I’m currently writing two manuscripts that each have a substantial data modelling component. Some of our previous papers have included computer code, but it was straightforward enough to have the code as a supplementary file or in a GitHub repo and leave it at that. Now with more substantial computation in the manuscript, I was wondering how best to describe it. How much detail is required?

Statistical hypothesis testing , commonly referred to as “statistics”, is a topic of consternation among cell biologists. This is a short practical guide I put together for my lab. Hopefully it will be useful to others. Note that statistical hypothesis testing is a huge topic and one post cannot hope to cover everything that you need to know.

This post follows on from “Getting Started“. In the lab we use IgorPRO for pretty much everything. We have many analysis routines that run in Igor, we have scripts for processing microscope metadata etc, and we use it for generating all figures for our papers. Even so, people in the lab engage with it to varying extents. The main battle is that the use of Excel is pretty ubiquitous.

Something that has driven me nuts for a while is the bug in FIJI/ImageJ when making montages of image stacks. This post is about a solution to this problem. What’s a montage? You have a stack of images and you want to array them in m rows by n columns. This is useful for showing a gallery of each frame in a movie or to separate the channels in a multichannel image.

More on the theme of “The Digital Cell“: using quantitative, computational approaches in cell biology. So you want to get started? Well, the short version of this post is: Programming I make no claim to be a computer wizard. My first taste of programming was the same as anyone who went to school in the UK in the 1980s: BBC Basic.

The future of cell biology, even for small labs, is quantitative and computational. What does this mean and what should it look like? My group is not there yet, but in this post I’ll describe where we are heading. The graphic below shows my current view of the ideal workflow for my lab. The graphic is pretty self-explanatory, but to walk you through: A lab member sets up a microscopy experiment.

If you are a cell biologist, you will have noticed the change in emphasis in our field. At one time, cell biology papers were – in the main – qualitative . Micrographs of “representative cells”, western blots of a “typical experiment”… This descriptive style gave way to more quantitative approaches, converting observations into numbers that could be objectively assessed.

I needed to generate a uniform random distribution of points inside a circle and, later, a sphere. This is part of a bigger project, but the code to do this is kind of interesting. There were no solutions available for IgorPro, but stackexchange had plenty of examples in python and mathematica. There are many ways to do this.

Today I saw a tweet from Manuel Théry (an Associate Ed at Mol Biol Cell ). Which said that he heard that the Editor-in-Chief of MBoC, David Drubin shops for interesting preprints on bioRxiv to encourage the authors to submit to MBoC.

I have written previously about Journal Impact Factors (here and here). The response to these articles has been great and earlier this year I was asked to write something about JIFs and citation distributions for one of my favourite journals. I agreed and set to work. Things started off so well. A title came straight to mind.

An occasional series in esoteric programming issues. As part of a larger analysis project I needed to implement a short program to determine the closest distance of two line segments in 3D space. This will be used to sort out which segments to compare… like I say, part of a bigger project. The best method to do this is to find the closest distance one segment is to the other when the other one is represented as an infinite line.