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!