GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1823
- Type
- D - Journal article
- DOI
-
10.1038/s41467-018-05988-7
- Title of journal
- Nature Communications
- Article number
- ARTN 3685
- First page
- -
- Volume
- 9
- Issue
- 1
- ISSN
- 2041-1723
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In this work we show how to embed the expression profiles of individual cells derived from single-cell RNA-seq experiments in an intuitive way in 2D. This allows biologists to identify the progress of specific subpopulations in their differentiation process.
Works derived by our paper include applications to analyse the immune system and the identification of bipotent-like cells associated with breast cancer risk and outcome (10.1038/s42003-019-0554-8).
The tools developed in this work have been integrated in the popular Galaxy platform (https://galaxyproject.org) for accessible, reproducible and collaborative biomedical analyses and can now be used in a simple way by biologists.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -