ProtoCloud : a generative prototypical self-explanatory model for cell type annotation
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Guo, Kaiyun
issued: 2025-09-30T07:00:00Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Single-cell RNA sequencing has fundamentally transformed our understanding of cellular diversity within tissues. The complexity of scRNA-seq data presents several challenges, including complex batch effects, varying sequencing depths, and ambient RNA contamination. Effective data analysis requires robust computational tools, with cell type annotation being a critical component. As the scale of the scRNA-seq dataset grows, several deep-learning models have been developed to offer automated and accurate cell type annotations. However, the lack of interpretability and explainability in these black-box models makes it difficult to understand the reasoning behind their decision-making processes and introduces concerns regarding the reliability of the results. To address this drawback, we propose ProtoCloud, a generative self-explaining model based on prototypical variational autoencoders. The model simultaneously learns cell-type representational prototypes, ensuring interpretability throughout the training process. Additionally, it quantifies the certainty of each prediction by calculating a similarity score between the cell embedding and the prototypes. The classification results demonstrate that ProtoCloud outperforms non-interpretable state-of-the-art methods in cell type annotation, as evidenced by the accuracy, macro F1 score, and Cohen's kappa score on seven extit{in vivo} datasets. To facilitate the explainability of ProtoCloud, this thesis extends layerwise relevance propagation (LRP) to encompass the case of linear layers. By applying LRP on prototypes, ProtoCloud not only underscores known marker genes but also identifies potentially relevant genes and transcriptional signatures. The model has been tested under a variety of experimental conditions in different scenarios, including datasets sourced from various technologies and organs. Its performance has been proven to be robust and consistent, providing accurate and reliable explanations. Finally, ProtoCloud …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89164
note: ProtoCloud: A GenerativePrototypical Self-explanatory Modelfor Cell Type AnnotationbyKaiyun GuoA THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)August 2024© Kaiyun Guo 2024The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:ProtoCloud: A Generative Prototypical Self-explanatory Modelfor Cell Type Annotationsubmitted by Kaiyun Guo in partial fulfillment of the requirements for thedegree of Master of Science in Computer Science.Examining Committee:Jiarui Ding, Assistant Professor, Department of Computer Science, UBCSupervisorAnne Condon, Professor, Department of Computer Science, UBCSupervisory Committee MemberiiAbstractSingle-cell RNA sequencing has fundamentally transformed our understand-ing of cellular diversity within tissues. The complexity of scRNA-seq datapresents several challenges, including complex batch effects, varying sequenc-ing depths, and ambient RNA contamination. Effective data analysis re-quires robust computational tools, with cell type annotation being a criticalcomponent. As the scale of the scRNA-seq dataset grows, several deep-learning models have been developed to offer automated and accurate celltype annotations. However, the lack of interpretability and explainabilityin these black-box models makes it difficult to understand the reasoning be-hind their decision-making processes and introduces concerns regarding thereliability of the results.To address this drawback, we propose ProtoCloud, a generative self-explainingmodel based on prototypical variational autoencoders. The model simulta-neously learns cell-type representational prototypes, ensuring interpretabil-ity throughout the training process. Additionally, it quantifies the certaintyof each prediction by calculating a similari…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0445300
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Ding, Jiarui
type: Text
identifier URI: http://hdl.handle.net/2429/89164