A bit late this week, since we’re reaching the holidays, but here we are anyway. Remember I mostly skim through these, I take these briefings as a way to engage in the content, and that most of my sources come from Twitter and Mastodon:

• Things could be better: A very interesting read, all the more because the way it is written, on why the human mind tend to imagine how things could be better when prompted to imagine how things could be different. As in, we immediately jump to think about the better version of something, and not the worse version of something, when asked how a given thing could be different –no matter the wording of this question, the language it is asked in, etc . Are we poised for expecting or demanding improvement? I also find it amazingly well written and accessible. Papers should read like this.
• Moonlight2 for identifying driver genes: originally thought for cancer research, a nice mix of GRN inference and data mining to identify marker and driver genes. Could it be adapted for other types of data if provided with a different type of annotation –not cancer data and literature, but e.g. a given biological process of interest?
• scTensor detects many-to-many cell–cell interactions from single cell RNA-sequencing data: a tool that relies on ligand-receptor annotation and gene co-expression to infer interactions between cells. Especially interesting when describing de novo datasets.

More later this week!

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.