Visium_Brain_deconvolution giotto解卷积

title: “Visium_Brain_deconvolution”
output: html_document

#https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets
#{r setup, include=FALSE}
#https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Adult_Mouse_Brain
library(Seurat) # 假设大家都会分析单细胞数据了，这个假设坏不坏，坏
library(SeuratData)
library(ggplot2)
library(cowplot)
library(dplyr)
library(Giotto)
library(patchwork)
library(tidyverse)
library(data.table)
#############################test visium
getwd()
path=“G:/silicosis/sicosis/gitto/brain_visum_deconv/”
setwd(path)

#raw_matrix<-get10Xmatrix(“raw_feature_bc_matrix/”,gene_column_index = 2)
raw_matrix<-get10Xmatrix(“G:/silicosis/sicosis/gitto/brain_visum_deconv/V1_Adult_Mouse_Brain_raw_feature_bc_matrix.tar/V1_Adult_Mouse_Brain_raw_feature_bc_matrix/raw_feature_bc_matrix/”,
gene_column_index = 2)#10x的rawdata 表达矩阵不是filtered data！！

#raw_matrix<-get10Xmatrix(“G:/silicosis/sicosis/gitto/brain_visum_deconv/V1_Adult_Mouse_Brain_filtered_feature_bc_matrix/filtered_feature_bc_matrix/”,
# gene_column_index = 2)
head(raw_matrix)

library(data.table)
spatial_results<-fread(“G:/silicosis/sicosis/gitto/brain_visum_deconv/tissue_positions_list.csv”
)
head(spatial_results)

#library(reticulate)
#py_config()
python_path<-“C:/Users/yll/AppData/Local/r-miniconda/envs/giotto_env/python.exe”
instrs = createGiottoInstructions(python_path = python_path,
show_plot = F, return_plot = T, save_plot = T,
dpi = 600, height = 9, width = 9)

spatial_results = spatial_results[match(colnames(raw_matrix), V1)]
head(spatial_results)
colnames(spatial_results) = c(‘barcode’, ‘in_tissue’, ‘array_row’, ‘array_col’, ‘col_pxl’, ‘row_pxl’)
visium_brain <- createGiottoObject(raw_exprs = raw_matrix,
spatial_locs = spatial_results[,.(row_pxl,-col_pxl)],
instructions = instrs,
cell_metadata = spatial_results[,.(in_tissue, array_row, array_col)])
metadata = pDataDT(visium_brain)
head(metadata)

in_tissue_barcodes = metadata[in_tissue == 1]$cell_ID
visium_brain = subsetGiotto(visium_brain, cell_ids = in_tissue_barcodes)
visium_brain <- filterGiotto(gobject = visium_brain,
expression_threshold = 1,
gene_det_in_min_cells = 50,
min_det_genes_per_cell = 1000,
expression_values = c(‘raw’),
verbose = T)
########export filtered matrix
visium_brain <- normalizeGiotto(gobject = visium_brain)
visium_brain <- calculateHVG(gobject = visium_brain)
gene_metadata = fDataDT(visium_brain)
head(gene_metadata)

featgenes = gene_metadata[hvg == ‘yes’]$gene_ID
visium_brain <- runPCA(gobject = visium_brain, genes_to_use = featgenes, scale_unit = F)
signPCA(visium_brain, genes_to_use = featgenes, scale_unit = F)
visium_brain <- runUMAP(visium_brain, dimensions_to_use = 1:10)
visium_brain <- createNearestNetwork(gobject = visium_brain, dimensions_to_use = 1:10, k = 15)
visium_brain <- doLeidenCluster(gobject = visium_brain, resolution = 0.4, n_iterations = 1000)
plotUMAP(gobject = visium_brain, cell_color = ‘leiden_clus’, point_size = 2)#show_NN_network = T,
spatDimPlot(gobject = visium_brain, cell_color = ‘leiden_clus’,
dim_point_size = 1.5, spat_point_size = 1.5)
#```

##Perform deconvolution
#```{r}
getwd()
#save(visium_brain,file=“visium_brain_deconv.rds”)

load(“G:/silicosis/sicosis/gitto/brain_visum_deconv/visium_brain_deconv.rds”)
load(“sig_ct_exp.RData”)
head(Sig,5)

visium_brain <- runDWLSDeconv(visium_brain,sign_matrix = Sig, n_cell = 20)

##Deconvolution based on signature gene expression and Giotto object
#visium_brain <- runDWLSDeconv(gobject = visium_brain, sign_matrix = Sig_exp)
##The result for deconvolution is stored in visium_brain@spatial_enrichmentDWLS.Thefollowingcodesarevisualizationdeconvolutionresultsusingpieplotplotdata<−as.data.frame(visiumbrain@spatialenrichmentDWLS. The following codes are visualization deconvolution results using pie plot plot_data <- as.data.frame(visium_brain@spatial_enrichmentDWLS.Thefollowingcodesarevisualizationdeconvolutionresultsusingpieplotplotdata<−as.data.frame(visiumbrain@spatialenrichmentDWLS)[-1]
plot_data

plot_col <- colnames(plot_data)
plot_col
plot_datax<−as.numeric(as.character(visiumbrain@spatiallocsx <- as.numeric(as.character(visium_brain@spatial_locsx<−as.numeric(as.character(visiumbrain@spatiallocssdimx))
plot_datay<−as.numeric(as.character(visiumbrain@spatiallocsy <- as.numeric(as.character(visium_brain@spatial_locsy<−as.numeric(as.character(visiumbrain@spatiallocssdimy))
plot_data

min_x <- min(plot_datax)plotdatax) plot_datax)plotdataradius <- 0.4
df <- data.frame()
str(plot_data)
plot_data[2,]

library(ggplot2)
p1 <- ggplot(df) + geom_point() + xlim(min(plot_datax)−1,max(plotdatax)-1, max(plot_datax)−1,max(plotdatax)+1) + ylim(min(plot_datay)−1,max(plotdatay)-1, max(plot_datay)−1,max(plotdatay)+1)
library(scatterpie)
p1 + geom_scatterpie(aes(x=x, y=y, r=radius), data=plot_data, cols=plot_col, color=NA, alpha=.8) +
geom_scatterpie_legend(plot_data$radius, x=1, y=1) + theme_classic()