Unsupervised cell type identification for spatial transcriptomics
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Updated
Jul 13, 2022 - R
Unsupervised cell type identification for spatial transcriptomics
Analysis scripts for the manuscript: "Spatial transcriptomics unveils ZBTB11 as a regulator of cardiomyocyte degeneration in arrhythmogenic cardiomyopathy".
Repo linked to our recent publication in Science Advances
This repository will continuously collect annotated spatial transcriptomic datasets. 本仓库将持续收集空间转录组数据集
A Python package for the Scalale and accurate identification condition-relevant niches from spatial -omics data.
Advanced computational framework for integrated analysis of molecular and electrical brain network dynamics at the cellular level
总结和比较目前发表的处理单细胞空间转录组学(Spatial Transcriptomic)的聚类方法。Conclusion and comparison of the current clustering method for ST data.
meta-analysis of new and published human WAT single cell data
Repository containing the computational code for Single Nucleus RNA Sequencing Demonstrates an Autosomal Dominant Alzheimer’s Disease Profile and Possible Mechanism of Disease Protection manuscript by Almeida et al.
HEST library (H&E and Spatial Transcriptomics framework)
Analysing single-cell and DSP data to test the effect of copper chelation with TEPA in Neuroblastoma mice
R package containing functions useful for stitching Visium capture areas. Related to https://github.com/LieberInstitute/LS_visiumStitched
The pipeline to process Novaseq dataset, from fastq to nge.
Visium SPG AD project (n = 10) using Visium Spatial Proteogenomics (Visium-SPG) on dissections from the inferior temporal cortex (ITC) from Alzheimer's disease cases and controls.
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MUSTANG: reference-free MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance
Awesome Deep Learning Methods in Spatial Transcriptomics
Pseudo-label supervised graph neural network for robust, fine-grained, interpretable spatial domain identification.
Powerful and lightweight package to identify tissue compartments in spatial transcriptomics datasets.
CellContrast: Reconstructing Spatial Relationships in Single-Cell RNA Sequencing Data via Deep Contrastive Learning
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