Unsupervised trajectory inference using graph mining
| Autor: | Leen De Baets, Yvan Saeys, Sofie Van Gassen, Tom Dhaene |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
0301 basic medicine
Technology and Engineering Computer science business.industry Cellular differentiation Inference Pattern recognition 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Graph (abstract data type) IBCN False positive rate Artificial intelligence Cluster analysis business 030217 neurology & neurosurgery |
| Zdroj: | Computational Intelligence Methods for Bioinformatics and Biostatistics, Lecture Notes in Bioinformatics Computational Intelligence Methods for Bioinformatics and Biostatistics ISBN: 9783319443317 CIBB |
| Popis: | Cell differentiation is a complex dynamic process and although the main cellular states are well studied, the intermediate stages are often still unknown. Single cell data (such as obtained by flow cytometry) is typically analysed by clustering the cells into distinct cell types, which does not model these gradual changes. Alternative approaches that explicitly model such gradual changes using seriation methods seems promising, but are only able to model a single differentiation pathway. In this paper, we introduce a new, graph-based approach that is able to model multiple branching differentiation pathways as continuous trajectories. Results on synthetic and real data show that this is a promising approach which is moreover robust to parameter changes. |
| Databáze: | OpenAIRE |
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