columns in object metadata, PC scores etc. # for anything calculated by the object, i.e. # FeatureScatter is typically used to visualize feature-feature relationships, but can be used VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) We filter cells that have >5% mitochondrial counts.We filter cells that have unique feature counts over 2,500 or less than 200.In the example below, we visualize QC metrics, and use these to filter cells. You can find them stored in the object meta data.The number of unique genes and total molecules are automatically calculated during CreateSeuratObject.Pbmc] <- PercentageFeatureSet(pbmc, pattern = "^MT-") # The [[ operator can add columns to object metadata. We use the set of all genes starting with MT- as a set of mitochondrial genes.We calculate mitochondrial QC metrics with the PercentageFeatureSet function, which calculates the percentage of counts originating from a set of features.Low-quality / dying cells often exhibit extensive mitochondrial contamination.The percentage of reads that map to the mitochondrial genome.Similarly, the total number of molecules detected within a cell (correlates strongly with unique genes).Cell doublets or multiplets may exhibit an aberrantly high gene count.Low-quality cells or empty droplets will often have very few genes.The number of unique genes detected in each cell.A few QC metrics commonly used by the community include Seurat allows you to easily explore QC metrics and filter cells based on any user-defined criteria. QC and selecting cells for further analysis Sparse.size # 29861992 bytes dense.size/sparse.size # 23.8 bytes dense.size <- object.size(as.matrix(pbmc.data))ĭense.size # 709548272 bytes sparse.size <- object.size(pbmc.data) This results in significant memory and speed savings for Drop-seq/inDrop/10x data. Since most values in an scRNA-seq matrix are 0, Seurat uses a sparse-matrix representation whenever possible. values in the matrix represent 0s (no molecules detected). Pbmc.data # 3 x 30 sparse Matrix of class "dgCMatrix" What does data in a count matrix look like? # Lets examine a few genes in the first thirty cells
# 13714 features across 2700 samples within 1 assay Pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200) # Initialize the Seurat object with the raw (non-normalized data). Pbmc.data <- Read10X(data.dir = "./data/pbmc3k/filtered_gene_bc_matrices/hg19/") For example, the count matrix is stored in library(dplyr) For a technical discussion of the Seurat object structure, check out our GitHub Wiki. 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. We next use the count matrix to create a Seurat object. The values in this matrix represent the number of molecules for each feature (i.e. gene row) that are detected in each cell (column). The Read10X function reads in the output of the cellranger pipeline from 10X, returning a unique molecular identified (UMI) count matrix. There are 2,700 single cells that were sequenced on the Illumina NextSeq 500. For this tutorial, we will be analyzing the a dataset of Peripheral Blood Mononuclear Cells (PBMC) freely available from 10X Genomics.
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