For this tutorial, we will be analyzing the a dataset of Peripheral Blood Mononuclear Cells (PBMC) freely available from 10X Genomics. There are 2,700 single cells that were sequenced on the Illumina NextSeq 500. The raw data can be found here.
We start by reading in the data. The
Read10X function reads in the output of the cellranger pipeline from 10X, returning a unique molecular identified (UMI) count matrix. The values in this matrix represent the number of molecules for each feature (i.e. gene; row) that are detected in each cell (column).
We next use the count matrix to create a
Seurat object. The object serves as a container that contains both data (like the count matrix) and analysis (like PCA, or clustering results) for a single-cell dataset. For a technical discussion of the
Seurat object structure, check out our GitHub Wiki. For example, the count matrix is stored in
library(dplyr) library(Seurat) library(patchwork) # Load the PBMC dataset pbmc.data <- Read10X(data.dir = "../data/pbmc3k/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
## An object of class Seurat ## 13714 features across 2700 samples within 1 assay ## Active assay: RNA (13714 features, 0 variable features)
What does data in a count matrix look like?
# Lets examine a few genes in the first thirty cells pbmc.data[c("CD3D", "TCL1A", "MS4A1"), 1:30]
## 3 x 30 sparse Matrix of class "dgCMatrix" ## ## CD3D 4 . 10 . . 1 2 3 1 . . 2 7 1 . . 1 3 . 2 3 . . . . . 3 4 1 5 ## TCL1A . . . . . . . . 1 . . . . . . . . . . . . 1 . . . . . . . . ## MS4A1 . 6 . . . . . . 1 1 1 . . . . . . . . . 36 1 2 . . 2 . . . .
. values in the matrix represent 0s (no molecules detected). Since most values in an scRNA-seq matrix are 0, Seurat uses a sparse-matrix representation whenever possible. This results in significant memory and speed savings for Drop-seq/inDrop/10x data.
dense.size <- object.size(as.matrix(pbmc.data)) dense.size
## 709591472 bytes
sparse.size <- object.size(pbmc.data) sparse.size
## 29905192 bytes
## 23.7 bytes
The steps below encompass the standard pre-processing workflow for scRNA-seq data in Seurat. These represent the selection and filtration of cells based on QC metrics, data normalization and scaling, and the detection of highly variable features.
Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. A few QC metrics commonly used by the community include
PercentageFeatureSetfunction, which calculates the percentage of counts originating from a set of features
MT-as a set of mitochondrial genes
# The [[ operator can add columns to object metadata. This is a great place to stash QC stats pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
Where are QC metrics stored in Seurat?
# Show QC metrics for the first 5 cells head(firstname.lastname@example.org, 5)
In the example below, we visualize QC metrics, and use these to filter cells.
# Visualize QC metrics as a violin plot VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)