-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathMerge_EC_MACs_SCHW_MES_Carr.R
218 lines (157 loc) · 7.64 KB
/
Merge_EC_MACs_SCHW_MES_Carr.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
library(Seurat)
library(ggplot2)
packageVersion("Seurat")
# The signaling crosstalk between ECs and other cellTypes involved in nerve injury was investigated by merging
# ECs from our intact/7 dpi scRNA-seq datasets, together with Schwann cells, macrophages, and Mesenchymal cells
# from other studies {Toma, 2020; Ydens, 2020; Carr, 2019 }.
# In parallel the same analysis was performed with MES cells from our study instead of Carr's.
# In this script we use Carr's MES.
# Merge intact datasets
merged_int <- merge(EC_Intact, y = c(MES_Intact,MACROPHAGES_Intact, SCHWANN_Intact),
add.cell.ids = c("EC", "MES", "MACROPHAGES", "SCHWANN"), project = "Intact")
merged_int[["percent.mt"]] <- PercentageFeatureSet(merged_int, pattern = "^mt-")
merged_int[["percent.Rpl"]] <- PercentageFeatureSet(merged_int, pattern = "Rpl")
merged_int <- NormalizeData(merged_int, verbose = FALSE)
merged_int <- FindVariableFeatures(merged_int, selection.method = "vst", nfeatures = 2000)
#ScaleDate and regress for MTpercent and nFeature_RNA
merged_int <- ScaleData(merged_int, vars.to.regress = c("percent.mt", "nFeature_RNA"))
merged_int <- RunPCA(merged_int, npcs = 20, verbose = FALSE)
nPC = 20
res = 0.5
merged_int <- FindNeighbors(merged_int, dims = 1:nPC)
merged_int <- RunUMAP(merged_int, dims = 1:nPC)
merged_int <- RunTSNE(merged_int, dims = 1:nPC)
DimPlot(merged_int, label = T, repel = T)
DefaultAssay(merged_int) = "RNA"
getwd()
#saveRDS(merged_int, "Intact_EC_MES_MACS_SCHWANN_forCDB_090222_merged_seurat.Rds")
#merged_int = readRDS("Intact_EC_MES_MACS_SCHWANN_forCDB_090222_merged_seurat.Rds")
# Merge injury datasets
merged_inj <- merge(EC_Injury, y = c(MES_Injury,MACROPHAGES_Injury, SCHWANN_Injury),
add.cell.ids = c("EC", "MES", "MACROPHAGES", "SCHWANN"), project = "Injury")
merged_inj[["percent.mt"]] <- PercentageFeatureSet(merged_inj, pattern = "^mt-")
merged_inj[["percent.Rpl"]] <- PercentageFeatureSet(merged_inj, pattern = "Rpl")
merged_inj <- NormalizeData(merged_inj, verbose = FALSE)
merged_inj <- FindVariableFeatures(merged_inj, selection.method = "vst", nfeatures = 2000)
#ScaleDate and regress for MTpercent and nFeature_RNA
merged_inj <- ScaleData(merged_inj, vars.to.regress = c("percent.mt", "nFeature_RNA"))
merged_inj <- RunPCA(merged_inj, npcs = 20, verbose = FALSE)
nPC = 20
res = 0.5
merged_inj <- FindNeighbors(merged_inj, dims = 1:nPC)
merged_inj <- RunUMAP(merged_inj, dims = 1:nPC)
merged_inj <- RunTSNE(merged_inj, dims = 1:nPC)
DimPlot(merged_inj, label = T, repel = T)
DefaultAssay(merged_inj) = "RNA"
#saveRDS(merged_inj, "Injury_EC_MES_MACS_SCHWANN_forCDB_090222_merged_seurat.Rds")
#merged_inj = readRDS("Injury_EC_MES_MACS_SCHWANN_forCDB_090222_merged_seurat.Rds")
# Intact + Injury datasets
DefaultAssay(merged_inj) = "RNA"
DefaultAssay(merged_int) = "RNA"
EC_Intact <- subset(x = merged_int, subset = stim == "1")
EC_Injury <- subset(x = merged_inj, subset = stim == "2")
MACROPHAGES_Intact <- subset(x = merged_int, subset = stim == "macs_Intact")
MACROPHAGES_Injury <- subset(x = merged_inj, subset = stim == "macs_Injury")
SCHWANN_Intact <- subset(x = merged_int, subset = stim == "schwann_Intact")
SCHWANN_Injury <- subset(x = merged_inj, subset = stim == "schwann_Injury")
MES_Intact <- subset(x = merged_int, subset = stim == "mes_Intact")
MES_Injury <- subset(x = merged_inj, subset = stim == "mes_Injury")
# Merge intact and injury datasets
merged <- merge(EC_Intact, y = c(MES_Intact,MACROPHAGES_Intact, SCHWANN_Intact, EC_Injury,MES_Injury,MACROPHAGES_Injury, SCHWANN_Injury),
add.cell.ids = c("EC_Intact", "MES_Intact", "MACROPHAGES_Intact", "SCHWANN_Intact", "EC_Injury", "MES_Injury", "MACROPHAGES_Injury", "SCHWANN_Injury"), project = "IntactInjury")
merged[["percent.mt"]] <- PercentageFeatureSet(merged, pattern = "^mt-")
merged[["percent.Rpl"]] <- PercentageFeatureSet(merged, pattern = "Rpl")
merged <- NormalizeData(merged, verbose = FALSE)
merged <- FindVariableFeatures(merged, selection.method = "vst", nfeatures = 2000)
#ScaleDate and regress for MTpercent and nFeature_RNA
merged <- ScaleData(merged, vars.to.regress = c("percent.mt", "nFeature_RNA"))
merged <- RunPCA(merged, npcs = 20, verbose = FALSE)
nPC = 20
res = 0.5
merged <- FindNeighbors(merged, dims = 1:nPC)
merged <- RunUMAP(merged, dims = 1:nPC)
merged <- RunTSNE(merged, dims = 1:nPC)
#saveRDS(merged, merged_all_cell_types.Rds)
#merged = readRDS("merged_all_cell_types.Rds")
merged@meta.data$stim[merged@meta.data$stim == "1"] <- "EC_Intact"
merged@meta.data$stim[merged@meta.data$stim == "2"] <- "EC_Injury"
# Semaphorin DotPlot
# upload semaphorin genes to plot
sema = readLines("/Users/maurizio.aurora/Downloads/sema.txt")
merged$stim <- factor(merged$stim,
levels=c("EC_Intact", "EC_Injury",
"mes_Intact", "mes_Injury",
"macs_Intact", "macs_Injury",
"schwann_Intact", "schwann_Injury"))
DefaultAssay(merged) = "RNA"
table(Idents(merged))
new.cluster.ids.lit <- c('EC',
'EC',
'EC',
'EC',
'MACS',
'MES_DIFF',
'MES_ENDO',
'MES_EPI',
'MES_PERI',
'SCHWANN',
'EC',
'EC',
'EC',
'EC',
'EC'
)
names(new.cluster.ids.lit) <- levels(merged)
object_new <- RenameIdents(merged, new.cluster.ids.lit)
test = c("EC", "MES_DIFF",
"MES_ENDO", "MES_EPI",
"MES_PERI", "MACS",
"SCHWANN")
levels(object_new) <- test
object_new$dataset <- factor(object_new$stim,
levels=c("EC_Intact", "EC_Injury",
"mes_Intact", "mes_Injury",
"macs_Intact", "macs_Injury",
"schwann_Intact", "schwann_Injury"))
# Fig S3 Semaphorin dotplot
DefaultAssay(object_new) = "RNA"
pdf("Semadotplot_splitbystim_newcols_green_violet_test.pdf", 8, 7)
DotPlot(object_new,
split.by = "dataset",
features = sema,
dot.scale = 8,
cols = c("limegreen","darkviolet","limegreen","darkviolet","limegreen","darkviolet","limegreen","darkviolet"),
assay = "RNA") +
coord_flip() +
RotatedAxis()
dev.off()
#KDR dotplot for referees
pdf("DotPlot_Kdr_splitbystim_newcols_green_violet.pdf", 8, 3)
DotPlot(object_new,
split.by = "dataset",
features = "Kdr",
dot.scale = 8,
cols = c("limegreen","darkviolet","limegreen","darkviolet","limegreen","darkviolet","limegreen","darkviolet"),
assay = "RNA") +
coord_flip() +
RotatedAxis()
dev.off()
#KDR vlnplots for referees
pdf("VlnPlot_Kdr_splitbystim_newcols_green_violet_median_line.pdf", 8, 4)
VlnPlot(object_new, "Kdr", group.by = "dataset", pt.size = 0, cols = c("limegreen","darkviolet","limegreen","darkviolet","limegreen","darkviolet","limegreen","darkviolet")) +
stat_summary(fun.y=median, geom="point", color="black", shape = 95, size = 12)+
theme(legend.position="none")
dev.off()
pdf("VlnPlot_Kdr_median_line.pdf", 8, 4)
VlnPlot(object_new, "Kdr", pt.size = 0) +
stat_summary(fun.y=median, geom="point", color="black", shape = 95, size = 15)+
theme(legend.position="none")
dev.off()
pdf("DotPlot_Kdr.pdf", 8, 4)
DotPlot(object_new,
features = "Kdr",
dot.scale = 8,
assay = "RNA") +
coord_flip() +
RotatedAxis()
dev.off()