poseidonchan/TAPE
Deep learning-based tissue compositions and cell-type-specific gene expression analysis with tissue-adaptive autoencoder (TAPE)
This project helps biological researchers and computational biologists understand the exact cellular makeup of a tissue sample and how specific genes are expressed within different cell types. By taking bulk RNA sequencing data and single-cell RNA sequencing data as input, it provides predictions of cell type proportions within a tissue and reveals which genes are active in particular cell types.
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Use this if you need to accurately determine the composition of different cell types within a tissue sample and understand gene expression specific to those cell types, even from complex bulk RNA-seq data.
Not ideal if your data does not include both single-cell reference data and bulk RNA-seq data for the same cell types.
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59
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10
Language
Jupyter Notebook
License
GPL-3.0
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Last pushed
Apr 03, 2024
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