Lucas Moitinho-Silva

You guys are doing great stuff! Thanks for keeping it compatible and updated

This is gold!!

Side-by-side comparison of two multi-panel bubble charts faceted by world region. The left column shows the default facet labels placed above each panel (“Africa”, “Americas”, “Asia”, “Europe”, “Oceania”). The right column shows the same charts, but the facet labels are moved inside each panel at the top-left using a negative margin. In the center, there is a title reading “Want to place your facet labels inside each panel?” with an arrow pointing right, followed by a short ggplot2 theme code snippet demonstrating how to move strip text inside the panel.

I ignored the strip.clip argument in #ggplot2 for way too long 😲

Combined with a small negative margin tweak, you can place facet labels inside each panel. A tiny trick that makes small multiples feel so much cleaner.

🔵 no manual coordinates
🔵 inherits theme styling
🔵 scales nicely when resizing

Yes!!!

filtro package hex on a background with dots; the Posit logo is in the corner

New releases for tidymodels! 📦

We've launched filtro and important for supervised feature selection in #RStats. These tools simplify predictor ranking, reduce overfitting, and include advanced dynamic importance calculations.

Learn more: tidyverse.org/blog/2025/11...

The commitments in the SAFE Labs Handbook.

A new community-driven lab handbook for reducing conflict and creating more positive and equitable work environments gets strong support from a survey of 200 researchers.
buff.ly/K7CGFLV

tidyomics project 🔛 R-bloggers!

www.r-bloggers.com/2025/10/the-...

Thanks to @stemang.bsky.social, Maria Doyle @bioconductor.bsky.social, @jhsanchez.bsky.social, Mengyuan Shen, Jonathan Carroll and others in tidyomics for testing and getting this up and running

Sounds pretty Cool!

Yeah, pretty exciting! Keep up the great work! Will definitely discuss in our next journal club.

This is pretty cool! I am wondering why I am not seeing more bacterial scRNA studies. Maybe due to my biased metagenome bubble?

mSphere of Influence: How the single cell contributes to the collective | mSphere In microbiology, we know that size matters. Your typical bacterium is only ~1 µm3, and thus, the micron scale is the fundamental level at which bacteria interact with their environments, a level that is hard to appreciate as humans (1). This challenge is one of the things that draws me to microbiology. I love the puzzle of understanding how things work that are too small for us to see. Through much of my PhD and postdoctoral training, I used sequencing approaches, including RNA-seq, to show how bacteria behave across different environments. But I always wondered if I had captured the whole story. For example, when I analyzed the gene expression of an oral pathogen from tooth scrapings taken from people with periodontitis (2), the samples were prepared from millions of microbes across millimeters of a tooth surface. I would ask, are all cells of this pathogen producing the same virulence factors and eating the same carbon sources? Likely not. Not all bacteria are in an identical environment, surrounded by the same host and bacterial cells at the micron level. Furthermore, targeted studies have shown repeatedly that bacterial gene expression varies across clonal cells for traits such as growth rate, antibiotic resistance, and competence due to micron-scale environmental differences, but also bistability, stochasticity, and genealogical effects (3, 4). Then, in 2020 and 2021, two papers, by Blattman et al. (5,6) and Kuchina et al. (6,6), opened up a new way to study bacterial gene expression at this single-cell level using bacterial single-cell RNA-seq (scRNA-seq).

My new perspective article is out on the opportunities to use single-cell RNA-seq to better understand heterogeneity in bacteria! journals.asm.org/doi/10.1128/...

#mSphereofInfluence @asm.org #scRNA-seq #microsky

Title slide: Will your code run again? Tips for making code reproducible in R

If you missed my talk but still want to learn how to make your R code more reproducible, my slides are here 🙂:

daxkellie.quarto.pub/will-your-co...

All the links to packages and resources I mentioned are there, so hopefully this can be a nice reference, too!

#ESAus2024 #rstats #quartopub 🧪🌏

Introducing the {messy} package | Nicola Rennie The {messy} R package takes a clean dataset, and randomly adds mess to create data more similar to that which you'd find in the real world. This is an easy way for educators to create data sets that g...

🎉 The {messy} package is now available on CRAN! 🎉

Read the introductory blog post here: nrennie.rbind.io/blog/introdu...

#RStats #StatsEd #DataScience

Re-upping this one. I have curated a playlist for molecular biology. Could be useful you are training undergrads in the lab.

Great, thx for sharing!

A common issue when learning new tools is that you normally don't know from where to begin. This could be your way into nextflow.

NEW Publication!😄

Here, we show how #PhyKIT — a broadly applicable toolkit for #phylogenomics — can be used to construct phylogenomic data matrices (& quantify biases therein), detect anomalies in predicted orthology, calculate gene-gene coevolution, & more!

🔗: tinyurl.com/yc5ja4xz

I made a starter pack for microbiome:

go.bsky.app/Fq36egy

Here’s a fun bunch 🙌 Let me know if you’d like to be added to it!

Bluesky for Science Bluesky starter packs for genomics, bioinformatics, R, and Nextflow

Bluesky for Science

Starter packs for genomics, bioinformatics, #Rstats, Nextflow. Moderation lists. Feeds. Let's rebuild the old scitwitter community and keep this place nice

blog.stephenturner.us/p/bluesky-fo... 🧬🖥️🧪