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[[HWr1]] {{Dot}} [https://youtu.be/qHdtopqSiXA Video introduction from an older edition of the course]
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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 <tt>bash</tt> or <tt>make</tt>.

==Introduction==
* [http://www.r-project.org/ 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/ 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:<br><tt>R --vanilla --slave < file.R</tt>

'''Option 3:''' Use <tt>rstudio</tt> command to open a [https://www.rstudio.com/products/RStudio/ 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 [https://towardsdatascience.com/how-to-use-r-in-google-colab-b6e02d736497].

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.
<syntaxhighlight lang="r">
x = c(1:10)
plot(x, x * x)
</syntaxhighlight>

'''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==
* [http://cran.r-project.org/doc/manuals/R-intro.html Official tutorial]
* [http://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf Seefeld, Linder: Statistics Using R with Biological Examples (pdf book)]
* [http://www.burns-stat.com/pages/Tutor/R_inferno.pdf Patrick Burns: The R Inferno] (intricacies of the language) 
* [https://www.r-project.org/doc/bib/R-books.html Other books]
* Built-in help: <tt>? plot</tt> displays help for <tt>plot</tt> 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. "[https://academic.oup.com/femsyr/article/7/6/819/533265 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 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5926 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 <tt>2mic_D_protein, AAC3, AAD15</tt> are identifiers of genes.
<pre>
,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,...
</pre>