There are 23 repositories under spatial-transcriptomics topic.
Tools for computational pathology
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
Spatiotemporal modeling of spatial transcriptomics
Python package to perform enrichment analysis from omics data.
Bayesian Segmentation of Spatial Transcriptomics Data
a spatial deconvolution method based on deep learning frameworks, which converts bulk transcriptomes into spatially resolved single-cell expression profiles
Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).
Code for the spatialLIBD R/Bioconductor package and shiny app
From geospatial to spatial -omics
spatial transcriptome, single cell
Spatial Transcriptomics human DLPFC pilot study part of the spatialLIBD project
ST Pipeline contains the tools and scripts needed to process and analyze the raw files generated with the Spatial Transcriptomics method in FASTQ format.
characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities
Technology-invariant pipeline for spatial-omics analysis (Xenium / MERSCOPE / CosMx / PhenoCycler / MACSima / Hyperion) that scales to millions of cells
Pipeline for processing spatially-resolved gene counts with spatial coordinates and image data. Designed for 10x Genomics Visium transcriptomics.
ClusterMap for multi-scale clustering analysis of spatial gene expression
Deciphering tumor ecosystems at super-resolution from spatial transcriptomics with TESLA
Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices
Beyondcell is a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq and Spatial Transcriptomics data.
WebGL-based viewer for spatially-resolved transcriptomics data
MUSE is a deep learning approach characterizing tissue composition through combined analysis of morphologies and transcriptional states for spatially resolved transcriptomics data.
A Python implementation of the model described in our publication "A convolutional neural network for common-coordinate registration of high-resolution histology images" developed principally for applications to registration of spatial transcriptomics image data.
'Best Practices for Spatial Transcriptomics Analysis with Bioconductor' online book
Code for the "Spatial genomics maps the structure, nature and evolution of cancer clones" paper