scGNNLTMG: LTMG implement in scGNN network

# devtools::install_github("BMEngineeR/scGNNLTMG")

# x <- read.csv("d:/my_analysis/BRIC_TEST/2.Yan/Yan_expression.csv",header = T,row.names = 1,check.names = F)
# library(scGNNLTMG)
# object <- CreateLTMGObject(x)
# object <-RunLTMG(object,Gene_use = 100)
# WriteSparse(object,path = "d:/BMBL",gene.name = FALSE, cell.name = FALSE)
# library(Seurat)
# library(reticulate)
# #py_install("umap-learn")
# library(umap)
# library(ggplot2)
# setwd("d:/my_analysis/XuDong/visualization/zeisel/zeisel/")
# orginal_matrix <- read.csv("original_expression.csv",header = T,row.names = 1,check.names = F,stringsAsFactors = F)
# embedding_matrix <- read.csv("zeisel_z.csv",header = T,row.names = 1,check.names = F, stringsAsFactors = F)
# label_benchmarker <- read.csv("Zeisel_7_label.csv",stringsAsFactors = F)
# identical(label_benchmarker$Cell,colnames(orginal_matrix))
# label_benchmarker <- label_benchmarker$Label
# #label_benchmarker <- as.factor(label_benchmarker)
# unique(label_benchmarker)
#
# cell_label <- read.csv("zeisel_result.csv",header = T,check.names = F,stringsAsFactors = F)
# cell_label[,1] <- colnames(orginal_matrix)
# # embedding umap
# rownames(embedding_matrix)<- colnames(orginal_matrix)
# umap_import <- import(module = "umap", delay_load = TRUE)
# set.seed(123)
# embedding_umap <-  umap(
#   d = embedding_matrix,
#   n_neighbors = as.integer(30),
#   n_components = as.integer(2),
#   metric = "cosine",
#   n_epochs = 100,
#   learning_rate = 1.0,
#   min_dist = 0.3,
#   spread = 1.0,
#   set_op_mix_ratio = 1.0,
#   local_connectivity = 1L ,
#   repulsion_strength = 1,
#   negative_sample_rate = 5,
#   a = NA,
#   b = NA,
#   fast_sgd = FALSE,
#   verbose = FALSE
# )
# my_embeding_matrix <- cbind(embedding_umap$layout[,1],embedding_umap$layout[,2],cell_label,label_benchmarker)
# colnames(my_embeding_matrix)<- c("UMAP1","UMAP2","NAME","CLUSTER","Benchmark")
# my_embeding_matrix$Benchmark <- as.factor(my_embeding_matrix$Benchmark)
# p<-ggplot(my_embeding_matrix,aes(x=UMAP1,y=UMAP2,color = Benchmark))
# p<- p+geom_point(size = 1)
# p
#
# # Seurat original imputed_matrix
# MAT <- orginal_matrix
# MAT <- MAT[rowSums(MAT)>0,colSums(MAT)>0]
#   Gene_use_name <-rownames(MAT)[order(apply(MAT, 1, var),decreasing = T)[1:2000]]
#   MAT <-MAT[Gene_use_name,]
# pbmc<-CreateSeuratObject(MAT,project = "pbmc3k", min.cells = 3, min.features = 200)
# pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
# pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
# all.genes <- rownames(pbmc)
# pbmc <- ScaleData(pbmc, features = all.genes)
# pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
# print(pbmc[["pca"]], dims = 1:5, nfeatures = 5)
# pbmc <- RunUMAP(pbmc, dims = 1:16,n.epochs = 100)
# pbmc <- FindNeighbors(pbmc, dims = 1:10)
# pbmc <- FindClusters(pbmc, resolution = 0.5)
# orginal_label <- pbmc$RNA_snn_res.0.5
# pbmc$celllabel <- as.factor(label_benchmarker)
# Idents(pbmc)<-pbmc$celllabel
# DimPlot(pbmc, reduction = "umap",pt.size = 1)
#
# # Seurat  imputed_matrix
# imputed_matrix <- read.csv("zeisel_recon.csv",header = T,row.names = 1,check.names = F,stringsAsFactors = F)
# rownames(imputed_matrix)<- rownames(MAT)
# colnames(imputed_matrix)<- colnames(MAT)
# pbmc<-CreateSeuratObject(orginal_matrix,project = "pbmc3k", min.cells = 3, min.features = 200)
# pbmc@assays$RNA@data <-as.sparse(imputed_matrix)
# pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
# all.genes <- rownames(pbmc)
# pbmc <- ScaleData(pbmc, features = all.genes)
# pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
# print(pbmc[["pca"]], dims = 1:5, nfeatures = 5)
# pbmc <- RunUMAP(pbmc, dims = 1:16,n.epochs = 100)
# pbmc <- FindNeighbors(pbmc, dims = 1:10)
# pbmc <- FindClusters(pbmc, resolution = 0.5)
# pbmc$celllabel <- as.factor(label_benchmarker)
# imputed_label <- pbmc$RNA_snn_res.0.5
# Idents(pbmc)<-pbmc$celllabel
# DimPlot(pbmc, reduction = "umap",pt.size = 1)
#
# # ARI scGNN
# library(MixGHD)
# ARI(cell_label$`0`,label_benchmarker)
# ARI(label_benchmarker,orginal_label)
# ARI(label_benchmarker,imputed_label )
#
# input_path <- "d:/my_analysis/XuDong/visualization/13/13/"
# original_matrix <- read.csv("d:/my_analysis/XuDong/visualization/13/13/Use_expression.csv",head=T,row.names=1,check.names = F)
# object <- CreateVisObject(original_matrix = original_matrix, input_path=input_path )
# object <- RunUmap(object)
# object <- FindMarkerGene(object)

# PlotUmap(object)
 #PlotGenes(object, feature.name = "Cd47")
# PlotHeatmap(object)
# PlotScatter(object, feature.name = c("Lmbrd1","Bclaf1"))
# PlotViolin(object,feature.name = "Slc1a3")
# PlotNetwork(object)
#PlotNetworkOneCluster(object,cluster.idx = 3)
# label<-rbind("0" = "a","1"= "b","2"="c","3"="d","4"="e","5"="f","6"="g")