Institute for Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom.
Institute of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom.
PLoS Comput Biol. 2022 Apr 8;18(4):e1009975. doi: 10.1371/journal.pcbi.1009975. eCollection 2022 Apr.
Cell-cell interactions are vital for numerous biological processes including development, differentiation, and response to inflammation. Currently, most methods for studying interactions on scRNA-seq level are based on curated databases of ligands and receptors. While those methods are useful, they are limited to our current biological knowledge. Recent advances in single cell protocols have allowed for physically interacting cells to be captured, and as such we have the potential to study interactions in a complemantary way without relying on prior knowledge. We introduce a new method based on Latent Dirichlet Allocation (LDA) for detecting genes that change as a result of interaction. We apply our method to synthetic datasets to demonstrate its ability to detect genes that change in an interacting population compared to a reference population. Next, we apply our approach to two datasets of physically interacting cells to identify the genes that change as a result of interaction, examples include adhesion and co-stimulatory molecules which confirm physical interaction between cells. For each dataset we produce a ranking of genes that are changing in subpopulations of the interacting cells. In addition to the genes discussed in the original publications, we highlight further candidates for interaction in the top 100 and 300 ranked genes. Lastly, we apply our method to a dataset generated by a standard droplet-based protocol not designed to capture interacting cells, and discuss its suitability for analysing interactions. We present a method that streamlines detection of interactions and does not require prior clustering and generation of synthetic reference profiles to detect changes in expression.
细胞间相互作用对于许多生物过程至关重要，包括发育、分化和对炎症的反应。目前，研究 scRNA-seq 水平相互作用的大多数方法都基于配体和受体的已编目数据库。虽然这些方法很有用，但它们仅限于我们目前的生物学知识。单细胞方案的最新进展允许捕获物理相互作用的细胞，因此我们有可能以互补的方式研究相互作用，而无需依赖先验知识。我们引入了一种基于潜在狄利克雷分配（LDA）的新方法，用于检测由于相互作用而发生变化的基因。我们将我们的方法应用于合成数据集，以证明其能够检测与参考群体相比在相互作用群体中发生变化的基因。接下来，我们将我们的方法应用于两个物理相互作用的细胞数据集，以识别由于相互作用而发生变化的基因，例如粘附和共刺激分子，这些基因证实了细胞之间的物理相互作用。对于每个数据集，我们都会生成一个基因排名，这些基因在相互作用细胞的亚群中发生变化。除了原始出版物中讨论的基因外，我们还强调了前 100 名和 300 名基因中进一步的相互作用候选基因。最后，我们将我们的方法应用于一个不是为了捕获相互作用细胞而设计的基于标准液滴的协议生成的数据集，并讨论其分析相互作用的适用性。我们提出了一种方法，该方法简化了相互作用的检测，并且不需要预先聚类和生成合成参考谱来检测表达变化。
