CSAMA 2017: Statistical Data Analysis for Genome-Scale Biology
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
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