澳大利亚悉尼大学的Jean Yee Hwa Yang课题组提供了一种单细胞数据高阶相互作用的研究算法。 这一研究成果发表在2020年7月13日出版的《自然—方法学》上。

研究人员提供了一种灵活和统计学上稳健的用于确定基因中高阶相互作用的方法scHOT。scHOT可以应用于分析细胞的连续轨迹或跨空间信息，适用于包括变异性或相关性在内的各种高阶测量。小组通过研究小鼠肝脏胚胎发育过程中高阶相互作用的协同变化来展示了scHOT的使用。

此外，scHOT能够通过分析小鼠嗅球的空间转录组学数据识别空间上基因-基因相关性的微小变化。scHOT显著地改善了一阶微分表达式测试，并为从单细胞数据中挖掘高阶相互作用提供了框架。

据了解，单细胞基因组改变了科学家检查细胞命运选择的能力。研究细胞随着计算机有序“拟时间”的变化提供了解开关键基因在变异性和共变上微小变化的可能。

附：英文原文

Title: Investigating higher-order interactions in single-cell data with scHOT

Author: Shila Ghazanfar, Yingxin Lin, Xianbin Su, David Ming Lin, Ellis Patrick, Ze-Guang Han, John C. Marioni, Jean Yee Hwa Yang

Issue&Volume: 2020-07-13

Abstract: Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered ‘pseudotime’ offers the potential to unpick subtle changes in variability and covariation among key genes. We describe an approach, scHOT—single-cell higher-order testing—which provides a flexible and statistically robust framework for identifying changes in higher-order interactions among genes. scHOT can be applied for cells along a continuous trajectory or across space and accommodates various higher-order measurements including variability or correlation. We demonstrate the use of scHOT by studying coordinated changes in higher-order interactions during embryonic development of the mouse liver. Additionally, scHOT identifies subtle changes in gene–gene correlations across space using spatially resolved transcriptomics data from the mouse olfactory bulb. scHOT meaningfully adds to first-order differential expression testing and provides a framework for interrogating higher-order interactions using single-cell data.

DOI: 10.1038/s41592-020-0885-x

Source: https://www.nature.com/articles/s41592-020-0885-x