5 Load data

This workshop follows the introductory Guided Clustering Tutorial tutorial from Seurat.

The bulk of this workshop may be found in its original format there.

5.1 Setup the Seurat Object

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).

Note: What does the data look like?

What do the input files look like? It varies, but this is the output of the CellRanger pipleine, described here

├── analysis
│   ├── clustering
│   ├── diffexp
│   ├── pca
│   ├── tsne
│   └── umap
├── cloupe.cloupe
├── filtered_feature_bc_matrix
│   ├── barcodes.tsv.gz
│   ├── features.tsv.gz
│   └── matrix.mtx.gz
├── filtered_feature_bc_matrix.h5
├── metrics_summary.csv
├── molecule_info.h5
├── possorted_genome_bam.bam
├── possorted_genome_bam.bam.bai
├── raw_feature_bc_matrix
│   ├── barcodes.tsv.gz
│   ├── features.tsv.gz
│   └── matrix.mtx.gz
├── raw_feature_bc_matrix.h5
└── web_summary.html

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 the GitHub Wiki. For example, the count matrix is stored in pbmc@assays$RNA@counts.

library(dplyr)
library(ggplot2)
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)
#>  2 layers present: counts, data

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"
#>   [[ suppressing 30 column names 'AAACATACAACCAC-1', 'AAACATTGAGCTAC-1', 'AAACATTGATCAGC-1' ... ]]
#>                                                            
#> CD3D  4 . 10 . . 1 2 3 1 . . 2 7 1 . . 1 3 . 2  3 . . . . .
#> TCL1A . .  . . . . . . 1 . . . . . . . . . . .  . 1 . . . .
#> MS4A1 . 6  . . . . . . 1 1 1 . . . . . . . . . 36 1 2 . . 2
#>              
#> CD3D  3 4 1 5
#> TCL1A . . . .
#> MS4A1 . . . .

The . 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
dense.size / sparse.size
#> 23.7 bytes