ttgump/spaVAE
Dependency-aware deep generative models for multitasking analysis of spatial genomics data
This tool helps computational biologists and bioinformaticians analyze complex spatial genomics data. It takes raw spatial transcriptomics, ATAC-seq, or multi-omics data as input, and outputs insights like refined gene expression patterns, cell clusters, batch-corrected data, and identified spatially variable genes. Researchers can use it to better understand cell organization and function within tissues.
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Use this if you need to perform multiple analytical tasks on spatial genomics data, such as dimensionality reduction, clustering, batch integration, denoising, or detecting spatially variable features, and require models that account for spatial dependencies.
Not ideal if your data is not spatially resolved or if you only need very basic, non-spatial analyses that don't require advanced deep generative models.
Stars
43
Forks
4
Language
Jupyter Notebook
License
Apache-2.0
Last pushed
Jul 10, 2024
Commits (30d)
0
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