Molecular Biology of The Cell , the official journal of the American Society for Cell Biology, recently joined a number of other periodicals in issuing guidelines for manuscripts, concerning statistics and reproducibility.
Ten years ago today I became a PI. Well, that’s not quite true. On that day, I took up my appointment as a Lecturer at University of Liverpool, but technically I was not a PI. I had no lab space (it was under construction), I had no people, and I also had no money for research. I arrived for work.
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: Find something that you need to automate and get going! Programming http://www.instruction-manuals.co.uk/imageIM/four/seven/bbc.gif I make no claim to be a computer wizard.
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.
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.
