繪圖

1.降維圖形繪制

降維圖可以通過plot_scdata()函數(shù)繪制：

plot_scdata(scRNA_int, pal_setup = pal)

UMAP 繪圖，按cluster著色

plot_scdata()有三個可選參數(shù)：color_by赚导，split_by击费，和pal_setup烹困。至于color_by參數(shù)莫其，默認(rèn)情況下魂迄，這個函數(shù)會給不同的"seurat_clusters"上色粗截，并且它可以被修改為metadata中的任何因素，比如"sample"或"group"：

plot_scdata(scRNA_int, color_by = "group", pal_setup = pal)

UMAP 繪圖捣炬，按group上色

如果split_by參數(shù)被指定為metadat中的一個因子熊昌，圖形將被該因子分割成不同的塊（可參考ggplot2分面）：

plot_scdata(scRNA_int, split_by = "sample", pal_setup = pal)

UMAP 繪圖，按樣本分割

與plot_qc()函數(shù)類似湿酸，pal_setup參數(shù)可以是RColorBrewer調(diào)色板名稱婿屹、調(diào)色板設(shè)置數(shù)據(jù)框或手動指定的顏色向量。

plot_scdata(scRNA_int, pal_setup = "Dark2")

UMAP 繪圖推溃，按簇著色昂利，RColorBrewer Dark2 調(diào)色板

plot_scdata(scRNA_int, color_by = "sample", pal_setup = c("red","orange","yellow","green","blue","purple"))

UMAP 繪圖，按簇著色，手動指定調(diào)色板

2.統(tǒng)計數(shù)據(jù)繪制

集群的計數(shù)和比例統(tǒng)計可以通過函數(shù)plot_stat()繪制蜂奸，plot_type參數(shù)必須提供為三個值之一:“group_count”犁苏、“prop_fill”和“prop_multi”。他們的圖表如下:

plot_stat(scRNA_int, plot_type = "group_count")

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plot_stat(scRNA_int, "group_count", group_by = "seurat_clusters", pal_setup = pal)

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plot_stat(scRNA_int, plot_type = "prop_fill", 
          pal_setup = c("grey90","grey80","grey70","grey60","grey50","grey40","grey30","grey20"))

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plot_stat(scRNA_int, plot_type = "prop_multi", pal_setup = "Set3")

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該group_by參數(shù)"sample"用作默認(rèn)分組變量扩所，并且可以指定為元數(shù)據(jù)中的其他因素（例如 "group"）围详。

plot_stat(scRNA_int, plot_type = "prop_fill", group_by = "group")

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plot_stat(scRNA_int, plot_type = "prop_multi", group_by = "group", pal_setup = c("sienna","bisque3"))

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3.熱圖繪制

熱圖的繪制需要 Seurat 找到聚類標(biāo)記：

markers <- FindAllMarkers(scRNA_int, logfc.threshold = 0.1, min.pct = 0, only.pos = T)

然后，用plot_heatmap()繪制每個聚類中的top基因祖屏。每個群集n中繪制的基因數(shù)量的默認(rèn)值是8助赞。在熱圖中，每一行代表一個基因袁勺，每一列代表一個細(xì)胞雹食。細(xì)胞可以按sort_var排序，如果默認(rèn)設(shè)置為c("seurat_clusters")期丰，這意味著細(xì)胞按集群標(biāo)識排序群叶。可以在sort_var中指定多個變量咐汞，細(xì)胞將按變量的順序排序盖呼。熱圖上方是注釋欄，可以通過指定anno_var參數(shù)顯示metadata數(shù)據(jù)中的分類或連續(xù)變量化撕，變量名作為字符向量。anno_colors參數(shù)是一個列表约炎，它為相應(yīng)的注釋變量指定注釋顏色植阴，因此它應(yīng)該與anno_var相同的長度。建議對分類變量和連續(xù)變量使用適當(dāng)?shù)恼{(diào)色板圾浅。和前面一樣掠手，支持RColorBrewer調(diào)色板和手工指定的調(diào)色板，并且三色向量可以用于連續(xù)變量注釋狸捕。

plot_heatmap(dataset = scRNA_int, 
              markers = markers,
              sort_var = c("seurat_clusters","sample"),
              anno_var = c("seurat_clusters","sample","percent.mt","S.Score","G2M.Score"),
              anno_colors = list("Set2",                                             # RColorBrewer palette
                                 c("red","orange","yellow","purple","blue","green"), # color vector
                                 "Reds",
                                 c("blue","white","red"),                            # Three-color gradient
                                 "Greens"))

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此外喷鸽，hm_limit和hm_colors用于指定熱圖主體的顏色梯度和限制。

plot_heatmap(dataset = scRNA_int,
             n = 6,
             markers = markers,
             sort_var = c("seurat_clusters","sample"),
             anno_var = c("seurat_clusters","sample","percent.mt"),
             anno_colors = list("Set2",
                                c("red","orange","yellow","purple","blue","green"),
                                "Reds"),
             hm_limit = c(-1,0,1),
             hm_colors = c("purple","black","yellow"))

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4.GO分析

GO分析結(jié)果可以通過plot_cluster_go()和plot_all_cluster_go()繪制灸拍。前者繪制一個特定的集群做祝，而后者迭代所有集群。plot_cluster_go()中的topn參數(shù)指定用于GO分析的top基因的數(shù)量鸡岗，默認(rèn)值為100混槐。org參數(shù)指定生物體，“human”和“mouse”是可接受的值轩性。plot_all_cluster_go()是plot_cluster_go()的包裝器声登，后者又是clusterProfilter:: richgo()`的包裝器。因此,…參數(shù)可以傳遞給內(nèi)部函數(shù)。

plot_cluster_go(markers, cluster_name = "1", org = "human", ont = "CC")

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plot_all_cluster_go(markers, org = "human", ont = "CC")

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5.Measures繪圖

Measures被定義為metadata中的連續(xù)變量以及基因表達(dá)值悯嗓。plot_measure()和plot_measure_dim()將這些變量分別歸納為箱線圖件舵、小提琴圖和降維圖。像group_by脯厨、split_by和pal_setup這樣的參數(shù)可以像上面描述的那樣使用芦圾。

plot_measure(dataset = scRNA_int, 
             measures = c("KRT14","percent.mt"), 
             group_by = "seurat_clusters", 
             pal_setup = pal)

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plot_measure_dim(dataset = scRNA_int, 
                 measures = c("nFeature_RNA","nCount_RNA","percent.mt","KRT14"))

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plot_measure_dim(dataset = scRNA_int, 
                 measures = c("nFeature_RNA","nCount_RNA","percent.mt","KRT14"),
                 split_by = "sample")

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6.GSEA分析

為了進(jìn)行GSEA分析，我們將首先通過find_diff_genes()找到差異表達(dá)基因(DEGs)和相關(guān)measures俄认。然后个少，通過test_GSEA()輸入經(jīng)過排序的列表進(jìn)行GSEA分析。(注:Seurat可能需要很長時間才能找到DEG眯杏。建議使用future包進(jìn)行多線程分析處理)夜焦。最后，可以使用plot_GSEA()繪制輸出岂贩，并提供用于調(diào)整p值截止和顏色漸提供附加參數(shù)茫经。

de <- find_diff_genes(dataset = scRNA_int, 
                      clusters = as.character(0:7),
                      comparison = c("group", "CTCL", "Normal"),
                      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"))

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參考文獻(xiàn)：
https://github.com/xmc811/Scillus