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CSAMA 2017: Statistical Data Analysis for Genome-Scale Biology

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Visits number : 463
Category : CourseAdvisor
Keywords : Genomics, RNA sequencing, Gene expression
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This ressource provides R course materials for genomic data analyses.

Main contents include:

- Introduction to R and Bioconductor for gene expression analyses and gene annotation

- Basics of sequence alignment and aligners

- RNA-Seq data analysis and differential expression

- Multiple testing

- Experimental design, batch effects and confounding

- Robust statistics: median, MAD, rank test, Spearman, robust linear model

- Visualization, the grammar of graphics and ggplot2

- Clustering and classification

- Resampling: cross-validation, bootstrap, and permutation tests

- Analysis of microbiome marker gene data

- Gene set enrichment analysis