R – Page 3 – IRIC's Bioinformatics Platform

Simple multiprocessing in R

Continuing my effort to help you get the most out of your CPUs, I figured we could look into using some multiprocessing functionality available for your R scripts. While there are a few different options for running multi-core treatments on your data, we'll focus on something really simple to put in place. A while back, I was putting together a script to run a large series of logistic regressions (using the glm package) in an attempt to model some data. [...]

By Jean-Philippe|2017-04-29T15:33:26+00:00March 14, 2016|Categories: Performance, R|Tags: multiprocessing|1 Comment

What’s the fastest? – R edition

When I started using R, about ten years ago, the community was much smaller. No R-bloggers to get inspired or ggplot2 to make nice graphs. It was the beginning of an other implementation of R (other than CRAN's) known as Revolution R from Revolution Analytics. Their R targeted enterprise and was designed to be faster and more scalable. They also offer an open source version of their product called RRO. In April 2015, the company was acquired by Microsoft! May [...]

By Geneviève|2017-04-29T15:32:29+00:00February 12, 2016|Categories: Performance, R|0 Comments

Beginner R: functions that make your life easier

Let’s get to know my top 10 R’s neat little functions and tricks that make our life easier when manipulating data in R. Sequences Want to make long sequences of numbers or letters but don’t feel like writing them all out into a vector? R let’s you make a sequence with “:” for numbers. You can also use seq() if you are looking for a regular sequence that is not incremented by one. letters[] let’s you make continuous letter sequences, [...]

By Assya|2017-04-29T17:06:48+00:00January 28, 2016|Categories: Bioinformatics, R|Tags: beginner|0 Comments

Applying PCA to Leucegene data

GEO offers an extremely rich source of transcriptional profile data, but downloading and preparing a dataset is often an obstacle to aspiring bioinformaticians. I'll walk you through one way to do it using the Leucegene dataset as an example. Once this data is loaded and ready to use in R, I'll then present a very simplified and practical perspective on the use of PCA for exploratory analysis. Loading data A dataset of 285 transcriptional profiles of acute myeloid leukemia (AML) [...]

By Sébastien|2017-04-29T23:05:21+00:00November 17, 2015|Categories: Data Analysis, R|Tags: gene expression, PCA|0 Comments

Formatting data for Circos with R

When generating a Circos plot, the formatting of the data to be represented is a crucial step. Here are some pointers on how to avoid the dreadful *** CIRCOS ERROR ***. All data files must be in text format. For instance, using R, I would generate a myData.txt file that I would then call within a specific plot block (<plot>...</plot>). Data files are used for 2-dimensional graphical representations (histogram, scatter plot, heatmap, tiles), labels (which are technically also a type [...]

By Caroline|2017-04-29T15:36:21+00:00October 29, 2015|Categories: Data Visualization, R|Tags: Circos, data analysis, data manipulation|0 Comments