SpatialData is a user-friendly computational framework developed by the Stegle, Theis, and Moore labs for exploring, analyzing, annotating, aligning, and storing spatial omics data. The framework can handle large multimodal datasets seamlessly. Additionally, SpatialData establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations, and alignment to common coordinate systems. SpatialDDLS, an R package, is also introduced to deconvolute spatial transcriptomics data using neural networks.
SpatialData: an open and universal data framework for spatial omics | Nature Methods https://t.co/paALp66HK6 #Bioinformatics
SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks | Bioinformatics https://t.co/qoYHa6xXZE #Bioinformatics https://t.co/Y0xZbLQCgL
SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems.
SpatialData: an open and universal data framework for spatial omics https://t.co/RVSi7R9F51 https://t.co/liGq53bu2E
Interpretable Spatial Gradient Analysis for Spatial Transcriptomics Data https://t.co/pCPpopeIYu #biorxiv_bioinfo
Out today from the Stegle, Theis, and Moore labs! SpatialData is a user-friendly computational framework for exploring, analyzing, annotating, aligning and storing spatial omics data that can seamlessly handle large multimodal datasets. https://t.co/ReCFeXVFoS https://t.co/hvkdhf62NK