作者，追风少年i~~~ 镜子面前，自己永远都是个少年

随着S12八强赛的结束，lpl就剩下JDG的一根独苗了，还依稀记得赛前各种吹捧，可能4强就是4支lpl的队伍，现实却狠狠打脸，唯一的独苗，还是战胜的RGE，这样的结果，不知道大家是否能承受。8强赛DK vs GenG，DK惜败，showmaker的发言刷屏了抖音，一个年仅20岁的少年，就知道失败总是贯穿始终，这就是人生。

前段时间跟着一位组织形态学的医生开了几次会，关于空间转录组的组织形态学分析一直是一个头疼的问题，因为需要很强的背景知识，以下图为例。

经验丰富的医院老师立马就可以识别出不同的区域，这里我标注一下，希望做空间转录组的同学也能虚心多多学习，真正把空间转录组的内容利用起来。

至于组织为什么会呈现如此的变化，病理学老师讲了很多关于组织结构，形态学方面的知识，包括如何通过切片的形态判断癌变的程度，是否是良性的等等等等。确实是一门很深的学问，如果你在做空间转录组，那么我希望你能扎扎实实提升功底。

好了，在这个基础上，聊一聊关于通讯分析的策略。

结合单细胞、空间和配受体对，推断细胞间的相互作用主要包括一下几个步骤

1 、Cell investigation.Cells are in vestigated from scRNA-seq data to evaluate expression levels of all genes.
2、Gene expression matrix construction.A gene expression matrix is constructed based on the transcript data of each gene across different cells.
3、Original LRI（配受体对） data arrangement.Interacting proteins involved in cell–cell communication(for example,interactions between ligands and receptors)are cap-tured from available data resources.
4、Gene screening.Genes relevant to the interacting proteins are retained in the above gene expression matrix.
5、LRI score computation.The gene expression values are used as inputs to calculate the interaction score for each ligand–receptor pair that mediate two cell types.
6、Intercellular communication inference.The interac-tion scores from all LR Is that mediate two cell types are aggregated to obtain an overall state of crosstalk between the two cell types.
7、Visualization.Visualization tools are used to interpret the aggregated cell–cell communication scores.

The pipeline for cell–cell communication prediction

我们来一一回顾一下这些内容

配受体对

Known ligand–receptor interaction data bases

可视化工具

Visualization tools of cell–cell communication

Cell–cell communication scoring strategies（单细胞 + 空间）

Input,case study and code of inter cellular communication inference methods

Machine learning-based cell–cell communication inference methods

The pipeline of PyMINEr for cell–cell communication inference

The pipeline of decision tree classifier-based cell–cell commu-nication prediction method

Spatial information-based cell–cell communication inference methods（空间通讯推断）

The pipeline of SpaOTsc for cell–cell communication inference

The pipeline of Giotto for cell–cell communication inference

Evaluation and validation of cell–cell communication

Advantages and disadvantages of cell–cell communication inference methods

Challenges and further research directions（分析的挑战）

1、单细胞数据、空间数据和图片等信息数据的整合方法
2、细胞类型的识别
3、推断细胞相互作用的多组学数据

而通讯的关键点在于

1、 Intercellular communication inference in tumour microenvironments contributes to tumor-targeted therapy and has been thus increasingly concerned.
2、We introduced the pipeline for cell–cell communication quantification, known LRI data resources, and visualization tools.
3、We highlighted classical intercellular communication scoring strategies, mainly analyzed four types of representative cell–cell communication methods including network-based methods, machine learning-based methods, spatial information-based methods, and other methods, and illustrated evaluation and validation strategies.
4、Based on our observations, we further discussed current challenges during cell–cell communication extraction as well as provided several novel directions to underpin the further development of cell–cell communication analysis

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