In the post-genomic era of big data in biology, computational approaches to integrate multiple heterogeneous data sets become increasingly important. Despite the availability of large amounts of omics data, the prioritisation of genes relevant for a specific functional pathway based on genetic screening experiments, remains a challenging task. Here, we introduce netprioR, a probabilistic generative model for semi-supervised integrative prioritisation of hit genes. The model integrates multiple network data sets representing gene–gene similarities and prior knowledge about gene functions from the literature with gene-based covariates, such as phenotypes measured in genetic perturbation screens, for example, by RNA interference or CRISPR/Cas9. We evaluate netprioR on simulated data and show that the model outperforms current state-of-the-art methods in many scenarios and is on par otherwise. In an application to real biological data, we integrate 22 network data sets, 1784 prior knowledge class labels and 3840 RNA interference phenotypes in order to prioritise novel regulators of Notch signalling in Drosophila melanogaster. The biological relevance of our predictions is evaluated using in silico and in vivo experiments. An efficient implementation of netprioR is available as an R package at http://bioconductor.org/packages/netprioR.

中文翻译：

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netprioR：遗传筛选综合命中优先级的概率模型

在生物学大数据的后基因组时代，整合多个异构数据集的计算方法变得越来越重要。尽管可以获得大量组学数据，但基于遗传筛选实验对与特定功能途径相关的基因进行优先排序仍然是一项具有挑战性的任务。在这里，我们介绍netprior，一种概率生成模型，用于对命中基因进行半监督综合优先级排序。该模型将代表基因-基因相似性的多个网络数据集以及来自文献的基因功能的先验知识与基于基因的协变量（例如通过 RNA 干扰或 CRISPR/Cas9 在遗传扰动筛选中测量的表型）整合在一起。我们评估netprior在模拟数据上，并表明该模型在许多情况下都优于当前最先进的方法，并且在其他情况下是相当的。在对真实生物数据的应用中，我们整合了 22 个网络数据集、1784 个先验知识类别标签和 3840 个 RNA 干扰表型，以便优先考虑黑腹果蝇中 Notch 信号传导的新调节因子。我们预测的生物学相关性使用以下方法评估计算机和体内实验。有效实施netprior可作为 R 包在http://bioconductor.org/packages/netprioR.