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Multi-omic Single-cell Data Integration Using Deep Neural Networks

Omidi, Alireza | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54502 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Sharifi Zarchi, Ali
  7. Abstract:
  8. The advent and advance of single-cell technologies have enabled us to measure the cell function and identity by using different assays and viewing it by different technologies. Nowadays, we are able to measure multiple feature vectors from same- single cells from multiple abstract molecular levels (genome, transcriptome, proteome, ...) simultaneously. Hence, the analysts can view the cell from different yet correlated angles and study their behaviours. Such progress in joint single-cell assessments plus the development and spread of more common single-cell assays - that measure one feature vector per cell - caused the growing need for computational tools to integrate these datasets in order to analyze the heterogeneity of the cells more effective.In this paper, we propose Multigrate, a novel method based on variational auto-encoders (VAEs) capable of integrating paired and unpaired single-cell datasets. We evaluate Multigrate on various real-world single-cell datasets, from paired ones to unpaired ones. Furthermore, we propose another novelty by which we make Multigrate able to map query datasets to a previously-learned reference integration space. The latter novelty helps us to analyze and annotate newly coming datasets incorporating reference datasets without the need to have those reference datasets available at the query time and with a cost of a few fine-tuning iterations
  9. Keywords:
  10. Deep Neural Networks ; Variational Autoencoder ; Single Cell Sequencing ; Data Aggregation ; Multi-Omic Data ; Query to Reference Mapping

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