Spatial Single Cell Analysis in Python

Tools for computational pathology

High dimensional weighted gene co-expression network analysis

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

Spatiotemporal modeling of spatial 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

Python package to perform enrichment analysis from omics data.

HEST: Bringing Spatial Transcriptomics and Histopathology together - NeurIPS 2024

Bayesian Segmentation of Spatial Transcriptomics Data

Technology-invariant pipeline for spatial omics analysis that scales to millions of cells (Xenium / Visium HD / MERSCOPE / CosMx / PhenoCycler / MACSima / etc)

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

Spatial-Linked Alignment Tool

Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).

From geospatial to spatial -omics