L09

HW09

Program for today: basics of R (applied to biology examples)

• very short intro as a lecture
• tutorial as HW: read a bit of text, try some commands, extend/modify them as requested

In this course we cover several languages popular for scripting in bioinformatics: Perl, Python, R

• their capabilities overlap, many extensions emulate strengths of one in another
• choose a language based on your preference, level of knowledge, existing code for the task, rest of the team
• quickly learn a new language if needed
• also possibly combine, e.g. preprocess data in Perl or Python, then run statistical analyses in R, automate entire pipeline with bash or make

Introduction

• R is an open-source system for statistical computing and data visualization
• Programming language, command-line interface
• Many built-in functions, additional libraries
  • For example http://bioconductor.org/ for bioinformatics
• We will concentrate on useful commands rather than language features

Working in R

• Run command R, type commands in command-line interface
  • supports history of commands (arrows, up and down, Ctrl-R) and completing command names with tab key

> 1+2
[1] 3

• Write a script to file, run it from command-line: R --vanilla --slave < file.R
• Use rstudio to open a graphics IDE [1]
  • Windows with editor of R scripts, console, variables, plots
  • Ctrl-Enter in editor executes current command in console

x=c(1:10)
plot(x,x*x)

• ? plot displays help for plot command

Suggested workflow

• work interactively in Rstudio or on command line, try various options
• select useful commands, store in a script
• run script automatically on new data/new versions, potentially as a part of a bigger pipeline

Additional information

• Official tutorial
• Seefeld, Linder: Statistics Using R with Biological Examples (pdf book)
• Patrick Burns: The R Inferno (intricacies of the language)
• Other books

Gene expression data

• Gene expression: DNA->mRNA->protein
• Level of gene expression: Extract mRNA from a cell, measure amounts of mRNA
• Technologies: microarray, RNA-seq

Gene expression data

• Rows: genes
• Columns: experiments (e.g. different conditions or different individuals)
• Each value is expression of a gene, i.e. relative amount of mRNA for this gene in the sample

We will use microarray data for yeast:

• Strassburg, Katrin, et al. "Dynamic transcriptional and metabolic responses in yeast adapting to temperature stress." Omics: a journal of integrative biology 14.3 (2010): 249-259. [2]
• Downloaded from GEO database [3]
• Data already preprocessed: normalization, log2, etc
• We have selected only cold conditions, genes with absolute change at least 1
• Data: 2738 genes, 8 experiments in a time series, yeast moved from normal temperature 28 degrees C to cold conditions 10 degrees C, samples taken after 0min, 15min, 30min, 1h, 2h, 4h, 8h, 24h in cold