Generate the UMI count matrix from CEL-Seq2 sequencing data
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Updated
Oct 12, 2018 - Python
Generate the UMI count matrix from CEL-Seq2 sequencing data
Reconstruction of spatial context of gene expression with optimal transport
Analysis scripts for the Ebbing et manuscript: "Spatially-resolved transcriptomics in C. elegans identifies sex-specific differences in gene expression patterns"
ST Pipeline contains the tools and scripts needed to process and analyze the raw files generated with the Spatial Transcriptomics method in FASTQ format.
A GUI tool for easy and smooth visualisation and analysis of Spatial Transcriptomics datasets
A toolset for analysis and visualisation of Spatial Transcriptomics datasets.
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.
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Analysis scripts for the manuscript: "Spatial transcriptomics unveils ZBTB11 as a regulator of cardiomyocyte degeneration in arrhythmogenic cardiomyopathy".
ClusterMap for multi-scale clustering analysis of spatial gene expression
MUSE is a deep learning approach characterizing tissue composition through combined analysis of morphologies and transcriptional states for spatially resolved transcriptomics data.
characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities
A collection of novel and standard statistical techniques for detecting spatially variable (SV) genes in spatial transcriptomics (ST) data.
Spatial transcriptomics of cardiac tissue 24 h after ischemic injury
Unsupervised cell type identification for spatial transcriptomics
Statistical analysis for spatial omics data
A deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics
A bare bones tutorial on how to analyse spatial transcriptomics data from raw sequencing reads to visualising spatially distinct features
Course Assignment on Clustering of Spatial Transcriptomics Data
Code for the "Spatial genomics maps the structure, nature and evolution of cancer clones" paper
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