Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development
- Submitting institution
-
University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20753011
- Type
- D - Journal article
- DOI
-
10.1093/bioinformatics/btu777
- Title of journal
- Bioinformatics
- Article number
- -
- First page
- 1060
- Volume
- 31
- Issue
- 7
- ISSN
- 1367-4803
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2014
- 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
- Yes
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 25
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel methodology in this paper in inferring probabilistic Boolean gene regulatory networks from single cell data is the use of genetic algorithms for optimization. This is an important problem due to the variability in the state of each cell in a population, and this variability controls decisions made by cells during development. Work done via a collaboration with Nanyang Technological University and A* Research Institute, Singapore. In follow-up work, we have (a) demonstrated the limitations of transfer of such inference between mouse models and human data (https://tinyurl.com/v9sn8lm) and (b) derived robust outlier detection algorithms for single cell data: (https://tinyurl.com/umfw2va).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
