https://github.com/CABSEL/CALISTA

CALISTA: Clustering And Lineage Inference in Single Cell Transcriptional Analysis
https://github.com/CABSEL/CALISTA

network-analysis single-cell-analysis

Last synced: 24 days ago
JSON representation

CALISTA: Clustering And Lineage Inference in Single Cell Transcriptional Analysis

• Host: GitHub
• URL: https://github.com/CABSEL/CALISTA
• Owner: CABSEL
• License: bsd-3-clause
• Created: 2018-01-26T10:35:58.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-05-16T14:20:18.000Z (about 5 years ago)
• Last Synced: 2024-02-24T12:37:40.592Z (4 months ago)
• Topics: network-analysis, single-cell-analysis
• Language: HTML
• Size: 102 MB
• Stars: 8
• Watchers: 5
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome_single_cell - CALISTA - [R] - CALISTA provides a user-friendly toolbox for the analysis of single cell expression data. CALISTA accomplishes three major tasks: 1) Identification of cell clusters in a cell population based on single-cell gene expression data, 2) Reconstruction of lineage progression and produce transition genes, and 3) Pseudotemporal ordering of cells along any given developmental paths in the lineage progression. (Software packages / RNA-seq)
• awesome-single-cell - CALISTA - [R] - CALISTA provides a user-friendly toolbox for the analysis of single cell expression data. CALISTA accomplishes three major tasks: 1) Identification of cell clusters in a cell population based on single-cell gene expression data, 2) Reconstruction of lineage progression and produce transition genes, and 3) Pseudotemporal ordering of cells along any given developmental paths in the lineage progression. (Software packages / Pseudotime and trajectory inference)
• awesome-single-cell - CALISTA - [R] - CALISTA provides a user-friendly toolbox for the analysis of single cell expression data. CALISTA accomplishes three major tasks: 1) Identification of cell clusters in a cell population based on single-cell gene expression data, 2) Reconstruction of lineage progression and produce transition genes, and 3) Pseudotemporal ordering of cells along any given developmental paths in the lineage progression. (Software packages / RNA-seq)

README

        


CALISTA provides a user-friendly toolbox for the analysis of single cell expression data. 

Please visit [CALISTA website](https://www.cabselab.com/calista) for more info about installation and how to run CALISTA.

## License

Redistribution and use in source and binary forms, with or without modification, are permitted provided agreeing to the BSD 3-Clause Style [License](https://github.com/CABSEL/CALISTA/blob/master/LICENSE).

## References

Papili Gao N., Hartmann T., Fang T. and Gunawan R., CALISTA: Clustering And Lineage Inference in Single Cell Transcriptional Analysis. [Abstract](https://www.biorxiv.org/content/early/2018/01/31/257550)

## Acknowledgement

This work is supported by funding from Swiss National Science Foundation.