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R for Molecular Biosciences: Learning Resources

The guide was created to support the course, R for Molecular Biosciences, an introductory undergraduate course in data science.

Books

We will use R for Data Science, https://r4ds.had.co.nz/, as a reference in class.  There are also many other books available online through the library catalog.  Do not purchase a hard copy of a book for R until you are absolutely certain that you will use it.  I have several books on my shelf that I rarely open because it is usually easier to find help online. 

You can find other books from the library if you search for 'R (Computer program language)' as a subject.

Grolemund, Garrett, and Hadley Wickham. R for Data Science. Accessed February 27, 2019. https://r4ds.had.co.nz/.

Online Resources

There are a host of online resources such as forums and blogs that can help you learn R.  If you are having a problem, the chances are very good that someone else has had the same problem.  Here are a few places that you can look for help.

Remember to follow all guidelines and codes of conduct when you are on an online forum.  The chances are very good that you will find the information that you need if you simply search the forum.  If you think that you must ask a question, be considerate and clear.  People can be incredibly helpful on these forums, but they will not have patience with anyone that is not following the rules of the forum, does not ask a clear question, or simply seems to be to lazy to search for answers. 

Rseekhttps://rseek.org/, is a search engine that targets specific resources that are likely to be useful if you are having issues with R.  It is better that a simple Google search because the engine already knows that you need help with R.  

Not sure what you need?  You might try browsing R-bloggers, https://www.r-bloggers.com/, a blog aggregator for people who blog about R.  This can be a great place to learn about new packages. 

The R Graph Gallery, https://www.r-graph-gallery.com/, is exactly what it sounds like--a collection of data visualizations that have been created with R.  The gallery can be a great source of inspiration, and there is a separate section dedicated to ggplot2, the package that we will use for most visualizations. 

Stack Overflow is a forum used by programmers of all stripes to share their problems and expertise.  This site is aggregated by Rseek, but it may be helpful to come here directly, https://stackoverflow.com/questions/tagged/r.  Please be considerate of the forum rules if you seek help here. 

Required Readings

 

Bioinformatics curriculum guidelines: toward a definition of core competencies. Welch L, Lewitter F, Schwartz R, Brooksbank C, Radivojac P, Gaeta B, Schneider MV. PLoS Comput Biol. 2014 Mar 6;10(3):e1003496. doi: 10.1371/journal.pcbi.1003496. eCollection 2014 Mar. PMID:24603430.

Ten quick tips for using the gene ontology. Blake JA. PLoS Comput Biol. 2013;9(11):e1003343. doi: 10.1371/journal.pcbi.1003343. Epub 2013 Nov 14. PMID: 24244145.

CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. Reinhold WC, Sunshine M, Liu H, Varma S, Kohn KW, Morris J, Doroshow J, Pommier Y. Cancer Res. 2012 Jul 15;72(14):3499-511. doi: 10.1158/0008-5472.CAN-12-1370. PMID: 22802077.

The Different Mechanisms of Cancer Drug Resistance: A Brief Review. Mansoori B, Mohammadi A, Davudian S, Shirjang S, Baradaran B. Adv Pharm Bull. 2017 Sep;7(3):339-348. doi: 10.15171/apb.2017.041. Epub 2017 Sep 25. Review. PMID: 29071215.

New articles in Bioinformatics

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Bioinformatics Virtual Issues

The Oxford Journal Bioinformatics maintains "virtual issues" on Next Generation Sequencing and Phylogenetics.  The NGS issue is quite extensice and gathers many articles and tools relevant for the analysis of NGS data.

Nature Statistics Collection

Nature maintains the web collection, Statistics for Biologists.  This site contains a variety of short articles on statistical topics of interest to biologists.  In general, these articles are written with non-statisticians in mind with the goal of demystifying concepts such as Bayesain statistics, multiple correction testing and p-value hacking.