Question: [Course] CSAMA 2011: Computational Statistics for Genome Biology (Ninth Edition)
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6.9 years ago by
stefano iacus430
stefano iacus430 wrote:
CSAMA 2011: Computational Statistics for Genome Biology (Ninth Edition) This one week intensive course is intended to give insights into recent advances in statistical and computational aspects of the design and interpretation of microarray experiments. The topics will include all aspects of the data analysis of microarray experiments for transcript profiling and ChIP-chip. The course is intended mainly for researchers with a basic understanding of microarray technology and its statistical and computational challenges. The four practical sessions of the course will be most beneficial for participants that are able to converse in a programming language such as R. What will it cover? (tentative program) * Introduction to R and Bioconductor * RNA-Seq and ChiP-Seq data analysis * Microarray analysis * Statistics for differential expression * Sequence manipulation * Annotation of genes, genomic features and variants * Gene set enrichment analysis * Machine Learning * High-throughput image analysis Where? Brixen-Bressanone (Alps!), Italy When? 26th June -- 1st July, 2011 Please see List of speakers Vincent J. Carey, Channing Laboratory, Harvard Medical School (USA) Robert Gentleman, Genentech (USA) Kasper Daniel Hansen, Johns Hopkins Bloomberg School of Public Health (USA) Wolfgang Huber, European Molecular Biology Laboratory (DE) Martin Morgan, Fred Hutchinson Cancer Research Center (USA) Few places left, registration closes soon! Best regards Stefano Tentative day by day program follows *Mon* Introduction to R and Bioconductor Bioconductor packages for short read I/O, data management and analysis Reproducible workflows with R Basics of HT sequencing technologies *Tue* Short read alignment using externals tools like bowtie, bwa, SOAP Biostrings, ShortRead, Rsamtools, gapped alignments IRanges, GenomicRanges What you still need to know about microarrays: normalisation, quality assessment, strengths and weaknesses, differential expression, use of existing datasets for integrative analyses *Wed* t-test and linear model - the general statistical concepts RNA-Seq transcript quantification and differential expression: what to do once you have read counts per gene, transcript, exon or region. GLMs with the negative binomial distribution; DESeq, edgeR Multiple testing and independent filtering Emerging topic *Thu* Gene and genome annotation - AnnotationDbi, GenomicFeatures Methods for visualising your data along genomic coordinates - Rtracklayer, IGB, GenomeGraphs ChIP-Seq related topics eQTL-type analyses *Fri* Clustering and Classification Aggregation - gene set enrichment analysis Image Analysis and pattern recognition in cellular assays, perhaps something about genetic interactions Emerging topic ----------------------------------- Stefano M. Iacus Department of Economics, Business and Statistics University of Milan Via Conservatorio, 7 I-20123 Milan - Italy Ph.: +39 02 50321 461 Fax: +39 02 50321 505 ---------------------------------------------------------------------- -------------- Please don't send me Word or PowerPoint attachments if not absolutely necessary. See:
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