TomMakesThings/Clustering-and-TDA-of-scRNA-seq-Data
Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks
This project helps biological researchers and bioinformaticians analyze single-cell RNA sequencing (scRNA-seq) data to uncover distinct cell populations or hidden gene expression patterns. It takes your raw scRNA-seq datasets and applies various clustering and topological data analysis methods to output detailed visualizations and groupings of cells, highlighting similarities and differences within your samples. This tool is for scientists working with genomic data who need to identify cell types or states.
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Use this if you need to identify hidden cell populations or subtle gene expression signatures within complex single-cell RNA sequencing data.
Not ideal if you are looking for a fully automated, point-and-click software solution without any interaction with code.
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Jupyter Notebook
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Last pushed
Aug 08, 2021
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