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.
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CC - Attribution
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
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