As part of the series on development of early career researchers in the lab, we spent three sessions over three weeks learning the basics of R. In my book “The Digital Cell”, I advocate R as the main number-crunching software but the R literacy in my lab is actually quite mixed. In order to know how to pitch the training, I conducted a quick poll in our lab Slack workspace to find out what R skills people in the lab already had.
I wrote a short opinion piece for the December Newsletter for the American Society for Cell Biology (ASCB). The content is reproduced below, or you can read the newsletter version here on page 14 of the PDF. The theme of this year’s ASCB|EMBO Meeting is Cell Biology for the 21st Century. So what skills are essential for a cell biologist to master in the 21st century?
It’s real! I recently received a physical copy of my book, The Digital Cell: Cell Biology as a Data Science . It’s real! It is published by Cold Spring Harbor Laboratory Press and is available here. The official release date is 1st December 2019. As I’ve described before, “The Digital Cell” a handbook to doing cell, developmental and molecular biology in the 21st Century; integrating computational and quantitative approaches.
Long-time readers might remember the short-lived series on quantixed called The Digital Cell. There is a reason why I stopped these posts, which I can now reveal… The Digital Cell will soon be a book! Published by Cold Spring Harbor Laboratory Press, The Digital Cell is a handbook to help cell and developmental biologists get to grips with programming, image analysis, statistics and much more.
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.
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.
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.