## Data Analysis for Genomics

The repository of the R markdown files (.Rmd) for the labs shown here is:

http://github.com/genomicsclass/labs

### Resources

### Introduction (week 1)

- Introduction
- Exploratory Data Analysis
- Installing Bioconductor and finding help
- R refresher
- Robust summaries

### Microarray and NGS basics (week 2)

- Installing packages from Github
- Reading microarray data
- Downloading data from GEO using GEOquery
- EDA plots for microarray
- Basic Bioconductor infrastructure
- EDA plots for next generation sequencing

### Statistical inference and linear modeling (week 3)

- Inference
- Expressing design formula in R
- Linear models
- Basic inference for microarray
- Rank tests
- Monte Carlo methods

### Background, modeling and normalization (week 4)

### Distance and prediction (week 5)

- Distance lecture
- Distance and clustering lab
- Dimension reduction and heatmaps
- Prediction lecture
- Cross-validation

### Batch effect (week 6)

### Advanced differential expression (week 7)

- Hierarchical modeling and using limma
- Mapping features to genes
- Gene set analysis lecture
- Gene set testing in R
- Multiple testing

### Advanced workflows (week 8)

- Visualizing NGS data
- Counting NGS reads in features
- Methylation
- Reading 450K idat files with the minfi package
- Interactive visualization of DNA methylation data analysis
- ChIP-seq
- RNA-seq
- Genome variation