Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Bioinformatics. 2021 May 17;37(7):968-975. doi: 10.1093/bioinformatics/btaa769.
Recent technological advances enable the profiling of spatial single-cell expression data. Such data present a unique opportunity to study cell-cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of these data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact.
We developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single-cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells.
MESSI is available at: https://github.com/doraadong/MESSI.
Supplementary data are available at Bioinformatics online.
最近的技术进步使对空间单细胞表达数据的分析成为可能。这些数据为研究细胞间相互作用以及介导这些相互作用的信号基因提供了独特的机会。然而，目前大多数用于分析这些数据的方法都侧重于无监督描述性建模，难以识别关键的信号基因并定量评估它们的影响。
我们开发了一种用于空间信号基因识别的专家混合模型（MESSI）方法，以识别细胞内和细胞间的活性信号基因。专家混合策略使 MESSI 能够将细胞细分为亚类。MESSI 依赖于使用来自相邻细胞的信息进行多任务学习，以提高对细胞内反应基因的预测。将这些方法应用于三个空间单细胞表达数据集，我们表明 MESSI 可以准确预测反应基因的水平，优于先前的方法，并提供有关关键信号基因和兴奋性神经元细胞亚型的有用生物学见解。
MESSI 可在以下网址获得：https://github.com/doraadong/MESSI。
补充数据可在 Bioinformatics 在线获得。
