-
Notifications
You must be signed in to change notification settings - Fork 2
/
analyze_SCP259.R
219 lines (187 loc) · 9.4 KB
/
analyze_SCP259.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
212
213
214
215
216
217
218
219
library(Seurat)
library(dplyr)
library(ggplot2)
library(ggsci)
library(ggpubr)
# prepare data
if(T){
# load data
seu = readRDS("./AvivRegev_2019cell_uc.raw.rds")
seu@meta.data %>% colnames()
colnames(seu@meta.data)[4] = "cell_id"
# check data
seu@assays$RNA@data[1,] %>% sum
# Normalize Data
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
# Cluster
result = seu@meta.data %>% group_by(Cluster) %>% summarise(count=n())
result
# Disease status
result = seu@meta.data %>% group_by(Health) %>% summarise(count=n())
result
}
# violin plot
# Expression of LAMP3, CCR7
# group by cluster
# Extended Data Figure 4d
if(T){
# load myeloid
imm = read.table("/home/niehu/niehu/ibd_public_data_20210821/AvivRegev_2019Cell_uc/download/SCP259/metadata/all.meta2.txt", header = T, stringsAsFactors = F, sep = "\t")
myeloid_cells = imm[imm$Cluster %in% c("Macrophages","DC1","DC2","CD69+ Mast","CD69- Mast","Inflammatory Monocytes","Cycling Monocytes"),]$NAME
genes = c("LAMP3","CCR7")
cells = myeloid_cells
title = "Gene expression"
gex = data.frame(seu@assays$RNA@data[genes,cells]) %>% t %>% as.data.frame()
rownames(gex) = rownames(seu@meta.data[cells, ])
gex$cell_id = seu@meta.data[cells, ]$cell_id
gex = reshape2::melt(gex)
gex = left_join(gex, seu@meta.data[,c("cell_id","Health",'Cluster')])
gex %>% head
colnames(gex)[2:3] = c("gene","exp")
ggplot(gex, aes(x = Cluster, y = log2(exp+0.1), fill = Cluster, color = Cluster)) +
facet_wrap(~gene, scales = "free_y", ncol = 4) +
geom_violin(trim = TRUE, scale = "width") +
scale_fill_d3(palette = 'category10')+
scale_color_d3(palette = 'category10') +
stat_summary(fun = "mean", geom = "crossbar", width = 0.3, colour = "lightblue4") +
theme_classic() +
labs(x="",y="Gene expression\nlog2(Norm + 0.1)", title =title) +
theme(legend.position = "right",
axis.text.x = element_text(family = "ArialMT", size = 0, color = "black", angle = 45, hjust = 1),
#axis.text.x = element_text(family = "ArialMT", size = 16, color = "black"),
axis.text.y = element_text(family = "ArialMT", size = 16, color = 'black'),
axis.title.y = element_text(family = "ArialMT",size = 16, color = 'black'),
legend.text = element_text(family = "ArialMT",size = 16, color ='black'),
legend.title = element_text(family = "ArialMT",size = 16, color ='black'),
strip.text = element_text(family = "ArialMT",size = 16, color ='black', face = 'italic'),
strip.background = element_blank(),
plot.title = element_text(family = "ArialMT",size = 18, color ='black', hjust = 0.5)
)
#ggsave("LAMP3_CCR7_in_Myeloid.SCP259.pdf", width = 8, height = 3)
}
# violin plot
# Expression of DUOX2, DUOXA2
# group by disease status
# Extended Data Figure 4e
if(T){
epi = read.table("/home/niehu/niehu/ibd_public_data_20210821/AvivRegev_2019Cell_uc/download/SCP259/cluster/Epi.tsne.txt", header = F, stringsAsFactors = F, sep = "\t")
epi_cells = epi$V1
genes = c("DUOX2","DUOXA2")
cells = epi_cells
title = ""
gex = data.frame(seu@assays$RNA@data[genes,cells]) %>% t %>% as.data.frame()
rownames(gex) = rownames(seu@meta.data[cells, ])
gex$cell_id = seu@meta.data[cells, ]$cell_id
gex = reshape2::melt(gex)
gex = left_join(gex, seu@meta.data[,c("cell_id","Health",'Cluster')])
gex %>% head
colnames(gex)[2:3] = c("gene","exp")
gex = gex %>%
filter(Cluster == 'Enterocytes')
my_comparisons <- list( c("Inflamed", "Healthy"), c("Non-inflamed", "Healthy"),
c("Inflamed","Non-inflamed"))
ggplot(gex, aes(x = Health, y = log2(exp+0.1), fill = Health, color = Health)) +
facet_wrap(~gene, scales = "free_y", ncol = 4) +
geom_violin(trim = TRUE, scale = "width") +
scale_fill_d3(palette = 'category10')+
scale_color_d3(palette = 'category10') +
stat_summary(fun = "mean", geom = "crossbar", width = 0.3, colour = "lightblue4") +
theme_classic() +
labs(x="",y="Gene expression\nlog2(Norm + 0.1)", title =title) +
theme(legend.position = "right",
axis.text.x = element_text(family = "ArialMT", size = 0, color = "black", angle = 45, hjust = 1),
#axis.text.x = element_text(family = "ArialMT", size = 16, color = "black"),
axis.text.y = element_text(family = "ArialMT", size = 16, color = 'black'),
axis.title.y = element_text(family = "ArialMT",size = 16, color = 'black'),
legend.text = element_text(family = "ArialMT",size = 16, color ='black'),
legend.title = element_text(family = "ArialMT",size = 16, color ='black'),
strip.text = element_text(family = "ArialMT",size = 16, color ='black', face = 'italic'),
strip.background = element_blank(),
plot.title = element_text(family = "ArialMT",size = 18, color ='black', hjust = 0.5)
) + stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")#, label = "p.signif")
# ggsave("DUOX2_DUOXA2_in_Enterocytes.SCP259.pdf", width = 7, height = 4)
# summary
gex %>% colnames()
gex$Health %>% table
gex %>% group_by(Health) %>% count()
# statistic test
wilcox.test(gex[gex$gene == "DUOX2" & gex$Health == "Healthy", ]$exp,
gex[gex$gene == "DUOX2" & gex$Health == "Inflamed", ]$exp)
}
# violin plot
# Expression of DUOX2, DUOXA2
# group by cluster
# Extended Data Figure 4f
if(T){
epi = read.table("/home/niehu/niehu/ibd_public_data_20210821/AvivRegev_2019Cell_uc/download/SCP259/cluster/Epi.tsne.txt", header = F, stringsAsFactors = F, sep = "\t")
epi_cells = epi$V1
genes = c("DUOX2","DUOXA2")
cells = epi_cells
title = "Gene expression"
gex = data.frame(seu@assays$RNA@data[genes,cells]) %>% t %>% as.data.frame()
rownames(gex) = rownames(seu@meta.data[cells, ])
gex$cell_id = seu@meta.data[cells, ]$cell_id
gex = reshape2::melt(gex)
gex = left_join(gex, seu@meta.data[,c("cell_id","Health",'Cluster')])
gex %>% head
colnames(gex)[2:3] = c("gene","exp")
ggplot(gex, aes(x = Cluster, y = log2(exp+0.1), fill = Cluster, color = Cluster)) +
facet_wrap(~gene, scales = "free_y", ncol = 4) +
geom_violin(trim = TRUE, scale = "width") +
scale_fill_d3(palette = 'category20b')+
scale_color_d3(palette = 'category20b') +
stat_summary(fun = "mean", geom = "crossbar", width = 0.3, colour = "lightblue4") +
theme_classic() +
labs(x="",y="Gene expression\nlog2(Norm + 0.1)", title =title) +
theme(legend.position = "right",
axis.text.x = element_text(family = "ArialMT", size = 0, color = "black", angle = 45, hjust = 1),
#axis.text.x = element_text(family = "ArialMT", size = 16, color = "black"),
axis.text.y = element_text(family = "ArialMT", size = 16, color = 'black'),
axis.title.y = element_text(family = "ArialMT",size = 16, color = 'black'),
legend.text = element_text(family = "ArialMT",size = 16, color ='black'),
legend.title = element_text(family = "ArialMT",size = 16, color ='black'),
strip.text = element_text(family = "ArialMT",size = 16, color ='black', face = 'italic'),
strip.background = element_blank(),
plot.title = element_text(family = "ArialMT",size = 18, color ='black', hjust = 0.5)
)
# ggsave("DUOX2_DUOXA2_in_Epi.SCP259.pdf", width = 10, height = 4.3)
}
# violin plot
# Tc17 markers
# group by cluster
# Extended Data Figure 4g
if(T){
# load myeloid
imm = read.table("/home/niehu/niehu/ibd_public_data_20210821/AvivRegev_2019Cell_uc/download/SCP259/metadata/all.meta2.txt", header = T, stringsAsFactors = F, sep = "\t")
cd8t = c("CD8+ IELs","CD8+ IL17+","CD8+ LP")
genes = c("IL17A","IL22","IL26","RORC")
cells = imm[imm$Cluster %in% cd8t,]$NAME
title = "Gene expression"
gex = data.frame(seu@assays$RNA@data[genes,cells]) %>% t %>% as.data.frame()
rownames(gex) = rownames(seu@meta.data[cells, ])
gex$cell_id = seu@meta.data[cells, ]$cell_id
gex = reshape2::melt(gex)
gex = left_join(gex, seu@meta.data[,c("cell_id","Health",'Cluster')])
gex %>% head
colnames(gex)[2:3] = c("gene","exp")
ggplot(gex, aes(x = Cluster, y = log2(exp+0.1), fill = Cluster, color = Cluster)) +
facet_wrap(~gene, scales = "free_y", ncol = 4) +
geom_violin(trim = TRUE, scale = "width") +
scale_fill_d3(palette = 'category10')+
scale_color_d3(palette = 'category10') +
stat_summary(fun = "mean", geom = "crossbar", width = 0.3, colour = "lightblue4") +
theme_classic() +
labs(x="",y="Gene expression\nlog2(Norm + 0.1)", title =title) +
theme(legend.position = "bottom",
axis.text.x = element_text(family = "ArialMT", size = 0, color = "black", angle = 45, hjust = 1),
#axis.text.x = element_text(family = "ArialMT", size = 16, color = "black"),
axis.text.y = element_text(family = "ArialMT", size = 16, color = 'black'),
axis.title.y = element_text(family = "ArialMT",size = 16, color = 'black'),
legend.text = element_text(family = "ArialMT",size = 16, color ='black'),
legend.title = element_text(family = "ArialMT",size = 16, color ='black'),
strip.text = element_text(family = "ArialMT",size = 16, color ='black', face = 'italic'),
strip.background = element_blank(),
plot.title = element_text(family = "ArialMT",size = 18, color ='black', hjust = 0.5)
)
# ggsave("Tc17_markers_in_CD8T.SCP259.pdf", width = 6, height = 3)
}