Nav

GitHub - HiDiHlabs/sainsc: Segmentation-free Analysis of In Situ Capture data Segmentation-free Analysis of In Situ Capture data - HiDiHlabs/sainsc

Sainsc is a new tool for efficient whole organism spatial transcriptomics data analysis at the nanometre scale. It works with Stereo-seq, VisiumHD, Xenium and Nova-ST. Looking forward to see how it works with upcoming platforms (e.g. Illumina).
github.com/HiDiHlabs/sa...

Sainsc: A Computational Tool for Segmentation‐Free Analysis of In Situ Capture Data (Small Methods 5/2025) Segmentation-Free Analyses Sainsc: a new tool for efficient whole organism spatial transcriptomics data analysis at the nanometre scale. Shown is a blended composite spatial map of total gene express...

Spatial transcriptomics - helping authors wow reviewers with pretty pictures (... and interesting spatial biology).. and making it possible for bioinformaticians to submit journal cover art! Our Sainsc tool is featured as the back cover for Small Methods: onlinelibrary.wiley.com/doi/10.1002/...

And our tireless worker bees who dedicated every Wednesday evening for 18 months for this project: Jieran Sun, Kirti Biharie, Peiying Cai, Niklas Müller-Bötticher, Paul Kiessling, Meghan A. Turner, Søren H. Dam, Florian Heyl. 🙏

I forgot the most important bit! This would not have been possible without the wonderful and amazing SpaceHack community. Special shout outs to Brain Long, Ahmed Mahfouz and @markrobinsonca.bsky.social for their work, ideas and resources...

GitHub - SpatialHackathon/SACCELERATOR Contribute to SpatialHackathon/SACCELERATOR development by creating an account on GitHub.

We wrap up our analysis framework into SACCELERATOR, github.com/SpatialHacka..., and present two case studies on brain and colon cancer datasets to remind people that having domain expertise and performing interactive analysis is more important than picking the "best" tool.

General findings:
- benchmarking studies can be inconsistent
- methods do not scale to new datasets
- "ground truths" do not consider granularity of biological features
- looking at the differences between methods (entropy) is informative
- method performance doesn't match subjective importance

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcript...

Did you ever notice discrepancies in benchmarking of bioinformatics tools? We did too! Setting out to benchmark spatial clustering methods, we encountered major reproducibility issues in previous benchmarks and questionable "ground truths".
More in our preprint: www.biorxiv.org/content/10.1...

Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current c...

Accurate cell segmentation remains a major issue for spatial transcriptomics.
Elyas Heidari and colleagues from the Gerstung, Pe'er and Stegle Labs released Segger, a new algorithm that uses GNN to model both transcripts and cells. More details in their preprint: www.biorxiv.org/content/10.1...

Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows - Nature Methods This study presents a comprehensive evaluation of Xenium In Situ datasets and provides recommendations on analysis workflows.

🧬If you work with @10xgenomics.bsky.social Xenium, this might be of your interest.

Our new study on Xenium exploration best practice and comparing it with other commercial platforms is now out in @naturemethods.bsky.social

www.nature.com/articles/s41...

GitHub - HiDiHlabs/ovrl.py: A python tool to investigate cell overlaps in imaging-based spatial transcriptomics data. A python tool to investigate cell overlaps in imaging-based spatial transcriptomics data. - HiDiHlabs/ovrl.py

We also released the ovrl.py tool to identify areas of cell overlaps in imaging-based spatial transcriptomic data (e.g. #Xenium, #MERSCOPE, #cosMX).
GitHub: github.com/HiDiHlabs/ov...

2D, or not 2D? Investigating Vertical Signal Integrity of Tissue Slices Imaging-based spatially resolved transcriptomics can localise transcripts within cells in 3D. Cell segmentation precedes assignment of transcripts to cells and annotation of cell function. However, ce...

Have you been bothered that practically every #SpatialTranscriptomic analysis tool assumes data to be 2D? Well, you were right - overlapping cells are a really big problem. Check out our report on bioRxiv: 2D, or not 2D? Investigating Vertical Signal Integrity of Tissue Slices. t.ly/CQX8K

GitHub - HiDiHlabs/multiSPAETI: Implementation of MULTISPATI-PCA in Python Implementation of MULTISPATI-PCA in Python. Contribute to HiDiHlabs/multiSPAETI development by creating an account on GitHub.

We also sneak in a python implementation for the spatially aware MULTISPATI-PCA. We think this should pretty much replace "non-spatial" PCA for all spatial modalities in most cases.
GitHub: github.com/HiDiHlabs/mu...
RtD: multispaeti.readthedocs.io/1.0.0/genera...

SpatialLeiden integrates with the #scverse by leveraging #scanpy and #anndata but can also be used independently.
GitHub: github.com/HiDiHlabs/sp...
RtD: spatialleiden.readthedocs.io/stable/

SpatialLeiden: spatially aware Leiden clustering - Genome Biology Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Le...

SpatialLeiden is out in #GenomeBiology: t.ly/WQppF. It models both gene expression and spatial information for clustering. This allows you to perform e.g. spatially aware cell typing or domain identification. It's fast, scalable and intuitive for single-cell researchers. Try it out!

The HISTOMAP projects aims to use AI and spatial transcriptomics data to accelerate biomarker detection for bladder cancer. We will work closely with the clinic, pathology, and regulatory experts to investigate how best to get these models into clinical routine.

BIH PhD Program - Call for PhD candidates 2025 - News - BIH at Charité The BIH PhD program aims to promote interdisciplinarity and support young translational talents. The program co-funds PhD positions for excellent translational projects that fit one of the four BIH re...

Do you also think that #SpatialTranstriptomics and #AI will give rise to #DigitalPathology 2.0? We have an open PhD position for the HISTOMAP (Histology-based Molecular Analysis Platform) project to accelerate biomarker detection. Please apply here: www.bihealth.org/en/notices/b....
Spread the word!

I tried Sainsc for our MERSCOPE data and Naveed, it works wonderfully! Importantly, the documentation is excellent. Thank you so much for the clear tutorial and explanations. This is truly a life saver for my analysis at the moment. Props to the team, this is awesome work!

Everyone knows by now that 42 is the answer to the ultimate question of life, the universe, and everything. But as I turn 42 today, I’ve realised something profound: it’s not the answer that matters - it’s the prompt!

Am I really the first to repost Mats Nilsson? What an honour!

a cartoon of a man looking out a window at the stars ALT: a cartoon of a man looking out a window at the stars

Hello world. Our lab has a new home here on Bluesky! We will post about spatial omics, science, nice papers and lab activities. The account will be collectively managed by lab members. Happy spatial-omics everyone!

The calm before the storm... #SpaceHack starts tomorrow! Looking forward to 3 intense days of addressing unsolved data analysis problems for #SpatialTranscriptomics.
More here: spatialhackathon.github.io

SpatialLeiden adds spatial support to the Leiden clustering algrithm. It has 3 parameters. Two we know from scRNAseq analysis (resolution and num of neighbors), and the other is the weight of the spatial info. We thin it is the natural choice for spatial clustering for the single-cell community!

... we will also implement multi-omics and multi-sample support at #SpaceHack spatialhackathon.github.io

SpatialLeiden - Spatially-aware Leiden clustering Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Le...

Try out SpatialLeiden for identifying spatial domains/clusters in your #SpatialTranscriptomics data: www.biorxiv.org/content/10.1...
github.com/HiDiHlabs/sp...
It's as easy as Leiden, but as performant as the best in class. Give it a try and let us know if it works for you!

@gestaltsp.bsky.social welcome to BlueSky!

@jasmineplummer.bsky.social @ioavlachos.bsky.social

Have you performed a #VisiumHD run, and frustrated that you had to analyse data in 8/16 um bins?
We just updated #Sainsc to create maps of cell types at 2 um resolution for #VisiumHD: sainsc.readthedocs.io/tutorials/tu....
Check it out and let us know - happy to add features/fixes.

#Sainsc is a cell-segmentation-free tool to create cell-type maps.
Why segmentation-free? Cell segmentation is hard, and if not done well it can ruin expression profiles, even miss cells.
Sainsc is fast! Get first insights in <5 min for #Xenium, #MERSCOPE, and <1 hr for #STomics, #Stereo-seq.

Sainsc: A Computational Tool for Segmentation‐Free Analysis of In Situ Capture Data Spatially resolved transcriptomics methods can now profile the entire transcriptome, at full organism scale with nanometre resolution. Analysis frameworks that can efficiently and intuitively process....

Check out our new tools for #SpatialTranscriptomics. It's called Sainsc: onlinelibrary.wiley.com/doi/10.1002/....
#Sainsc can map cell types in high resolution spatial transcriptomics data, without segmenting cells! It's light weight, fast, and easy to use: sainsc.readthedocs.io/tutorials/in...

Whole-brain spatial transcriptional analysis at cellular resolution

Incredible study - 3D single cell imaging of the entire mouse brain - also on the cover of this week's #Science
🧪🔬🤯

www.science.org/doi/10.1126/...

Id love to be added :)