spatial-transcriptomics

Spatial Single Cell Analysis in Python

Tools for computational pathology

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

Bayesian Segmentation of Spatial Transcriptomics Data

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

characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities

Code for the spatialLIBD R/Bioconductor package and shiny app

WebGL-based viewer for spatially-resolved transcriptomics data

ST Pipeline contains the tools and scripts needed to process and analyze the raw files generated with the Spatial Transcriptomics method in FASTQ format.

Spatial Transcriptomics human DLPFC pilot study part of the spatialLIBD project

spatial transcriptome, single cell

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.

Generate the UMI count matrix from CEL-Seq2 sequencing data

ClusterMap for multi-scale clustering analysis of spatial gene expression

Pipeline for SpatialTranscriptomics and 10X Visium data

Reconstruction of spatial context of gene expression with optimal transport

Multiview Intercellular SpaTial modeling framework

A toolset for analysis and visualisation of Spatial Transcriptomics datasets.

Python package to infer biological activities from omics data using a collection of methods.

A probabilistic cell typing algorithm for spatial transcriptomics.

"Orchestrating Spatially Resolved Transcriptomics Analysis with Bioconductor" (OSTA) online book

Accurate and fast cell marker gene identification with COSG

A deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics

Accurate and fast cell marker gene identification with COSG

Interactive software tool for the assignment of cell types in single-cell studies.

Links to interesting courses, tutorials, papers, software etc. related to single-cell omics data analysis

ClusterMap for multi-scale clustering analysis of spatial gene expression

A GUI tool for easy and smooth visualisation and analysis of Spatial Transcriptomics datasets

R package for decontaminating the spot swapping effect and recovering true expression in spatial transcriptomics data