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Daniel Dimitrov

@dbdimitrov.bsky.social

85 followers 62 following 0 posts

Postdoc @steglelab.bsky.social; Interested in #ML for #Spatial #Omics

Posts Replies Media

Reposted by Daniel Dimitrov

Stegle Lab @steglelab.bsky.social · 07/01/2026

Fresh off the press in 2026! Interested in the challenge of how to advance from descriptive atlases to causal mechanisms and counterfactuals? 🔬 Take a look at our recent perspective: "Interpretation, extrapolation and perturbation of single cells"! (rdcu.be/eXeDY)

rdcu.be

Interpretation, extrapolation and perturbation of single cells

Nature Reviews Genetics - Causal and mechanistic modelling strategies, which aim to infer cause–effect relationships, provide insights into cellular responses to perturbations. The authors...

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Reposted by Daniel Dimitrov

Manu Saraswat @manusaraswat.bsky.social · 16/05/2025

🧠 Excited to share my main PhD project! We mapped the regulatory rules governing Glioblastoma plasticity using single-cell multi-omics and deep learning. This work is part of a two-paper series with @bayraktarlab.bsky.social @oliverstegle.bsky.social and @moritzmall.bsky.social, Preprint at end🧵👇

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Reposted by Daniel Dimitrov

Stegle Lab @steglelab.bsky.social · 18/03/2025

1/ New preprint! 🍳 @elihei.bsky.social and our team at @embl.org , @dkfz.bsky.social, and @mskcancercenter.bsky.social built #segger - a fast, accurate cell segmentation tool for spatial transcriptomics that assigns transcripts to their cell origins! doi.org/10.1101/2025...

Message passing intuition behind the segger’s link-prediction model and the network architecture

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Reposted by Daniel Dimitrov

Stegle Lab @steglelab.bsky.social · 06/03/2025

Want to map the origins and trajectories of cells in development and disease? Cell2Fate is here to help you! 🚀 🔗 Learn more: doi.org/10.1038/s415... 🖥️ Try it on your own datasets: github.com/BayraktarLab/cell2fate

doi.org

Cell2fate infers RNA velocity modules to improve cell fate prediction - Nature Methods

Cell2fate improves RNA velocity analysis of single-cell and spatial transcriptomics data by module decomposition of realistic biophysical models of transcription dynamics.

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