-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathscRNA-seq.R
228 lines (174 loc) · 7.41 KB
/
scRNA-seq.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
##reanalysis of the single RNA-seq data downloaded from GSE189358
source activate seurat
library(Seurat)
library(harmony)
library(dplyr)
library(patchwork)
library(ggplot2)
samples=list.files("/mnt/sda/jwz/data/TTS/GSE189358/cellranger")
samples
names(samples) = c("Control","ISO")
samples
##批量读取数据并创建Seurat对象
scRNAlist <- list()
for(i in 1:length(samples)){
counts <- Read10X(data.dir = samples[i])
scRNAlist[[i]] <- CreateSeuratObject(counts, min.cells = 3)
#scRNAlist[[i]] <- CreateSeuratObject(counts)
}
for(i in 1:length(scRNAlist)){
sc <- scRNAlist[[i]]
# 计算线粒体基因比例
sc[["mt_percent"]] <- PercentageFeatureSet(sc, pattern = "^mt-")
##计算核糖体基因比例
sc[["ribo_percent"]] <- PercentageFeatureSet(sc, pattern = "^Rp[sl]")
# 计算红细胞比例
HB_genes <- c("Hba-a1","Hba-a2","Hbegf","Hbb-y","Hbp1","Hbs1l","Hba-x")
HB_m <- match(HB_genes, rownames(sc@assays$RNA))
HB_genes <- rownames(sc@assays$RNA)[HB_m]
HB_genes <- HB_genes[!is.na(HB_genes)]
sc[["HB_percent"]] <- PercentageFeatureSet(sc, features=HB_genes)
scRNAlist[[i]] <- sc
rm(sc)
}
scRNAlist[[1]]@meta.data[1:6,1:6]
pdf("./qc/qc-before.pdf")
VlnPlot(scRNAlist[[1]], features = c("nFeature_RNA", "nCount_RNA", "mt_percent","ribo_percent"), ncol = 4, pt.size = 0)
VlnPlot(scRNAlist[[2]], features = c("nFeature_RNA", "nCount_RNA", "mt_percent","ribo_percent"), ncol = 4, pt.size = 0)
dev.off()
##批量过滤细胞
scRNAlist <- lapply(X = scRNAlist, FUN = function(x){
x <- subset(x,
subset = nFeature_RNA > 500 & nFeature_RNA < 6000 &
mt_percent < 25
)})
pdf("./qc/qc-filter.pdf")
VlnPlot(scRNAlist[[1]], features = c("nFeature_RNA", "nCount_RNA", "mt_percent","ribo_percent"), ncol = 4, pt.size = 0)
VlnPlot(scRNAlist[[2]], features = c("nFeature_RNA", "nCount_RNA", "mt_percent","ribo_percent"), ncol = 4, pt.size = 0)
dev.off()
##利用merge()合并Seurat对象
scRNAlist <- merge(scRNAlist[[1]],y=c(scRNAlist[[2]]))
head([email protected],5)
##增加metadata分组信息
library("stringr")
phe=str_split(rownames([email protected]),'_',simplify = T)
head(phe)
[email protected]$group=phe[,1]
head([email protected])
## 统计细胞数
table(scRNAlist[[]]$orig.ident)
dim(scRNAlist) #查看基因数和细胞总数
table([email protected]$orig.ident) #查看每个样本的细胞数
##1.数据标准化，LogNormalize的算法：A = log( 1 + ( UMIA ÷ UMITotal ) × 10000
scRNAlist <- NormalizeData(scRNAlist)
##2.鉴定高变基因
scRNAlist <- FindVariableFeatures(scRNAlist,selection.method = "vst",nfeatures = 2000)
# 查看最高变的10个基因
top10 <- head(VariableFeatures(scRNAlist), 10)
# 画出不带标签或带标签基因点图
plot1 <- VariableFeaturePlot(scRNAlist)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
pdf("VariableFeaturePlot.pdf")
plot1 + plot2
dev.off()
##3.ScaleData()
scRNAlist <- ScaleData(scRNAlist)
##降维可视化
##4. PCA降维
scRNAlist <- RunPCA(scRNAlist)
##查看PCA结果
print(scRNAlist[["pca"]], dims = 1:5, nfeatures = 5)
##可视化PCA结果
##展示主成分基因
pdf("PCA-gene.pdf")
VizDimLoadings(scRNAlist, dims = 1:2, reduction = "pca")
dev.off()
##热图，第一主成分
pdf("PCA-heatmap.pdf")
DimHeatmap(scRNAlist, dims = 1, cells = 500, balanced = TRUE)
dev.off()
##确定数据的维度
pdf("ElbowPlot-50.pdf")
ElbowPlot(scRNAlist, ndims = 50)
dev.off()
##保留20个主成分，累加贡献度达到90%
scRNAlist <- RunUMAP(scRNAlist,reduction = "pca", dims = 1:20, verbose = F)
scRNAlist <- RunTSNE(scRNAlist,reduction = "pca", dims = 1:20, verbose = F)
pdf("umap-before-integration-group.pdf")
DimPlot(scRNAlist,reduction = "umap", group.by = "group") + plot_annotation(title = "before integration")
dev.off()
pdf("tsne-before-integration-1.pdf")
DimPlot(scRNAlist,reduction = "tsne", group.by = "group") + plot_annotation(title = "before integration")
dev.off()
##harmony整合
scRNA_harmony <- RunHarmony(scRNAlist, group.by.vars = "orig.ident")
scRNA_harmony@reductions[["harmony"]][[1:5,1:5]]
# 聚类
scRNA_harmony <- FindNeighbors(scRNA_harmony, reduction = "harmony", dims = 1:20)
scRNA_harmony <- scRNA_harmony %>% FindClusters(resolution = 0.1)
# umap/tsne降维
scRNA_harmony <- RunTSNE(scRNA_harmony, reduction = "harmony", dims = 1:20)
scRNA_harmony <- RunUMAP(scRNA_harmony, reduction = "harmony", dims = 1:20)
pdf("umap-after-integration-group.pdf")
DimPlot(scRNA_harmony,reduction = "umap", group.by = "group") + plot_annotation(title = "after integration")
dev.off()
pdf("tsne-after-integration-group.pdf")
DimPlot(scRNA_harmony,reduction = "tsne", group.by = "group") + plot_annotation(title = "after integration")
dev.off()
pdf("harmony_umap-cluster.pdf")
DimPlot(scRNA_harmony,reduction = "umap",label = TRUE)
dev.off()
pdf("harmony_tsne-cluster.pdf")
DimPlot(scRNA_harmony,reduction = "tsne",label = TRUE)
dev.off()
# split.by 展示分组聚类
pdf("harmony_umap-split.pdf")
DimPlot(scRNA_harmony, reduction = "umap", split.by = "group",ncol=2,label = T)
dev.off()
pdf("harmony_tsne-split.pdf")
DimPlot(scRNA_harmony, reduction = "tsne", split.by = "group",ncol=2,label = T)
dev.off()
##寻找marker基因，注释细胞类型
markers <- FindAllMarkers(scRNA_harmony, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
markers_df = markers %>% group_by(cluster)
write.table(markers_df,file="all-markers.txt",quote=F,sep="\t",row.names=F,col.names=T)
markers_df = markers %>% group_by(cluster) %>% top_n(n = 200, wt = avg_log2FC)
head(markers_df)
write.table(markers_df,file="markers200.txt",quote=F,sep="\t",row.names=F,col.names=T)
#每个聚类前10个差异基因表达热图
top10 <- markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
pdf("marker-heatmap.pdf")
DoHeatmap(scRNA_harmony, features = top10$gene) + NoLegend()
dev.off()
##人工注释
#加上注释
new.cluster.ids <- c("Monocytes", "B cells", "Macrophages", "T cells", "Neutrophils", "NK cells", "T helper cells", "Dendritic Cells", "Monocytes", "Endothelial cells")
names(new.cluster.ids) <- levels(scRNA_harmony)
scRNA_harmony <- RenameIdents(scRNA_harmony, new.cluster.ids)
#在metadata中，添加Celltype信息
scRNA_harmony$celltype <- Idents(scRNA_harmony)
table(scRNA_harmony$celltype,[email protected]$seurat_clusters)
##可视化
pdf("harmony-cluster-name-umap.pdf")
DimPlot(scRNA_harmony, reduction = "umap", group.by = "celltype", label = FALSE, pt.size = 0.5)
dev.off()
pdf("harmony-cluster-split.pdf")
DimPlot(scRNA_harmony, reduction = "umap", group.by = "celltype", split.by="group", ncol=1, label = FALSE, pt.size = 0.5)
dev.off()
##细胞比例计算及可视化
table(scRNA_harmony$orig.ident) #查看各样本细胞数
table(Idents(scRNA_harmony), scRNA_harmony$orig.ident) #各样本不同细胞群细胞数
table(scRNA_harmony$group) #查看各组别细胞数
table(Idents(scRNA_harmony), scRNA_harmony$group) #各组别不同细胞群细胞数
Cellratio <- prop.table(table(Idents(scRNA_harmony), scRNA_harmony$group), margin = 2)#计算各组别不同细胞群比例
Cellratio
Cellratio <- as.data.frame(Cellratio)
Cellratio$Var2=factor(Cellratio$Var2,levels=c("WT","BLM","AGGF1"))
colourCount = length(unique(Cellratio$Var1))
library(ggplot2)
pdf("cell-ratio.pdf")
ggplot(Cellratio) + geom_bar(aes(x =Var2, y= Freq, fill = Var1),stat = "identity",width = 0.7,size = 0.5,colour = '#222222')+
theme_classic() +
labs(x='Group',y = 'Cell Type Ratio')+
theme(panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"))
dev.off()