Creat Seurat: Standard Workflow

library(dplyr)

library(Seurat)

library(patchwork)

# Load the PBMC dataset

pbmc.data <- Read10X(data.dir = "/brahms/mollag/practice/filtered_gene_bc_matrices/hg19/")

# Initialize the Seurat object with the raw (non-normalized data).

pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)

pbmc

# The [[ operator can add columns to object metadata. This is a great place to stash QC stats

pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")

# Visualize QC metrics as a violin plot

VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)