# Introduction to R for Bioinformatics - BC203

## Vincenzo Lagani, Christoforos Nikolaou

The course will introduce the R statistical software as a tool for performing data analysis tasks in the bioinformatics field. At the beginning, the basics of the R language will be explained, along with the main concepts related to the R software and its modular architecture. Most advanced concepts will then be introduced, as for example data structure in R, functional programming, graphical visualization and the creation of R packages. The second part of the course will focus on the Bioconductor initiative and its repository of R packages for bioinformatics. Particularly, functionalities for analyzing RNA-seq and microarray data will be explored in detail.

LessThe course will introduce the R statistical software as a tool for performing data analysis tasks in the bioinformatics field. At the beginning, the basics of the R language will be explained, along with the main concepts related to the R software and its modular architecture. Most advanced concepts will then be introduced, as for example data structure in R, functional programming, graphical visualization and the creation of R packages. The second part of the course will focus on the Bioconductor initiative and its repository of R packages for bioinformatics. Particularly, functionalities for analyzing RNA-seq and microarray data will be explored in detail.

The course will introduce the R statistical software as a tool for performing data analysis tasks in the bioinformatics field. At the beginning, the basics of the R language will be explained, along with the main concepts related to the R software and its modular architecture. Most advanced concepts will then be introduced, as for example data structure in R, functional programming, graphical visualization and the creation of R packages. The second part of the course will focus on the Bioconductor initiative and its repository of R packages for bioinformatics. Particularly, functionalities for analyzing RNA-seq and microarray data will be explored in detail.

### Course Units

This lesson provides a short introduction to R and its syntax

Further explaining R syntax and functions

The lesson shows how to perform analysis in R, starting from data loading up to plotting results

This lesson focuses on the R package ggplot2 (visualization) and functions specialized for plotting heatmap

Tools for (interactive) and automatic visualization and reporting

This lecture gives a short introduction to linear modelling and multiple testing correction

This lesson discusses the principles behind the microarray technology, particularly in gene expression. Furthermore, the principal commands for loading and preprocessing CEL files in R are presented.

This lecture focuses on the main aspect of microarra data analysis, including quality control, explorative visualization and linear modelling

This lesson introduces the basis of the cytometry technology

This lesson describe some principles of RNA-seq technology and how to identify differentially expressed genes in RNA-seq count data with R

This lesson illustrates how to access some of the most known bio-related on-line databases

This lesson introduces the key concepts of functional (enrichment) analysis and illustrates how to implement functional analysis in R

This section contains the projects for the final exam

### Calendar

### Announcements

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