Seurat 中的数据可视化方法

SeuratData::InstallData("pbmc3k")

library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
pbmc3k.final <- LoadData("pbmc3k", type = "pbmc3k.final")
pbmc3k.final$groups <- sample(c("group1", "group2"), size = ncol(pbmc3k.final), replace = TRUE)
features <- c("LYZ", "CCL5", "IL32", "PTPRCAP", "FCGR3A", "PF4")
pbmc3k.final

## An object of class Seurat 
## 13714 features across 2638 samples within 1 assay 
## Active assay: RNA (13714 features, 2000 variable features)
##  3 layers present: data, counts, scale.data
##  2 dimensional reductions calculated: pca, umap

# Ridge plots - from ggridges. Visualize single cell expression distributions in each cluster
RidgePlot(pbmc3k.final, features = features, ncol = 2)

# Violin plot - Visualize single cell expression distributions in each cluster
VlnPlot(pbmc3k.final, features = features)

# Violin plots can also be split on some variable. Simply add the splitting variable to object
# metadata and pass it to the split.by argument
VlnPlot(pbmc3k.final, features = "percent.mt", split.by = "groups")

# Feature plot - visualize feature expression in low-dimensional space
FeaturePlot(pbmc3k.final, features = features)

# Plot a legend to map colors to expression levels
FeaturePlot(pbmc3k.final, features = "MS4A1")

# Adjust the contrast in the plot
FeaturePlot(pbmc3k.final, features = "MS4A1", min.cutoff = 1, max.cutoff = 3)

# Calculate feature-specific contrast levels based on quantiles of non-zero expression.
# Particularly useful when plotting multiple markers
FeaturePlot(pbmc3k.final, features = c("MS4A1", "PTPRCAP"), min.cutoff = "q10", max.cutoff = "q90")

# Visualize co-expression of two features simultaneously
FeaturePlot(pbmc3k.final, features = c("MS4A1", "CD79A"), blend = TRUE)

# Split visualization to view expression by groups (replaces FeatureHeatmap)
FeaturePlot(pbmc3k.final, features = c("MS4A1", "CD79A"), split.by = "groups")

# Dot plots - the size of the dot corresponds to the percentage of cells expressing the
# feature in each cluster. The color represents the average expression level
DotPlot(pbmc3k.final, features = features) + RotatedAxis()

# SplitDotPlotGG has been replaced with the `split.by` parameter for DotPlot
DotPlot(pbmc3k.final, features = features, split.by = "groups") + RotatedAxis()

## Single cell heatmap of feature expression
DoHeatmap(subset(pbmc3k.final, downsample = 100), features = features, size = 3)

# DimPlot replaces TSNEPlot, PCAPlot, etc. In addition, it will plot either 'umap', 'tsne', or
# 'pca' by default, in that order
DimPlot(pbmc3k.final)

pbmc3k.final.no.umap <- pbmc3k.final
pbmc3k.final.no.umap[["umap"]] <- NULL
DimPlot(pbmc3k.final.no.umap) + RotatedAxis()

# DoHeatmap now shows a grouping bar, splitting the heatmap into groups or clusters. This can
# be changed with the `group.by` parameter
DoHeatmap(pbmc3k.final, features = VariableFeatures(pbmc3k.final)[1:100], cells = 1:500, size = 4,
    angle = 90) + NoLegend()

baseplot <- DimPlot(pbmc3k.final, reduction = "umap")
# Add custom labels and titles
baseplot + labs(title = "Clustering of 2,700 PBMCs")

# Use community-created themes, overwriting the default Seurat-applied theme Install ggmin
# with remotes::install_github('sjessa/ggmin')
baseplot + ggmin::theme_powerpoint()

# Seurat also provides several built-in themes, such as DarkTheme; for more details see
# ?SeuratTheme
baseplot + DarkTheme()

# Chain themes together
baseplot + FontSize(x.title = 20, y.title = 20) + NoLegend()

# Include additional data to display alongside cell names by passing in a data frame of
# information.  Works well when using FetchData
plot <- FeaturePlot(pbmc3k.final, features = "MS4A1")
HoverLocator(plot = plot, information = FetchData(pbmc3k.final, vars = c("ident", "PC_1", "nFeature_RNA")))

pbmc3k.final <- RenameIdents(pbmc3k.final, DC = "CD14+ Mono")
plot <- DimPlot(pbmc3k.final, reduction = "umap")
select.cells <- CellSelector(plot = plot)

# LabelClusters and LabelPoints will label clusters (a coloring variable) or individual points
# on a ggplot2-based scatter plot
plot <- DimPlot(pbmc3k.final, reduction = "pca") + NoLegend()
LabelClusters(plot = plot, id = "ident")

# Both functions support `repel`, which will intelligently stagger labels and draw connecting
# lines from the labels to the points or clusters
LabelPoints(plot = plot, points = TopCells(object = pbmc3k.final[["pca"]]), repel = TRUE)

plot1 <- DimPlot(pbmc3k.final)
# Create scatter plot with the Pearson correlation value as the title
plot2 <- FeatureScatter(pbmc3k.final, feature1 = "LYZ", feature2 = "CCL5")
# Combine two plots
plot1 + plot2

# Remove the legend from all plots
(plot1 + plot2) & NoLegend()