-
Notifications
You must be signed in to change notification settings - Fork 0
/
sl2_cl_integration.Rmd
163 lines (146 loc) · 6.37 KB
/
sl2_cl_integration.Rmd
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
---
title: "introduction"
output: rmarkdown::html_vignette
vignette: >
%/VignetteIndexEntry{introduction}
%/VignetteEngine{knitr::rmarkdown}
%/VignetteEncoding{UTF-8}
---
```{r message=FALSE}
library(Seurat)
library(dplyr)
library(ggplot2)
library(patchwork)
library(sqldf)
library(dplyr)
library(RColorBrewer)
library(plotly)
library(spatstat)
library(gplots)
library(corrplot)
library(stringr)
library(VennDiagram)
library(grid)
library(reshape2)
library(ggplot2)
study_name<-"human_SLCL_Visium"
#PLEASE ADD YOUR WORKING PATH HERE
study_path<-"your_path_here/"
samples <-c("30i","35i","40i", "53i", "47i", "48i")
#, "57i")
sample_label <-c("P1","P2","P3", "P4", "P5", "P6")
#Create a sub-folder in your
#work path where all outputs
#will be stored
save_path<-paste0(study_path,"R/")
save_on<-1
```
###AS LONG AS YOU HAVE FOLDERS STARTING WITH 'V' IN 'study' PATH AND IF YOU###
###HAVE GIVEN THE CORRECT SAMPLE NAMES (SEE 'samples' ABOVE) THE FOLLOWING CODE###
### WILL PICK UP .H5 FILES ASSOCIATED WITH THESE SAMPLES###
```{r loadSeurat h5}
# Start the clock!
ptm <- proc.time()
seuratSpatial <- list()
counter<-1
for (sample in samples){
sample_dir <- Sys.glob(paste0(study_path,"V*/*",sample,"/"))
sample_path <- Sys.glob(paste0(study_path,"V*/*",sample,"/","*.h5"))
sample_path_file <- tail(strsplit(sample_path, split = "/")[[1]], n = 1)
seuratSpatial[[sample]]<-Load10X_Spatial(
sample_dir,
filename = sample_path_file,
assay = "Spatial",
slice = sample,
filter.matrix = TRUE,
to.upper = FALSE
)
seuratSpatial[[sample]]$orig.ident <- sample
seuratSpatial[[sample]]$label <- sample_label[[counter]]
counter<-counter+1
}
# Stop the clock
time_taken<- proc.time() - ptm
print(paste0("Time elapsed: ", sprintf((time_taken[3]/60), fmt = '%#.2f')," minutes"))
```
#QC plots
```{r data_pre_processing}
pdf(file = paste0(save_path, study_name, "_overall_counts.pdf"))
for(object in seuratSpatial){
plot1 <- VlnPlot(object, features = "nCount_Spatial", pt.size = 0.1) + NoLegend()
plot2 <- SpatialFeaturePlot(object, features = "nCount_Spatial") + theme(legend.position = "right")
plot3 <- SpatialFeaturePlot(object, features = "nFeature_Spatial") + theme(legend.position = "right")
print(wrap_plots(plot1, plot2, plot3))
}
dev.off()
```
#Normalize
```{r SCTRansform & merge, warning=FALSE}
# Start the clock!
ptm <- proc.time()
for(sample in samples){
seuratSpatial[[sample]] <- SCTransform(seuratSpatial[[sample]], assay = "Spatial", verbose = FALSE, vars.to.regress = c("nCount_Spatial", "nFeature_Spatial"))
}
# Stop the clock
time_taken<- proc.time() - ptm
print(paste0("Time elapsed: ", sprintf((time_taken[3]/60), fmt = '%#.2f')," minutes"))
```
#Integrate across samples
```{r integrate_workflow_for_spatial}
# Start the clock!
ptm <- proc.time()
features <- SelectIntegrationFeatures(object.list = seuratSpatial, nfeatures = 3000)
seuratSpatial <- PrepSCTIntegration(object.list = seuratSpatial, anchor.features = features)
immune.anchors <- FindIntegrationAnchors(object.list = seuratSpatial, normalization.method = "SCT", anchor.features = features)
nd2117SLCL_skin_merged <- IntegrateData(anchorset = immune.anchors, normalization.method = "SCT")
# Stop the clock
time_taken<- proc.time() - ptm
print(paste0("Time elapsed: ", sprintf((time_taken[3]/60), fmt = '%#.2f')," minutes"))
```
#Cluster
```{r clustering}
# Start the clock!
ptm <- proc.time()
nd2117SLCL_skin_merged <- RunPCA(nd2117SLCL_skin_merged, assay = "integrated", verbose = FALSE)
ElbowPlot(nd2117SLCL_skin_merged)
res=0.9
dims=15
spot_colours = c('0'='#F68282','1'='#31C53F','2'='#1FA195','3'='#B95FBB','4'='#D4D915',
'5'='#28CECA','6'='#ff9a36', '7'='#2FF18B','8'='#aeadb3', '9'='#faf4cf','10'='#CCB1F1','11'='#25aff5')
levels(Idents(nd2117SLCL_skin_merged))
my_cols2 <- spot_colours[order(as.integer(names(spot_colours)))]
#clustering
nd2117SLCL_skin_merged <- FindNeighbors(nd2117SLCL_skin_merged, reduction = "pca", dims = 1:dims)
nd2117SLCL_skin_merged <- FindClusters(nd2117SLCL_skin_merged, verbose = FALSE, resolution = res)
#umap/tsne for visualisation
nd2117SLCL_skin_merged <- RunUMAP(nd2117SLCL_skin_merged, reduction = "pca", dims = 1:dims)
nd2117SLCL_skin_merged <- RunTSNE(nd2117SLCL_skin_merged, reduction = "pca", dims = 1:dims)
#prepare for de
nd2117SLCL_skin_merged <- PrepSCTFindMarkers(nd2117SLCL_skin_merged, assay = "SCT")
# Stop the clock
time_taken<- proc.time() - ptm
print(paste0("Time elapsed: ", sprintf((time_taken[3]/60), fmt = '%#.2f')," minutes"))
pdf(paste0(save_path, "with47_48_DimPlot_PC1to",dims,"res",res,".pdf"))
DimPlot(nd2117SLCL_skin_merged, reduction = "tsne", label = TRUE, pt.size = 0.8, shuffle = TRUE, cols = my_cols2)
DimPlot(nd2117SLCL_skin_merged, reduction = "umap", label = TRUE, pt.size = 0.8, shuffle = TRUE, cols = my_cols2,label.box = TRUE, repel = TRUE)
DimPlot(nd2117SLCL_skin_merged, reduction = "tsne", group.by = "orig.ident")
DimPlot(nd2117SLCL_skin_merged, reduction = "umap", group.by = "label", pt.size = 1.5) + ggtitle(label = NULL)
DimPlot(nd2117SLCL_skin_merged, reduction = "tsne", group.by = "label", pt.size = 0.8, order = c("35i"))+ ggtitle(label = NULL)
DimPlot(nd2117SLCL_skin_merged, reduction = "umap", group.by = "label")+ ggtitle(label = NULL)
dev.off()
prop.table(table(Idents(nd2117SLCL_skin_merged), nd2117SLCL_skin_merged$orig.ident), margin = 2)
#write proportion per cell type
write.csv(prop.table(table(Idents(nd2117SLCL_skin_merged), nd2117SLCL_skin_merged$orig.ident), margin = 2), paste0(save_path, "_ident_proportions_sample.csv"), row.names =TRUE)
pdf(paste0(save_path, "with47_48_Spatial_DimPlot_PC1to",dims,"res",res,".pdf"))
x<-SpatialDimPlot(nd2117SLCL_skin_merged,crop = FALSE, pt.size.factor = 1, image.alpha = 0,label = FALSE, label.size = 3, repel = TRUE, cols = my_cols2)
y<-SpatialDimPlot(nd2117SLCL_skin_merged, alpha = c(0.9, 0.9), crop = FALSE, pt.size.factor = 0.9, cols = my_cols2)
for(i in 1:6){
print(x[[i]])
}
for(i in 1:6){
print(y[[i]])
}
dev.off()
#ONLY SAVE Rds IF RE-RUNNING PIPELINE FROM SCRATCH - NOT REQUIRED FOR PLOTTING MANUSCRIPT FIGURES
#saveRDS(nd2117SLCL_skin_merged, paste0(save_path, "with47_48_nd2117SLCL_skin_merged.Rds"))
```