Package: StatisticalComputing2013
Type: Package
Title: Introduction to Statistical Computing with R and Bioconductor
Version: 0.3.1
Author: Herv\'e Pag\`es, Marc Carlson, Valerie Obenchain, Paul
    Shannon, Dan Tenenbaum, Martin Morgan
Maintainer: Biocore Team c/o BioC user list
        <bioconductor@stat.math.ethz.ch>
License: Artistic-2.0
Description: This hands-on workshop introduces use of R and
    Bioconductor for the analysis and comprehension of high-throughput
    genomic data. We assume basic familiarity with R, e.g., entering
    R commands and writing short scripts. Users will engage in
    exercises and activities to familiarize themselves with workshop
    concepts; all software is provided. The morning provides a
    systematic review of essential R data types (vector, matrix,
    data.frame) and programming concepts (vectorization, functions,
    packages). Bioinformatic data is large and complicated, and data
    provenance and reproducibility are important. For these reasons,
    we emphasize `best practices' for writing efficient R code, and
    the use of classes and methods for working with complicated data
    objects. The morning highlights essential R packages, and provides
    an overview of Bioconductor resources for working with modern
    genomic data.  The afternoon uses `RNA-seq' approaches to
    assessing differential expression of known genes as an exemplar
    work flow for modern high-throughput genomic analysis. The work
    flow includes core activities of data manipulation, statistical
    analysis, and interpretation of results in biological context. The
    work flow provides insight into unique statistical challenges of
    high-throughput data, including sample normalization, use of
    appropriate models, approaches to maximizing information in highly
    structured data, and reducing the false discovery rate.  Analogous
    statistical issues are central to many genomic data sets, and are
    informed by lessons learned from analysis of micro-arrays.  The
    workshop concludes with static and interactive approaches to
    effective, accurate and informed presentation of statistical
    results in the context of interdisciplinary research teams.
Depends: SequenceAnalysisData (>= 0.1.4), DESeq2, parathyroidSE,
        pasilla (>= 0.2.10), vsn, gplots,
Imports: methods, Biostrings, IRanges, GenomicRanges
Suggests: AnnotationHub, biomaRt, Biostrings,
        BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19,
        edgeR, ensemblVEP, gmapR, GenomicFeatures, GenomicRanges,
        ggplot2, lattice, limma, LungCancerLines, org.Dm.eg.db,
        parallel, rbenchmark, rtracklayer, ShortRead,
        SIFT.Hsapiens.dbSNP132, SNPlocs.Hsapiens.dbSNP.20101109,
        TxDb.Dmelanogaster.UCSC.dm3.ensGene,
        TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation
Packaged: 2013-10-07 17:24:28 UTC; biocbuild
