可视化单细胞亚群的标记基因的5个方法的升级版

FeaturePlot(object = obj, features = "Gene", split.by = "Meta_Name", order = T) & theme(legend.position = "right")

m_featureplot <- FeaturePlot(M_Aggregated_seurat, features = "Lepr", reduction = "tsne",
                             split.by = "orig.ident", pt.size = 1.8, repel = F, label = F,
                             order = T, max.cutoff = 1)
m_featureplot <- m_featureplot & scale_x_continuous(breaks=seq(-30, 20, 10)) ## 改x轴刻度标签
m_featureplot <- m_featureplot & scale_y_continuous(breaks=seq(-30, 20, 10)) ## 改y轴刻度标签
m_featureplot <- m_featureplot +  theme(axis.text.y = element_blank()) +   ## 删去所有刻度标签
                                  theme(axis.ticks.y = element_blank()) +  ##则只删去 Y 轴的刻度线
                                  theme(axis.line.y = element_blank())
m_featureplot <- m_featureplot + ylab("")

p1 <- Seurat::VlnPlot(F_Aggregated_seurat, features = ImDEGs, group.by = "small_celltype",
                      stack = T, split.by = "orig.ident",
                      fill.by="ident", flip = T, split.plot = F,adjust=1.3, same.y.lims = T)
p1$data$split <- factor(rep(F_Aggregated_seurat$try, length(ImDEGs)), levels = levels_define)

## GABA1 ob/ob 加6个细胞,  GABA2 ob/ob 加4个细胞 GABA6 ob/ob加8个细胞 那样这三个细胞数目很少的细胞总共就有10个细胞了
## 因为Seurat::VlnPlot有个bug就是当用split的时候 细胞数目小于3个的时候 就不会画出来
GABA1_ob <- 6
GABA2_ob <- 4
GABA6_ob <- 8
add_cell_num <- GABA1_ob + GABA2_ob + GABA6_ob
row_num <- nrow(p1$data) + length(ImDEGs)*add_cell_num
col_num <- ncol(p1$data)
original_row <- nrow(p1$data)
try_data <- matrix(0, row_num, col_num)
try_data[1:original_row, 1:col_num] <- as.matrix(p1$data)
colnames(try_data) <- colnames(p1$data)

GABA1_ob_data <- matrix(0, length(ImDEGs) * GABA1_ob, col_num)
GABA1_ob_data[ ,1] <- rep(ImDEGs, GABA1_ob)
GABA1_ob_data[ ,3] <- rep("GABA1", length(ImDEGs) * GABA1_ob)
GABA1_ob_data[ ,4] <- rep("GABA1_ob/ob", length(ImDEGs) * GABA1_ob)
GABA1_ob_row <- (original_row+nrow(GABA1_ob_data))
try_data[(original_row+1):GABA1_ob_row, 1:4] <- GABA1_ob_data

GABA2_ob_data <- matrix(0, length(ImDEGs) * GABA2_ob, col_num)
GABA2_ob_data[ ,1] <- rep(ImDEGs, GABA2_ob)
GABA2_ob_data[ ,3] <- rep("GABA2", length(ImDEGs) * GABA2_ob)
GABA2_ob_data[ ,4] <- rep("GABA2_ob/ob", length(ImDEGs) * GABA2_ob)
GABA2_ob_row <- GABA1_ob_row + nrow(GABA2_ob_data)
try_data[(GABA1_ob_row+1):GABA2_ob_row, 1:4] <- GABA2_ob_data

GABA6_ob_data <- matrix(0, length(ImDEGs) * GABA6_ob, col_num)
GABA6_ob_data[ ,1] <- rep(ImDEGs, GABA6_ob)
GABA6_ob_data[ ,3] <- rep("GABA6", length(ImDEGs) * GABA6_ob)
GABA6_ob_data[ ,4] <- rep("GABA6_ob/ob", length(ImDEGs) * GABA6_ob)
GABA6_ob_row <- GABA2_ob_row + nrow(GABA6_ob_data)
try_data[(GABA2_ob_row+1):GABA6_ob_row, 1:4] <- GABA6_ob_data

try_data <- as.data.frame(try_data)
try_data$feature <- factor(try_data$feature, levels = ImDEGs)
try_data$expression <- as.numeric(try_data$expression)
try_data$ident <- factor(try_data$ident, levels = levels(p1$data$ident))
try_data$split <- factor(try_data$split, levels = levels(p1$data$split))

dim(try_data)

## 加上所有GABA Glu的细胞
All <- FetchData(F_Aggregated_seurat, vars = ImDEGs)
library(reshape)
All <- melt(All)
colnames(All) <- c("feature", "expression")
head(All)
All$ident <- rep(F_Aggregated_seurat$big_celltype, length(ImDEGs))
All_label <- rep(as.character(F_Aggregated_seurat$orig.ident), length(ImDEGs))
All$split <- paste(All$ident, All_label, sep="_")
colnames(All) <- colnames(try_data)
head(All)
dim(All)

final_data <- rbind(try_data, All)
dim(final_data)

final_data$split <- factor(final_data$split,
                           levels = c(levels_define[1:12], 
                                      "GABA_WT", "GABA_ob/ob",
                                      levels_define[13:18],
                                      "Glu_WT", "Glu_ob/ob"))
final_data$ident <- factor(final_data$ident, 
                           levels = c(levels(try_data$ident)[1:6],
                                      "GABA",
                                      levels(try_data$ident)[7:9],
                                      "Glu"))

levels(final_data$split)
levels(final_data$ident)
levels(final_data$feature)

dim(final_data)

p1$data <- final_data
dim(p1$data)
p3 <- p1 + scale_fill_manual(values = c('#F8766D','#F8766D', '#D39200','#D39200',
                                        '#93AA00', '#93AA00', '#00BA38', '#00BA38',
                                        '#00C19F', '#00C19F', '#00B9E3', '#00B9E3',
                                        "#FC8D62", "#FC8D62",
                                        # "#FFFF33", "#FFFF33",
                                        '#619CFF','#619CFF', '#DB72FB','#DB72FB',
                                        '#FF61C3','#FF61C3',
                                        "#E41A1C", "#E41A1C"))
pdf("20210426_Vlnplot_Female_Gene_Related_to_Energy.pdf", 12, 10)
p3
dev.off()

dotplot_f_data_1 <- DotPlot(F_Aggregated_seurat, features = rev(ImDEGs),
                       group.by = "try",col.min=-2, col.max=2)$data
head(dotplot_f_data_1)
dim(dotplot_f_data_1)

dotplot_f_data_2 <- DotPlot(F_Aggregated_seurat, features = rev(ImDEGs),
                            group.by = "try2",col.min=-2, col.max=2)$data
head(dotplot_f_data_2)
dim(dotplot_f_data_2)

dotplot_f_data <- rbind(dotplot_f_data_1, dotplot_f_data_2)
head(dotplot_f_data)
dim(dotplot_f_data)
table(dotplot_f_data$id)

p_dotplot_f <- DotPlot(F_Aggregated_seurat, features = rev(ImDEGs),col.min=-2, col.max=2)
p_dotplot_f$data <- dotplot_f_data
p_dotplot_f <- p_dotplot_f + coord_flip()
p_dotplot_f <- p_dotplot_f+ scale_color_gradient2(high="red",mid = "lightgrey",low ="darkblue", midpoint = 0) + theme_classic()+ 
  theme(axis.text.x = element_text(angle = -45,hjust = -0.1,vjust = 0.8)) 
p_dotplot_f$data$id <- factor(p_dotplot_f$data$id,
                              levels = c(levels_define[1:12], 
                                         "GABA_WT", "GABA_ob/ob",
                                         levels_define[13:18],
                                         "Glu_WT", "Glu_ob/ob"))
p_dotplot_f