Configuring the R BatchJobs package for Torque batch queues

I was asked recently to look at some R code which performs “embarrassingly parallel” computations (the same function, multiple times, different parameters) and see whether I could modify it to run on one of our high-performance computing clusters. The machine has 63 virtual compute nodes and uses the TORQUE batch queue system to allocate nodes to compute jobs.

First stop: the CRAN Task View High-Performance and Parallel Computing with R. Two promising packages there: BatchJobs and BatchExperiments. Their documentation is quite extensive with useful examples, but I found it a little disjointed and confusing. What I wanted was a simple, step-by-step guide to setting up for a first-time user. So here is my attempt. As always, it’s for “Linux-like” systems.
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PMRetract: PubMed retraction reporting rewritten as an interactive RMarkdown document

Back in 2010, I wrote a web application called PMRetract to monitor retraction notices in the PubMed database. It was written primarily as a way for me to explore some technologies: the Ruby web framework Sinatra, MongoDB (hosted at MongoHQ, now Compose) and Heroku, where the app was hosted.

I automated the update process using Rake and the whole thing ran pretty smoothly, in a “set and forget” kind of way for four years or so. However, the first era of PMRetract is over. Heroku have shut down git pushes to their “Bamboo Stack” – which runs applications using Ruby version 1.8.7 – and will shut down the stack on June 16 2015. Currently, I don’t have the time either to update my code for a newer Ruby version or to figure out the (frankly, near-unintelligible) instructions for migration to the newer Cedar stack.

So I figured now was a good time to learn some new skills, deal with a few issues and relaunch PMRetract as something easier to maintain and more portable. Here it is. As all the code is “out there” for viewing, I’ll just add few notes here regarding this latest incarnation.
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From PMID to BibTeX via BioRuby

Chris writes:

The blog post in question concerns conversion of PubMed PMIDs to BibTeX citations. However, a few things have changed since 2010.

Here’s what currently works.


# pmid2bibtex.rb
# convert a PubMed PMID to BibTeX citation format
# updated version of http://chrisamiller.com/science/2010/12/13/using-bioruby-to-fetch-citations-from-pubmed/
# works as of 2015-03-18
require 'bio'
Bio::NCBI.default_email = "me@me.com" # required for EUtils
id = "18265351"
pm = Bio::PubMed::efetch(id) # array of MEDLINE-formatted string
med = Bio::MEDLINE.new(pm[0]) # MEDLINE object
bib = med.reference.format("bibtex") # format is a method of Reference object
# "@article{PMID:18265351,\n author = {Brown, T. and Mackey, K. and Du, T.},\n title = {Analysis of RNA by northern
# and slot blot hybridization.},\n journal = {Curr Protoc Mol Biol},\n year = {2004},\n volume = {Chapter
# 4},\n pages = {Unit 4.9},\n url = {http://www.ncbi.nlm.nih.gov/pubmed/18265351},\n}\n"

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pmid2bibtex.rb

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Make prettier documents by reusing chunks in RMarkdown

No revelations here, just a little R tip for generating more readable documents.

Screenshot-RStudio.png

Original with lots of code at the top

There are times when I want to show code in a document, but I don’t want it to be the first thing that people see. What I want to see first is the output from that code. In this silly example, I want the reader to focus their attention on the result of myFunction(), which is 49.
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Bioinformatics journals: time from submission to acceptance, revisited

Before we start: yes, we’ve been here before. There was the Biostars question “Calculating Time From Submission To Publication / Degree Of Burden In Submitting A Paper.” That gave rise to Pierre’s excellent blog post and code + data on Figshare.

So why are we here again? 1. It’s been a couple of years. 2. This is the R (+ Ruby) version. 3. It’s always worth highlighting how the poor state of publicly-available data prevents us from doing what we’d like to do. In this case the interesting question “which bioinformatics journal should I submit to for rapid publication?” becomes “here’s an incomplete analysis using questionable data regarding publication dates.”

Let’s get it out of the way then.
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When life gives you coloured cells, make categories

Let’s start by making one thing clear. Using coloured cells in Excel to encode different categories of data is wrong. Next time colleagues explain excitedly how “green equals normal and red = tumour”, you must explain that (1) they have sinned and (2) what they meant to do was add a column containing the words “normal” and “tumour”.

I almost hesitate to write this post but…we have to deal with the world as it is, not as we would like it to be. So in the interests of just getting the job done: here’s one way to deal with coloured cells in Excel, should someone send them your way.
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Converting a spreadsheet of SMILES: my first OSM contribution

I’ve long admired the work of the Open Source Malaria Project. Unfortunately time and “day job” constraints prevent me from being as involved as I’d like.

So: I was happy to make a small contribution recently in response to this request for help:

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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.

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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.