Seuratаæ½Ì³Ì:New data visualization methods in v3.0

ibrary(Seurat)
library(ggplot2)
pbmc <- readRDS(file = "D:\\Users\\Administrator\\Desktop\\NovoÖÜÔËÀ´\\SingleCell\\scrna_tools/pbmc3k_final.rds")
pbmc$groups <- sample(c("group1", "group2"), size = ncol(pbmc), replace = TRUE)
features <- c("LYZ", "CCL5", "IL32", "PTPRCAP", "FCGR3A", "PF4")
pbmc

An object of class Seurat 
13714 features across 2638 samples within 1 assay 
Active assay: RNA (13714 features)
 3 dimensional reductions calculated: pca, umap, tsne

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

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

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

# 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(pbmc, features = features) + RotatedAxis()

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

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

# Adjust the contrast in the plot
FeaturePlot(pbmc, 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(pbmc, features = c("MS4A1", "PTPRCAP"), min.cutoff = "q10", max.cutoff = "q90")

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

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

# 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(pbmc, features = "percent.mt", split.by = "groups")

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

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

pbmc.no.umap <- pbmc
pbmc.no.umap[["umap"]] <- NULL
DimPlot(pbmc.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(pbmc, features = VariableFeatures(pbmc)[1:100], cells = 1:500, size = 4, angle = 90) + 
    NoLegend()

baseplot <- DimPlot(pbmc, 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
# devtools::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(pbmc, features = "MS4A1")

HoverLocator(plot = plot, information = FetchData(pbmc, vars = c("ident", "PC_1", "nFeature_RNA")))
Warning messages:
1: In if (is.na(col)) { :
  the condition has length > 1 and only the first element will be used
2: In if (is.na(col)) { :
  the condition has length > 1 and only the first element will be used
3: `error_y.color` does not currently support multiple values. 
4: `error_x.color` does not currently support multiple values. 
5: `line.color` does not currently support multiple values. 
6: The titlefont attribute is deprecated. Use title = list(font = ...) instead. 

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

Click around the cluster of points you wish to select
ie. select the vertecies of a shape around the cluster you
are interested in. Press <Esc> when finished (right click for R-terminal users)

> head(select.cells)
[1] "AAGCCATGAACTGC" "ACGTCGCTCCTGAA" "ACTTAAGATTACTC"
[4] "ACTTCAACAAGCAA" "AGCACTGATGCTTT" "CACCGGGACTTCTA"

Idents(pbmc, cells = select.cells) <- "NewCells"

# Now, we find markers that are specific to the new cells, and find clear DC markers
newcells.markers <- FindMarkers(pbmc, ident.1 = "NewCells", ident.2 = "CD14+ Mono", min.diff.pct = 0.3, 
                                only.pos = TRUE)
head(newcells.markers)

                p_val avg_logFC pct.1 pct.2    p_val_adj
HLA-DQA2 6.579989e-24  1.635741 0.619 0.045 9.023796e-20
SPECC1   1.928970e-21  0.547701 0.333 0.009 2.645390e-17
FCER1A   3.069126e-21  2.062739 0.524 0.036 4.208999e-17
HLA-DQB1 1.476429e-20  1.823891 0.905 0.142 2.024774e-16
HLA-DRB5 3.013802e-20  1.994758 0.810 0.112 4.133129e-16
HLA-DMA  6.237886e-20  1.363932 0.762 0.092 8.554637e-16

pbmc <- CellSelector(plot = plot, object = pbmc, ident = "selected")
levels(pbmc)
[1] "selected"     "NewCells"     "CD14+ Mono"   "Naive CD4 T" 
 [5] "Memory CD4 T" "B"            "CD8 T"        "FCGR3A+ Mono"
 [9] "NK"           "Platelet"    

# LabelClusters and LabelPoints will label clusters (a coloring variable) or individual points
# on a ggplot2-based scatter plot
plot <- DimPlot(pbmc, 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 = pbmc[["pca"]]), repel = TRUE)

# Plotting multiple plots at once is easy with CombinePlots
plot1 <- DimPlot(pbmc)
plot2 <- FeatureScatter(pbmc, feature1 = "LYZ", feature2 = "CCL5")
# CombinePlots takes a list of ggplot2-based plots
CombinePlots(plots = list(plot1, plot2))

# CombinePlots can also easily remove the legend from the plots by passing `legend = 'none'`;
# legends can also be combined with `legend = 'right'`
CombinePlots(plots = list(plot1, plot2), legend = "none")

# CombinePlots used cowplot:plot_grid under the hood, and will pass on additional arguments to
# cowplot::plot_grid