This tool performs integrated analysis of two samples: it clusters the cells and visualizes the clusters using UMAP, tSNE or PCA. As an input, give the Seurat object generated with "Seurat v3 -Combine two samples" tool.
This tool clusters the cells by constructing a Shared Nearest Neighbor (SNN) graph. It first determines the K (20) nearest neighbors of each cell and then uses this KNN graph to construct the SNN graph by calculating the neighborhood overlap (Jaccard index) between every cell and its 20 nearest neighbors. It performs the clustering in the principal component (PC) space and allows the user to define how many PCs to use.
The resolution parameter sets the 'granularity' of the clustering, with increased values leading to a greater number of clusters. Setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K cells. Optimal resolution often increases for larger datasets -use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities.
Clustering results are visualized with either UMAP, tSNE or PCA plot, and you can decide the point size for a cell in these plots.
The tool can also return a table with expression for an 'average' single cell in each cluster. Read more about the AverageExpression function.
For more details, please check the Seurat tutorials for multiple sample analysis.