Spatial-eXpression-R: Cell type identification (including cell type mixtures) and cell type-specific differential expression for spatial transcriptomics
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Updated
Jun 4, 2024 - HTML
Spatial-eXpression-R: Cell type identification (including cell type mixtures) and cell type-specific differential expression for spatial transcriptomics
Open-ST: profile and analyze tissue transcriptomes in 3D with high resolution in your lab
Spatial Transcriptomics human DLPFC pilot study part of the spatialLIBD project
A fast and accurate deconvolution algorithm based on regularized matrix completion algorithm (ENIGMA)
an integrative algorithm to distinguish spatially variable cell subclusters by reconstructing cells onto a pseudo space with spatial transcriptome references
spatialDLPFC project involving Visium (n = 30), Visium SPG (n = 4) and snRNA-seq (n = 19) samples
A bare bones tutorial on how to analyse spatial transcriptomics data from raw sequencing reads to visualising spatially distinct features
Visium SPG AD project (n = 10) using Visium Spatial Proteogenomics (Visium-SPG) on dissections from the inferior temporal cortex (ITC) from Alzheimer's disease cases and controls.
Repo linked to our recent publication in Science Advances
This Github repository holds data, Notebooks and results of running SDePER on both Simulated and Real datasets, and Notebooks for figure panels in manuscript, as well as the codes for running other cell type deconvolution methods.
Pseudo-label supervised graph neural network for robust, fine-grained, interpretable spatial domain identification.
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