Nebulosa 1.15.0
Due to the sparsity observed in single-cell data (e.g. RNA-seq, ATAC-seq), the
visualization of cell features (e.g. gene, peak) is frequently affected and
unclear, especially when it is overlaid with clustering to annotate cell
types. Nebulosa is an R package to visualize data from single cells based on
kernel density estimation. It aims to recover the signal from dropped-out
features by incorporating the similarity between cells allowing a “convolution”
of the cell features.
For this vignette, let’s use Nebulosa with the Seurat package.
First, we’ll do a brief/standard data processing.
library("Nebulosa")
library("Seurat")## Loading required package: SeuratObject## Loading required package: sp##
## Attaching package: 'sp'## The following object is masked from 'package:IRanges':
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## %over%##
## Attaching package: 'SeuratObject'## The following object is masked from 'package:SummarizedExperiment':
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## Assays## The following object is masked from 'package:GenomicRanges':
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## intersect## The following object is masked from 'package:S4Vectors':
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## Attaching package: 'Seurat'## The following object is masked from 'package:SummarizedExperiment':
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## Assayslibrary("BiocFileCache")Let’s download a dataset of 3k PBMCs (available from 10X Genomics). This same dataset is commonly used in Seurat vignettes. The code below will download, store, and uncompress the data in a temporary directory.
bfc <- BiocFileCache(ask = FALSE)
data_file <- bfcrpath(bfc, file.path(
"https://s3-us-west-2.amazonaws.com/10x.files/samples/cell",
"pbmc3k",
"pbmc3k_filtered_gene_bc_matrices.tar.gz"
))
untar(data_file, exdir = tempdir())Then, we can read the gene expression matrix using the Read10X from Seurat
data <- Read10X(data.dir = file.path(tempdir(),
"filtered_gene_bc_matrices",
"hg19"
))Let’s create a Seurat object with features being expressed in at least 3 cells and cells expressing at least 200 genes.
pbmc <- CreateSeuratObject(
counts = data,
project = "pbmc3k",
min.cells = 3,
min.features = 200
)## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')Remove outlier cells based on the number of genes being expressed in each cell (below 2500 genes) and expression of mitochondrial genes (below 5%).
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA < 2500 & percent.mt < 5)Let’s use SCTransform to stabilize the variance of the data by regressing out
the effect of the sequencing depth from each cell.
pbmc <- SCTransform(pbmc, verbose = FALSE)Once the data is normalized and scaled, we can run a Principal Component Analysis (PCA) first to reduce the dimensions of our data from 26286 features to 50 principal components. To visualize the principal components, we can run a Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) using the first 30 principal components to obtain a two-dimentional space.
pbmc <- RunPCA(pbmc)
pbmc <- RunUMAP(pbmc, dims = 1:30)To assess cell similarity, let’s cluster the data by constructing a Shared Nearest Neighbor (SNN) Graph using the first 30 principal components and applying the Louvain algorithm.
pbmc <- FindNeighbors(pbmc, dims = 1:30)
pbmc <- FindClusters(pbmc)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 2638
## Number of edges: 108643
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8406
## Number of communities: 13
## Elapsed time: 0 secondsNebulosaThe main function from Nebulosa is the plot_density. For usability, it
resembles the FeaturePlot function from Seurat.
Let’s plot the kernel density estimate for CD4 as follows
plot_density(pbmc, "CD4")