Basic Usage on PBMC3k Data • recall

suppressPackageStartupMessages({
library(Seurat)
library(SeuratData)
library(recall)
})

First, we use the SeuratData data package to first download and then load 2700 PBMCs. The loaded SeuratObject, pbmc3k, is from an old version of Seurat, and so we update the object to v5.

set.seed(123)

SeuratData::InstallData("pbmc3k")
data("pbmc3k")

pbmc3k <- UpdateSeuratObject(pbmc3k)

Now, we use Seurat to perform the usual preprocessing steps that are performed prior to clustering.

pbmc3k <- NormalizeData(pbmc3k)
pbmc3k <- FindVariableFeatures(pbmc3k)
pbmc3k <- ScaleData(pbmc3k)
pbmc3k <- RunPCA(pbmc3k)
pbmc3k <- FindNeighbors(pbmc3k)
pbmc3k <- RunUMAP(pbmc3k, dims = 1:10)

The recall algorithm can be run with a single function call as a drop-in replacement for the Seurat function FindClusters.

pbmc3k <- FindClustersRecall(pbmc3k)

The recall clusters are set to the idents of the SeuratObject that is returned by FindClustersRecall

DimPlot(pbmc3k)

Cluster labels from FindClustersRecall care stored in the metadata in the column pbmc3k@meta.data$recall_clusters.

DimPlot(pbmc3k, group.by = "recall_clusters")