Spatial Single Cell Analysis in Python
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Updated
Aug 19, 2024 - Python
Spatial Single Cell Analysis in Python
Tools for computational pathology
DANCE: a deep learning library and benchmark platform for single-cell analysis
Spatial-eXpression-R: Cell type identification (including cell type mixtures) and cell type-specific differential expression for spatial transcriptomics
R toolkit for inference, visualization and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
Haplotype-aware CNV analysis from single-cell RNA-seq
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
Technology-invariant pipeline for spatial omics analysis (Xenium / MERSCOPE / CosMx / PhenoCycler / MACSima / Hyperion) that scales to millions of cells
HEST: Bringing Spatial Transcriptomics and Histopathology together
Code for the spatialLIBD R/Bioconductor package and shiny app
Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).
Open-ST: profile and analyze tissue transcriptomes in 3D with high resolution in your lab
Spatial-Linked Alignment Tool
From geospatial to spatial -omics
Spatial Transcriptomics human DLPFC pilot study part of the spatialLIBD project
spatial transcriptome, single cell
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