/
Today I learned about the Girvan–Newman algorithm. It's a fairly quick way to break up networks of interacting components into clustered communities. It doesn't really join components in any way they aren't already connected. It just removes connections which don't meet betweenness requirements. If the starting network is a hairball, the final product is either distinct clusters or just a bunch of smaller hairballs (I seem to be getting the latter, for the most part, but my current networks are several thousand nodes with more than 40 thousand edges).

The clusterMaker2 plugin for Cytoscape has an implementation of this kind of community clustering. It uses GLay. The plugin hasn't been quite as fast for me as the GLay authors report but I'm content to blame that on my old* Core2 Quad 2.4GHz and/or the massive changes in Cytoscape over the last few years.


*This one came with the lab and was originally used for working with sequencing data. I'm going to use it until it dies and/or catches on fire.
/
I read this Nature news piece about phage therapy today. It's great that phage might finally be taken seriously as an alternative to carpet-bombing bacterial infections with antibiotics.

This sentence caught my eye: "Nature provides an almost inexhaustible supply: no two identical phages have ever been found." That claim isn't entirely true. While bacteriophage genomes are staggeringly diverse, many contain very similar conserved sequences. If we're only considering two phages to be identical if each nucleotide in one genome is present in the other, then it's very close to true. We could, of course, pick two phage out of the same culture and have a good chance of their genomes being 100% identical. Depending on the phage, we may only be talking about few thousand nucleotides (ΦX174 is only about 5 thousand base pairs, for example!). 

Let's not even start on the issue of how frequently we isolate genetically identical bacteria. We'd like to think that a culture of any one bacterial species is a monoculture, but how many cells in that culture are completely genetically identical at any one time? How about their transcriptomes? I suppose that the original idea remains true: bacteria can produce great genetic variation, so bacteriophage can do so just as well.

Bar none

/
"Kick the bar chart habit", they say. "Use box plots instead and you can show a distribution of your data points", they claim. They're right, of course. I suspect that most people, whether they're scientists or just members of the 24-hour-TV-news audience (there's some overlap between the two groups, of course) prefer bar charts because they're so straightforward. Higher bars are greater in value. What could be easier?

Besides scientific papers and TV news, the biggest influence on chart type is likely Excel. Hell, the newer versions let users create tiny, almost-unreadable sparkline bar charts. The only worse option may be pie charts. My introductory statistics professor was always wary of using the wrong chart at the wrong time, but he constantly warned us that there was never a right time for a pie chart. I'm usually inclined to agree.

So, bottom line: avoid using the default chart options in Excel and/or chart types which sound like desserts.
/
Nothing feels less productive than fighting with Git. The official documentation is the real problem: it's roughly as arcane as Norse runes.* I managed to find a few tutorials today, at least:

  • Atlassian Git tutorial - just a basic rundown of the common commands.
  • Try Git - I find that the Github documentation is just as bad as the Git docs: it makes the basics sound easy but doesn't provide enough detail to know what to do when things go totally wrong. This does the same thing in an interactive way. It's great for the basics but not much more than that.

*They all read like this.