Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 1.68 MB
Add to list
  1. Search results
  2. SPECS: a non-parametric method to ide...

SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups

Celine Everaert (UGent) , Pieter-Jan Volders (UGent) , Annelien Morlion (UGent) , Olivier Thas (UGent) and Pieter Mestdagh (UGent)
(2020) BMC Bioinformatics. 21(1).
Author
Organization
Abstract
Background To understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they can’t handle replicates at all. Results We describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be. Conclusions SPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications.
Keywords
Biochemistry, Applied Mathematics, Molecular Biology, Structural Biology, Computer Science Applications

Downloads

  • Download
    s12859-020-3407-z.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 1.68 MB

Citation

Please use this url to cite or link to this publication:

MLA
Everaert, Celine, et al. “SPECS: A Non-Parametric Method to Identify Tissue-Specific Molecular Features for Unbalanced Sample Groups.” BMC Bioinformatics, vol. 21, no. 1, BMC, 2020, doi:10.1186/s12859-020-3407-z.
APA
Everaert, C., Volders, P.-J., Morlion, A., Thas, O., & Mestdagh, P. (2020). SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups. BMC Bioinformatics, 21(1). https://doi.org/10.1186/s12859-020-3407-z
Chicago author-date
Everaert, Celine, Pieter-Jan Volders, Annelien Morlion, Olivier Thas, and Pieter Mestdagh. 2020. “SPECS: A Non-Parametric Method to Identify Tissue-Specific Molecular Features for Unbalanced Sample Groups.” BMC Bioinformatics 21 (1). https://doi.org/10.1186/s12859-020-3407-z.
Chicago author-date (all authors)
Everaert, Celine, Pieter-Jan Volders, Annelien Morlion, Olivier Thas, and Pieter Mestdagh. 2020. “SPECS: A Non-Parametric Method to Identify Tissue-Specific Molecular Features for Unbalanced Sample Groups.” BMC Bioinformatics 21 (1). doi:10.1186/s12859-020-3407-z.
Vancouver
1.
Everaert C, Volders P-J, Morlion A, Thas O, Mestdagh P. SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups. BMC Bioinformatics. 2020;21(1).
IEEE
[1]
C. Everaert, P.-J. Volders, A. Morlion, O. Thas, and P. Mestdagh, “SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups,” BMC Bioinformatics, vol. 21, no. 1, 2020.
@article{8658757,
  abstract     = {{Background
To understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they can’t handle replicates at all.
Results
We describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be.
Conclusions
SPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications.}},
  articleno    = {{58}},
  author       = {{Everaert, Celine and Volders, Pieter-Jan and Morlion, Annelien and Thas, Olivier and Mestdagh, Pieter}},
  issn         = {{1471-2105}},
  journal      = {{BMC Bioinformatics}},
  keywords     = {{Biochemistry,Applied Mathematics,Molecular Biology,Structural Biology,Computer Science Applications}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BMC}},
  title        = {{SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups}},
  url          = {{http://doi.org/10.1186/s12859-020-3407-z}},
  volume       = {{21}},
  year         = {{2020}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: