Bioinformatics

Good resources to learn R

Since it's the summer vacations, why not take some time to learn R. There are numerous free resources online to dive into this powerful language. For whomever wants to learn it, the challenge more related to finding the time rather than finding resources. Videos Coursera is an inevitable for online learning. There are a few good video courses offered for R beginners that are more or less oriented toward genomics : https://www.coursera.org/learn/r-programming https://www.coursera.org/learn/exploratory-data-analysis https://www.coursera.org/learn/bioconductor (Bioconductor is a life science packages [...]

By | 2017-04-29T16:57:17+00:00 July 11, 2016|Categories: Bioinformatics, R|Tags: |0 Comments

The language(s) of bioinformatics

The most recurrent question I get regarding bioinformatics is unfortunately the one that leads to the least productive discussions I've participated in: Which programming language should I use for bioinformatics? Don't get me wrong, in a pub, over a beer, this can lead to some lively entertainment among the nerd intelligentsia... but rarely does it lead to enlightenment that persists in the morning! Here, I'd like to share the current answer I have honed over the past years. It is based [...]

By | 2017-04-29T16:59:22+00:00 April 18, 2016|Categories: Bioinformatics|Tags: |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 | 2017-04-29T17:06:48+00:00 January 28, 2016|Categories: Bioinformatics, R|Tags: |0 Comments

Generating Synthetic Genomic Data

Applying statistical methods is a large part of the work of a bioinformatician. Apart from some more classical techniques, machine learning algorithms are also regularly applied to clinical and biological data (notably, clustering techniques such as k-means). Some techniques such as artificial neural networks have recently found great success in areas such as image recognition and natural language processing. However, these techniques do not perform as well on small datasets with high dimensionality, a problem known as "the curse of dimensionality". [...]

By | 2017-04-29T23:00:58+00:00 January 7, 2016|Categories: Bioinformatics, Python|Tags: , |0 Comments

Table-reading: loading data into R without a hassle

The first thing I have learned in R is how to load a table. Usually, when you start your R journey, someone more knowledgeable will tell you how to do this very first action. It will typically be: data<-read.table("~/SomeFolder/datafile.txt") You probably will be adding various parameters into the brackets such as "row.names=0" or "header=TRUE" or, "sep="\t"", to make sure you are reading your file correctly. And this is perfectly fine, as a loading method of small datasets. However, to maximize [...]

By | 2017-04-29T17:14:58+00:00 February 5, 2015|Categories: Bioinformatics, R|Tags: |1 Comment