Category Archives: research diary

utils4bioinformatics: all those “little snippets” in one place

Over the years, I’ve written a lot of small “utility scripts”. You know the kind of thing. Little code snippets that facilitate research, rather than generate research results. For example: just what are the fields that you can use to qualify Entrez database searches?

Typically, they end up languishing in long-forgotten Dropbox directories. Sometimes, the output gets shared as a public link. No longer! As of today, “little code snippets that do (hopefully) useful things” have a new home at Github.

Also as of today: there’s not much there right now, just the aforementioned Entrez database code and output. I’m not out to change the world here, just to do a little better.

This is why code written by scientists gets ugly

There’s a lot of discussion around why code written by self-taught “scientist programmers” rarely follows what a trained computer scientist would consider “best practice”. Here’s a recent post on the topic.

One answer: we begin with exploratory data analysis and never get around to cleaning it up.

An example. For some reason, a researcher (let’s call him “Bob”) becomes interested in a particular dataset in the GEO database. So Bob opens the R console and use the GEOquery package to grab the data:

Update: those of you commenting “should have used Python instead” have completely missed the point. Your comments are off-topic and will not be published. Doubly-so when you get snarky about it.

Read the rest…

When is db=all not db=all? When you use Entrez ELink.

Just a brief technical note.

I figured that for a given compound in PubChem, it would be interesting to know whether that compound had been used in a high-throughput experiment, which you might find in GEO. Very easy using the E-utilities, as implemented in the R package rentrez:

library(rentrez)
links <- entrez_link(dbfrom = "pccompound", db = "gds", id = "62857")
length(links$pccompound_gds)
# [1] 741

Browsing the rentrez documentation, I note that db can take the value “all”. Sounds useful!

links <- entrez_link(dbfrom = "pccompound", db = "all", id = "62857")
length(links$pccompound_gds)
# [1] 0

That’s odd. In fact, this query does not even link pccompound to gds:

length(names(links))
# [1] 39
which(names(links) == "pccompound_gds")
# integer(0)

It’s not a rentrez issue, since the same result occurs using the E-utilities URL.

The good people at ropensci have opened an issue, contacting NCBI for clarification. We’ll keep you posted.

New publication: A panel of genes methylated with high frequency in colorectal cancer

I’m pleased to announce an open-access publication with my name on it:

Mitchell, S.M., Ross, J.P., Drew, H.R., Ho, T., Brown, G.S., Saunders, N.F.W., Duesing, K.R., Buckley, M.J., Dunne, R., Beetson, I., Rand, K.N., McEvoy, A., Thomas, M.L., Baker, R.T., Wattchow, D.A., Young, G.P., Lockett, T.J., Pedersen, S.K., LaPointe L.C. and Molloy, P.L. (2014). A panel of genes methylated with high frequency in colorectal cancer. BMC Cancer 14:54.

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R: how not to use savehistory() and source()

Admitting to stupidity is part of the learning process. So in the interests of public education, here’s something stupid that I did today.

You’re working in the R console. Happy with your exploratory code, you decide to save it to a file.

savehistory(file = "myCode.R")

Then, you type something else, for example:

ls()
# more lines here

And then, decide that you should save again:

savehistory(file = "myCode.R")

You quit the console. Returning to it later, you recall that you saved your code and so can simply run source() to get back to the same point:

source("myCode.R")

Unfortunately, you forget that the sourced file now contains the savehistory() command. Result: since your new history contains only the single line source() command, then that is what gets saved back to the file, replacing all of your lovely code.

Possible solutions include:

  • Remember to edit the saved file, removing or commenting out any savehistory() lines
  • Generate a file name for savehistory() based on a timestamp so as not to overwrite each time
  • Suggested by Scott: include a prompt in the code before savehistory()

Microarrays, scan dates and Bioconductor: it shouldn’t be this difficult

When dealing with data from high-throughput experimental platforms such as microarrays, it’s important to account for potential batch effects. A simple example: if you process all your normal tissue samples this week and your cancerous tissue samples next week, you’re in big trouble. Differences between cancer and normal are now confounded with processing time and you may as well start over with new microarrays.

Processing date is often a good surrogate for batch and it was once easy to extract dates from Affymetrix CEL files using Bioconductor. It seems that this is no longer the case.
Read the rest…

Interestingly: the sentence adverbs of PubMed Central

Scientific writing – by which I mean journal articles – is a strange business, full of arcane rules and conventions with origins that no-one remembers but to which everyone adheres.

I’ve always been amused by one particular convention: the sentence adverb. Used with a comma to make a point at the start of a sentence, as in these examples:

Surprisingly, we find that the execution of karyokinesis and cytokinesis is timely…
Grossly, the tumor is well circumscribed with fibrous capsule…
Correspondingly, the short-term Smad7 gene expression is graded…

The example that always makes me smile is interestingly. “This is interesting. You may not have realised that. So I said interestingly, just to make it clear.”

With that in mind, let’s go looking for sentence adverbs in article abstracts.
Read the rest…

Basic R: rows that contain the maximum value of a variable

File under “I keep forgetting how to do this basic, frequently-required task, so I’m writing it down here.”

Let’s create a data frame which contains five variables, vars, named A – E, each of which appears twice, along with some measurements:

df.orig <- data.frame(vars = rep(LETTERS[1:5], 2), obs1 = c(1:10), obs2 = c(11:20))
df.orig
#    vars obs1 obs2
# 1     A    1   11
# 2     B    2   12
# 3     C    3   13
# 4     D    4   14
# 5     E    5   15
# 6     A    6   16
# 7     B    7   17
# 8     C    8   18
# 9     D    9   19
# 10    E   10   20

Now, let’s say we want only the rows that contain the maximum values of obs1 for A – E. In bioinformatics, for example, we might be interested in selecting the microarray probeset with the highest sample variance from multiple probesets per gene. The answer is obvious in this trivial example (6 – 10), but one procedure looks like this:
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