I’ve been meaning to do a newsletter to keep my readings/tabs organised for a while but I am terrible at getting things started. This is part of a motivation influx I had two months ago after a couple of congresses (the other part being having learned to code in functions and using markdowns) and after being subscribed to learnbyexample’s newsletter for almost a year now. I’ll do my best to explain in simple lay words the concept behind the papers/resources, most times having read only the abstract or skimmed through very quickly.

Let’s get started:

• Unclearing microscopy : a novel method to visualise cells without the need of a microscope, by “revealing” the cell membranes using a chemical reaction that is expanded volumetrically while keeping the original cell shape.
• Predicting evolution: a review on the major advances in evolutionary biology and population genetics, and how we can infer patterns on clonal competition across all biological systems studied. This hints at potential predictions of the principles of a modern evolutionary theory.
• Anticor_features: finding anticorrelation patterns in single cell data that can help identify divergent cell types. Also a way of performing negative controls in these studies to prevent excessive sub-clustering. A twitter thread here.

More soon!

I have two different Gene Ontology annotations for the genome annotation I am working with, and I’ve been trying different ways to quickly visualize Gene Ontology information, such as GO enrichment analysis using p-values as a proxy of significance (as per usual in genomic analyses). I thought of adapting this quick tutorial on wordcloud, but soon I noticed that sometimes the GO term human-readable descriptions are excruciatingly long, and the plots can become a total mess.

One option could be to inject a jumpline character after N words in the GO term string, which the R plotting engine seems to work fine with, but my first I thought was to abbreviate the words in the description. Perhaps unsurprised I learned that the foundational knowledge of R base has what I was looking for: the abbreviate() function.