` is selection of genomic features `G`

    and experimental samples `S` from archive `X`

   + Archive containers: `ExpressionSet`, `SummarizedExperiment`

   + Getting acquainted with some classic experiments

     - Expression arrays

     - Methylation arrays

     - Genotyping studies

     - NGS studies: RNA-seq, ChIP-seq

 * Lecture 2: Statistical concepts for genomic data analysis

   + Exploratory data analysis

     - distributions, density estimation

     - scatterplot matrices

     - PCA

     - distances, clustering, silhouette

     - Example: identifying batch effects

   + Hypothesis testing

     - Two-sample problem: parametric, nonparametric

     - regression/ANOVA

     - censored response

     - correlated response

   + Shrinkage concepts for high-dimensional data

   + Visualizations and reports: standard, "shiny", ReportingTools

 * Lab 3: Statistical explorations of genomic data: interfaces

for exploratory multivariate analysis, machine learning,

multiple comparisons, enumerating significantly distinctive

features, functional interpretation of feature sets.  [Early version.](StatsLab/stats.Rnw.md)

Day 2

=====

 * Lecture 3: R and Bioconductor for high-throughput computing

 * Lab 4: Case studies

   + Microarray differential expression

   + Differential methylation, _bsseq_

   + RNA-seq, _DESeq2_

   + ChIP-seq, _DiffBind_

   + Integrative analyses