Lr1

HWr1 · Video introduction from an older edition of the course

Program for this lecture: basics of R

• A very short introduction will be given as a lecture.
• Exercises have the form of a tutorial: read a bit of text, try some commands, extend/modify them as requested in individual tasks

In this course we cover several languages popular for scripting and data processing: 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, the 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 the 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 Bioconductor for bioinformatics
• We will concentrate on useful commands rather than language features.

Working in R

Option 1: Run command R, type commands in a command-line interface

• It supports history of commands (arrows, up and down, Ctrl-R) and completing command names with the tab key

Option 2: Write a script to a file, run it from the command-line as follows:
R --vanilla --slave < file.R

Option 3: Use rstudio command to open a graphical IDE

• Sub-windows with editor of R scripts, console, variables, plots.
• Ctrl-Enter in editor executes the current command in console.
• You can also install RStudio on your home computer and work there.

Option 4: If you like Jupyter notebooks, you can use them also to run R code, see for example an explanation of how to enable it in Google Colab [1].

In R, you can easily create plots. In command-line interface these open as a separate window, in Rstudio they open in one of the sub-windows.

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

Suggested workflow

• Work interactively in Rstudio, notebook or on command line, try various options.
• Select useful commands, store in a script.
• Run the 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
• Built-in help: ? plot displays help for plot command

Gene expression data

• DNA molecules contain regions called genes, which "recipes" for making proteins.
• Gene expression is the process of creating a protein according to the "recipe".
• It works in two stages: first a gene is copied (transcribed) from DNA to RNA, then translated from RNA to protein.
• Different proteins are created in different quantities and their amount depends on the needs of a cell.
• There are several technologies (microarray, RNA-seq) for measuring the amount of RNA for individual genes, this gives us some measure how active each gene is under given circumstances.

Gene expression data is typically a table with numeric values.

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

We will use a data set for yeast:

• Abbott, Derek A., et al. "Generic and specific transcriptional responses to different weak organic acids in anaerobic chemostat cultures of Saccharomyces cerevisiae." FEMS yeast research 7.6 (2007): 819-833.
• Downloaded from the GEO database
• Data was preprocessed: normalized, converted to logarithmic scale.
• Only 1220 genes with the biggest changes in expression are included in our dataset.
• The dataset contains gene expression measurements under 5 conditions:
  • Control: yeast grown in a normal environment.
  • 4 different acids added so that cells grow 50% slower (acetic, propionic, sorbic, benzoic).
• From each condition (reference and each acid) we have 3 replicates, together 15 experiments.
• The goal is to observe how the acids influence the yeast and the activity of its genes.

Part of the file (only the first 4 experiments and first 3 genes shown), strings 2mic_D_protein, AAC3, AAD15 are identifiers of genes.

,control1,control2,control3,acetate1,acetate2,acetate3,...
2mic_D_protein,1.33613934199651,1.13348900359964,1.2726678684356,1.42903234691804,...
AAC3,0.558482767397578,0.608410781454015,0.6663002997292,0.231622581964063,...
AAD15,-0.927871996497105,-1.04072379902481,-1.01885986692013,-2.62459941525552,...