我们将使用我们之前从 2，700个 PBMC 教程中计算的 Seurat 对象在 Seurat 中演示可视化技术。您可以从这里下载此数据集
SeuratData::InstallData("pbmc3k")
library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
data("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)
## 2 dimensional reductions calculated: pca, umap
marker基因可视化的5种方法
# Ridge plots - from ggridges. Visualize single cell expression distributions in each cluster
RidgePlot(pbmc3k.final, features = features, ncol = 2)
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# Violin plot - Visualize single cell expression distributions in each cluster
VlnPlot(pbmc3k.final, features = features)
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# Feature plot - visualize feature expression in low-dimensional space
FeaturePlot(pbmc3k.final, features = features)
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# 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()
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# Single cell heatmap of feature expression
DoHeatmap(subset(pbmc3k.final, downsample = 100), features = features, size = 3)
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FeaturePlot新增功能
# Plot a legend to map colors to expression levels
FeaturePlot(pbmc3k.final, features = "MS4A1")
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# Adjust the contrast in the plot
FeaturePlot(pbmc3k.final, features = "MS4A1", min.cutoff = 1, max.cutoff = 3)
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# 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")
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# Visualize co-expression of two features simultaneously
FeaturePlot(pbmc3k.final, features = c("MS4A1", "CD79A"), blend = TRUE)
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# Split visualization to view expression by groups (replaces FeatureHeatmap)
FeaturePlot(pbmc3k.final, features = c("MS4A1", "CD79A"), split.by = "groups")
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FeaturePlot()]可视化功能更新和扩展
除了更改之外，其他几个绘图功能也已更新和扩展，具有新功能，并接管了现已废弃的函数的功能
# 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")
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# SplitDotPlotGG has been replaced with the `split.by` parameter for DotPlot
DotPlot(pbmc3k.final, features = features, split.by = "groups") + RotatedAxis()
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# DimPlot replaces TSNEPlot, PCAPlot, etc. In addition, it will plot either 'umap', 'tsne', or
# 'pca' by default, in that order
DimPlot(pbmc3k.final)
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pbmc3k.final.no.umap <- pbmc3k.final
pbmc3k.final.no.umap[["umap"]] <- NULL
DimPlot(pbmc3k.final.no.umap) + RotatedAxis()
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# 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()
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将主题应用于绘图
与 Seurat，所有绘图功能默认情况下返回基于 ggplot2 的绘图，允许人们像任何其他基于 ggplot2 的绘图一样轻松调整绘图。
baseplot <- DimPlot(pbmc3k.final, reduction = "umap")
# Add custom labels and titles
baseplot + labs(title = "Clustering of 2,700 PBMCs")
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# Use community-created themes, overwriting the default Seurat-applied theme Install ggmin with
# remotes::install_github('sjessa/ggmin')
baseplot + ggmin::theme_powerpoint()
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# Seurat also provides several built-in themes, such as DarkTheme; for more details see
# ?SeuratTheme
baseplot + DarkTheme()
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# Chain themes together
baseplot + FontSize(x.title = 20, y.title = 20) + NoLegend()
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交互式绘图功能
苏拉特利用 R 的绘图库创建交互式绘图。此交互式绘图功能适用于任何基于 ggplot2 的散点图（需要一层）。要使用，只需制作基于 ggplot2 的散射图（如或），并将生成的绘图传递给geom_point``[DimPlot()](https://satijalab.org/seurat/reference/DimPlot.html)``[FeaturePlot()](https://satijalab.org/seurat/reference/FeaturePlot.html)``[HoverLocator()](https://satijalab.org/seurat/reference/HoverLocator.html)
# 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")))
Seurat 提供的另一个交互式功能是能够手动选择单元格以供进一步调查。我们发现，这对于小集群特别有用，这些小集群并不总是使用不偏不倚的群集进行分离，但看起来却截然不同。现在，您可以通过创建基于 ggplot2 的散射图（例如使用或传递返回的绘图）来选择这些单元格。 将返回带有所选点名称的矢量，以便您可以将它们设置为新的标识类并执行差额表达式。[DimPlot()](https://satijalab.org/seurat/reference/DimPlot.html)``[FeaturePlot()](https://satijalab.org/seurat/reference/FeaturePlot.html)``[CellSelector()](https://satijalab.org/seurat/reference/CellSelector.html)``[CellSelector()](https://satijalab.org/seurat/reference/CellSelector.html)
例如，让我们假装 DCs 与聚类中的单核细胞合并，但我们想根据它们在 tSNE 绘图中的位置来了解它们的独特之处。
pbmc3k.final <- RenameIdents(pbmc3k.final, DC = "CD14+ Mono")
plot <- DimPlot(pbmc3k.final, reduction = "umap")
select.cells <- CellSelector(plot = plot)
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然后，我们可以改变这些细胞的身份，把它们变成他们自己的迷你集群。
head(select.cells)
## [1] "AAGATTACCGCCTT" "AAGCCATGAACTGC" "AATTACGAATTCCT" "ACCCGTTGCTTCTA"
## [5] "ACGAGGGACAGGAG" "ACGTGATGCCATGA"
Idents(pbmc3k.final, cells = select.cells) <- "NewCells"
# Now, we find markers that are specific to the new cells, and find clear DC markers
newcells.markers <- FindMarkers(pbmc3k.final, ident.1 = "NewCells", ident.2 = "CD14+ Mono", min.diff.pct = 0.3,
only.pos = TRUE)
head(newcells.markers)
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## FCER1A 3.239004e-69 3.7008561 0.800 0.017 4.441970e-65
## SERPINF1 7.761413e-36 1.5737896 0.457 0.013 1.064400e-31
## HLA-DQB2 1.721094e-34 0.9685974 0.429 0.010 2.360309e-30
## CD1C 2.304106e-33 1.7785158 0.514 0.025 3.159851e-29
## ENHO 5.099765e-32 1.3734708 0.400 0.010 6.993818e-28
## ITM2C 4.299994e-29 1.5590007 0.371 0.010 5.897012e-25
<details style="box-sizing: border-box; display: block;"><summary style="box-sizing: border-box; display: list-item;">使用自动分配单元格标识CellSelector</summary></details>
绘图配件
随着新功能为绘图添加交互式功能，Seurat 还提供新的配件功能来操纵和组合绘图。
# 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")
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# 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)
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以前通过该功能绘制多个绘图。我们弃用此功能，转而支持拼凑系统。下面是一个简短的演示，但请参阅此处的拼凑包网站，了解更多详细信息和示例。CombinePlot()
plot1 <- DimPlot(pbmc3k.final)
plot2 <- FeatureScatter(pbmc3k.final, feature1 = "LYZ", feature2 = "CCL5")
# Combine two plots
plot1 + plot2
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# Remove the legend from all plots
(plot1 + plot2) & NoLegend()
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