# Visualizing BLAST score distributions in RStudio¶

If we want to take a look at the results of computing orthologs between mouse and cow, we can’t page through that big text file and understand it usefully. We have to use another program to look at it. Here, we’re going to use R and RStudio!

...but first we have to install it.

## Installing and running RStudio on Jetstream¶

### Install RStudio!¶

sudo apt-get update && sudo apt-get -y install gdebi-core r-base


wget https://download2.rstudio.org/rstudio-server-1.0.143-amd64.deb
sudo gdebi -n rstudio-server-1.0.143-amd64.deb


You should see now see text indicating an RStudio server has started:

Jun 27 11:33:40 js-17-66.jetstream-cloud.org systemd[1]: Starting RStudio Server...
Jun 27 11:33:40 js-17-66.jetstream-cloud.org systemd[1]: Started RStudio Server.


### Figure out the Web site to connect to.¶

Because we’re using the cloud to run things, everyone will have a different computer that they’re running RStudio on. To find out what Web site to connect to to access YOUR server, run:

echo My RStudio Web server is running at: http://$(hostname):8787/  After running this, copy/paste the URL into your Web browser; you should see login page. Enter the XSEDE username and password you were given (should be tx160085 username, with associated password). If the login is unsuccessful, return to the terminal and run: sudo passwd tx160085  to change your password for this instance. You will be prompted to enter a new password: Enter new UNIX password: Retype new UNIX password:  but note that the text will not echo to the screen (because it’s a password!) Return to the browser login page and enter your new password. Note this will not change the global XSEDE login info (i.e. it only affects this instance). Once R is up and running, we’ll give you a quick tour of the RStudio Web interface for those of you who haven’t seen it. ## Enter some R commands¶ (Enter the below commands into RStudio, not the command line.) Download the precomputed data, and give it a better name; you could also use your precomputed shmlast results – download.file("https://github.com/ngs-docs/angus/raw/17a0ba3b1d915de90a5b8bd1fbc1027eba47baf8/_static/shmlast/mouse.1.rna.fna.gz.x.cow.faa.crbl.csv.gz", "shmlast_mouse.rna.fna.gz.x.cow.faa.crbl.csv")  Next, read the object in to R, and name it something that you might remember shmlast_out <- read.csv("shmlast_mouse.rna.fna.gz.x.cow.faa.crbl.csv")  Now we can take a look at the data in a slightly nicer way! This is called a dataframe, which is a sort of R-ish version of a spreadsheet with named columns. Use the head() command to take a look at the beginning of it all: head(shmlast_out)  this is a generic R command that acts very much like the UNIX command line head command but gives you a slightly nicer more structured view. In RStudio you can also use the view command, View(shmlast_out)  which is much nicer altogether! Another useful command is dim which will tell you the DIMENSIONS of this data frame: dim(shmlast_out)  That’s a big data frame! 132,900 rows (and 17 columns!) Let’s do some data visualization to get a handle on what our blast output looked like: first, let’s look at the E_scaled column. hist(shmlast_out$E_scaled)


This is telling us that MOST of the values in the E_scaled column are quite high. What does this mean? How do we figure out what this is?

(Hint: the shmlast documentation should tell us! Go to that page and search for “To fit the model”.)

So these are a lot of low e-values. Is that good or bad? Should we be happy or concerned that

We can take a look at some more stats – let’s look at the bitscore column:

hist(shmlast_outbitscore)  what are we looking for here? (And how would we know?) (Hint: longer bitscores are better, but even bitscores of ~1000 mean a nucleotide alignment of 1000 bp - which is pretty good, no? Here we really want to rescale the x axis to look at the distribution of bitscores in the 100-300 range.) We can also look at the length of the queries, which are the mouse sequences in this case. hist(shmlast_outq_len)


Compare this to the bitscores... do things match? Are most mouse sequences getting matched by something of equivalent length?

Well, we can ask this directly with plot:

plot(shmlast_out$q_len, shmlast_out$bitscore)


why does this plot look the way it does? (This may take a minute to show up, note!)

(The bitscores are limited by the length of the sequences! You can’t get a longer bitscore than you have bases to align.)

### Summary points¶

This is an example of initial exploratory data analysis, in which we poke around with data to see roughly what it looks like. This is opposed to other approaches where we might be trying to do statistical analysis to confirm a hypothesis.

Typically with small sample sizes (n < 5) it is hard to do confirmatory data analysis or hypothesis testing, so a lot of NGS work is done for hypothesis generation and then confirmed via additional experimental work.

## Some questions for discussion/points to make:¶

• Why are we using R for this instead of the UNIX command line, or Excel?

One important thing to note here is that we’re looking at a pretty large data set - with ease. It would be much slower to do this in Excel.

• What other things could we look at?

• Have we done a basic check of just looking at the data? Go back and look at the data frame! Do the gene name assignments look right?

How might we do this a bit more systematically, while still “looking” at things? Try googling ‘choose random rows from data frame’ and then run

shmlast_sub = shmlast_out[sample(nrow(shmlast_out), 10),]
View(shmlast_sub)

• What does the following code do?

tmp <- subset(shmlast_out, q_len >= 8000 & q_len <= 11000 & bitscore <=2000)
functions <- tmp[, c("q_name", "s_name")]


and do you notice anything interesting about the names? (They’re all predicted/inferred genes.) What does this suggest about that “quadrant” of the data?