MagesLab retweeted
📢 Out in Nat Med: Single-cell, nucleus & 4 types of spatial expression data across the clinicopathological diversity of metastatic breast cancer!
We find surprising within-patient stability, spatial EMT phenotypes, local immune exclusion, and more + comparison across methods🔗➡️
MagesLab retweeted
📢BoReMi for manual registration of spatial expression data 🧬 and microscopy images 🔬. When automated tools fail 🥺 BoReMi is your friend💪. Lightweight Jupyter interface, Single cell matching, Non-linear alignment, and more. #spatial_omics #registration #groundtruth
MagesLab retweeted
scPrisma is out in @NatureBiotech : scPrisma is a framework for enhancement and filtering of topological signals from #SingleCell data based on spectral template matching. Together with Yoni Bornfeld and @mor_nitzan.
Paper: go.nature.com/3IxEk9H
Code: github.com/nitzanlab/scPrism…
scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching go.nature.com/3IxEk9H
🌮Tacco is now published in Nature Biotechnology: nature.com/articles/s41587-0… And even more importantly, the package is now also available on conda 🐍: anaconda.org/conda-forge/tac… #SingleCell #spatialomics
🌮From molecular to tissue scale (and beyond): TACCO helps you crack your (spatial) omics data!
Check out our preprint doi.org/10.1101/2022.10.02.5… and Python package github.com/simonwm/tacco
Joint work with @moriel_noa @InbalDavidi @KlughammerLab #AvivRegev @mor_nitzan et al.
🧵1/n
MagesLab retweeted
Our “epigenetic zoo” is finally published! DNA methylation profiles for 580 animal species and 2443 tissue samples provide an evolutionary perspective on the epigenome.
Full paper: nature.com/articles/s41467-0… (open access) #epigenome #evolution #crossspecies #epigenetics [1/9]
🌮TACCO would not have been possible without support from Evan Murray, Fei Chen @insitubiology, Orit Rozenblatt-Rosen, and Leslie Gaffney, and the inspiring environments at the @broadinstitute, @GeneCenter_LMU, and @HebrewU!
🌮Don't like optimal transport? Want to integrate and develop your own methods? TACCO's analysis framework can be easily wrapped around any other annotation method as well.
🌮No GPU? TACCO is based on optimal transport and runs fast without GPU using only highly optimized CPU code!
🌮Want to quickly annotate your spatial data? TACCO annotates 40 Slide-seq pucks in less than 12 minutes!
🌮No time to learn a new API? TACCO uses a scanpy-style API and can annotate your data in a single python function call.
🌮TACCO annotates in the presence of technical artefacts like high dropout levels or ambient contributions, and also predicts cell type differentiation of progenitor cells ⬇️.
🌮TACCO annotates spot-based spatial transcriptomics data compositionally and single molecule resolved spatial transcriptomics data at molecular resolution ⬇️ using a single cell reference.
🌮TACCO efficiently transfers annotations between omics datasets using optimal transport in a wide variety of use cases by representing them as mixture annotation problems, and provides efficient tools for the analysis and visualisation of (spatial) omics data
🌮From molecular to tissue scale (and beyond): TACCO helps you crack your (spatial) omics data!
Check out our preprint doi.org/10.1101/2022.10.02.5… and Python package github.com/simonwm/tacco
Joint work with @moriel_noa @InbalDavidi @KlughammerLab #AvivRegev @mor_nitzan et al.
🧵1/n