Combining Pathway Analysis and Supervised Machine Learning for the Functional Classification of Single-Cell Transcriptomic Data
| Autor: | Ajdini Bajram, Chara Mastrokalou, Eleftherios Pilalis, Thodoris Koutsandreas, Aristotelis Chatziioannou, Ilias Maglogiannis |
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| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
Cell type business.industry Computer science Semantic analysis (machine learning) Cell Supervised learning Construct (python library) Machine learning computer.software_genre Expression (mathematics) Transcriptome 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure medicine Feature (machine learning) Artificial intelligence business computer 030217 neurology & neurosurgery 030304 developmental biology |
| Zdroj: | BIBE |
| Popis: | The revolution of single-cell technologies established a novel framework to investigate gene expression profiles in the level of individual cells. Scientists are able to investigate the biological variability of the same tissue, producing isolated transcriptomic data for each single cell. As a result, each transcriptomic experiment could extract a unique expression profile for each cell, posing new challenges in the translation analysis of all these profiles. Pathway analysis tools need to be adapted, not only to analyze simultaneously numerous gene expression profiles, but also to compare them, detecting functional differences and commonalities among the cells of the same issue, separating them to functional subclusters. In this study, we used the output of a single-cell experiment in the hematopoietic system, in order to determine a novel framework for the functional comparison of single cells, based on their pathway analysis with Gene Ontology annotation. Thousands of expression profiles of single cells, congregated in 15 different hematopoietic classes, were translated into networks of significant biological mechanisms, through the use of BioInfoMiner platform. We propose a novel framework to exploit these results and construct appropriate feature spaces of functional omponents, with a view to perform supervised learning to different hematopoietic cell types and separate their respective single cells, according to their functional profile. The constructed classification model performed interestingly high precision and sensitivity scores for some cell types, while the overall performance needs to be improved with further conceptual and technical refinements. |
| Databáze: | OpenAIRE |
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