Welcome to our team, Gesa Voigt! 👋

During her molecular biology studies, Gesa developed an interest in applying mathematics and ML to understand complex biological systems. Her PhD research will focus on inferring GRNs by integrating insights from population genetics and genetic perturbation data.

Please join me in welcoming Stefanie Mantz to our lab! 👋

Steffi joins us as a PhD student after completing her Master’s at Heidelberg University. She will explore gene regulation and cell differentiation using organoid models and is excited to analyze these complex datasets and develop ML methods.

3️⃣ We applied MOFA-FLEX to breast cancer data combining Xenium (166K cells; ~300-gene spatial) and Chromium (30K cells; ~3K-gene non-spatial) datasets from adjacent FFPE slides, revealing latent factors capturing global cell-type variation and transcriptome-wide spatially resolved gene programs. 9/n

2️⃣ When applying MOFA-FLEX to a multi-omic CITE-seq dataset from murine spleen and lymph nodes (N=14,870 cells), collected in two independent batches on different days, it was able to disentangle both batch and cell type variation. 8/n

1️⃣ We applied MOFA-FLEX to an scRNA-seq dataset of PBMCs from lupus patients (N=13,576 cells) treated with or without IFN-β, showing it can identify IFN-β gene programs consistent with known biology and reveal new program-linked genes (e.g., TNFSF10). 7/n

Are you using any of our factor models, such as MOFA? 🛵
You might’ve found it challenging to tailor them to your specific use cases - not anymore!

Introducing MOFA-FLEX: a flexible, modular factor analysis framework designed for customizable modeling across diverse multi-omics data scenarios. 1/n

📢 Announcing the MOPITAS Autumn School on Spatial Transcriptomics Data Analysis – taking place November 12–14, in Munich. Don't miss this excellent opportunity to deepen your expertise in data science and multi-OMICS analysis - especially as a PhD student in the data science community.

#ELSAGeneralAssembly Flashback: At last week’s ELSA General Assembly in Brussels, our lab member Hakime represented the @embl.org partnership in the @elsa-ai.eu project. 🧬✨

Hakime shared reflections on the recently concluded Health Privacy Challenge and also organized a follow-up workshop. 🛡️

Our lab members recently attended the @www.helmholtz.de - @ellis.eu workshop on #FoundationModels in science, where academia and industry united in Berlin to explore AI’s role in genomics and more. Exciting insights fueling innovation! 🧠

Huge thanks to the organizers for such a fantastic event! 🙌

8/ Please check our preprint (biorxiv.org/content/10.1...) and documentation (elihei2.github.io/segger_dev/), test #segger (github.com/EliHei2/segg...), and join in - PRs & issues invited!

6/ On @10xgenomics.bsky.social Xenium tests (with/without membrane staining), #segger outperforms other approaches - higher sensitivity & specificity, fewer edge errors, cleaner profiles, more reliable downstream results, in a split of compute time.

5/ Cell-assigned transcripts form #segger 's “cells”. Optionally, the rest (up to 50%) could be grouped as “fragments”. First time to split these two entities semantically—key for handling cut-up, overlapping, or unstained cells!

4/ #segger embeds cells & transcripts in a shared latent space - closer means likelier transcript-to-cell matches. It assigns each transcript to the cell with the highest score within a radius, in the physical space.

3/ #segger ’s trick? Graph Attention Networks (GATs) + GPU acceleration on heterogeneous graphs. It represents transcripts and cells as nodes of a heterogeneous graph, and computes on gene identities, transcript locations (in 3D), nucleus/membrane stains, and their morphology.

2/ If you’ve worked with Spatial Transcriptomics (ST) data, you’ve indeed had some troubles with segmentation accuracy. Segmentation problems mess up downstream analysis & obscure cell-cell interactions - which are ST’s biggest promises. #segger

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...

⏰ A quick reminder that Track I of the Health Privacy Challenge is still running strong, exploring privacy preservation in bulk RNA-seq datasets! We’re excited to see the innovative solutions Blue Teams and Red Teams will bring to the competition! #🫐🍅 #CAMDAConference #ISMB/ECCB2025

Choose Your Side: Will you join the Blue Team (🫐), to advance the privacy-preserving generative methods for biological datasets, or align with the Red Team (🍅), to develop robust and realistic membership inference attack methods tailored for biological context?

Choose Your Side: Will you join the Blue Team (🫐), to advance the privacy-preserving generative methods for biological datasets, or align with the Red Team (🍅), to develop robust and realistic membership inference attack methods tailored for biological context?

Two new @natureportfolio.bsky.social publications:

1️⃣ Multi-omic profiling of medulloblastoma characterizes subclonal variation of chromothripsis (doi.org/10.1038/s414...)

2️⃣ Spatio-temporal profiling explores mechanisms of treatment resistance (doi.org/10.1038/s414...)

Thanks to all involved! 🙌