دورية أكاديمية
A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics
| العنوان: | A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics |
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| المؤلفون: | Felipe Segato Dezem, Maycon Marção, Bassem Ben-Cheikh, Nadya Nikulina, Ayodele Omotoso, Destiny Burnett, Priscila Coelho, Judith Hurley, Carmen Gomez, Tien Phan-Everson, Giang Ong, Luciano Martelotto, Zachary R. Lewis, Sophia George, Oliver Braubach, Tathiane M. Malta, Jasmine Plummer |
| المصدر: | BMC Genomics, Vol 24, Iss 1, Pp 1-12 (2023) |
| بيانات النشر: | BMC, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Biotechnology LCC:Genetics |
| مصطلحات موضوعية: | Single cell, Spatial, Machine learning, Cancer stem, Proteomic, Transcriptomic, Biotechnology, TP248.13-248.65, Genetics, QH426-470 |
| الوصف: | Abstract Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2164 |
| Relation: | https://doaj.org/toc/1471-2164 |
| DOI: | 10.1186/s12864-023-09722-6 |
| URL الوصول: | https://doaj.org/article/b9b9187442f749489a02407b0cc07d3c |
| رقم الأكسشن: | edsdoj.b9b9187442f749489a02407b0cc07d3c |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 14712164 |
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| DOI: | 10.1186/s12864-023-09722-6 |