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Biochim Biophys Acta Gene Regul Mech. 2019 Nov 20:194441. doi: 10.1016/j.bbagrm.2019.194441. [Epub ahead of print]

Data generation and network reconstruction strategies for single cell transcriptomic profiles of CRISPR-mediated gene perturbations.

Author information

1
Department of Biology, University of York, York, UK; York Biomedical Research Institute, University of York, York, UK; CRUK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK; The Alan Turing Institute, 96 Euston Road, Kings Cross, London, UK.
2
Department of Biology, University of York, York, UK.

Abstract

Recent advances in single-cell RNA-sequencing (scRNA-seq) in combination with CRISPR/Cas9 technologies have enabled the development of methods for large-scale perturbation studies with transcriptional readouts. These methods are highly scalable and have the potential to provide a wealth of information on the biological networks that underlie cellular response. Here we discuss how to overcome several key challenges to generate and analyse data for the confident reconstruction of models of the underlying cellular network. Some challenges are generic, and apply to analysing any single-cell transcriptomic data, while others are specific to combined single-cell CRISPR/Cas9 data, in particular barcode swapping, knockdown efficiency, multiplicity of infection and potential confounding factors. We also provide a curated collection of published data sets to aid the development of analysis strategies. Finally, we discuss several network reconstruction approaches, including co-expression networks and Bayesian networks, as well as their limitations, and highlight the potential of Nested Effects Models for network reconstruction from scRNA-seq data. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

KEYWORDS:

CRISPR; Cas9; Machine learning; Nested Effects Models; Network biology; Networks; Sequencing; Single-cell genomics; scRNA-seq

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