![]() ![]() While there is generally going to be a loss in power, the speed increases can be significant and the most highly differentially expressed features will likely still rise to the top. This will downsample each identity class to have no more cells than whatever this is set to. As another option to speed up these computations, can be set. You can set both of these to 0, but with a dramatic increase in time - since this will test a large number of features that are unlikely to be highly discriminatory. The min.pct argument requires a feature to be detected at a minimum percentage in either of the two groups of cells, and the thresh.test argument requires a feature to be differentially expressed (on average) by some amount between the two groups. 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. ![]()
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