SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses.

Genome-wide transcriptome profiling identifies genes that are prone to differential expression (DE) across contexts, as well as genes with changes specific to the experimental manipulation. Distinguishing genes that are specifically changed in a context of interest from common differentially expressed genes (DEGs) allows more efficient prediction of which genes are specific to a given biological process under scrutiny. Currently, common DEGs or pathways can only be identified through the laborious manual curation of experiments, an inordinately time-consuming endeavor. Here we pioneer an approach, Specific cOntext Pattern Highlighting In Expression data (SOPHIE), for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be generated. We apply SOPHIE to diverse datasets including those from human, human cancer, and bacterial pathogen Pseudomonas aeruginosa. SOPHIE identifies common DEGs in concordance with previously described, manually and systematically determined common DEGs. Further molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional changes. SOPHIE’s measure of specificity can complement log2 fold change values generated from traditional DE analyses. For example, by filtering the set of DEGs, one can identify genes that are specifically relevant to the experimental condition of interest. Consequently, these results can inform future research directions. All scripts used in these analyses are available at https://github.com/greenelab/generic-expression-patterns. Users can access https://github.com/greenelab/sophie to run SOPHIE on their own data.

全基因组转录组分析鉴定出在不同情境下易于差异表达（DE）的基因，以及因实验操作而发生特定变化的基因。将在特定研究情境下发生特异性变化的基因与常见的差异表达基因（DEGs）区分开来，可以更有效地预测哪些基因对于所研究的特定生物过程具有特异性。目前，常见的差异表达基因或通路只能通过费力的人工整理实验来确定，这是一项极其耗时的工作。在此，我们开创了一种方法，即表达数据中的特定情境模式突出显示（SOPHIE），利用生成式神经网络区分常见和特定的转录模式，创建一组实验背景，从中可以生成基因和通路变化的零分布。我们将SOPHIE应用于多种数据集，包括来自人类、人类癌症以及细菌病原体铜绿假单胞菌的数据。SOPHIE鉴定出的常见差异表达基因与先前描述的、通过人工和系统确定的常见差异表达基因一致。进一步的分子验证表明，SOPHIE能够检测到高度特异性但幅度较低的具有生物学相关性的转录变化。SOPHIE的特异性度量可以补充传统差异分析产生的log2倍变化值。例如，通过对差异表达基因集进行筛选，可以鉴定出与所关注的实验条件特异性相关的基因。因此，这些结果可以为未来的研究方向提供参考。这些分析中使用的所有脚本可在https://github.com/greenelab/generic - expression - patterns获取。用户可以访问https://github.com/greenelab/sophie在自己的数据上运行SOPHIE。