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Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty.

Abstract：

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 data.

journal_name

BMC Genomics

journal_title

BMC genomics

authors

Hajiramezanali E,Imani M,Braga-Neto U,Qian X,Dougherty ER

doi

10.1186/s12864-019-5720-3

subject

Has Abstract

pub_date

2019-06-13 00:00:00

pages

435

issue

Suppl 6

issn

1471-2164

pii

10.1186/s12864-019-5720-3

journal_volume

20

pub_type

杂志文章

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