Using Spatial Information of Cells in Clustering Cells of Transcriptomics Samples

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Faez, Sabereh | 2021

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54252 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Rabiee, Hamid Reza; Rohban, Mohammad Hossein
Abstract:
Spatial transcriptomics is a new technology that, in addition to transcriptomic cell information, provides spatial information for each of the sample cells and, if possible, histological images of the cells. Despite much research on cell indexing, little research has been done on using cell spatial information to cluster cells, and existing methods can be improved. The aim of this study is to use cell spatial data to extract more information from the samples and to better identify the cell conditions in the images, leading to better clustering than current methods. In the proposed method, in order to use spatial location data and transcriptomics simultaneously, the samples are modeled using a graph and then clustering is performed on the vertices of the graph. Finally, the efficiency of the proposed method is evaluated on 12 brain samples using the adjusted rand index criterion and compared with a number of existing methods and it is shown that the proposed method has significantly improved compared to these previous methods. Also, the speed of the proposed method of this research is significantly higher than the previous best work compared
Keywords:
Machine Learning ; Clustering ; Spatial Transcriptomics ; Spatial Information