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Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data

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Item Type:Article
Title:Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data
Creators Name:Ocone, A. and Haghverdi, L. and Mueller, N.S. and Theis, F.J.
Abstract:MOTIVATION: High-dimensional single-cell snapshot data are becoming widespread in the systems biology community, as a mean to understand biological processes at the cellular level. However, as temporal information is lost with such data, mathematical models have been limited to capture only static features of the underlying cellular mechanisms. RESULTS: Here, we present a modular framework which allows to recover the temporal behaviour from single-cell snapshot data and reverse engineer the dynamics of gene expression. The framework combines a dimensionality reduction method with a cell time-ordering algorithm to generate pseudo time-series observations. These are in turn used to learn transcriptional ODE models and do model selection on structural network features. We apply it on synthetic data and then on real hematopoietic stem cells data, to reconstruct gene expression dynamics during differentiation pathways and infer the structure of a key gene regulatory network. AVAILABILITY AND IMPLEMENTATION: C++ and Matlab code available at https://www.helmholtz-muenchen.de/fileadmin/ICB/software/inferenceSnapshot.zip.
Keywords:Algorithms, Gene Expression Profiling, Gene Regulatory Networks, Genetic Models, Hematopoiesis, Hematopoietic Stem Cells, Kinetics, Single-Cell Analysis, Systems Biology
Publisher:Oxford University Press
Page Range:i89-i96
Date:15 June 2015
Official Publication:https://doi.org/10.1093/bioinformatics/btv257
PubMed:View item in PubMed

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