-
Notifications
You must be signed in to change notification settings - Fork 2
/
Toma_reanalysis.R
240 lines (164 loc) · 8.68 KB
/
Toma_reanalysis.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
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
suppressMessages(library(ggbeeswarm))
suppressMessages(library(dplyr))
suppressMessages(library(Seurat))
suppressMessages(library(Matrix))
suppressMessages(library(gplots))
suppressMessages(library(ggplot2))
suppressMessages(library(openxlsx))
suppressMessages(library(cowplot))
suppressMessages(library(patchwork))
library(pheatmap)
library(reshape2)
library(scales)
library(viridis)
library(SeuratWrappers)
library(slingshot)
require(BiocStyle)
library(SingleCellExperiment)
packageVersion("Seurat")
###############################################
# Examine Uninjured post injurey samples from Toma et al. 2020 GSE147285_RAW
# Peripheral Nerve Single-Cell Analysis Identifies Mesenchymal Ligands that Promote Axonal Growth
# Sciatic nerve, GSM4423509
###############################################
TomaUninj <-read.table(gzfile("/Users/maurizio.aurora/Dropbox (HSR Global)/WORKSPACE/Bonanomi/Bonanomi_1287_scRNA_injury/7_bioinfo/public_data/Toma/GSE147285_RAW/GSM4423509_Uninj_Sciatic.txt.gz"), header=TRUE)
rownames(TomaUninj) = TomaUninj$GENE
TomaUnin = TomaUninj[-c(1)]
TU <- CreateSeuratObject(counts = TomaUnin, project = "TOMA_Uninj", min.cells = 5, min.features = 200)
TU[["percent.mt"]] <- PercentageFeatureSet(TU, pattern = "^mt-")
TU[["percent.Rpl"]] <- PercentageFeatureSet(TU, pattern = "Rpl")
TU$stim <- "Toma_Uninj"
VlnPlot(TU, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
min_nFeature_RNA = 200
max_nFeature_RNA = 5000
max_percent_MT = 20
TU.subset <- subset(TU,
subset = nFeature_RNA > min_nFeature_RNA & nFeature_RNA < max_nFeature_RNA & percent.mt < max_percent_MT)
FeatureScatter(TU.subset, feature1 = "nCount_RNA", feature2 = "percent.mt",cols = NULL)
FeatureScatter(TU.subset, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
TU.subset <- NormalizeData(TU.subset, verbose = FALSE)
TU.subset <- FindVariableFeatures(TU.subset, selection.method = "vst", nfeatures = 2000)
#ScaleDate and regress for MTpercent and nFeature_RNA
TU.subset <- ScaleData(TU.subset,
vars.to.regress = c("percent.mt", "nFeature_RNA"))
TU.subset <- RunPCA(TU.subset, npcs = 20, verbose = FALSE)
print(TU.subset[["pca"]], dims = 1:5, nfeatures = 5)
VizDimLoadings(TU.subset, dims = 1:3, reduction = "pca")
DimPlot(TU.subset, reduction = "pca")
DimHeatmap(TU.subset, dims = 1, cells = 500, balanced = TRUE)
DimHeatmap(TU.subset, dims = 1:15, cells = 500, balanced = TRUE)
ElbowPlot(TU.subset)
nPC = 20
res = 4
TU.subset <- FindNeighbors(TU.subset, dims = 1:nPC)
TU.subset <- FindClusters(TU.subset, resolution = res)
TU.subset <- RunUMAP(TU.subset, dims = 1:nPC)
TU.subset <- RunTSNE(TU.subset, dims = 1:nPC)
DimPlot(TU.subset, reduction = "umap")
DimPlot(TU.subset, reduction = "tsne")
#EC
FeaturePlot(TU.subset, "Pecam1", order = T, reduction = "umap", label = T)
FeaturePlot(TU.subset, c("Vwf"), order = T, reduction = "umap", label = T)
FeaturePlot(TU.subset, c("Cdh5"), order = T, reduction = "umap", label = T)
FeaturePlot(TU.subset, c("Mest"), order = T, reduction = "umap", label = T)
#prolif
FeaturePlot(TU.subset, c("Top2a") , order = T, reduction = "umap")
#pericites
FeaturePlot(TU.subset, "Rgs5", order = T, reduction = "umap", label = T)
#fibro
FeaturePlot(TU.subset, "Pdgfra", order = T, reduction = "umap", label = T)
#schwann non myel
FeaturePlot(TU.subset, "Ngfr", order = T, reduction = "umap", label = T)
FeaturePlot(TU.subset, "L1cam", order = T, reduction = "umap", label = T)
#schwann myelinating
FeaturePlot(TU.subset, "Mbp", order = T, reduction = "umap", label = T)
FeaturePlot(TU.subset, "Plp1", order = T, reduction = "umap", label = T)
FeaturePlot(TU.subset, "L1cam", order = T, reduction = "umap", label = T)
#t nk cells
FeaturePlot(TU.subset, "Cxcr6", order = T, reduction = "umap", label = T)
FeaturePlot(TU.subset, "Nkg7", order = T, reduction = "umap", label = T)
#macrophages
FeaturePlot(TU.subset, "Aif1", order = T, reduction = "umap", label = T)
#among all different clusters select only the EC ones
FeaturePlot(TU.subset, "nFeature_RNA", order = T, reduction = "umap", label = T)
FeaturePlot(TU.subset, "percent.mt", order = T, reduction = "umap", label = T)
#among all different clusters select only the EC ones
EC_TU.subset = subset(TU.subset, idents = c("23", "6", "24", "20", "16", "11", "12", "14", "25"))
#among all different clusters select only the Schwann ones
TU_shwann.subset = subset(TU.subset, idents = c("0", "1", "9"))
# all Toma uninjured cells
saveRDS(TU.subset, "Toma_Uninjured.Rds")
# only EC cells among all Toma uninjured cells
saveRDS(EC_TU.subset, "EC_Toma_Uninjured.Rds")
# only Schwann cells among all Toma uninjured cells
saveRDS(TU_shwann.subset, "Toma_Uninjured_Schwann.Rds")
###############################################
# Examine 3D post injury samples from Toma et al.
# Sciatic nerve, 3 days post injury, (myelin removal beads) GSM4423506
###############################################
Toma3d <-read.table(gzfile("/Users/maurizio.aurora/Dropbox (HSR Global)/WORKSPACE/Bonanomi/Bonanomi_1287_scRNA_injury/7_bioinfo/public_data/Toma/GSE147285_RAW/GSM4423506_Inj_Sciatic_3d.txt.gz"), header=TRUE)
rownames(Toma3d) = Toma3d$GENE
Toma3D = Toma3d[-c(1)]
head(Toma3D[1:2,1:2])
T3D <- CreateSeuratObject(counts = Toma3D, project = "TOMA3D", min.cells = 5, min.features = 200)
T3D[["percent.mt"]] <- PercentageFeatureSet(T3D, pattern = "^mt-")
T3D[["percent.Rpl"]] <- PercentageFeatureSet(T3D, pattern = "Rpl")
T3D$stim <- "Toma_3D"
#options(repr.plot.width=8, repr.plot.height=6)
VlnPlot(T3D, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
min_nFeature_RNA = 200
max_nFeature_RNA = 6000
max_percent_MT = 20
T3D.subset <- subset(T3D,
subset = nFeature_RNA > min_nFeature_RNA & nFeature_RNA < max_nFeature_RNA & percent.mt < max_percent_MT)
FeatureScatter(T3D.subset, feature1 = "nCount_RNA", feature2 = "percent.mt",cols = NULL)
FeatureScatter(T3D.subset, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
T3D.subset <- NormalizeData(T3D.subset, verbose = FALSE)
T3D.subset <- FindVariableFeatures(T3D.subset, selection.method = "vst", nfeatures = 2000)
#ScaleDate and regress for MTpercent and nFeature_RNA
T3D.subset <- ScaleData(T3D.subset,
vars.to.regress = c("percent.mt", "nFeature_RNA"))
T3D.subset <- RunPCA(T3D.subset, npcs = 15, verbose = FALSE)
print(T3D.subset[["pca"]], dims = 1:5, nfeatures = 5)
VizDimLoadings(T3D.subset, dims = 1:3, reduction = "pca")
DimPlot(T3D.subset, reduction = "pca")
DimHeatmap(T3D.subset, dims = 1, cells = 500, balanced = TRUE)
DimHeatmap(T3D.subset, dims = 1:15, cells = 500, balanced = TRUE)
ElbowPlot(T3D.subset)
nPC = 15
res = 4
T3D.subset <- FindNeighbors(T3D.subset, dims = 1:nPC)
T3D.subset <- FindClusters(T3D.subset, resolution = res)
T3D.subset <- RunUMAP(T3D.subset, dims = 1:nPC)
T3D.subset <- RunTSNE(T3D.subset, dims = 1:nPC)
DimPlot(T3D.subset, reduction = "umap")
DimPlot(T3D.subset, reduction = "tsne")
DimPlot(T3D.subset, label=T, cells.highlight=check, cols.highlight = c("cyan"), cols= "grey", reduction = "tsne")
T3D.subset$stim <- "Toma_3D"
FeaturePlot(T3D.subset, c("Mest", "Mki67","Birc5","Cdk1","Top2a") , order = T, reduction = "tsne")
FeaturePlot(T3D.subset, c("Mest", "Mki67","Birc5","Cdk1","Top2a") , order = T, reduction = "umap")
#endothelial
FeaturePlot(T3D.subset, "Cdh5", order = T, reduction = "umap", label = T)
FeaturePlot(T3D.subset, "Cdh5", order = T, reduction = "tsne", label = T)
FeaturePlot(T3D.subset, "Pecam1", order = T, reduction = "umap", label = T)
FeaturePlot(T3D.subset, "Pecam1", order = T, reduction = "tsne", label = T)
#pericites
FeaturePlot(T3D.subset, "Rgs5", order = T, reduction = "tsne", label = T)
FeaturePlot(T3D.subset, "Rgs5", order = T, reduction = "umap", label = T)
#fibro
FeaturePlot(T3D.subset, "Pdgfra", order = T, reduction = "tsne", label = T)
FeaturePlot(T3D.subset, "Pdgfra", order = T, reduction = "umap", label = T)
#schwann
FeaturePlot(T3D.subset, "Ngfr", order = T, reduction = "tsne", label = T)
FeaturePlot(T3D.subset, "Ngfr", order = T, reduction = "umap", label = T)
#tcells
FeaturePlot(T3D.subset, "Cxcr6", order = T, reduction = "tsne", label = T)
FeaturePlot(T3D.subset, "Cxcr6", order = T, reduction = "umap", label = T)
#macrophages
FeaturePlot(T3D.subset, "Aif1", order = T, reduction = "tsne", label = T)
FeaturePlot(T3D.subset, "Aif1", order = T, reduction = "umap", label = T)
#among all different clusters select only the EC ones
EC_T3D.subset = subset(T3D.subset, idents = c("7", "34", "35", "9", "20"))
FeaturePlot(EC_T3D.subset, c("Mest", "Mki67","Birc5","Cdk1","Top2a") , order = T, reduction = "umap")
FeaturePlot(T3D.subset, "nFeature_RNA", order = T, reduction = "tsne", label = T)
saveRDS(EC_T3D.subset, "EC_Toma_3D.Rds")