简介

scCustomize是一个单细胞转录组数据可视化的R包，里面集合了一些常用的数据可视化方法，可以与Seurat包进行很好的联用，支持Seurat，LIGER和SCE等常用对象的数据。

R包安装

直接使用devtools包进行安装

devtools::install_github(repo = "samuel-marsh/scCustomize") remotes::install_github(repo = "samuel-marsh/scCustomize")

实例演示

在本教程中，我将使用 SeuratData 包中的 HCA 骨髓单细胞数据。

QC plot

所有 scRNA-seq 数据分析的第一步就是执行一些 QC 检查和绘图，以便可以适当地过滤数据。scCustomize 包含许多功能，可用于快速轻松地生成一些最相关的 QC 图。

library(tidyverse) library(patchwork) library(Seurat) library(scCustomize) library(qs) # Load Example Dataset hca_bm <- hcabm40k.Seuratdata::hcabm40k # Add pseudo group variable just for this vignette hca_bm@meta.data$group[hca_bm@meta.data$orig.ident == "MantonBM1" | hca_bm@meta.data$orig.ident == "MantonBM2" | hca_bm@meta.data$orig.ident == "MantonBM3" | hca_bm@meta.data$orig.ident == "MantonBM4"] <- "Group 1" hca_bm@meta.data$group[hca_bm@meta.data$orig.ident == "MantonBM5" | hca_bm@meta.data$orig.ident == "MantonBM6" | hca_bm@meta.data$orig.ident == "MantonBM7" | hca_bm@meta.data$orig.ident == "MantonBM8"] <- "Group 2"

添加线粒体和核糖体基因百分比

# These defaults can be run just by providing accepted species name hca_bm <- Add_Mito_Ribo_Seurat(seurat_object = hca_bm, species = "Human")


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注意：目前Add_Mito_Ribo_Seurat函数只支持以上物种计算线粒体和核糖体基因百分比，如果需要，请在 GitHub 上提交问题以获取其他默认物种。请包括线粒体和核糖体基因的正则表达式模式或基因列表，我将在函数中添加额外的内置默认值。

# Using gene name patterns hca_bm <- Add_Mito_Ribo_Seurat(seurat_object = hca_bm, species = "other", mito_pattern = "regexp_pattern", ribo_pattern = "regexp_pattern") # Using feature name lists mito_gene_list <- c("gene1", "gene2", "etc") ribo_gene_list <- c("gene1", "gene2", "etc") hca_bm <- Add_Mito_Ribo_Seurat(seurat_object = hca_bm, species = "other", mito_features = mito_gene_list, ribo_features = ribo_gene_list) # Using combination of gene lists and gene name patterns hca_bm <- Add_Mito_Ribo_Seurat(seurat_object = hca_bm, species = "Human", mito_features = mito_gene_list, ribo_pattern = "regexp_pattern")

Plotting QC Metrics

• VlnPlot-based QC Plots
  scCustomize 包提供了以下4种函数绘制VlnPlot-based QC Plots.

• QC_Plots_Genes() Plots genes per cell/nucleus.
• QC_Plots_UMIs() Plots UMIs per cell/nucleus.
• QC_Plots_Mito() Plots mito% (named “percent_mito”) per cell/nucleus.
• QC_Plots_Feature() Plots “feature” per cell/nucleus. Using parameter feature to allow plotting of any applicable named feature in object@meta.data slot.

# All functions contain p1 <- QC_Plots_Genes(seurat_object = hca_bm, low_cutoff = 800, high_cutoff = 5500) p2 <- QC_Plots_UMIs(seurat_object = hca_bm, low_cutoff = 1200, high_cutoff = 45000) p3 <- QC_Plots_Mito(seurat_object = hca_bm, high_cutoff = 20) wrap_plots(p1, p2, p3, ncol = 3)


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同时，还提供了一下可选择的自定义参数：

• plot_title: Change plot title
• x_axis_label/y_axis_label: Change axis labels.
• x_lab_rotate: Should x-axis label be rotated 45 degrees?
• y_axis_log: Should y-axis in linear or log10 scale.

p1 <- QC_Plots_UMIs(seurat_object = hca_bm, low_cutoff = 1200, high_cutoff = 45000, pt.size = 0.1) p2 <- QC_Plots_UMIs(seurat_object = hca_bm, low_cutoff = 1200, high_cutoff = 45000, pt.size = 0.1, y_axis_log = TRUE) wrap_plots(p1, p2, ncol = 2)


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• FeatureScatter-based QC Plots
  scCustomize 包提供了以下3种函数绘制FeatureScatter-based QC Plots.

• QC_Plot_UMIvsGene() Plots genes vs UMIs per cell/nucleus
• QC_Plot_GenevsFeature() Plots Genes vs. “feature” per cell/nucleus. Using parameter feature1 to allow plotting of any applicable named feature in object@meta.data slot.
• QC_Plot_UMIvsFeature() Plots UMIs vs. “feature” per cell/nucleus. Using parameter feature1 to allow plotting of any applicable named feature in object@meta.data slot.

# All functions contain QC_Plot_UMIvsGene(seurat_object = hca_bm, low_cutoff_gene = 800, high_cutoff_gene = 5500, low_cutoff_UMI = 500, high_cutoff_UMI = 50000) QC_Plot_GenevsFeature(seurat_object = hca_bm, feature1 = "percent_mito", low_cutoff_gene = 800, high_cutoff_gene = 5500, high_cutoff_feature = 20)


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QC_Plot_UMIvsGene(seurat_object = hca_bm, meta_gradient_name = "percent_mito", low_cutoff_gene = 800, high_cutoff_gene = 5500, high_cutoff_UMI = 45000) QC_Plot_UMIvsGene(seurat_object = hca_bm, meta_gradient_name = "percent_mito", low_cutoff_gene = 800, high_cutoff_gene = 5500, high_cutoff_UMI = 45000, meta_gradient_low_cutoff = 20)


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计算每个样本的QC值的中位数

scCustomize 包提供了Median_Stats()函数，可以快速计算不同QC指标 (Genes/, UMIs/, %Mito/Cell, etc)的中位数。

median_stats <- Median_Stats(seurat_object = hca_bm, group_by_var = "orig.ident")


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绘制QC指标中位数值
scCustomize 包提供了以下函数绘制QC指标的中位数值

• Plot_Median_Genes()
• Plot_Median_UMIs()
• Plot_Median_Mito()
• Plot_Median_Other()

Plot_Median_Genes(seurat_object = hca_bm, group_by = "group") Plot_Median_UMIs(seurat_object = hca_bm, group_by = "group") Plot_Median_Mito(seurat_object = hca_bm, group_by = "group") Plot_Median_Other(seurat_object = hca_bm, median_var = "percent_ribo", group_by = "group")


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内置自定义颜色画板

（1）Continuous Palettes

• viridis_plasma_dark_high
• viridis_plasma_light_high
• viridis_magma_dark_high
• viridis_magma_light_high
• viridis_inferno_dark_high
• viridis_inferno_light_high
• viridis_dark_high
• viridis_light_high

（2）Discrete Palettes

• alphabet (24)
• alphabet2 (24)
• glasbey (32)
• polychrome (36)
• stepped (24)
• ditto_seq (40)
• varibow (Dynamic)

PalettePlot(palette = DiscretePalette_scCustomize(num_colors = 26, palette = "alphabet")) PalettePlot(palette = c("color1", "color2", ...)


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# Save as variable to global environment polychrome_pal <- DiscretePalette_scCustomize(num_colors = 36, palette = "polychrome") # Call within another function DimPlot(object = obj_name, cols = DiscretePalette_scCustomize(num_colors = 26, palette = "polychrome")) DimPlot_scCustom(seurat_object = pbmc)


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