Thrilled to announce our new Nature Methods paper: Giotto Suite: an extensible, technology-agnostic ecosystem for spatial multiomics analysis. Big thanks to co-first authors @jiaji_g_chen & @josschavezf1, and all Giotto devs in @RnDries & @gc_yuan labs!

Our team will be doing a hands-on workshop in August and showcase both basic and advanced spatial omics data analysis!

Our website also includes many other examples from spatial technologies and datasets that we couldn't all highlight in our manuscript, but that are certainly worth exploring, including @SpatialGenomics, @nanostringtech #CosMx, #SeqScope, #DBiTseq and others

For more information you can visit our website at https://t.co/4OxY54NJsS. We are working very hard to further improve the website and add additional vignettes, and also lots of practical information for both first-time users and external developers.

To demonstrate its power we showcase how our new framework, together with existing spatial interpolation tools (kriging), allowed us to quickly build a method to super-enhance the resolution of spatial domains from a #Visium dataset (from 55 µm to single-cells)

Last, but not least, we seek to work on top of the shoulders of giants and tried to integrate Giotto Suite into the much larger and established (geo)spatial ecosystem in R, including #terra, #sf, and #stars.

We made major efforts to make Giotto Suite interoperable with upstream and downstream tools. This also includes other popular spatial data software, including #SpatialExperiment, #Seurat, and #AnnData. We hope to expand to other more recent developments soon

We also developed #RShiny tools to offer users an interactive exploratory data analysis experience, including selecting spatial regions or in silico slicing of 3D data.

With this dataset we demonstrate our flexible tilling and binning methods that allow you to work at multiple scales or create pseudo-Visium data with no effort.

In another vignette we utilize the @ST_Omics (stereo-seq) MOSTA dataset to demonstrate working with large-scale data.

We also implemented flexible data structures to work with spatial multi-modal data such as that generated by the #Visium CytAssist from @10xGenomics .

In another example we demonstrate how Giotto Suite can be used to jointly test multiple #segmentation methods and notice that this can result in significant changes in cell type distributions.

In one example we demonstrate how we used STalign from @JEFworkslab and systematically assessed the spatial correlation between #Xenium and #Visium expression levels.

Using multi-modal spatial data from the @10xGenomics #Xenium Breast Cancer dataset we illustrate how Giotto Suite supports various segmentation and registration approaches.

In our manuscript we show both simple and more advanced analyses, such as working with spatial data at multiple scales, including at the individual transcript level as demonstrated with #MERSCOPE data from @vizgen_inc.

Key in designing our core framework was the separation of features (i.e. molecular analytes) and spatial units (e.g. cells, spots), and the creation of objects that can work with any type of data. Together they facilitate working and integrating any type of (spatial) omics data.

Our dedicated Giotto team, led by the amazing @jiaji_g_chen & @josschavezf1 from the @RnDries and @gc_yuan labs, created a modular suite of R packages that can represent and analyze virtually any dataset.

Dear spatial afficionados, check out our latest contribution to the spatial data analysis landscape. In this work we present Giotto Suite, our completely new ecosystem designed to work with the most exciting and challenging spatial datasets.

Last minute deals! I will be giving a major update on Giotto Suite at 11 am today (!) for the VUES on Spatial Biology group. Please, share with anyone interested. Registration required: https://t.co/oZCBrdIBy7 More exciting updates will follow very soon!

Three members of our team presented Giotto Suite (v3.3.1) at the annual @Bioconductor meeting in Boston! The presentation and demo code can be found at https://t.co/a5gxtIZuij