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.

中文翻译：

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CRISPR 介导的基因扰动的单细胞转录组谱的数据生成和网络重建策略。


单细胞 RNA 测序 (scRNA-seq) 与 CRISPR/Cas9 技术相结合的最新进展使得利用转录读数进行大规模扰动研究的方法得以开发。这些方法具有高度可扩展性，并且有可能提供有关细胞反应基础的生物网络的丰富信息。在这里，我们讨论如何克服几个关键挑战来生成和分析数据，以便可靠地重建底层蜂窝网络模型。一些挑战是通用的，适用于分析任何单细胞转录组数据，而其他挑战则特定于组合单细胞 CRISPR/Cas9 数据，特别是条形码交换、敲低效率、感染的多重性和潜在的混杂因素。我们还提供精选的已发布数据集，以帮助制定分析策略。最后，我们讨论了几种网络重建方法，包括共表达网络和贝叶斯网络及其局限性，并强调了嵌套效应模型在 scRNA-seq 数据网络重建方面的潜力。本文是由 Federico Manuel Giorgi 博士和 Shaun Mahony 博士编辑的特刊的一部分，题为：转录谱和调控基因网络。