Monet: An open-source Python package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces
Author(s): Florian Wagner, Department of Medicine, University of Chicago
Environment
Python
Ubuntu
Packages:
Monet, Wget, Monet, Numpy, Pandas, Plotly, scikit-learn
Code
Python using Jupyter and Jupterlab
Data
Single cell gene expression data from human peripheral blood mononuclear cells (PBMCs)
Results
What's inside
Single-cell RNA sequencing (scRNA-seq) can reveal complex and rare cell populations, uncover regulatory relationships between genes, and track the trajectories of distinct cell lineages in development. However, noisy and high-dimensional data are challenging to analyze.
The Capsule uses Jupyter notebooks and provides tutorials for how Monet can be used to analyze scRNA-Seq data, e.g. for generating simple t-SNE visualizations.
Who uses it
Computational Biologists and scientists analyzing single cell RNA sequencing data in basic as well as applied research
Why we like this
Powerful tool to analyze and integrate noisy, high-dimensional single-cell RNA sequencing data Powerful tool to analyze and integrate noisy, high-dimensional single-cell RNA sequencing data
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For any questions about the Compute Capsule, please contact us at [email protected].
