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, [...]
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". [...]
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 [...]
Usually, there is more than one way to accomplish a task. Some are better, some are worse and others are just as good. Assessing which one to use is often related to the computing time, the ease of use and/or to personal preferences and abilities. Say I have a matrix of thousands of chromosomal features with the following column names : Feature, Start, End. All the positions are found on the same chromosome and the widths of my features are variable. [...]
Fisher's exact test is widely applied in bioinformatics (it is the core computation in gene-set or pathway enrichment analysis). I won't introduce the test itself as others have done it several times (here), but will rather point to a disconnect between what it does and what is often needed. In Fisher's exact test, the null hypothesis is that there is no enrichment between the two variables studied. When using this test with large numbers (such as the number of genes [...]
