-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathFigure 6F-H.R
211 lines (155 loc) · 8.25 KB
/
Figure 6F-H.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
library(Seurat)
library(org.Hs.eg.db)
library(clusterProfiler)
library(patchwork)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(cowplot)
library(ggpubr)
library(readr)
library(tidyr)
library(ggforce)
library(pals)
library(pheatmap)
library(scales)
library(ggthemes)
library(clustree)
library(ComplexHeatmap)
library(circlize)
library(ggrastr)
library(ggrepel)
library(purrr)
library(reshape2)
library(scatterpie)
setwd("/mnt/public3/chaohy/pancancer/fig3/")
rds <- readRDS("/mnt/public3/chaohy/pancancer/fig3/Ductal0115_umap.rds")
rds
library(monocle3)
##创建CDS对象并预处理数据
data <- GetAssayData(rds, assay = 'RNA', slot = 'counts')
cell_metadata <- rds@meta.data
gene_annotation <- data.frame(gene_short_name = rownames(data))
rownames(gene_annotation) <- rownames(data)
cds <- new_cell_data_set(data,
cell_metadata = cell_metadata,
gene_metadata = gene_annotation)
#preprocess_cds函数相当于seurat中NormalizeData+ScaleData+RunPCA
cds <- preprocess_cds(cds, num_dim = 50)
#umap降维
cds <- reduce_dimension(cds, preprocess_method = "PCA")
p1 <- plot_cells(cds, reduction_method="UMAP", color_cells_by="major_cluster") + ggtitle('cds.umap')
p1
##从seurat导入整合过的umap坐标
cds.embed <- cds@int_colData$reducedDims$UMAP
int.embed <- Embeddings(rds, reduction = "umap")
int.embed <- int.embed[rownames(cds.embed),]
cds@int_colData$reducedDims$UMAP <- int.embed
p2 <- plot_cells(cds, reduction_method="UMAP", color_cells_by="major_cluster") + ggtitle('int.umap')
p2
## Monocle3聚类分区
cds <- cluster_cells(cds)
plot_cells(cds, show_trajectory_graph = FALSE) + ggtitle("label by clusterID")
## 识别轨迹
cds <- learn_graph(cds)
plot_cells(cds, label_groups_by_cluster = FALSE, label_leaves = FALSE, label_branch_points = FALSE) -> p
##细胞按拟时排序
# # 使用辅助线选择root细胞
# p + geom_vline(xintercept = seq(-9.5,-9,0.25)) + geom_hline(yintercept = seq(0,0.25,0.25))
# embed <- data.frame(Embeddings(scRNAsub, reduction = "umap"))
# embed <- subset(embed, UMAP_1 > -8 & UMAP_1 < -7.5 & UMAP_2 > 0.24 & UMAP_2 < 0.25)
# root.cell <- rownames(embed)
# cds <- order_cells(cds, root_cells = root.cell)
# 使用在线工具选择
cds <- order_cells(cds)
# pdf("/mnt/public3/chaohy/pancancer/fig3/pseudotime.pdf",width=6,height=5)
png(filename = "/mnt/public3/chaohy/pancancer/fig3/pseudotime.png",width=6,height=5, units = "cm",bg = "white",res = 300)
plot_cells(cds, color_cells_by = "pseudotime",
label_cell_groups = FALSE,
label_leaves = FALSE,
label_branch_points = FALSE,
label_roots = FALSE,
show_trajectory_graph = FALSE,
rasterize=T) + NoAxes() + NoLegend()
dev.off()
png(filename = "/mnt/public3/chaohy/pancancer/fig3/pseudotimebyCourse.png",width=13,height=10, units = "cm",bg = "white",res = 300)
plot_cells(cds, color_cells_by = "Course",
label_cell_groups = FALSE,
label_leaves = FALSE,
label_branch_points = FALSE,
label_roots = FALSE,
show_trajectory_graph = TRUE,
rasterize=T)
dev.off()
plot_cells(cds, color_cells_by = "major_cluster", label_cell_groups = FALSE,
label_leaves = FALSE, label_branch_points = FALSE,rasterize=TRUE)
plot_cells(cds, color_cells_by = "Course", label_cell_groups = FALSE,
label_leaves = FALSE, label_branch_points = FALSE,rasterize=TRUE)
png(filename = "/mnt/public3/chaohy/pancancer/fig3/pseudotimebyGeneExp.png",width=18,height=9, units = "cm",bg = "white",res = 300)
plot_genes_in_pseudotime(cds[c("HSALNG0050644","HSALNG0136338",
"HSALNG0051132","HSALNG0093627",
"HSALNG0103881","HSALNG0092235"),],
color_cells_by="Course",
min_expr=1, nrow = 3,ncol=3)
dev.off()
png(filename = "/mnt/public3/chaohy/pancancer/fig3/pseudotimebyGeneExp_low2high.png",width=9,height=4.5, units = "cm",bg = "transparent",res = 300)
plot_genes_in_pseudotime(cds[c("FXYD2",),],
color_cells_by="pseudotime",
min_expr=0.5,nrow=3)
dev.off()
png(filename = "/mnt/public3/chaohy/pancancer/fig3/pseudotimebyGeneExp_high2low.png",width=18,height=9, units = "cm",bg = "transparent",res = 300)
plot_genes_in_pseudotime(cds[c("TFF1","LINC01133","HSALNG0092235"),],
color_cells_by="pseudotime",
min_expr=0.5,nrow=3) -> p1
dev.off()
overlap_normal <- read.table("./overlap_normal.txt",header = F,sep = "\t")
read.table("/mnt/public3/chaohy/pancancer/fig2/gene.bed", sep="\t", header=F) -> gene_bed
gene_bed[gene_bed$V5=="lncRNA",] -> gene_bed_lncRNA
Track_genes_overlap_normal <- Track_genes[Track_genes$gene_short_name %in% overlap_normal$V1, ] %>% arrange(desc(morans_I))
Track_genes_overlap_normal_lnc <- Track_genes_overlap_normal[Track_genes_overlap_normal$gene_short_name %in% gene_bed_lncRNA$V4, ] %>% arrange(desc(morans_I))
plot_genes_in_pseudotime(cds[c("TFF1","HSALNG0051132","HSALNG0092235","FXYD2","HSALNG0111708","HSALNG0052479"),],
color_cells_by="pseudotime",
min_expr=0.5,nrow=3,ncol=2) -> p2
png(filename = "/mnt/public3/chaohy/pancancer/fig3/pseudotimebyGeneExp_ALL.png",width=15,height=10, units = "cm",bg = "transparent",res = 300)
p2
dev.off()
##寻找拟时轨迹差异基因
#graph_test分析最重要的结果是莫兰指数（morans_I），其值在-1至1之间，0代表此基因没有空间共表达效应，1代表此基因在空间距离相近的细胞中表达值高度相似。
Track_genes <- graph_test(cds, neighbor_graph="principal_graph", cores=50)
Track_genes %>% dplyr::filter(p_value < 0.001 & morans_I > 0.2) -> Track_genes_filter
write.table(Track_genes_filter, "/mnt/public3/chaohy/pancancer/fig3/Track_genes_by_monolce3.txt",col.names = T,sep = "\t", quote = F, row.names = F)
saveRDS(Track_genes,"./Track_genes.rds")
# 读取overlap的基因
overlap <- read.table("/mnt/public3/chaohy/pancancer/fig3/overlap.txt",header = F,sep="\t")
Track_genes_overlap <- Track_genes[Track_genes$gene_short_name %in% overlap$V1, ] %>% arrange(morans_I)
read.table("/mnt/public3/chaohy/pancancer/fig2/gene.bed", sep="\t", header=F) -> gene_bed
gene_bed[gene_bed$V5=="lncRNA",] -> gene_bed_lncRNA
Track_genes_overlap_lnc <- Track_genes_overlap[Track_genes_overlap$gene_short_name %in% gene_bed_lncRNA$V4, ] %>% arrange(desc(morans_I))
Track_genes_lnc <- Track_genes[Track_genes$gene_short_name %in% gene_bed_lncRNA$V4, ] %>% arrange(desc(morans_I))
png(filename = "/mnt/public3/chaohy/pancancer/fig3/pseudotimebyGeneExp11Lnc.png",width=18,height=18, units = "cm",bg = "white",res = 300)
plot_genes_in_pseudotime(cds[Track_genes_overlap_lnc$gene_short_name,],
color_cells_by="Course",
min_expr=0.5,nrow=6,ncol=2)
dev.off()
#挑选top10画图展示
Track_genes_sig <- Track_genes %>% top_n(n=10, morans_I) %>%
pull(gene_short_name) %>% as.character()
#基因表达趋势图
plot_genes_in_pseudotime(cds[c()], color_cells_by="Course", min_expr=0.5)
ggsave("/mnt/public3/chaohy/pancancer/fig3/fig3e.pdf",p,width =7, height =4)
plot_genes_violin(cds["HSALNG0051132"], group_cells_by = "Course") + theme(axis.text.x = element_text(angle = 45,hjust = 1))
#FeaturePlot图
plot_cells(cds, genes=Track_genes_sig, show_trajectory_graph=FALSE,
label_cell_groups=FALSE, label_leaves=FALSE)
##寻找共表达模块
genelist <- Track_genes %>% filter(q_value < 0.001 & morans_I > 0.3 ) %>% pull(gene_short_name) %>% as.character()
gene_module <- find_gene_modules(cds[genelist,], resolution=1e-2, cores = 10)
cell_group <- tibble::tibble(cell=row.names(colData(cds)),
cell_group=colData(cds)$predicted.id)
agg_mat <- aggregate_gene_expression(cds, gene_module, cell_group)
row.names(agg_mat) <- stringr::str_c("Module ", row.names(agg_mat))
pheatmap::pheatmap(agg_mat, scale="column", clustering_method="ward.D2")
plot_cell_trajectory(cds,color_by = "Course")
# 教程：https://www.jianshu.com/p/c402b6588e17
Track_genes %>% filter(q_value < 0.001 & morans_I > 0.1) %>% grepl("^HSALNG", gene) %>% sum()
Track_genes %>% filter(q_value < 0.001 & morans_I > 0.1 & grepl("^HSALNG", gene_short_name)) %>% dim()