#request 2
.libPaths(c( "/home/data/t040413/R/x86_64-pc-linux-gnu-library/4.2",
"/home/data/t040413/R/yll/usr/local/lib/R/site-library",
"/home/data/refdir/Rlib/", "/usr/local/lib/R/library"))
## We load the required packages
library(Seurat)
library(decoupleR)
# Only needed for data handling and plotting
library(dplyr)
library(tibble)
library(tidyr)
library(patchwork)
library(ggplot2)
library(pheatmap)
library(CellChat)
library(patchwork)
library(ggplot2)
library(ggalluvial)
library(svglite)
library(Seurat)
library(xlsx)
library(harmony)
#https://mojaveazure.github.io/seurat-disk/reference/Convert.html
#https://github.com/saezlab/visium_heart/blob/db4d51ff033a6f25b24c99a275ea415128741f63/cellxgene_submission/convert_rds_to_h5ad_snATAC.ipynb
load("/home/data/t040413/silicosis/spatial_transcriptomics/silicosis_ST_harmony_SCT_r0.5.rds")
#save(d.all,file ="/home/data/t040413/silicosis/spatial_transcriptomics/silicosis_ST_harmony_SCT_r0.5.rds" )
load("/home/data/t040413/silicosis/silicosis_cluster_merge.rds")
dim(d.all)
dim(All.merge)
print(getwd())
dir.create("/home/data/t040413/silicosis/data/")
setwd("/home/data/t040413/silicosis/data/")
dir.create("visium_objs")
setwd("visium_objs/")
Embeddings(object = d.all) %>%head()
Embeddings(object = d.all,reduction = "umap") %>%head()
Reductions(d.all)
SpatialFeaturePlot(d.all,features = "Inmt")
FeaturePlot(d.all,features = "Inmt" )
DefaultAssay(d.all)
head([email protected])
d.all$Sample=d.all$orig.ident
d.all$cell_type=d.all$SCT_snn_res.0.5
obj_s=SplitObject(d.all)
obj_s
class(obj_s)
lapply(X = obj_s,FUN = function(obj){
obj_name=unique(obj$orig.ident)
# obj=obj_s[[1]]
names(obj)
Images(obj)
1. #
names( obj@images)=obj_name
# obj@images[2] %>%names()
# obj@assays$Spatial@data
# dim(obj@images[[1]])>3
#
2.
meta.data <- [email protected]
head(meta.data)
unique(obj$orig.ident)=="SiO2_7"
meta.data <- meta.data[, c("Sample", "cell_type")]
meta.data$cell_type <- as.character(meta.data$cell_type)
head(meta.data)
3.
DefaultAssay(obj)
dim(obj@assays$SCT@counts)
counts=GetAssayData(object = obj,slot = "counts",assay = "SCT")
obj_visum<- CreateSeuratObject(counts = counts,
meta.data = meta.data,
assay = "Spatial",
names.delim = "-")
# obj_visum@assays$RNA@counts
# obj_visum@assays$RNA@data
# identical(obj_visum@assays$RNA@counts,
# obj_visum@assays$RNA@data)
# head([email protected])
#
4.
umap_embedding <- Embeddings(obj, reduction = "umap")
rownames(umap_embedding) <- colnames(obj_visum)
colnames(umap_embedding) <- c("UMAP_1", "UMAP_2")
head(umap_embedding)
obj_visum[["umap"]] <- CreateDimReducObject(embeddings = umap_embedding, key = "UMAP_", assay = DefaultAssay(obj_visum))
#DimPlot(obj_visum, reduction = "umap", pt.size = 0.5, group.by = "cell_type", label = TRUE)
obj_name=unique(obj$orig.ident)
# packageVersion("Seurat")
# packageVersion("spatial")
#
5.
###有的时候需要使用SetAssayData()函数---------------------------
obj_visum@images=obj@images[paste0(obj_name)]
#head(obj@images$slice1@coordinates)
# Images(object = obj)
# Assays(obj)
# slotNames(obj)
#
# Seurat::Images(object = obj_visum)
6.
SaveH5Seurat(obj_visum, filename = paste0(obj_name,".h5Seurat") ,overwrite = TRUE )
print("start---------save to--h5ad")
Convert( paste0(obj_name,".h5Seurat") , dest = "h5ad",overwrite = TRUE)
print(getwd())
print(obj_name)
}
)
SpatialDimPlot(object = obj_visum, group.by = "cell_type", label = TRUE,pt.size.factor = 1)
??Convert
Idents(d.all)
Idents(d.all) %>%table()
head(d.all$SCT_snn_res.0.5)
d.all$orig.ident=factor(x = d.all$orig.ident,
levels = c("NS_7" ,"SiO2_7","NS_56","SiO2_56"))
table(d.all$stim)
library(dplyr)
d.all$stim=case_when(d.all$stim=="sio2_56"~"SiO2_56",
d.all$stim=="sio2_7"~"SiO2_7",
d.all$stim=="NS_7"~"NS_7",
d.all$stim=="NS_56"~"NS_56"
)
table(d.all$stim)
Idents(d.all)=d.all$stim
table(d.all$stim)
d.all$group=case_when(
d.all$stim=="SiO2_56"~"SiO2_56",
d.all$stim=="SiO2_7"~"SiO2_7",
d.all$stim=="NS_7"~"NS",
d.all$stim=="NS_56"~"NS"
)
table(d.all$group)
