Spatial Single Cell Analysis in Python
-
Updated
Oct 14, 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
Spatiotemporal modeling of spatial transcriptomics
Haplotype-aware CNV analysis from single-cell RNA-seq
Python package to perform enrichment analysis from omics data.
Bayesian Segmentation of Spatial Transcriptomics Data
HEST: Bringing Spatial Transcriptomics and Histopathology together - NeurIPS 2024
Technology-invariant pipeline for spatial omics analysis (Xenium / Visium HD / MERSCOPE / CosMx / PhenoCycler / MACSima / ...) that scales to millions of cells
a spatial deconvolution method based on deep learning frameworks, which converts bulk transcriptomes into spatially resolved single-cell expression profiles
A Toolbox for Spatial Transcriptomics Analysis
Code for the spatialLIBD R/Bioconductor package and shiny app
Open-ST: profile and analyze tissue transcriptomes in 3D with high resolution in your lab
Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).
Spatial-Linked Alignment Tool
From geospatial to spatial -omics
Spatial Transcriptomics human DLPFC pilot study part of the spatialLIBD project
Add a description, image, and links to the spatial-transcriptomics topic page so that developers can more easily learn about it.
To associate your repository with the spatial-transcriptomics topic, visit your repo's landing page and select "manage topics."