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Tutorials on how to use Monet for analyzing single-cell RNA-Seq data

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data
data
 
 
output
output
 
 
010 - Loading and saving expression data.ipynb
010 - Loading and saving expression data.ipynb
 
 
020 - Importing data from Scanpy and exporting data to Scanpy.ipynb
020 - Importing data from Scanpy and exporting data to Scanpy.ipynb
 
 
030 - Visualizing data with t-SNE.ipynb
030 - Visualizing data with t-SNE.ipynb
 
 
040 - Clustering data with Galapagos (t-SNE plus DBSCAN).ipynb
040 - Clustering data with Galapagos (t-SNE plus DBSCAN).ipynb
 
 
060 - Denoising data with ENHANCE.ipynb
060 - Denoising data with ENHANCE.ipynb
 
 
070 - Train a Monet model (for integrative analyses).ipynb
070 - Train a Monet model (for integrative analyses).ipynb
 
 
080 - Plot a batch-corrected t-SNE using mutual nearest neighbors (Haghverdi et al., 2018).ipynb
080 - Plot a batch-corrected t-SNE using mutual nearest neighbors (Haghverdi et al., 2018).ipynb
 
 
090 - Label transfer using K-nearest neighbor classification.ipynb
090 - Label transfer using K-nearest neighbor classification.ipynb
 
 
README.md
README.md
 
 
clustering.ipynb
clustering.ipynb
 
 

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Monet Tutorials

This repository contains Jupyter notebooks with tutorials for how to use Monet for analyzing scRNA-Seq data. All the data files required to run these notebooks are contained in the "data/" folder.

Copyright and License

Copyright (c) 2020 Florian Wagner

These tutorials are licensed under a Creative Commons Attribution 4.0 International License.

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Tutorials on how to use Monet for analyzing single-cell RNA-Seq data

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