单细胞Scillus可视化R包学习

rm(list = ls())
library(Scillus)
library(Seurat)
library(ggplot2)
library(qs)
scRNA <- qread("sc_dataset.qs")

DimPlot(scRNA,label = T)
colnames(scRNA@meta.data)

plot_scdata(scRNA, 
            color_by = "celltype",
            split_by = NA,
            pal_setup = "Set1"
)
ggsave("celltype.pdf",width = 9,height = 7)


plot_scdata(scRNA, 
            color_by = "celltype",
            split_by = "hpv",
            pal_setup = "Set1" # Dark2,也可以自定义颜色
)
ggsave("celltype.pdf",width = 18,height = 7)

#"group_count", "prop_fill", and "prop_multi". 三种模式
plot_stat(scRNA, 
          plot_type = "group_count", #"group_count", "prop_fill", and "prop_multi".
          group_by = "hpv",
          pal_setup = "Set2",
          plot_ratio = 1,
          text_size = 10,
          tilt_text = FALSE)

plot_stat(scRNA, 
          plot_type = "prop_fill", #"group_count", "prop_fill", and "prop_multi".
          group_by = "hpv",
          pal_setup = "Set2",
          plot_ratio = 1,
          text_size = 10,
          tilt_text = FALSE)

plot_stat(scRNA, 
          plot_type = "prop_multi", #"group_count", "prop_fill", and "prop_multi".
          group_by = "hpv",
          pal_setup = "Set2",
          plot_ratio = 1,
          text_size = 10,
          tilt_text = FALSE)

# 正式绘图
markers <- FindAllMarkers(scRNA, logfc.threshold = 0.1, min.pct = 0, only.pos = T)
plot_heatmap(dataset = scRNA, 
              markers = markers,
              sort_var = c("celltype","hpv"),
              anno_var = c("celltype","hpv"),
              anno_colors = list("Set2",
                                 c("tomato","#009CB8")
                                 ))


plot_heatmap(dataset = scRNA,
             n = 6,# 每个聚类要绘制的基因数量。若指定标记基因，则此参数将不会被使用。
             markers = markers,
             sort_var = c("celltype","hpv"),
             anno_var = c("celltype","hpv"),
             anno_colors = list("Set2",
                                 c("tomato","#009CB8")
                                 ),
             hm_limit = c(-1,0,1),
             hm_colors = c("purple","black","yellow"))

# 默认提取前100个基因
# 采用的是FinderMarker后的差异基因
plot_cluster_go(markers, 
                cluster_name = "myeloid cells", 
                org = "human", # 物种
                ont = "CC") # Go分析的方法

# 全部细胞
plot_all_cluster_go(markers, org = "human", ont = "CC")

#在元数据中，这些测量指标被定义为连续变量，与基因表达值类似。函数 plot_measure() 和 plot_measure_dim()分别用于以箱线图或小提琴图，以及降维可视化的方式对这些变量进行汇总展示。参数 group_by、split_by 和 pal_setup 的使用方式与前述部分一致。
plot_measure(dataset = scRNA, 
             measures = c("KRT14","percent.mt"), 
             group_by = "seurat_clusters", 
             pal_setup = "Set2")

plot_measure_dim(dataset = scRNA, 
                 measures = c("nFeature_RNA"))
                # split_by = "GSE_num")

de <- find_diff_genes(dataset = scRNA, 
                      clusters = "fibroblasts",
                      comparison = c("hpv", 
                                     "HPV-","HPV+"),
                      logfc.threshold = 0, # threshold of 0 is used for GSEA
                      min.cells.group = 1)  # To include clusters with only 1 cell

gsea_res <- test_GSEA(de, 
                      pathway = pathways.hallmark)

plot_GSEA(gsea_res, p_cutoff = 0.1, 
          colors = c("#0570b0", "grey", "#d7301f"))