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". [...]
** Special collaboration from the proteomic platform** Following your sample's analysis by mass spectrometry, you will usually receive your results as a list of proteins. During the treatment of the data, some factors inevitably influence the proteins found in the final list. Fig. 1 Overview of bottom-up proteomics. Figure modified from Angel et al. (2011) Let's begin by briefly explaining how this protein list is generated by the bottom-up approach usually used (see Figure 1). In this [...]
Your shell, along with the myriad command line programs it exposes is clearly a great friend when it comes to file manipulation. And let's face it, file manipulation is a big part of a bioinformatician's daily workload. Now, since we rarely have the time to review all the options offered by the different programs I thought I'd list some really useful ones from grep. I expect everyone to know what grep is and what it does so let's just get [...]
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) [...]
