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Title: Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty
Background: Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty. Results:The goal of this paper is to develop optimal classification of single-cell trajectories accounting for potential model uncertainty. Partially-observed Boolean dynamical systems (POBDS) are used for modeling gene regulatory networks observed through noisy gene-expression data. We derive the exact optimal Bayesian classifier (OBC) for binary classification of single-cell trajectories. The application of the OBC becomes impractical for large GRNs, due to computational and memory requirements. To address this, we introduce a particle-based single-cell classification method that is highly scalable for large GRNs with much lower complexity than the optimal solution. Conclusion:The performance of the proposed particle-based method is demonstrated through numerical experiments using a POBDS model of the well-known T-cell large granular lymphocyte (T-LGL) leukemia network with noisy time-series gene-expression  more » « less
Award ID(s):
1718924
NSF-PAR ID:
10108039
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
BMC genomics
Volume:
20
Issue:
S6
ISSN:
1471-2164
Page Range / eLocation ID:
435
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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