FindAllMarkers() automates this process for all clusters, but you can also test groups of clusters vs. each other, or against all cells. By default, it identifies positive and negative markers of a single cluster (specified in ident.1), compared to all other cells. Seurat can help you find markers that define clusters via differential expression. The clusters can be found using the Idents() function.įinding differentially expressed features (cluster biomarkers) Optimal resolution often increases for larger datasets. We find that setting this parameter between 0.4-1.2 typically returns good results for single-cell datasets of around 3K cells. The FindClusters() function implements this procedure, and contains a resolution parameter that sets the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters. To cluster the cells, we next apply modularity optimization techniques such as the Louvain algorithm (default) or SLM, to iteratively group cells together, with the goal of optimizing the standard modularity function. This step is performed using the FindNeighbors() function, and takes as input the previously defined dimensionality of the dataset (first 10 PCs). Briefly, these methods embed cells in a graph structure - for example a K-nearest neighbor (KNN) graph, with edges drawn between cells with similar feature expression patterns, and then attempt to partition this graph into highly interconnected ‘quasi-cliques’ or ‘communities’.Īs in PhenoGraph, we first construct a KNN graph based on the euclidean distance in PCA space, and refine the edge weights between any two cells based on the shared overlap in their local neighborhoods (Jaccard similarity). Our approach was heavily inspired by recent manuscripts which applied graph-based clustering approaches to scRNA-seq data and CyTOF data. However, our approach to partitioning the cellular distance matrix into clusters has dramatically improved. Importantly, the distance metric which drives the clustering analysis (based on previously identified PCs) remains the same. Seurat v3 applies a graph-based clustering approach, building upon initial strategies in ( Macosko et al). For example, performing downstream analyses with only 5 PCs does significantly and adversely affect results. We advise users to err on the higher side when choosing this parameter.As you will observe, the results often do not differ dramatically. We encourage users to repeat downstream analyses with a different number of PCs (10, 15, or even 50!). However, these groups are so rare, they are difficult to distinguish from background noise for a dataset of this size without prior knowledge.
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