Things I would tell a budding bioinformatician to learn.

I recently read Ewan Birney's blog post, which I found echoed a lot of my own thoughts about the use of statistical in computational biology. I thought I would compile my own similar list but for bioinformatics  / computational biology in general. I have not been and in the field as long as Ewan and I certainly still have a lot to learn, particularly about statistics due to my biological background, but I have learnt some things over the last ten years, that like Ewan, I wish someone had told me long ago.  The points are in no particular order.

R: Basic R Skills - Splitting and Plotting

I am giving a short R course next year, so I am going to make a series of blog posts to help get my thoughts and example code in order. The aim is to introduce people with little or no experience of R to the language with self contained examples. The order of the posts are not going to reflect any order in the course, just what I feel like doing at the time.

This first post is going to deal with splitting and plotting data. It is a common occurrence to have data in such a form that you want to split the data in one column based on the data in another column. Maybe you want to split an experimental result by age or gender for example. Perhaps you want to see if there is a difference in the distribution of results in males and females. The example code below goes through one such hypothetical example.

The figure shows the output you should get from running the code. Essentially the example is designed to illustrate the split function and the ~ (tilde) character. 


The split function will do what it says, split a vector of data (A), based on another vector (B). It returns a list, with each element of the list being all of the element in A that match each element in B. For example 

A <- c(1,2,3,4)

B <- c("X","Y","X","Y")

sp <- split(A,B)

sp

$X

[1] 1 3

$Y

[1] 2 4

Now we have a list, and we can operate on each element of the list using the apply functions, such as lapply. 

lapply(sp,sum)

$X

[1] 4

$Y

[1] 6

There are lots off different apply functions, a good introduction is here.

The other main way of splitting is using the ~ (tilde) operation. In my head I always read this as 'given', such as plot(A ~ B) is "plot A given B". This is an example of the formula notation in R, but here we are using it very simply. It essentially does the same thing as split.

Note: You actually need to do plot(A ~ factor(B)) if B isn't already a factor.

Lots of functions support the function call, such as t.test in the example, for others you can use the lapply and split version, such as for density in the example. 

I also mention the aggregate function, which essentially is the same as lapply and split but seems slower on large datasets. 

R: Using RColorBrewer to colour your figures in R

RColorBrewer is an R packages that uses the work from http://colorbrewer2.org/ to help you choose sensible colour schemes for figures in R. For example if you are making a boxplot with eight boxes, what colours would you use, or if you are drawing six lines on an x-y plot what colours would you use so you can easily distinguish the colours and look them up on a key? RColorBrewer help you to do this.

Below is some example R code that generates a few plots, coloured by RColorBrewer.



The colours are split into three group, sequential, diverging, and qualitative.

1. Sequential - Light colours for low data, dark for high data
2. Diverging -  Light colours for mid-range data, low and high contrasting dark colours
3. Qualitative - Colours designed to give maximum visual difference between classes

The main function is brewer.pal, which you simply give the number of colours you want, and the name of the palette, which you can choose from running display.brewer.all()

There are limits on the number of colours you can get, but if you want to extend the Sequential or Diverging groups you can do so with the colorRampPalatte command, for example :

colorRampPalette(brewer.pal(9,"Blues"))(100)

This will generate 100 colours based on the 9 from the 'Blues' palette. See image below for a contrast.

From compBiomeBlog