https://github.com/lgatto/rintro
An introduction to R for beginners, using microarrays as main thread
https://github.com/lgatto/rintro
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An introduction to R for beginners, using microarrays as main thread
- Host: GitHub
- URL: https://github.com/lgatto/rintro
- Owner: lgatto
- Created: 2013-05-10T09:54:31.000Z (almost 12 years ago)
- Default Branch: master
- Last Pushed: 2014-11-12T21:09:28.000Z (over 10 years ago)
- Last Synced: 2025-01-20T23:12:56.652Z (3 months ago)
- Language: R
- Homepage: http://lgatto.github.io/RIntro/
- Size: 14.1 MB
- Stars: 1
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
A 1 day `R` introductory course for non-programmers, using
microarrays as main thread. Also includes an intro to Bioconductor and
the `eSet` infrastructure. Initially set up for the
[diXa](http://www.dixa-fp7.eu/dixa-training/dixa-training-agenda/dixa-microarray-training)
Microarray Analysis using R and Bioconductor training (see tags for
specific courses). Partially based on the
[Beginners guide to solving biological problems in `R`](http://www.training.cam.ac.uk/gsls/course/gsls-rintro)
(see also [here](http://logic.sysbiol.cam.ac.uk/teaching/Rcourse/))
course by Robert Stojnić, Rob Foy, John Davey, Laurent Gatto and Ian
Roberts.
## Slides
The [slides](https://github.com/lgatto/RIntro/blob/master/RIntro.pdf?raw=true)
provide a general introduction to [`R`](http://www.r-project.org/) and
the main data structures. Scripting and plotting is presented by means
of exercises using microarray data as example. Finally,
[Bioconductor](http://bioconductor.org/) and the microarray
`eSet`/`ExpressionSet` classes are introduced and compared to the
previous introductory material and exercises.
## Exercises
1. Using `R` interactively and running a script.
2. [Vectors](https://github.com/lgatto/RIntro/blob/master/Exercises/Exercise-02.md)
3. [How to store microarray data](https://github.com/lgatto/RIntro/blob/master/Exercises/Exercise-03.md)
* expression data and meta data
* matrices, data frames and lists.
4. [A short microarray analysis](https://github.com/lgatto/RIntro/blob/master/Exercises/Exercise-04.md)
* reading spreadsheets into `R`
* saving/loading objects
* basic plotting
* `for` loops: counting differentially expressed genes in three
microarray result data
5. [Another microarray analysis](https://github.com/lgatto/RIntro/blob/master/Exercises/Exercise-05.md)
* combining multiple expression matrices and produce a heatmap
* extracting, parsing and visualising genes of interest
6. [A short Bioconductor data analysis](https://github.com/lgatto/RIntro/blob/master/Exercises/Exercise-06.md)
* Quality control
* Exploratory data analysis
## See also
See the [TeachingMaterial](https://github.com/lgatto/TeachingMaterial)
repository for more material.
This material is licensed under the
[Creative Commons Attribution-ShareAlike 3.0 License](http://creativecommons.org/licenses/by-sa/3.0/).