I am now running a new module for masters students, MD997. The aim is to introduce the class to a range of advanced research methods and to get them to think about how to formulate their own research question(s). The module is built around a paper which is allocated in the first session. I had to come up with a list of methods-type papers, which I am posting below. There are 16 students and I picked 23 papers.

Another post using R and looking at Twitter data. As I was typing out a tweet, I had the feeling that my vocabulary is a bit limited. Papers I tweet about are either “great”, “awesome” or “interesting”. I wondered what my most frequently tweeted words are. Like the last post you can (probably) do what I’ll describe online somewhere, but why would you want to do that when you can DIY in R? First, I requested my tweets from Twitter.

I wondered how many of the people that I follow on Twitter do not follow me back.

We have a new paper out. The title is New tools for ‘hot-wiring’ clathrin-mediated endocytosis with temporal and spatial precision . You can read it here. Cells have a plasma membrane which is the barrier between the cell’s interior and the outside world. In order to import material from outside, cells have a special process called endocytosis.

We have a new paper out! This post is to explain what it’s about. Cancer cells often have gene fusions . This happens because the DNA in cancer cells is really messed up. Sometimes, chromosomes can break and get reattached to a different one in a strange way. This means you get a fusion between one gene and another which makes a new gene, called a gene fusion.

In the UK there is an advertising disclaimer that “the value of your investments may go down as well as up.” Since papers are our main commodity in science and citations are something of a return, surely the “value” of a published paper only ever increases over time. Doesn’t it? I think this is true when citations to a paper are tracked at a conventional database (Web of Science for example). Citations are added and very rarely taken away.

To validate our analyses, I’ve been using randomisation to show that the results we see would not arise due to chance. For example, the location of pixels in an image can be randomised and the analysis rerun to see if – for example – there is still colocalisation. A recent task meant randomising live cell movies in the time dimension , where two channels were being correlated with one another.

We have a new paper out! You can access it here. The people This paper really was a team effort. Faye Nixon and Tom Honnor are joint-first authors. Faye did most of the experimental work in the final months of her PhD and Tom came up with the idea for the mathematical modelling and helped to rewrite our analysis method in R. Other people helped in lots of ways.

I’ve generated a lot of code for IgorPro. Keeping track of it all has got easier since I started using GitHub – even so – I have found myself writing something only to discover that I had previously written the same thing. I was thinking that it would be good to make a list of all functions that I’ve written to locate long lost functions.

Caution: this post is for nerds only. I watched this numberphile video last night and was fascinated by the point pattern that was created in it. I thought I would quickly program my own version to recreate it and then look at patterns made by more points. I didn’t realise until afterwards that there is actually a web version of the program used in the video here. It is a bit limited though so my code was still worthwhile.