Seurat中对细胞分群（Cluster）的操作

Posted 2023-04-19

参考技术A

FindNeighbors ：
Constructs a Shared Nearest Neighbor (SNN) Graph for a given dataset. We first determine the k-nearest neighbors of each cell. We use this knn graph to construct the SNN graph by calculating the neighborhood overlap (Jaccard index) between every cell and its k.param nearest neighbors.

FindClusters ：
Identify clusters of cells by a shared nearest neighbor (SNN) modularity optimization based clustering algorithm. First calculate k-nearest neighbors and construct the SNN graph. Then optimize the modularity function to determine clusters.

FindClusters()函数
该函数是基于FindNeighbors()构建的SNN图来进行分群。其中参数 resolution 是设置下游聚类分群重要参数，该参数一般设置在0.3-1之间即可，还需针对每个单独的实验数据进行优化。分辨率值越高，簇的数量就越多，对于较大的数据集且复杂组织来说高分辨率能够区分更多的细胞。

resolution参数支持多个分辨率值输入，结果可以通过pbmc@metadata进行查看，每个分辨率的结果都有单独一列。

之后对非线性降维结果可视化时可以通过 Idents()函数来指定分辨率。

RenameIdents()函数 : 细胞簇注释名更改

无论是通过已知Makergene还是单纯对cluster名字进行注释更改，都可以通过RenameIdents()函数来进行更改。

Seurat | 单细胞分析工具

Seurat是一个老牌的单细胞分析工具了（satija的力作），我之前测试过，但是没怎么用。

最近发现这个工具又publish在了NBT上，所以很有必要看一下这篇文章。

 

Integrating single-cell transcriptomic data across different conditions, technologies, and species

主要目的：identifying subpopulations of cells that are present across multiple data sets

主要就是为了多样品之间的比较，甚至不同物种间的对比。

 

Overview of Seurat alignment workflow
We aimed to develop a diverse integration strategy that could compare scRNA-seq data sets across different conditions, technologies, or species.

 

Identifying shared correlation structures across data sets

Machine-learning techniques for ‘data fusion’ aim to integrate information from multiple experiments into a consistent representation.

 

待续~