Over the last few years the number of methods for analysing scRNA-seq has exploded and there is now well over 200 software tools available. Each of these tools need to make a choice about how they store and represent the data used during their analysis. One attempt to standardise the data structures that are used is the SingleCellExperiment package created by Davide Risso and Aaron Lun, with help from Keegan Korthauer.
The Bioconductor 3.7 release was announced this week. I thought I would have a look through the new packages and changes to existing packages and point out some of my highlights. The descriptions below are my summaries, if you want to see more detail you can read the full release notes here. Single-cell RNA-seq My interest is in single-cell RNA-seq analysis, so I am going to start off with packages related to this.
Over the last few years I have followed a lot of the work done by FiveThiryEight, particularly their attempts to model and predict sport. More recently I have discovered there is a community of people trying to do similar things for the AFL, including The Arc, Squiggle, Matter of Stats and Hurling People Now. Many of these modelling projects are based around the Elo system. If you haven’t heard of it before this model is a ranking system originally designed for chess by a Hungarian physicist.
Welcome to Blogdown! I’ve just finished migrating this blog from Ghost to Blogdown. Ghost was great in a lot a ways but Blogdown adds the ability to write in RMarkdown which I’m hoping will encourage me to post more often, particularly on things that include some code or analysis. Thanks to everyone who has written about their experiences with Blogdown. I didn’t keep track of the resources I found useful so I can’t point them out but they definitely made the process easier.
Today I attended the Joining the Dots visualisation symposium. You can see the slides for my talk about clustering trees here. It was a great event and hope we see more meetings like this in the future. Here is an analysis of the Twitter activity on the #jtdwehi hashtag, thanks to code from Neil Saunders. You can see it on Github. Introduction An analysis of tweets from the Joining the Dots symposium.
For my PhD I am working on methods for analysing single-cell RNA-sequencing (scRNA-seq) data which measure the expression of genes in individual cells. One of the most common analyses done on this type of data is to cluster the cells, often in an attempt to find out what cell types are present in a sample. In a recent seminar I showed some images of what I am calling a “clustering tree” (you can see the slides here if you are interested).
Over the weekend I attended PyCon Australia. This was my first time at a purely tech conference and I couldn’t help but compare it to my previous experiences at scientific conferences. DISCLAIMER: Like I said this was my first tech conference and my scientific conference experience is also fairly limited so some of the comments I make might be generalisations that don’t always apply. PyCon started with miniconfs on Friday and continued coding sprints on Monday and Tuesday.
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