https://github.com/KChen-lab/cyclum

Identify circular trajectories in scRNA-seq data using an autoencoder with sinusoidal activations
https://github.com/KChen-lab/cyclum

artifical-neural-network latent-process-inference single-cell-rna-seq trajectory-inference-methods

Last synced: 24 days ago
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Identify circular trajectories in scRNA-seq data using an autoencoder with sinusoidal activations

• Host: GitHub
• URL: https://github.com/KChen-lab/cyclum
• Owner: KChen-lab
• License: mit
• Created: 2019-03-12T18:28:36.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-12-17T00:49:45.000Z (over 2 years ago)
• Last Synced: 2024-02-24T12:38:58.732Z (4 months ago)
• Topics: artifical-neural-network, latent-process-inference, single-cell-rna-seq, trajectory-inference-methods
• Language: Python
• Homepage:
• Size: 129 MB
• Stars: 19
• Watchers: 2
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome_single_cell - cyclum - [python] - Cyclum is a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. [bioRxiv](https://www.biorxiv.org/content/10.1101/625566v1) (Software packages / RNA-seq)
• awesome-single-cell - cyclum - [python] - Cyclum is a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. [bioRxiv](https://www.biorxiv.org/content/10.1101/625566v1) (Software packages / RNA-seq)
• awesome-single-cell - cyclum - [python] - Cyclum is a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. [bioRxiv](https://www.biorxiv.org/content/10.1101/625566v1) (Software packages / RNA-seq)