Rémi Trimbour - MD-PhD student

Yes, the way Cicero normalizes counts and computes pseudo cells from UMAP coordinates was really deleterious in our evaluation..

Maybe improvement will come with scATAC atlases, to compensate for data sparsity! 😊

Very happy to see this work finally online 🥳

It was really interesting to write a short manuscript mixing method presentation & broader insights on data preprocessing. 📝

Thank you to my co-supervisors @cantinilab.bsky.social & @juliosaezrod.bsky.social for their feedback on this side work! 🫶

5/5

CIRCE is a Python package based on #AnnData and fully compatible with the #scverse environment.

You can extract DNA region modules & visualize CIRCE's interactions over gene locations.

CIRCE also facilitates #CellOracle or #HuMMuS usage, making it unnecessary to run Cicero (code in R) first.

4/5

We explore the impact of preprocessing on DNA interactions inference. 👩‍🔬

We evaluated scATAC-seq preprocessings using promoter capture Hi-C data. 🧬

TLDR: Best performance came from single-cell inputs directly and from CIRCE metacells! 📊
In contrast, count normalization had a negative impact.

3/5

We compared CIRCE & Cicero on 2 datasets, demonstrating near identical results from the same input.

Both use pseudo-cells to reduce sparsity. CIRCE proposes a new strategy, whose output is closer to the single-cell profiles. 🛠️

On average, CIRCE uses 5x less memory and runs 150x faster ! 📈

2/5

I'm presenting #CIRCE, a Python package to infer co-accessible DNA region networks 🧬

Based on #Cicero 's algorithm (Pliner et al.), it runs ~150x faster, processing an atlas of 700k cells in less than 40 min! ⛷️

Short paper: doi.org/10.1101/2025...
Code: github.com/cantinilab/CIRCE

1/5 ⬇️

I'm curious if other mid career PIs have considered completely abandoning the trad publication model. We've been making gradual moves towards that end goal. But it's been difficult to completely jump ship for one main reason for me at least. 1/

GitHub - cantinilab/scPRINT: single cell foundation model for Gene network inference and more single cell foundation model for Gene network inference and more - cantinilab/scPRINT

On Tuesday at 2025-02-04 18:00 CET, @jkobject will talk about scPrint, a transformer model that infers gene networks from scRNA-seq data, at our 2nd community meeting of 2025! For more information, check out the GitHub: https://buff.ly/40RJ3gT & pre-print https://buff.ly/4hwK4Av

GRETA graphical abstract

We present Gene Regulatory nETwork Analsyis (GRETA), a framework to infer, compare and evaluate gene regulatory networks #GRNs. With it, we have benchmarked multimodal and unimodal GRN inference methods. Check the results here 👇
Paper: doi.org/10.1101/2024.12.20.629764
Code: github.com/saezlab/greta

We present NetworkCommons, a unified platform 🪐 for network biology, providing access to omics data, knowledge, and contextualization methods, all with a consistent API 👇🧵
Paper: doi.org/10.1101/2024...
Docs: networkcommons.readthedocs.io

Congrats Pau for all this work !! 🎉🎉

I learned a lot from being around you (and I still do) 😇
And I'm really looking forward to seeing what you'll bring to the field in the coming years 🔮

I would love to be in too ! 😊

Single-cell multiomics reveals the oscillatory dynamics of mRNA metabolism and chromatin accessibility during the cell cycle The cell cycle is a tightly regulated process that requires precise temporal expression of hundreds of cell cycledependent genes. However, the genome-wide dynamics of mRNA metabolism throughout the ce...

Hey, this place is getting fun! Here is a new version of our latest preprint on oscillatory dynamics of mRNA metabolism and chromatin accessibility during the cell cycle: www.biorxiv.org/content/10.1...

What are the key disrupted multicellular processes in heart failure? In our new work we combine 23 years of molecular data with recent single-cell atlases to draw a cross-study patient map
doi.org/10.1101/2024...

Hi, would like to join the science feed too please ! :)

I'm an MD-PhD student currently working on single cell method development to predict molecular regulations.
orcid.org/0000-0001-87...