#CORAL enables the prediction of cell-cell interactions, as demonstrated in the mouse thymus. The latent variables in the model capture spatial features that transition from the medulla to the cortex, associating with thymic development and T cell maturation. 🧬5/🪸

Given paired spatial proteomics and downsampled ST data (such as Visium), #CORAL performs comparably to #SpatialGlue in detecting functional domains, while also achieving high-res ST🔬4/🪸

Thrilled to introduce #CORAL🪸—a deep probabilistic graphical model that integrates spatial multiomics data!
🔹Resolves spatial resolution discrepancies across modalities
🔹Deconvolutes low-res data to achieve single-cell granularity
🔹Enables parallel integrative analysis 1/🪸

We applied Squidiff to model blood vessel #organoids using single-cell RNA sequencing to examine their response to neutron irradiation and pro-regenerative G-CSF treatment, simulating cosmic irradiation anticipated in #deepspace missions🧑‍🚀. 5/🦑

Given the scRNA data on day 0 and day 3, can we predict the data on day 1, day 2 and further? Squidiff predicts this differentiation of induced pluripotent stem cells (iPSCs) into mesendoderm and endoderm, guided by stimuli vectors. 3/🦑