Spatial Single Cell Analysis in Python
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Updated
May 6, 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
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
R toolkit for inference, visualization and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics
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
Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).
Code for the spatialLIBD R/Bioconductor package and shiny app
Technology-invariant pipeline for spatial-omics analysis (Xenium / MERSCOPE / CosMx / PhenoCycler / MACSima / Hyperion) that scales to millions of cells
Spatial-Linked Alignment Tool
From geospatial to spatial -omics
Spatial Transcriptomics human DLPFC pilot study part of the spatialLIBD project
spatial transcriptome, single cell
ST Pipeline contains the tools and scripts needed to process and analyze the raw files generated with the Spatial Transcriptomics method in FASTQ format.
Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data
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