Workshops/Saskatoon 2015-Introduction to R

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Introduction to R



 

Schedule

Please note: this schedule is a rough guideline only, we will be very flexible to adapt to class needs as we proceed.


Time Wednesday's Activities
09:00 – 10:30 Welcome, Introduction
Lecture and practicals: setup and environment
10:30 – 11:00 Coffee break
11:00 – 12:30 Lecture and practicals: R commands
12:30 – 13:30 Lunch break
13:30 – 15:00 Lecture and practicals: programming
15:00 – 15:30 Coffee break
13:30 – 15:00 Lecture and practicals: data


 

Setup

There may be a difference between R and R Studio regarding the location of installed packages.

We have noticed at previous workshops that RStudio couldn't find libraries that were not installed through the RStudio package manager. This appeared to have been version dependent, and may not affect current releases. You can check for this (in case you have problems running the library() command) by issuing the command ... 

.libPaths()

...in R as well as in R Studio. The path should be the same in both.

In case you need to "tell" RStudio the location, you can define the path in a startup file. Create or edit a file called .Renviron in your home directory. Inside there define: 

R_LIBS=<Library Path of R installed packages>

Alternatively, you could use the R Studio package manger to install libraries.



Task:

  1. Download the file Intro_to_R.R to your working directory;
  2. Load the file in R Studio;
  3. Proceed to the first CHECKPOINT in the script.


Resources

Script files for this section


Jaitin et al. (2014) Resources
Jaitin et al. (2014) Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343:776-9. (pmid: 24531970)

[ PubMed ] [ DOI ] In multicellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless, dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing (RNA-seq) approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell-type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed preprogrammed cell-type hierarchy. These data demonstrate single-cell RNA-seq as an effective tool for comprehensive cellular decomposition of complex tissues.


Other resources


 

Useful links


Notes



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