使用Nebulosa可视化scRNAseq,Featureplo

Overview

由于在单细胞数据（如RNA-seq、ATAC-seq）中观察到的稀疏性，细胞特征（如基因、峰）的可视化经常受到影响和不清楚，特别是当它与聚类重叠以注释细胞类型时。Nebulosa是一个基于核密度估计的R软件包，用于可视化单个细胞的数据。它的目的是通过合并单元之间的相似性来从丢失的特征中恢复信号，从而允许单元特征的“卷积”。

import some necessary packages

library("Nebulosa")
library("Seurat")
library("BiocFileCache")

Data pre-processing

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())

# read the gene expression matrix
data <- Read10X(data.dir = file.path(tempdir(),
  "filtered_gene_bc_matrices",
  "hg19"
))

# create a Seurat object
pbmc <- CreateSeuratObject(
  counts = data,
  project = "pbmc3k",
  min.cells = 3,
  min.features = 200
)

pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA < 2500 & percent.mt < 5)

Data normalization, Dimensionality reduction, and Clustering

pbmc <- SCTransform(pbmc, verbose = FALSE)
pbmc <- RunPCA(pbmc)
pbmc <- RunUMAP(pbmc, dims = 1:30)
pbmc <- FindNeighbors(pbmc, dims = 1:30)
pbmc <- FindClusters(pbmc)

Visualize data with Nebulosa 关键函数为plot_density

plot_density(pbmc, "CD4")  ==> Figure 1

FeaturePlot(pbmc, "CD4")  ==> Figure 2

FeaturePlot(pbmc, "CD4", order = TRUE)  ==> Figure 3

DimPlot(pbmc, label = TRUE, repel = TRUE)  ==> Figure 4

plot_density(pbmc, "CD3D")  ==> Figure 5

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5

根据上面结果 We can now easily identify that clusters 0 and 2 correspond to CD4+ T cells if we plot CD3D too.

Multi-feature visualization 主要参数joint

p3 <- plot_density(pbmc, c("CD8A", "CCR7"))
p3 + plot_layout(ncol = 1)

p4 <- plot_density(pbmc, c("CD8A", "CCR7"), joint = TRUE)
p4 + plot_layout(ncol = 1)

p_list <- plot_density(pbmc, c("CD8A", "CCR7"), joint = TRUE, combine = FALSE)
p_list[[length(p_list)]]

p3.png p4.png p_list.png

根据上面结果 When compared to the clustering results, we can easily identify that Naive CD8+ T cells correspond to cluster 8.

Conclusions

总之，星云图（Nebulosa density plots）可以用来恢复基因缺失的信号，并改善其在二维空间的可视化效果。我们建议使用Nebulosa，特别是对于dropped-out 的基因。对于表达良好的基因，直接可视化的基因表达可能更可取。我们鼓励用户使用Nebulosa以及来自Seurat和Bioconductor环境的核心可视化方法以及其他可视化方法，以便对他们的数据得出更明智的结论。

Visualization of gene expression with Nebulosa