Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors
- DOI
- 10.1186/s13059-023-03077-7
- Published
- 2023-10-20
- Container
- Genome Biology
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s13059-023-03077-7,
title = {Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors},
author = {Ariel A. Hippen and Dalia K. Omran and Lukas M. Weber and Euihye Jung and Ronny Drapkin and Jennifer A. Doherty and Stephanie C. Hicks and Casey S. Greene},
year = {2023},
journal = {Genome Biology},
doi = {10.1186/s13059-023-03077-7},
url = {https://doi.org/10.1186/s13059-023-03077-7}
}RIS
TY - JOUR TI - Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors AU - Ariel A. Hippen AU - Dalia K. Omran AU - Lukas M. Weber AU - Euihye Jung AU - Ronny Drapkin AU - Jennifer A. Doherty AU - Stephanie C. Hicks AU - Casey S. Greene PY - 2023 JO - Genome Biology DO - 10.1186/s13059-023-03077-7 UR - https://doi.org/10.1186/s13059-023-03077-7 ER -
APA
Hippen, A. A., Omran, D. K., Weber, L. M., Jung, E., Drapkin, R., Doherty, J. A., Hicks, S. C., & Greene, C. S. (2023). Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors. Genome Biology. https://doi.org/10.1186/s13059-023-03077-7
Source records
- crossref · retrieved 2026-09-26T02:12:36.616Z