From e22a5bb9e47dd688e912b52f16e44b877e6b72c2 Mon Sep 17 00:00:00 2001
From: Dee-chen <371761145@qq.com>
Date: Mon, 7 Mar 2022 16:02:29 +0800
Subject: [PATCH 01/17] Update data_preprocess_utility.R

---
 scGCN/data_preprocess_utility.R | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/scGCN/data_preprocess_utility.R b/scGCN/data_preprocess_utility.R
index c206395..0a9c609 100644
--- a/scGCN/data_preprocess_utility.R
+++ b/scGCN/data_preprocess_utility.R
@@ -71,15 +71,15 @@ GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
                                   
     objects <- list(object1,object2)    
     objects1 <- lapply(objects,function(obj){
-        obj <- NormalizeData(obj,verbose=F)
+        obj <- NormalizeData(obj,verbose=F)#标准化 
         obj <- FindVariableFeatures(obj,
                                        selection.method = "vst",
-                                       nfeatures = 2000,verbose=F)
+                                       nfeatures = 2000,verbose=F)#寻找前2000高可变基因
           obj <- ScaleData(obj,features=rownames(obj),verbose=FALSE)
-          obj <- RunPCA(obj, features=rownames(obj), verbose = FALSE)
+          obj <- RunPCA(obj, features=rownames(obj), verbose = FALSE)#对数据进行标准化和中心化
         return(obj)})
     #'  Inter-data graph  
-    object.nn <- FindIntegrationAnchors(object.list = objects1,k.anchor=K,verbose=F)
+    object.nn <- FindIntegrationAnchors(object.list = objects1,k.anchor=K,verbose=F) # 寻找数据间样本整合的数据点参考 Seurat包其中的FindIntegrationAnchors函数解析 - 简书 (jianshu.com)
     arc=object.nn@anchors
     d1.arc1=cbind(arc[arc[,4]==1,1],arc[arc[,4]==1,2],arc[arc[,4]==1,3]) 
     grp1=d1.arc1[d1.arc1[,3]>0,1:2]-1
@@ -95,7 +95,7 @@ GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
     }
     #'  Intra-data graph  
     d2.list <- list(objects1[[2]],objects1[[2]])
-    d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)    
+    d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)    #寻找data1内部的样本整合的数据点
     d2.arc=d2.nn@anchors
     d2.arc1=cbind(d2.arc[d2.arc[,4]==1,1],d2.arc[d2.arc[,4]==1,2],d2.arc[d2.arc[,4]==1,3])
     d2.grp=d2.arc1[d2.arc1[,3]>0,1:2]-1

From 1cda434b00933dbae03ea565a8c7f62add6f77b7 Mon Sep 17 00:00:00 2001
From: ianutin <yanyuting@genomics.cn>
Date: Mon, 7 Mar 2022 16:04:35 +0800
Subject: [PATCH 02/17] 3.7

---
 scGCN/data_preprocess.R         | 10 +++++++
 scGCN/data_preprocess_utility.R | 53 +++++++++++++++++++++++++++++++--
 2 files changed, 60 insertions(+), 3 deletions(-)

diff --git a/scGCN/data_preprocess.R b/scGCN/data_preprocess.R
index 3f2c41b..9fb4716 100644
--- a/scGCN/data_preprocess.R
+++ b/scGCN/data_preprocess.R
@@ -12,4 +12,14 @@ count.list <- readRDS('example_data/count.list.RDS')
 label.list <- readRDS('example_data/label.list.RDS')
 
 save_processed_data(count.list,label.list)
+#四个文件，一个参考基因表达矩阵，一个待测基因表达矩阵，一个参考标签数据框，一个待测标签数据框
+#> source('data_preprocess_utility.R')
+#> ref_c=read.table("参考基因表达矩阵",header=T)
+#> qur_c=read.table("待测基因表达矩阵",header=T)
+#> count.list=list(ref_c,qur_c)
+#> ref_t=read.table("ref.class.txt参考的标签数据框",header=T)
+#> qur_t=read.table("qur.class.txt待测的标签数据框",header=T)
+#> label.list=list(ref_t,qur_t)
+#> save_processed_data(count.list,label.list)
+
 
diff --git a/scGCN/data_preprocess_utility.R b/scGCN/data_preprocess_utility.R
index 0a9c609..ceb6e66 100644
--- a/scGCN/data_preprocess_utility.R
+++ b/scGCN/data_preprocess_utility.R
@@ -6,18 +6,22 @@
 #' @param inter_graph  inter-dataset graph in input folder
 
 #' @return This function returns a list of two statistical scores: entropy score and enrichment score that unknown cells have higher entropy score and lower enrichment score
+#熵值和富集值：未知类型的细胞具有较高的熵和较低的富集度；
 #' @examples: first load count.list and label.list from folder "example_data" and run 'data_preprocess.R'
 #' then run 'metrics(dat1,lab1,dat2,lab2,inter_graph,cluster)'
 
 suppressMessages(library(Seurat)); suppressMessages(library(entropy))
 
 metrics <- function(lab1,inter_graph,clusters){
+    #调用的时候是处理待测数据时
+    #scores <- metrics(lab1=Lab1,inter_graph=inter.graph,clusters=hc) 
+    #inter_graph和细胞整合锚点有关；clusters和细胞标识有关
     inter.graph <- inter_graph; hc <- clusters
     
     #' ------------ check cluster enrichment in cell types ------------    
     #' Examine in clusters
     
-    #' check enrichment
+    #' check enrichment 细胞富集度
     eval1 <- sapply(names(table(hc)),function(name){
         ps1 <- which(hc==name)
         foo <- table(lab1[inter_graph[which(inter_graph[,2]%in%ps1),1],1])
@@ -41,6 +45,7 @@ metrics <- function(lab1,inter_graph,clusters){
                 ind <- grep(names(foo)[i],names(tem))
                 r1 <- foo[i]/sum(foo)
                 return (r1/tem[ind])}))
+                #以上和之前一样？
             temp2 <- entropy.empirical(temp,unit='log')
             return (temp2)
         } else {return (0) }
@@ -61,6 +66,7 @@ metrics <- function(lab1,inter_graph,clusters){
 #' save_processed_data(count.list,label.list)
 
 GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
+    #调用的时候k的赋值为5
     object1 <- CreateSeuratObject(counts=Dat1,project = "1",assay = "Data1",
                                   min.cells = 0,min.features = 0,
                                   names.field = 1,names.delim = "_")
@@ -68,38 +74,68 @@ GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
     object2 <- CreateSeuratObject(counts=Dat2,project = "2",assay = "Data2",
                                   min.cells = 0,min.features =0,names.field = 1,
                                   names.delim = "_")
-                                  
+    #分别对ref和qur创建seurat对象                              
     objects <- list(object1,object2)    
     objects1 <- lapply(objects,function(obj){
         obj <- NormalizeData(obj,verbose=F)#标准化 
         obj <- FindVariableFeatures(obj,
                                        selection.method = "vst",
+<<<<<<< Updated upstream
                                        nfeatures = 2000,verbose=F)#寻找前2000高可变基因
+=======
+                                       nfeatures = 2000,verbose=F)
+                                       #这里也有一个基因数目的处理2000
+>>>>>>> Stashed changes
           obj <- ScaleData(obj,features=rownames(obj),verbose=FALSE)
           obj <- RunPCA(obj, features=rownames(obj), verbose = FALSE)#对数据进行标准化和中心化
         return(obj)})
+        #用seurat做一些常规的单细胞数据预处理
+
     #'  Inter-data graph  
+<<<<<<< Updated upstream
     object.nn <- FindIntegrationAnchors(object.list = objects1,k.anchor=K,verbose=F) # 寻找数据间样本整合的数据点参考 Seurat包其中的FindIntegrationAnchors函数解析 - 简书 (jianshu.com)
+=======
+    object.nn <- FindIntegrationAnchors(object.list = objects1,k.anchor=K,verbose=F)
+    #寻找锚点：Find a set of anchors between a list of Seurat objects, K=5 
+    #These anchors can later be used to integrate the objects using the IntegrateData function
+    #k.anchor:How many neighbors (k) to use when picking anchors
+>>>>>>> Stashed changes
     arc=object.nn@anchors
+    #将整体细胞的锚点信息赋值给变量
     d1.arc1=cbind(arc[arc[,4]==1,1],arc[arc[,4]==1,2],arc[arc[,4]==1,3]) 
     grp1=d1.arc1[d1.arc1[,3]>0,1:2]-1
+    #这里是？个性化的处理
     
     if (check.unknown){
         obj <- objects1[[2]]
         obj <- RunPCA(obj, features = VariableFeatures(object = obj),npcs=30,verbose=F)
         obj <- FindNeighbors(obj,verbose=F)
+        #Computes the k.param nearest neighbors for a given dataset. Can also optionally (via compute.SNN), 
+        #construct a shared nearest neighbor graph by calculating the neighborhood overlap (Jaccard index)
+        # between every cell and its k.param nearest neighbors.
         obj <- FindClusters(obj, resolution = 0.5,verbose=F)
         hc <- Idents(obj); inter.graph=grp1+1
+        #hc是用Idents函数获取细胞标识，那么seurat对象的标识是？
+        #inter.graph和细胞整合的锚点有关
         scores <- metrics(lab1=Lab1,inter_graph=inter.graph,clusters=hc)
+        #这里是对待测数据处理，用的label1？
         saveRDS(scores,file='./input/statistical_scores.RDS')
+        #检验待测细胞类型的熵和富集度
     }
     #'  Intra-data graph  
     d2.list <- list(objects1[[2]],objects1[[2]])
+<<<<<<< Updated upstream
     d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)    #寻找data1内部的样本整合的数据点
+=======
+    d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)   
+
+>>>>>>> Stashed changes
     d2.arc=d2.nn@anchors
     d2.arc1=cbind(d2.arc[d2.arc[,4]==1,1],d2.arc[d2.arc[,4]==1,2],d2.arc[d2.arc[,4]==1,3])
     d2.grp=d2.arc1[d2.arc1[,3]>0,1:2]-1
+    #个性化操作锚点
     final <- list(inteG=grp1,intraG=d2.grp)
+    #返回两个有关锚点的值
     return (final)
 }
 
@@ -178,6 +214,7 @@ selectHVFeature <- function(count.list,var.features,nfeatures = 2000){
 }
 
 #' This functions takes refrence data and labels to identify variable gene features
+#筛选高变基因的过程
 #' @param data reference data; rows are genes and columns are cells
 #' @param label data frame with rownames identical with colnames of data; the first column is cell type
 #' @param nf number of variable features
@@ -186,15 +223,20 @@ selectHVFeature <- function(count.list,var.features,nfeatures = 2000){
 #' @examples
 #' select_feature(data=reference.data,label=reference.label)
 select_feature <- function(data,label,nf=2000){
-    M <- nrow(data); new.label <- label[,1]
+    M <- nrow(data); new.label <- label[,1] 
+    #M是行数，参考基因表达矩阵的行数；new.label是细胞类型标签
     pv1 <- sapply(1:M, function(i){
         mydataframe <- data.frame(y=as.numeric(data[i,]), ig=new.label)
         fit <- aov(y ~ ig, data=mydataframe)
+        #aov函数是在做方差分析；分析同一个基因的不同细胞的关系？
         summary(fit)[[1]][["Pr(>F)"]][1]})
     names(pv1) <- rownames(data)
+    #rownames是基因名？
     pv1.sig <- names(pv1)[order(pv1)[1:nf]]
+    #nf是挑选的高变基因的数目，在此是2000个
     egen <- unique(pv1.sig)
     return (egen)
+    #返回的是高变基因特征
 }
 
 #' This functions takes refrence data and labels to identify variable gene features
@@ -206,7 +248,9 @@ select_feature <- function(data,label,nf=2000){
 #' pre_process(count_list,label_list)
 pre_process <- function(count_list,label_list){
     sel.features <- select_feature(count_list[[1]],label_list[[1]])
+    #提取参考基因表达矩阵和参考的细胞标签；进行高变基因的挑选
     count_list_new <- list(count_list[[1]][sel.features,],count_list[[2]][sel.features,])
+    #对参考基因表达矩阵做了处理，保留2000个高变基因的数据
     return (count_list_new)
 }
 
@@ -219,12 +263,15 @@ pre_process <- function(count_list,label_list){
 #' save_processed_data(count.list,label.list)
 save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=FALSE){
     count.list <- pre_process(count_list=count.list,label_list=label.list)
+    #对参考数据处理，这一步得到的是做了2000个高变基因提取的基因表达矩阵和标签
     #' save counts data to certain path: 'input'
     dir.create('input'); 
     write.csv(t(count.list[[1]]),file='input/Data1.csv',quote=F,row.names=T)
     write.csv(t(count.list[[2]]),file='input/Data2.csv',quote=F,row.names=T)
+    #分别保存两个基因表达矩阵：（1）对参考做了2000个高变基因处理（2）对待测未处理
 
     #' optional graph: R genreated graph has minor differnce with python, user can choose the one with better performance
+    #使用R或者python两种方法来生成graph
     if (Rgraph){
         #' use R generated graph
         new.dat1 <- count.list[[1]]; new.dat2 <- count.list[[2]]

From f5c8a3b0730161be7ad0039792c225be1c42e3b1 Mon Sep 17 00:00:00 2001
From: Dee-chen <371761145@qq.com>
Date: Mon, 7 Mar 2022 16:12:44 +0800
Subject: [PATCH 03/17] Update data.py

---
 scGCN/data.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/scGCN/data.py b/scGCN/data.py
index a1e1163..8b0caac 100644
--- a/scGCN/data.py
+++ b/scGCN/data.py
@@ -29,12 +29,15 @@ def input_data(DataDir,Rgraph=True):
     lab_data2 = data2.reset_index(drop=True)  #.transpose()
     lab_label1.columns = ['type']
     lab_label2.columns = ['type']
-
+    ##！！！此处通过物种1的数据中获得了type ，也许可以使用list(lab_label1['type']，lab_label2['type'])
+    # 来合并两个数据集的细胞类型。但也许会影响之后运行？
     types = np.unique(lab_label1['type']).tolist()
 
     random.seed(123)
     p_data = []
     p_label = []
+
+    ##此处循环类型列表从而从各个细胞类型中拆分训练、测试集
     for i in types:
         tem_index = lab_label1[lab_label1['type'] == i].index
         tem_label = lab_label1[lab_label1['type'] == i]

From 6e66b5b637647fd4987be99cf8e9614197cc1d37 Mon Sep 17 00:00:00 2001
From: Dee-chen <371761145@qq.com>
Date: Mon, 7 Mar 2022 16:55:15 +0800
Subject: [PATCH 04/17] Update utils.py

---
 scGCN/utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/scGCN/utils.py b/scGCN/utils.py
index e394433..5f1011e 100644
--- a/scGCN/utils.py
+++ b/scGCN/utils.py
@@ -50,7 +50,7 @@ def load_data(datadir,rgraph=True):
 
     data_train1, data_test1, data_val1, label_train1, label_test1, label_val1, lab_data2, lab_label2, types = tuple(
         objects)
-
+    #train2包含了两个数据集的数据
     train2 = pd.concat([data_train1, lab_data2])
     lab_train2 = pd.concat([label_train1, lab_label2])
 
@@ -61,7 +61,7 @@ def load_data(datadir,rgraph=True):
     index_guide = np.concatenate(
         (label_train1.index, lab_label2.index * (-1) - 1, label_val1.index,
          label_test1.index))
-
+    #同上，labels_train包含了两个数据集
     labels_train = np.array(lab_train2).flatten()
     labels_test = np.array(label_test1).flatten()
     labels_val = np.array(label_val1).flatten()
@@ -95,11 +95,11 @@ def load_data(datadir,rgraph=True):
     uniq = np.unique(Labels.values)
 
     rename = {}
-
+    #这里把类型转换成了数字，而此处的类型只有ref的
     for line in range(0, len(types)):
         key = types[line]
         rename[key] = int(line)
-
+    #！！！！这里把类型数字进行了替换，所以会导致data2内新类型仍然是文字而报错
     Label1 = Labels.replace(rename)
     indices = np.array(Label1.values, dtype='int').tolist()
 

From 14f2e395c979c7c2513c612d812f464cb528b6c7 Mon Sep 17 00:00:00 2001
From: ianutin <yanyuting@genomics.cn>
Date: Tue, 8 Mar 2022 09:26:34 +0800
Subject: [PATCH 05/17] 3.7

update Rscript
---
 scGCN/data.py                   |  1 +
 scGCN/data_preprocess_utility.R | 38 ++++++++++++++++-----------------
 2 files changed, 19 insertions(+), 20 deletions(-)

diff --git a/scGCN/data.py b/scGCN/data.py
index 8b0caac..c811c6e 100644
--- a/scGCN/data.py
+++ b/scGCN/data.py
@@ -27,6 +27,7 @@ def input_data(DataDir,Rgraph=True):
 
     lab_data1 = data1.reset_index(drop=True)  #.transpose()
     lab_data2 = data2.reset_index(drop=True)  #.transpose()
+    #重新给一个索引，将原来的索引赋值给第一列，将现在的索引设置为0/1/2/3、、、、、、
     lab_label1.columns = ['type']
     lab_label2.columns = ['type']
     ##！！！此处通过物种1的数据中获得了type ，也许可以使用list(lab_label1['type']，lab_label2['type'])
diff --git a/scGCN/data_preprocess_utility.R b/scGCN/data_preprocess_utility.R
index ceb6e66..8df9789 100644
--- a/scGCN/data_preprocess_utility.R
+++ b/scGCN/data_preprocess_utility.R
@@ -77,34 +77,26 @@ GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
     #分别对ref和qur创建seurat对象                              
     objects <- list(object1,object2)    
     objects1 <- lapply(objects,function(obj){
-        obj <- NormalizeData(obj,verbose=F)#标准化 
+        obj <- NormalizeData(obj,verbose=F)
         obj <- FindVariableFeatures(obj,
                                        selection.method = "vst",
-<<<<<<< Updated upstream
-                                       nfeatures = 2000,verbose=F)#寻找前2000高可变基因
-=======
                                        nfeatures = 2000,verbose=F)
                                        #这里也有一个基因数目的处理2000
->>>>>>> Stashed changes
           obj <- ScaleData(obj,features=rownames(obj),verbose=FALSE)
-          obj <- RunPCA(obj, features=rownames(obj), verbose = FALSE)#对数据进行标准化和中心化
+          obj <- RunPCA(obj, features=rownames(obj), verbose = FALSE)
         return(obj)})
         #用seurat做一些常规的单细胞数据预处理
 
     #'  Inter-data graph  
-<<<<<<< Updated upstream
-    object.nn <- FindIntegrationAnchors(object.list = objects1,k.anchor=K,verbose=F) # 寻找数据间样本整合的数据点参考 Seurat包其中的FindIntegrationAnchors函数解析 - 简书 (jianshu.com)
-=======
     object.nn <- FindIntegrationAnchors(object.list = objects1,k.anchor=K,verbose=F)
     #寻找锚点：Find a set of anchors between a list of Seurat objects, K=5 
     #These anchors can later be used to integrate the objects using the IntegrateData function
     #k.anchor:How many neighbors (k) to use when picking anchors
->>>>>>> Stashed changes
     arc=object.nn@anchors
     #将整体细胞的锚点信息赋值给变量
     d1.arc1=cbind(arc[arc[,4]==1,1],arc[arc[,4]==1,2],arc[arc[,4]==1,3]) 
     grp1=d1.arc1[d1.arc1[,3]>0,1:2]-1
-    #这里是？个性化的处理
+    #这里是？个性化的处理；最终得到的grp1是一个数据框，第一列是cell1的标识-1，第二列是cell2的标识-1，保留标准是score>0
     
     if (check.unknown){
         obj <- objects1[[2]]
@@ -124,18 +116,14 @@ GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
     }
     #'  Intra-data graph  
     d2.list <- list(objects1[[2]],objects1[[2]])
-<<<<<<< Updated upstream
-    d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)    #寻找data1内部的样本整合的数据点
-=======
     d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)   
 
->>>>>>> Stashed changes
     d2.arc=d2.nn@anchors
     d2.arc1=cbind(d2.arc[d2.arc[,4]==1,1],d2.arc[d2.arc[,4]==1,2],d2.arc[d2.arc[,4]==1,3])
     d2.grp=d2.arc1[d2.arc1[,3]>0,1:2]-1
     #个性化操作锚点
     final <- list(inteG=grp1,intraG=d2.grp)
-    #返回两个有关锚点的值
+    #返回两个有关锚点的值;一个是参考数据集和待测数据集之间的相似度，一个是数据集内部的相似度
     return (final)
 }
 
@@ -186,8 +174,10 @@ selectHVFeature <- function(count.list,var.features,nfeatures = 2000){
     var.features1 <- unname(unlist(var.features))
     var.features2 <- sort(table(var.features1), decreasing = TRUE)
     for (i in 1:length(count.list)) {
-        var.features3 <- var.features2[names(var.features2) %in% rownames(count.list[[i]])]}    
+        var.features3 <- var.features2[names(var.features2) %in% rownames(count.list[[i]])]}
+    #确保基因既在ref中也在qur中    
     tie.val <- var.features3[min(nfeatures, length(var.features3))]
+    #反正只保留2000个基因数目
     features <- names(var.features3[which(var.features3 > tie.val)])
     if (length(features) > 0) {
         feature.ranks <- sapply(features, function(x) {
@@ -279,16 +269,22 @@ save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=
         graphs <- suppressWarnings(GenerateGraph(Dat1=new.dat1,Dat2=new.dat2,
                                                  Lab1=new.lab1,K=5,
                                                  check.unknown=check_unknown))
+        #返回两个锚点信息
         write.csv(graphs[[1]],file='input/inter_graph.csv',quote=F,row.names=T)
+        #数据集内部锚点
         write.csv(graphs[[2]],file='input/intra_graph.csv',quote=F,row.names=T)
+        #数据集之间锚点
         write.csv(new.lab1,file='input/label1.csv',quote=F,row.names=F)
-        write.csv(new.lab2,file='input/label2.csv',quote=F,row.names=F)        
+        write.csv(new.lab2,file='input/label2.csv',quote=F,row.names=F)
+        #到此未对label2做处理        
     } else {
         #' use python generated graph
         dir.create('results')
         #' @param norm.list normalized data
         res1 <- normalize_data(count.list)
+        #得到标准化表达矩阵以及高变基因集
         norm.list <- res1[[1]]; hvg.features <- res1[[2]];
+
         #' @param scale.list scaled data
         scale.list <- scale_data(count.list,norm.list,hvg.features)
         outputdir <- 'process_data'; dir.create(outputdir)
@@ -300,6 +296,7 @@ save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=
             file.name=paste0(outputdir,'/count_data/count_data_',i,'.csv')
             write.csv(df,file=file.name,quote=F)
         }
+        #保存经过第一步预处理的基因表达矩阵
         
         for (i in 1:N){
             df = label.list[[i]]
@@ -307,19 +304,20 @@ save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=
             file.name=paste0(outputdir,'/label_data/label_data_',i,'.csv')
             write.csv(df,file=file.name,quote=F)
         }
-        
+        #保存label信息
         for (i in 1:N){
             df = norm.list[[i]]
             if (!dir.exists(paste0(outputdir,'/norm_data'))){dir.create(paste0(outputdir,'/norm_data'))}
             file.name=paste0(outputdir,'/norm_data/norm_data_',i,'.csv')
             write.csv(df,file=file.name,quote=F)
         }
-        
+        #保存标准化的基因表达矩阵
         for (i in 1:N){
             df = scale.list[[i]]
             if (!dir.exists(paste0(outputdir,'/scale_data'))){dir.create(paste0(outputdir,'/scale_data'))}
             file.name=paste0(outputdir,'/scale_data/scale_data_',i,'.csv')
             write.csv(df,file=file.name,quote=F)
         }
+        #保存scale后的基因表达矩阵
     }
 }

From a235db1e8c20ca792b0758fbe4de5c55c9bcaac4 Mon Sep 17 00:00:00 2001
From: ianutin <yanyuting@genomics.cn>
Date: Tue, 8 Mar 2022 18:05:39 +0800
Subject: [PATCH 06/17] 3.8

---
 scGCN/graph.py   | 22 +++++++++++++---------
 scGCN/utility.py | 17 ++++++++++++++---
 2 files changed, 27 insertions(+), 12 deletions(-)

diff --git a/scGCN/graph.py b/scGCN/graph.py
index c5488a8..2931759 100644
--- a/scGCN/graph.py
+++ b/scGCN/graph.py
@@ -8,46 +8,49 @@ def graph_construct(outputdir):
 
     #' import processed data 
     files1 = glob.glob(path0 + "/count_data/*.csv")
+    #获取该路径下的所有csv文件
     files1.sort()
     count_list = []
     for df in files1:
         print(df)
         count_list.append(pd.read_csv(df, index_col=0))
-
+    #得到所有的原始基因表达矩阵
     files2 = glob.glob(path0 + "/norm_data/*.csv")
     files2.sort()
     norm_list = []
     for df in files2:
         print(df)
         norm_list.append(pd.read_csv(df, index_col=0))
-
+    #得到所有的标准化基因表达矩阵
     files3 = glob.glob(path0 + "/scale_data/*.csv")
     files3.sort()
     scale_list = []
     for df in files3:
         print(df)
         scale_list.append(pd.read_csv(df, index_col=0))
-
+    #得到所有的scale后的基因表达矩阵
     files4 = glob.glob(path0 + "/label_data/*.csv")
     files4.sort()
     label_list = []
     for df in files4:
         print(df)
         label_list.append(pd.read_csv(df, index_col=0))
-
+    #得到label数据
     fpath = os.path.join(path0, 'sel_features.csv')
     features = pd.read_csv(fpath, index_col=False).values.flatten()
-
+    #得到高变基因信息
     #' graph construction    
     import itertools
-
+    #该模块处理迭代
     N = len(count_list)
     if (N == 1):
         combine = pd.Series([(0, 0)])
+        #combine的值是一个列，该列只有一行一个值为0 (0,0)
     else:
         combin = list(itertools.product(list(range(N)), list(range(N))))
         index = [i for i, x in enumerate([i[0] < i[1] for i in combin]) if x]
         combine = pd.Series(combin)[index]
+        #1 (0,1)
 
     pairss1 = generate_graph(count_list=count_list,
                              norm_list=norm_list,
@@ -69,13 +72,14 @@ def graph_construct(outputdir):
 
     graph1 = pairss1[0].iloc[:, 0:2].reset_index()
     graph1.to_csv('./input/inter_graph.csv')
-
+    #数据集间
     #'@param graph2: intra-dataset graph 
     graph2 = pairss2[0].iloc[:, 0:2].reset_index()
     graph2.to_csv('./input/intra_graph.csv')
-
+    #数据集内
     label1 = label_list[0]
     label1.to_csv('./input/Label1.csv', index=False)
-
+    #reference label
     label2 = label_list[1]
     label2.to_csv('./input/Label2.csv', index=False)
+    #query label
\ No newline at end of file
diff --git a/scGCN/utility.py b/scGCN/utility.py
index 094c012..7e62d8f 100644
--- a/scGCN/utility.py
+++ b/scGCN/utility.py
@@ -11,7 +11,7 @@ def svd1(mat, num_cc):
     u = U[:, 0:int(num_cc)]
     v = V[0:int(num_cc), :].transpose()
     return u, v, d
-
+#？
 def pls(x, y, num_cc):
     random.seed(42)
     plsca = PLSCanonical(n_components=int(num_cc), algorithm='svd')
@@ -39,8 +39,10 @@ def runcca(data1, data2, num_cc=20):
     object1 = scale2(data1)
     object2 = scale2(data2)
     mat3 = np.matmul(np.matrix(object1).transpose(), np.matrix(object2))
+    #返回两个数组的矩阵乘积
     a = svd1(mat=mat3, num_cc=int(num_cc))
     cca_data = np.concatenate((a[0], a[1]))
+    #数组的拼接函数
     ind = np.where(
         [cca_data[:, col][0] < 0 for col in range(cca_data.shape[1])])[0]
     cca_data[:, ind] = cca_data[:, ind] * (-1)
@@ -66,10 +68,16 @@ def l2norm(mat):
 #' @rdname runCCA
 #' @export feature loadings and embeddings
 def runCCA(data_use1, data_use2, features, count_names, num_cc):
+    #runCCA(data_use1=scale_data1,
+    #  data_use2=scale_data2,
+    #  features=features,
+    #  count_names=rowname,
+    #  num_cc=30)
     features = checkFeature(data_use1, features)
     features = checkFeature(data_use2, features)
     data1 = data_use1.loc[features, ]
     data2 = data_use2.loc[features, ]
+    #features是非零方差的数据
     cca_results = runcca(data1=data1, data2=data2, num_cc=num_cc)
     cell_embeddings = np.matrix(cca_results[0])
     combined_data = data1.merge(data2,
@@ -83,9 +91,9 @@ def runCCA(data_use1, data_use2, features, count_names, num_cc):
     return cca_results, loadings
 
 
-# Check if features have zero variance
+# Check if features have zero variance 零方差
 # @return Returns a vector of features that is the subset of features
-# that have non-zero variance
+# that have non-zero variance 非零方差
 #' @param data_use pandas data frame
 def checkFeature(data_use, features):
     data1 = data_use.loc[features, ]
@@ -223,6 +231,8 @@ def generate_graph(count_list, norm_list, scale_list, features, combine, k_filte
         scale_data1 = scale_list[i]
         scale_data2 = scale_list[j]
         rowname = counts1.index
+        #如果combine是1 (0,1) 2(0,2) 5(1,2);i 是0/0/1，j是1/2/2
+
         #' @param data_use1 pandas data frame
         #' @param data_use2 pandas data frame
         #' @export feature loadings and embeddings (pandas data frame)
@@ -231,6 +241,7 @@ def generate_graph(count_list, norm_list, scale_list, features, combine, k_filte
                                          features=features,
                                          count_names=rowname,
                                          num_cc=30)
+        #return cca_results, loadings
         norm_embedding = l2norm(mat=cell_embedding[0])
         #' identify nearest neighbor
         cells1 = counts1.columns

From 10a7a3370cecae6177afd46690d47e7cd52a855b Mon Sep 17 00:00:00 2001
From: ianutin <yanyuting@genomics.cn>
Date: Mon, 14 Mar 2022 22:03:44 +0800
Subject: [PATCH 07/17] 3-14

---
 scGCN/data.py   | 12 +++++++-----
 scGCN/layers.py |  5 ++---
 scGCN/models.py |  3 ++-
 scGCN/train.py  |  5 +++++
 scGCN/utils.py  | 36 +++++++++++++++++++++++-------------
 5 files changed, 39 insertions(+), 22 deletions(-)

diff --git a/scGCN/data.py b/scGCN/data.py
index c811c6e..cf2fe58 100644
--- a/scGCN/data.py
+++ b/scGCN/data.py
@@ -33,7 +33,9 @@ def input_data(DataDir,Rgraph=True):
     ##！！！此处通过物种1的数据中获得了type ，也许可以使用list(lab_label1['type']，lab_label2['type'])
     # 来合并两个数据集的细胞类型。但也许会影响之后运行？
     types = np.unique(lab_label1['type']).tolist()
-
+    #label1=lab_label1['type'].tolist()
+    #label2=lab_label2['type'].tolist()
+    #types=np.unique(label1.extend(label2))
     random.seed(123)
     p_data = []
     p_label = []
@@ -82,9 +84,9 @@ def input_data(DataDir,Rgraph=True):
     label_train1 = pd.concat(label_train)
     label_test1 = pd.concat(label_test)
     label_val1 = pd.concat(label_val)
-
-    train2 = pd.concat([data_train1, lab_data2])
-    lab_train2 = pd.concat([label_train1, lab_label2])
+    #原来划分训练集、测试集、验证集的只有reference数据集
+    #train2 = pd.concat([data_train1, lab_data2])
+    #lab_train2 = pd.concat([label_train1, lab_label2])
 
     #' save objects
 
@@ -93,7 +95,7 @@ def input_data(DataDir,Rgraph=True):
         data_train1, data_test1, data_val1, label_train1, label_test1,
         label_val1, lab_data2, lab_label2, types
     ]
-
+    #此处原来的types仅仅是Reference的types
     with open(PIK, "wb") as f:
         pkl.dump(res, f)
 
diff --git a/scGCN/layers.py b/scGCN/layers.py
index 6c3f0a3..d580e98 100644
--- a/scGCN/layers.py
+++ b/scGCN/layers.py
@@ -72,19 +72,18 @@ def __init__(self, input_dim, output_dim, placeholders, dropout=0.,
                  sparse_inputs=False, act=tf.nn.relu, bias=False,
                  featureless=False, **kwargs):
         super(GraphConvolution, self).__init__(**kwargs)
-
+        #这里是调用父类的__init__()
         if dropout:
             self.dropout = placeholders['dropout']
         else:
             self.dropout = 0.
-
         self.act = act
         self.support = placeholders['support']
         self.sparse_inputs = sparse_inputs
         self.featureless = featureless
         self.bias = bias
 
-        # helper variable for sparse dropout
+        # helper variable for sparse dropout 辅助变量
         self.num_features_nonzero = placeholders['num_features_nonzero']
 
         with tf.variable_scope(self.name + '_vars'):
diff --git a/scGCN/models.py b/scGCN/models.py
index 00ef87e..1460b86 100644
--- a/scGCN/models.py
+++ b/scGCN/models.py
@@ -88,9 +88,10 @@ def __init__(self, placeholders, input_dim, **kwargs):
         self.inputs = placeholders['features']
         self.input_dim = input_dim
         # self.input_dim = self.inputs.get_shape().as_list()[1]  # To be supported in future Tensorflow versions
+        #input_dim=features[2][1]
         self.output_dim = placeholders['labels'].get_shape().as_list()[1]
+        ####!!!!!!!!!!!标签的处理
         self.placeholders = placeholders
-
         self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
 
         self.build()
diff --git a/scGCN/train.py b/scGCN/train.py
index a71ce58..86f74a0 100644
--- a/scGCN/train.py
+++ b/scGCN/train.py
@@ -40,6 +40,10 @@
 # Load data
 adj, features, labels_binary_train, labels_binary_val, labels_binary_test, train_mask, pred_mask, val_mask, test_mask, new_label, true_label, index_guide = load_data(
     FLAGS.dataset,rgraph=FLAGS.graph)
+    #pred_mask 仅仅是ref
+    #new_label new_label是所有的真实的二进制的值
+    #true_label 这个变量中存储了所有的原本真实标签label
+    #index_guide 这里就是给未来的结果分配index,依次是ref_train/qur/ref_val/ref_test
 
 support = [preprocess_adj(adj)]
 num_supports = 1
@@ -60,6 +64,7 @@
     tf.placeholder_with_default(0., shape=()),
     'num_features_nonzero':
     tf.placeholder(tf.int32)  # helper variable for sparse dropout
+    #placeholder()在神经网络构建graph的时候在模型中的占位，此时并没有把要输入的数据传入模型，它只会分配必要的内存
 }
 
 # Create model
diff --git a/scGCN/utils.py b/scGCN/utils.py
index 5f1011e..b5052d9 100644
--- a/scGCN/utils.py
+++ b/scGCN/utils.py
@@ -29,6 +29,7 @@ def graph(matrix):
 def sample_mask(idx, l):
     """Create mask."""
     mask = np.zeros(l)
+    # 给定性状和类型的用0填充的数组
     mask[idx] = 1
     return np.array(mask, dtype=np.bool)
 
@@ -47,37 +48,38 @@ def load_data(datadir,rgraph=True):
     PIK = "{}/datasets.dat".format(datadir)
     with open(PIK, "rb") as f:
         objects = pkl.load(f)
-
     data_train1, data_test1, data_val1, label_train1, label_test1, label_val1, lab_data2, lab_label2, types = tuple(
         objects)
-    #train2包含了两个数据集的数据
+    # data_train1 data_test1 data_val1只有ref,label相对应
+    # lab_data2与lab_label2是qur
+    #此处原来的types仅仅是ref中的types
     train2 = pd.concat([data_train1, lab_data2])
     lab_train2 = pd.concat([label_train1, lab_label2])
-
+    #train2包含了两个数据集的数据，包含了从Ref中随机得到的train以及所有的qur
     datas_train = np.array(train2)
     datas_test = np.array(data_test1)
     datas_val = np.array(data_val1)
-
+    ####!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+    ####!!!!!!!!!!!!!!!!!!!!这里就是给未来的结果分配index,依次是ref_train/qur/ref_val/ref_test
     index_guide = np.concatenate(
         (label_train1.index, lab_label2.index * (-1) - 1, label_val1.index,
          label_test1.index))
-    #同上，labels_train包含了两个数据集
+    #numpy提供了numpy.concatenate((a1,a2,...), axis=0)函数。能够一次完成多个数组的拼接
     labels_train = np.array(lab_train2).flatten()
     labels_test = np.array(label_test1).flatten()
     labels_val = np.array(label_val1).flatten()
-
+    #同上，labels_train包含了两个数据集
     #' convert pandas data frame to csr_matrix format
     datas_tr = scipy.sparse.csr_matrix(datas_train.astype('Float64'))
     datas_va = scipy.sparse.csr_matrix(datas_val.astype('Float64'))
     datas_te = scipy.sparse.csr_matrix(datas_test.astype('Float64'))
 
     #' 3) set the unlabeled data in training set
-
     #' @param N; the number of labeled samples in training set
     M = len(data_train1)
 
+    #这里是有改动？
     #' 4) get the feature object by combining training, test, valiation sets
-
     features = sp.vstack((sp.vstack((datas_tr, datas_va)), datas_te)).tolil()
     features = preprocess_features(features)
 
@@ -91,16 +93,19 @@ def load_data(datadir,rgraph=True):
     Labels = pd.DataFrame(labels)
 
     true_label = Labels
+    #这个变量中存储了所有的原本真实标签label
     #' convert list to binary matrix
     uniq = np.unique(Labels.values)
 
     rename = {}
-    #这里把类型转换成了数字，而此处的类型只有ref的
+    #这里把类型转换成了数字，而此处的类型只有ref的;原始types变量里面只存储了唯一的Ref的label
     for line in range(0, len(types)):
         key = types[line]
         rename[key] = int(line)
     #！！！！这里把类型数字进行了替换，所以会导致data2内新类型仍然是文字而报错
     Label1 = Labels.replace(rename)
+    #Labels是原始的所有的真实标签label
+    #因此Lanel1是所有转换为数字？的真实标签
     indices = np.array(Label1.values, dtype='int').tolist()
 
     indice = [item for sublist in indices for item in sublist]
@@ -109,21 +114,25 @@ def load_data(datadir,rgraph=True):
     indptr = range(len(indice) + 1)
     dat = np.ones(len(indice))
     binary_label = scipy.sparse.csr_matrix((dat, indice, indptr))
-
+    #binary_label是所有的二进制标签
     #' new label with binary values
     new_label = np.array(binary_label.todense())
+    #new_label是所有的二进制的值
     idx_train = range(M)
+    #M = len(data_train1) data_train1是从ref中提取出来的train
     idx_pred = range(M, len(labels_tr))
+    #这个区间相当于是所有的qur
     idx_val = range(len(labels_tr), len(labels_tr) + len(labels_va))
+    #这个区间仅仅是从ref中提取出来的val
     idx_test = range(
         len(labels_tr) + len(labels_va),
         len(labels_tr) + len(labels_va) + len(labels_te))
-
+    #这个区间仅仅是从ref中提取出来的test
     train_mask = sample_mask(idx_train, new_label.shape[0])
     pred_mask = sample_mask(idx_pred, new_label.shape[0])
     val_mask = sample_mask(idx_val, new_label.shape[0])
     test_mask = sample_mask(idx_test, new_label.shape[0])
-
+    #给定形状和类型的用0填充的数组，也就是所有的标签做01处理
     labels_binary_train = np.zeros(new_label.shape)
     labels_binary_val = np.zeros(new_label.shape)
     labels_binary_test = np.zeros(new_label.shape)
@@ -148,7 +157,7 @@ def load_data(datadir,rgraph=True):
     fake2 = np.array([-1] * len(data_train1))
     fake3 = np.array([-1] * (len(data_val1) + len(data_test1)))
     find1 = np.concatenate((fake2, np.array(lab_data2.index), fake3)).flatten()
-
+    #这里是什么意思啊？师兄？
     #' ---------------------------------------------
     #'  intra-graph
     #' ---------------------------------------------
@@ -238,6 +247,7 @@ def uniform(shape, scale=0.05, name=None):
 
 
 def glorot(shape, name=None):
+    #Glorot深度学习中的参数初始化方法
     """Glorot & Bengio (AISTATS 2010) init."""
     init_range = np.sqrt(6.0 / (shape[0] + shape[1]))
     initial = tf.random_uniform(shape,

From 98fa6712594a4d222a3fd60f8ed52209d5c5bd6b Mon Sep 17 00:00:00 2001
From: Dee-chen <371761145@qq.com>
Date: Tue, 15 Mar 2022 16:33:32 +0800
Subject: [PATCH 08/17] =?UTF-8?q?=E6=89=B9=E9=87=8F=E6=B5=81=E7=A8=8B?=
 =?UTF-8?q?=E5=A4=84=E7=90=86?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main.py                             |  98 +++++++++
 scGCN/data_preprocess.R             |  20 +-
 scGCN/data_preprocess.raw.R         |  25 +++
 scGCN/data_preprocess_utility.R     |  67 +-----
 scGCN/data_preprocess_utility.raw.R | 323 ++++++++++++++++++++++++++++
 5 files changed, 466 insertions(+), 67 deletions(-)
 create mode 100644 main.py
 create mode 100644 scGCN/data_preprocess.raw.R
 create mode 100644 scGCN/data_preprocess_utility.raw.R

diff --git a/main.py b/main.py
new file mode 100644
index 0000000..ff00622
--- /dev/null
+++ b/main.py
@@ -0,0 +1,98 @@
+import argparse
+#import threading
+import time
+import os
+import configparser
+import logging
+import coloredlogs
+import sys
+import subprocess
+import multiprocessing
+import pandas as pd
+
+class NewConfigParser(configparser.ConfigParser):
+    def optionxform(self, optionstr):
+        return optionstr
+
+def err_exit():
+    sys.exit('\033[1;31;47m!!The program exited abnormally, please check the log file !!\033[0m')
+
+def main():
+    pwd= os.getcwd()
+    parser = argparse.ArgumentParser(description='scGCN pipline')
+    parser.add_argument('-i', type=str, required=True, metavar='input_list',help='The count matix and cell type.')
+    parser.add_argument('-o', type=str, metavar='outputdir', default=os.sep.join([pwd, 'output']),
+                        help='The output dir.default:' + os.sep.join([pwd, 'output']))
+    parser.add_argument('--log', type=str, metavar='logfile',help='log file name,or log will print on stdout')
+    parser.add_argument('--debug', action='store_true', default=False, help='The log file will contain the output of each software itself, which is convenient for finding errors (-log is required)')
+
+    args = parser.parse_args()
+
+
+    if not args.log:
+        coloredlogs.install(
+                fmt='%(asctime)s: %(levelname)s\t%(message)s',
+                evel='info'.upper(), stream=sys.stdout
+            )
+    else:
+        if args.debug:
+            l='debug'
+        else:
+            l='info'
+        print('Logs will be written to {}'.format(args.log))
+        if os.path.exists(args.log):
+            os.remove(args.log)
+        logging.basicConfig(
+                    filename=args.log,
+                    filemode='a',
+                    format='%(asctime)s: %(levelname)s\t%(message)s',
+                    datefmt='%H:%M:%S',
+                    level=l.upper()
+                    )
+    home_dir=os.path.dirname(os.path.abspath(__file__))
+
+    logging.info("Start checking the input file..")
+    try:
+        input_list=pd.read_csv(args.i, sep='\t', header=0)
+    except FileNotFoundError:
+        logging.error('Checking input file :[ERR] -- The input file :%s  does not exist' %(args.input_list))
+        err_exit()
+    if input_list.columns.tolist()!=["Species","count","type","dbclass"]:
+        logging.error('The input file :%s format error.' % (args.i))
+        err_exit()
+    for f in input_list['count']:
+        if not os.path.exists(f):
+            logging.error('The input count file :%s does not exist.'%f)
+            err_exit()
+    for f in input_list['type']:
+        if not os.path.exists(f):
+            logging.error('The cell type count file :%s does not exist.'%f)
+            err_exit()
+    
+    outpath=os.path.abspath(args.o)
+    os.mkdir(outpath)
+   
+    for ref in input_list[input_list.dbclass=="ref"].itertuples():
+        for query in input_list[input_list.dbclass=="query"].itertuples():
+            
+            os.mkdir(os.sep.join([outpath,ref.Species+"_"+query.Species]))
+            logging.info('Start processing %s to %s  type predictions.' % (ref.Species, query.Species))
+            logging.info('Data pre-processing...')
+            os.chdir(os.sep.join([outpath,ref.Species+"_"+query.Species]))
+            logging.info(['Rscript',os.sep.join([home_dir,'data_preprocess.R']),ref.count,ref.type,query.count,query.type])
+            stdout=subprocess.run(['Rscript',os.sep.join([home_dir,'data_preprocess.R']),ref.count,ref.type,query.count,query.type],stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            logging.debug('{} stdout:\n'.format('Data pre-processing') +stdout.stdout.decode('utf-8'))
+            logging.debug('{} stderr:\n'.format('Data pre-processing') +stdout.stderr.decode('utf-8'))
+            logging.info("Data pre-processing finish.")
+
+            logging.info("Cell type predictions...")
+            stdout = subprocess.run(['python',os.sep.join([home_dir,'train.py'])],stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            logging.debug('{} stdout:\n'.format('Data pre-processing') +stdout.stdout.decode('utf-8'))
+            logging.debug('{} stderr:\n'.format('Data pre-processing') +stdout.stderr.decode('utf-8'))
+            logging.info("Cell type predictions finish.")
+
+    logging.info("ALL finish.")
+
+if __name__ == '__main__':
+    main()
+
diff --git a/scGCN/data_preprocess.R b/scGCN/data_preprocess.R
index 9fb4716..4778719 100644
--- a/scGCN/data_preprocess.R
+++ b/scGCN/data_preprocess.R
@@ -8,18 +8,16 @@
 
 source('data_preprocess_utility.R')
 
-count.list <- readRDS('example_data/count.list.RDS')
-label.list <- readRDS('example_data/label.list.RDS')
+args=commandArgs(T)
+
+ref_c=read.table(args[1],header=T)
+qur_c=read.table(args[2],header=T)
+count.list=list(ref_c,qur_c)
+ref_t=read.table(args[3],header=T)
+qur_t=read.table(args[4],header=T)
+label.list=list(ref_t,qur_t)
+
 
 save_processed_data(count.list,label.list)
-#四个文件，一个参考基因表达矩阵，一个待测基因表达矩阵，一个参考标签数据框，一个待测标签数据框
-#> source('data_preprocess_utility.R')
-#> ref_c=read.table("参考基因表达矩阵",header=T)
-#> qur_c=read.table("待测基因表达矩阵",header=T)
-#> count.list=list(ref_c,qur_c)
-#> ref_t=read.table("ref.class.txt参考的标签数据框",header=T)
-#> qur_t=read.table("qur.class.txt待测的标签数据框",header=T)
-#> label.list=list(ref_t,qur_t)
-#> save_processed_data(count.list,label.list)
 
 
diff --git a/scGCN/data_preprocess.raw.R b/scGCN/data_preprocess.raw.R
new file mode 100644
index 0000000..9fb4716
--- /dev/null
+++ b/scGCN/data_preprocess.raw.R
@@ -0,0 +1,25 @@
+#' This functions takes raw counts and labels of reference/query set to generate scGCN training input
+#' @param count.list list of reference data and query data; rows are genes and columns are cells
+#' @param label.list list of reference label and query label (if any), both are data frames with rownames identical with colnames of data; the first column is cell type
+#' @return This function returns files saved in folders "input" & "process_data"
+#' @export: all files are saved in current path
+#' @examples: load count.list and label.list from folder "example_data"
+#' save_processed_data(count.list,label.list)
+
+source('data_preprocess_utility.R')
+
+count.list <- readRDS('example_data/count.list.RDS')
+label.list <- readRDS('example_data/label.list.RDS')
+
+save_processed_data(count.list,label.list)
+#四个文件，一个参考基因表达矩阵，一个待测基因表达矩阵，一个参考标签数据框，一个待测标签数据框
+#> source('data_preprocess_utility.R')
+#> ref_c=read.table("参考基因表达矩阵",header=T)
+#> qur_c=read.table("待测基因表达矩阵",header=T)
+#> count.list=list(ref_c,qur_c)
+#> ref_t=read.table("ref.class.txt参考的标签数据框",header=T)
+#> qur_t=read.table("qur.class.txt待测的标签数据框",header=T)
+#> label.list=list(ref_t,qur_t)
+#> save_processed_data(count.list,label.list)
+
+
diff --git a/scGCN/data_preprocess_utility.R b/scGCN/data_preprocess_utility.R
index 8df9789..e272f4a 100644
--- a/scGCN/data_preprocess_utility.R
+++ b/scGCN/data_preprocess_utility.R
@@ -6,22 +6,18 @@
 #' @param inter_graph  inter-dataset graph in input folder
 
 #' @return This function returns a list of two statistical scores: entropy score and enrichment score that unknown cells have higher entropy score and lower enrichment score
-#熵值和富集值：未知类型的细胞具有较高的熵和较低的富集度；
 #' @examples: first load count.list and label.list from folder "example_data" and run 'data_preprocess.R'
 #' then run 'metrics(dat1,lab1,dat2,lab2,inter_graph,cluster)'
 
 suppressMessages(library(Seurat)); suppressMessages(library(entropy))
 
 metrics <- function(lab1,inter_graph,clusters){
-    #调用的时候是处理待测数据时
-    #scores <- metrics(lab1=Lab1,inter_graph=inter.graph,clusters=hc) 
-    #inter_graph和细胞整合锚点有关；clusters和细胞标识有关
     inter.graph <- inter_graph; hc <- clusters
     
     #' ------------ check cluster enrichment in cell types ------------    
     #' Examine in clusters
     
-    #' check enrichment 细胞富集度
+    #' check enrichment
     eval1 <- sapply(names(table(hc)),function(name){
         ps1 <- which(hc==name)
         foo <- table(lab1[inter_graph[which(inter_graph[,2]%in%ps1),1],1])
@@ -45,7 +41,6 @@ metrics <- function(lab1,inter_graph,clusters){
                 ind <- grep(names(foo)[i],names(tem))
                 r1 <- foo[i]/sum(foo)
                 return (r1/tem[ind])}))
-                #以上和之前一样？
             temp2 <- entropy.empirical(temp,unit='log')
             return (temp2)
         } else {return (0) }
@@ -66,7 +61,6 @@ metrics <- function(lab1,inter_graph,clusters){
 #' save_processed_data(count.list,label.list)
 
 GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
-    #调用的时候k的赋值为5
     object1 <- CreateSeuratObject(counts=Dat1,project = "1",assay = "Data1",
                                   min.cells = 0,min.features = 0,
                                   names.field = 1,names.delim = "_")
@@ -74,56 +68,38 @@ GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
     object2 <- CreateSeuratObject(counts=Dat2,project = "2",assay = "Data2",
                                   min.cells = 0,min.features =0,names.field = 1,
                                   names.delim = "_")
-    #分别对ref和qur创建seurat对象                              
+                                  
     objects <- list(object1,object2)    
     objects1 <- lapply(objects,function(obj){
         obj <- NormalizeData(obj,verbose=F)
         obj <- FindVariableFeatures(obj,
                                        selection.method = "vst",
                                        nfeatures = 2000,verbose=F)
-                                       #这里也有一个基因数目的处理2000
           obj <- ScaleData(obj,features=rownames(obj),verbose=FALSE)
           obj <- RunPCA(obj, features=rownames(obj), verbose = FALSE)
         return(obj)})
-        #用seurat做一些常规的单细胞数据预处理
-
     #'  Inter-data graph  
     object.nn <- FindIntegrationAnchors(object.list = objects1,k.anchor=K,verbose=F)
-    #寻找锚点：Find a set of anchors between a list of Seurat objects, K=5 
-    #These anchors can later be used to integrate the objects using the IntegrateData function
-    #k.anchor:How many neighbors (k) to use when picking anchors
     arc=object.nn@anchors
-    #将整体细胞的锚点信息赋值给变量
     d1.arc1=cbind(arc[arc[,4]==1,1],arc[arc[,4]==1,2],arc[arc[,4]==1,3]) 
     grp1=d1.arc1[d1.arc1[,3]>0,1:2]-1
-    #这里是？个性化的处理；最终得到的grp1是一个数据框，第一列是cell1的标识-1，第二列是cell2的标识-1，保留标准是score>0
     
     if (check.unknown){
         obj <- objects1[[2]]
         obj <- RunPCA(obj, features = VariableFeatures(object = obj),npcs=30,verbose=F)
         obj <- FindNeighbors(obj,verbose=F)
-        #Computes the k.param nearest neighbors for a given dataset. Can also optionally (via compute.SNN), 
-        #construct a shared nearest neighbor graph by calculating the neighborhood overlap (Jaccard index)
-        # between every cell and its k.param nearest neighbors.
         obj <- FindClusters(obj, resolution = 0.5,verbose=F)
         hc <- Idents(obj); inter.graph=grp1+1
-        #hc是用Idents函数获取细胞标识，那么seurat对象的标识是？
-        #inter.graph和细胞整合的锚点有关
         scores <- metrics(lab1=Lab1,inter_graph=inter.graph,clusters=hc)
-        #这里是对待测数据处理，用的label1？
         saveRDS(scores,file='./input/statistical_scores.RDS')
-        #检验待测细胞类型的熵和富集度
     }
     #'  Intra-data graph  
     d2.list <- list(objects1[[2]],objects1[[2]])
-    d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)   
-
+    d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)    
     d2.arc=d2.nn@anchors
     d2.arc1=cbind(d2.arc[d2.arc[,4]==1,1],d2.arc[d2.arc[,4]==1,2],d2.arc[d2.arc[,4]==1,3])
     d2.grp=d2.arc1[d2.arc1[,3]>0,1:2]-1
-    #个性化操作锚点
     final <- list(inteG=grp1,intraG=d2.grp)
-    #返回两个有关锚点的值;一个是参考数据集和待测数据集之间的相似度，一个是数据集内部的相似度
     return (final)
 }
 
@@ -174,10 +150,8 @@ selectHVFeature <- function(count.list,var.features,nfeatures = 2000){
     var.features1 <- unname(unlist(var.features))
     var.features2 <- sort(table(var.features1), decreasing = TRUE)
     for (i in 1:length(count.list)) {
-        var.features3 <- var.features2[names(var.features2) %in% rownames(count.list[[i]])]}
-    #确保基因既在ref中也在qur中    
+        var.features3 <- var.features2[names(var.features2) %in% rownames(count.list[[i]])]}    
     tie.val <- var.features3[min(nfeatures, length(var.features3))]
-    #反正只保留2000个基因数目
     features <- names(var.features3[which(var.features3 > tie.val)])
     if (length(features) > 0) {
         feature.ranks <- sapply(features, function(x) {
@@ -204,7 +178,6 @@ selectHVFeature <- function(count.list,var.features,nfeatures = 2000){
 }
 
 #' This functions takes refrence data and labels to identify variable gene features
-#筛选高变基因的过程
 #' @param data reference data; rows are genes and columns are cells
 #' @param label data frame with rownames identical with colnames of data; the first column is cell type
 #' @param nf number of variable features
@@ -213,20 +186,15 @@ selectHVFeature <- function(count.list,var.features,nfeatures = 2000){
 #' @examples
 #' select_feature(data=reference.data,label=reference.label)
 select_feature <- function(data,label,nf=2000){
-    M <- nrow(data); new.label <- label[,1] 
-    #M是行数，参考基因表达矩阵的行数；new.label是细胞类型标签
+    M <- nrow(data); new.label <- label[,1]
     pv1 <- sapply(1:M, function(i){
         mydataframe <- data.frame(y=as.numeric(data[i,]), ig=new.label)
         fit <- aov(y ~ ig, data=mydataframe)
-        #aov函数是在做方差分析；分析同一个基因的不同细胞的关系？
         summary(fit)[[1]][["Pr(>F)"]][1]})
     names(pv1) <- rownames(data)
-    #rownames是基因名？
     pv1.sig <- names(pv1)[order(pv1)[1:nf]]
-    #nf是挑选的高变基因的数目，在此是2000个
     egen <- unique(pv1.sig)
     return (egen)
-    #返回的是高变基因特征
 }
 
 #' This functions takes refrence data and labels to identify variable gene features
@@ -238,9 +206,7 @@ select_feature <- function(data,label,nf=2000){
 #' pre_process(count_list,label_list)
 pre_process <- function(count_list,label_list){
     sel.features <- select_feature(count_list[[1]],label_list[[1]])
-    #提取参考基因表达矩阵和参考的细胞标签；进行高变基因的挑选
     count_list_new <- list(count_list[[1]][sel.features,],count_list[[2]][sel.features,])
-    #对参考基因表达矩阵做了处理，保留2000个高变基因的数据
     return (count_list_new)
 }
 
@@ -251,17 +217,14 @@ pre_process <- function(count_list,label_list){
 #' @export: all files are saved in current path
 #' @examples: load count.list and label.list from folder "example_data"
 #' save_processed_data(count.list,label.list)
-save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=FALSE){
+save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=FALSE,outdir="input"){
     count.list <- pre_process(count_list=count.list,label_list=label.list)
-    #对参考数据处理，这一步得到的是做了2000个高变基因提取的基因表达矩阵和标签
     #' save counts data to certain path: 'input'
-    dir.create('input'); 
+    dir.create(outdir); 
     write.csv(t(count.list[[1]]),file='input/Data1.csv',quote=F,row.names=T)
     write.csv(t(count.list[[2]]),file='input/Data2.csv',quote=F,row.names=T)
-    #分别保存两个基因表达矩阵：（1）对参考做了2000个高变基因处理（2）对待测未处理
 
     #' optional graph: R genreated graph has minor differnce with python, user can choose the one with better performance
-    #使用R或者python两种方法来生成graph
     if (Rgraph){
         #' use R generated graph
         new.dat1 <- count.list[[1]]; new.dat2 <- count.list[[2]]
@@ -269,22 +232,16 @@ save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=
         graphs <- suppressWarnings(GenerateGraph(Dat1=new.dat1,Dat2=new.dat2,
                                                  Lab1=new.lab1,K=5,
                                                  check.unknown=check_unknown))
-        #返回两个锚点信息
         write.csv(graphs[[1]],file='input/inter_graph.csv',quote=F,row.names=T)
-        #数据集内部锚点
         write.csv(graphs[[2]],file='input/intra_graph.csv',quote=F,row.names=T)
-        #数据集之间锚点
-        write.csv(new.lab1,file='input/label1.csv',quote=F,row.names=F)
-        write.csv(new.lab2,file='input/label2.csv',quote=F,row.names=F)
-        #到此未对label2做处理        
+        write.csv(new.lab1,file='input/Label1.csv',quote=F,row.names=F)
+        write.csv(new.lab2,file='input/Label2.csv',quote=F,row.names=F)        
     } else {
         #' use python generated graph
         dir.create('results')
         #' @param norm.list normalized data
         res1 <- normalize_data(count.list)
-        #得到标准化表达矩阵以及高变基因集
         norm.list <- res1[[1]]; hvg.features <- res1[[2]];
-
         #' @param scale.list scaled data
         scale.list <- scale_data(count.list,norm.list,hvg.features)
         outputdir <- 'process_data'; dir.create(outputdir)
@@ -296,7 +253,6 @@ save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=
             file.name=paste0(outputdir,'/count_data/count_data_',i,'.csv')
             write.csv(df,file=file.name,quote=F)
         }
-        #保存经过第一步预处理的基因表达矩阵
         
         for (i in 1:N){
             df = label.list[[i]]
@@ -304,20 +260,19 @@ save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=
             file.name=paste0(outputdir,'/label_data/label_data_',i,'.csv')
             write.csv(df,file=file.name,quote=F)
         }
-        #保存label信息
+        
         for (i in 1:N){
             df = norm.list[[i]]
             if (!dir.exists(paste0(outputdir,'/norm_data'))){dir.create(paste0(outputdir,'/norm_data'))}
             file.name=paste0(outputdir,'/norm_data/norm_data_',i,'.csv')
             write.csv(df,file=file.name,quote=F)
         }
-        #保存标准化的基因表达矩阵
+        
         for (i in 1:N){
             df = scale.list[[i]]
             if (!dir.exists(paste0(outputdir,'/scale_data'))){dir.create(paste0(outputdir,'/scale_data'))}
             file.name=paste0(outputdir,'/scale_data/scale_data_',i,'.csv')
             write.csv(df,file=file.name,quote=F)
         }
-        #保存scale后的基因表达矩阵
     }
 }
diff --git a/scGCN/data_preprocess_utility.raw.R b/scGCN/data_preprocess_utility.raw.R
new file mode 100644
index 0000000..8df9789
--- /dev/null
+++ b/scGCN/data_preprocess_utility.raw.R
@@ -0,0 +1,323 @@
+#' This functions provides the optional way to generate scGCN labels
+#' @param Dat1  reference data; rows are genes and columns are cells
+#' @param Dat2  query data; rows are genes and columns are cells
+#' @param Lab1  label of reference data; rows are genes and columns are cells
+#' @param Lab2  query data; rows are genes and columns are cells
+#' @param inter_graph  inter-dataset graph in input folder
+
+#' @return This function returns a list of two statistical scores: entropy score and enrichment score that unknown cells have higher entropy score and lower enrichment score
+#熵值和富集值：未知类型的细胞具有较高的熵和较低的富集度；
+#' @examples: first load count.list and label.list from folder "example_data" and run 'data_preprocess.R'
+#' then run 'metrics(dat1,lab1,dat2,lab2,inter_graph,cluster)'
+
+suppressMessages(library(Seurat)); suppressMessages(library(entropy))
+
+metrics <- function(lab1,inter_graph,clusters){
+    #调用的时候是处理待测数据时
+    #scores <- metrics(lab1=Lab1,inter_graph=inter.graph,clusters=hc) 
+    #inter_graph和细胞整合锚点有关；clusters和细胞标识有关
+    inter.graph <- inter_graph; hc <- clusters
+    
+    #' ------------ check cluster enrichment in cell types ------------    
+    #' Examine in clusters
+    
+    #' check enrichment 细胞富集度
+    eval1 <- sapply(names(table(hc)),function(name){
+        ps1 <- which(hc==name)
+        foo <- table(lab1[inter_graph[which(inter_graph[,2]%in%ps1),1],1])
+        tem <- table(lab1)/sum(table(lab1))
+        if (length(foo)>0){
+            temp <- unlist(sapply(1:length(foo),function(i){
+                ind <- grep(names(foo)[i],names(tem))
+                r1 <- foo[i]/sum(foo)
+                return (r1/tem[ind])}))
+            return (max(temp)/sum(temp))
+        } else {return (0)}
+        })    
+    
+    #' check mixtureness
+    eval2 <- sapply(names(table(hc)),function(name){
+        ps1 <- which(hc==name)
+        foo <- table(lab1[inter_graph[which(inter_graph[,2]%in%ps1),1],1])
+        tem <- table(lab1)/sum(table(lab1))
+        if (length(foo)>0){
+            temp <- unlist(sapply(1:length(foo),function(i){
+                ind <- grep(names(foo)[i],names(tem))
+                r1 <- foo[i]/sum(foo)
+                return (r1/tem[ind])}))
+                #以上和之前一样？
+            temp2 <- entropy.empirical(temp,unit='log')
+            return (temp2)
+        } else {return (0) }
+    })
+    return (list(eval1=eval1,eval2=eval2))
+}
+
+
+#' This functions provides the optional way to generate scGCN labels
+#' @param Dat1  reference data; rows are genes and columns are cells
+#' @param Dat2  query data; rows are genes and columns are cells
+#' @param Lab1  label of reference data; rows are genes and columns are cells
+#' @param Lab2  query data; rows are genes and columns are cells
+
+#' @return This function returns files saved in folders "input" & "process_data"
+#' @export: all files are saved in current path
+#' @examples: load count.list and label.list from folder "example_data"
+#' save_processed_data(count.list,label.list)
+
+GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
+    #调用的时候k的赋值为5
+    object1 <- CreateSeuratObject(counts=Dat1,project = "1",assay = "Data1",
+                                  min.cells = 0,min.features = 0,
+                                  names.field = 1,names.delim = "_")
+        
+    object2 <- CreateSeuratObject(counts=Dat2,project = "2",assay = "Data2",
+                                  min.cells = 0,min.features =0,names.field = 1,
+                                  names.delim = "_")
+    #分别对ref和qur创建seurat对象                              
+    objects <- list(object1,object2)    
+    objects1 <- lapply(objects,function(obj){
+        obj <- NormalizeData(obj,verbose=F)
+        obj <- FindVariableFeatures(obj,
+                                       selection.method = "vst",
+                                       nfeatures = 2000,verbose=F)
+                                       #这里也有一个基因数目的处理2000
+          obj <- ScaleData(obj,features=rownames(obj),verbose=FALSE)
+          obj <- RunPCA(obj, features=rownames(obj), verbose = FALSE)
+        return(obj)})
+        #用seurat做一些常规的单细胞数据预处理
+
+    #'  Inter-data graph  
+    object.nn <- FindIntegrationAnchors(object.list = objects1,k.anchor=K,verbose=F)
+    #寻找锚点：Find a set of anchors between a list of Seurat objects, K=5 
+    #These anchors can later be used to integrate the objects using the IntegrateData function
+    #k.anchor:How many neighbors (k) to use when picking anchors
+    arc=object.nn@anchors
+    #将整体细胞的锚点信息赋值给变量
+    d1.arc1=cbind(arc[arc[,4]==1,1],arc[arc[,4]==1,2],arc[arc[,4]==1,3]) 
+    grp1=d1.arc1[d1.arc1[,3]>0,1:2]-1
+    #这里是？个性化的处理；最终得到的grp1是一个数据框，第一列是cell1的标识-1，第二列是cell2的标识-1，保留标准是score>0
+    
+    if (check.unknown){
+        obj <- objects1[[2]]
+        obj <- RunPCA(obj, features = VariableFeatures(object = obj),npcs=30,verbose=F)
+        obj <- FindNeighbors(obj,verbose=F)
+        #Computes the k.param nearest neighbors for a given dataset. Can also optionally (via compute.SNN), 
+        #construct a shared nearest neighbor graph by calculating the neighborhood overlap (Jaccard index)
+        # between every cell and its k.param nearest neighbors.
+        obj <- FindClusters(obj, resolution = 0.5,verbose=F)
+        hc <- Idents(obj); inter.graph=grp1+1
+        #hc是用Idents函数获取细胞标识，那么seurat对象的标识是？
+        #inter.graph和细胞整合的锚点有关
+        scores <- metrics(lab1=Lab1,inter_graph=inter.graph,clusters=hc)
+        #这里是对待测数据处理，用的label1？
+        saveRDS(scores,file='./input/statistical_scores.RDS')
+        #检验待测细胞类型的熵和富集度
+    }
+    #'  Intra-data graph  
+    d2.list <- list(objects1[[2]],objects1[[2]])
+    d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)   
+
+    d2.arc=d2.nn@anchors
+    d2.arc1=cbind(d2.arc[d2.arc[,4]==1,1],d2.arc[d2.arc[,4]==1,2],d2.arc[d2.arc[,4]==1,3])
+    d2.grp=d2.arc1[d2.arc1[,3]>0,1:2]-1
+    #个性化操作锚点
+    final <- list(inteG=grp1,intraG=d2.grp)
+    #返回两个有关锚点的值;一个是参考数据集和待测数据集之间的相似度，一个是数据集内部的相似度
+    return (final)
+}
+
+#' This function normalize count data matrix from refrence and query set
+#' @param count.list list of (1) reference data and (2) query data, rows are genes and columns are cells
+#' @return This function returns normalized data list 
+#' @export
+#' @examples
+#' normalize_data(count.list)
+normalize_data <- function(count.list){
+    norm.list <- vector('list')
+    var.features <- vector('list')
+    for ( i in 1:length(count.list)){
+        norm.list[[i]] <- as.matrix(Seurat:::NormalizeData.default(count.list[[i]],verbose=F))
+        #' select variable features
+        hvf.info <- Seurat:::FindVariableFeatures.default(count.list[[i]],selection.method='vst',verbose=F)
+        hvf.info <- hvf.info[which(x = hvf.info[, 1, drop = TRUE] != 0), ]
+        hvf.info <- hvf.info[order(hvf.info$vst.variance.standardized, decreasing = TRUE), , drop = FALSE]
+        var.features[[i]] <- head(rownames(hvf.info), n = 2000)
+    }
+    #' select variable features
+    sel.features <- selectHVFeature(count.list,var.features)
+    return (list(norm.list,sel.features))}
+
+#' This function returns scaled  data matrix from refrence and query set
+#' @param count.list list of raw counts of (1) reference data and (2) query data, rows are genes and columns are cells
+#' @param norm.list list of normalized (1) reference data and (2) query data, rows are genes and columns are cells
+#' @param hvg.features variable features of data list
+#' @return This function returns scaled data list 
+#' @export
+#' @examples
+#' scale_data(count.list,norm.list,hvg.features)
+scale_data <- function(count.list,norm.list,hvg.features){
+    scale.list <- lapply(norm.list,function(mat){
+        Seurat:::ScaleData.default(object = mat, features = hvg.features,verbose=F)})
+    scale.list <- lapply(1:length(count.list),function(i){
+        return (scale.list[[i]][na.omit(match(rownames(count.list[[i]]),rownames(scale.list[[i]]))),])})
+    return (scale.list)}
+
+#' This function returns a set of highly variable features
+#' @param count.list list of raw counts of (1) reference data and (2) query data, rows are genes and columns are cells
+#' @param var.features variable features of data list
+#' @return This function returns a common set of highly variable features
+#' @export
+#' @examples
+#' selectHVFeature(count.list,var.features)
+selectHVFeature <- function(count.list,var.features,nfeatures = 2000){
+    var.features1 <- unname(unlist(var.features))
+    var.features2 <- sort(table(var.features1), decreasing = TRUE)
+    for (i in 1:length(count.list)) {
+        var.features3 <- var.features2[names(var.features2) %in% rownames(count.list[[i]])]}
+    #确保基因既在ref中也在qur中    
+    tie.val <- var.features3[min(nfeatures, length(var.features3))]
+    #反正只保留2000个基因数目
+    features <- names(var.features3[which(var.features3 > tie.val)])
+    if (length(features) > 0) {
+        feature.ranks <- sapply(features, function(x) {
+            ranks <- sapply(var.features, function(y) {
+                if (x %in% y) {
+                    return(which(x == y))
+                }
+                return(NULL)
+            })
+            median(unlist(ranks))
+        })
+        features <- names(sort(feature.ranks))
+    }
+    features.tie <- var.features3[which(var.features3 == tie.val)]
+    tie.ranks <- sapply(names(features.tie), function(x) {
+        ranks <- sapply(var.features, function(y) {
+            if (x %in% y) {return(which(x == y))}
+            return(NULL)
+        })
+        median(unlist(ranks))
+    })
+    features <- c(features, names(head(sort(tie.ranks), nfeatures - length(features))))
+    return(features)
+}
+
+#' This functions takes refrence data and labels to identify variable gene features
+#筛选高变基因的过程
+#' @param data reference data; rows are genes and columns are cells
+#' @param label data frame with rownames identical with colnames of data; the first column is cell type
+#' @param nf number of variable features
+#' @return This function returns variable gene features using ANOVA
+#' @export
+#' @examples
+#' select_feature(data=reference.data,label=reference.label)
+select_feature <- function(data,label,nf=2000){
+    M <- nrow(data); new.label <- label[,1] 
+    #M是行数，参考基因表达矩阵的行数；new.label是细胞类型标签
+    pv1 <- sapply(1:M, function(i){
+        mydataframe <- data.frame(y=as.numeric(data[i,]), ig=new.label)
+        fit <- aov(y ~ ig, data=mydataframe)
+        #aov函数是在做方差分析；分析同一个基因的不同细胞的关系？
+        summary(fit)[[1]][["Pr(>F)"]][1]})
+    names(pv1) <- rownames(data)
+    #rownames是基因名？
+    pv1.sig <- names(pv1)[order(pv1)[1:nf]]
+    #nf是挑选的高变基因的数目，在此是2000个
+    egen <- unique(pv1.sig)
+    return (egen)
+    #返回的是高变基因特征
+}
+
+#' This functions takes refrence data and labels to identify variable gene features
+#' @param count_list list of reference data and query data; rows are genes and columns are cells
+#' @param label_list list of reference label and query label (if any), both are data frames with rownames identical with colnames of data; the first column is cell type
+#' @return This function returns four elements, including the normalized data, scaled data, hvg features, and selected features
+#' @export
+#' @examples
+#' pre_process(count_list,label_list)
+pre_process <- function(count_list,label_list){
+    sel.features <- select_feature(count_list[[1]],label_list[[1]])
+    #提取参考基因表达矩阵和参考的细胞标签；进行高变基因的挑选
+    count_list_new <- list(count_list[[1]][sel.features,],count_list[[2]][sel.features,])
+    #对参考基因表达矩阵做了处理，保留2000个高变基因的数据
+    return (count_list_new)
+}
+
+#' This functions takes raw counts and labels of reference/query set to generate scGCN training input
+#' @param count.list list of reference data and query data; rows are genes and columns are cells
+#' @param label.list list of reference label and query label (if any), both are data frames with rownames identical with colnames of data; the first column is cell type
+#' @return This function returns files saved in folders "input" & "process_data"
+#' @export: all files are saved in current path
+#' @examples: load count.list and label.list from folder "example_data"
+#' save_processed_data(count.list,label.list)
+save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=FALSE){
+    count.list <- pre_process(count_list=count.list,label_list=label.list)
+    #对参考数据处理，这一步得到的是做了2000个高变基因提取的基因表达矩阵和标签
+    #' save counts data to certain path: 'input'
+    dir.create('input'); 
+    write.csv(t(count.list[[1]]),file='input/Data1.csv',quote=F,row.names=T)
+    write.csv(t(count.list[[2]]),file='input/Data2.csv',quote=F,row.names=T)
+    #分别保存两个基因表达矩阵：（1）对参考做了2000个高变基因处理（2）对待测未处理
+
+    #' optional graph: R genreated graph has minor differnce with python, user can choose the one with better performance
+    #使用R或者python两种方法来生成graph
+    if (Rgraph){
+        #' use R generated graph
+        new.dat1 <- count.list[[1]]; new.dat2 <- count.list[[2]]
+        new.lab1 <- label.list[[1]]; new.lab2 <- label.list[[2]]
+        graphs <- suppressWarnings(GenerateGraph(Dat1=new.dat1,Dat2=new.dat2,
+                                                 Lab1=new.lab1,K=5,
+                                                 check.unknown=check_unknown))
+        #返回两个锚点信息
+        write.csv(graphs[[1]],file='input/inter_graph.csv',quote=F,row.names=T)
+        #数据集内部锚点
+        write.csv(graphs[[2]],file='input/intra_graph.csv',quote=F,row.names=T)
+        #数据集之间锚点
+        write.csv(new.lab1,file='input/label1.csv',quote=F,row.names=F)
+        write.csv(new.lab2,file='input/label2.csv',quote=F,row.names=F)
+        #到此未对label2做处理        
+    } else {
+        #' use python generated graph
+        dir.create('results')
+        #' @param norm.list normalized data
+        res1 <- normalize_data(count.list)
+        #得到标准化表达矩阵以及高变基因集
+        norm.list <- res1[[1]]; hvg.features <- res1[[2]];
+
+        #' @param scale.list scaled data
+        scale.list <- scale_data(count.list,norm.list,hvg.features)
+        outputdir <- 'process_data'; dir.create(outputdir)
+        write.csv(hvg.features,file=paste0(outputdir,'/sel_features.csv'),quote=F,row.names=F)
+        N <- length(count.list)
+        for (i in 1:N){
+            df = count.list[[i]]
+            if (!dir.exists(paste0(outputdir,'/count_data'))){dir.create(paste0(outputdir,'/count_data'))}
+            file.name=paste0(outputdir,'/count_data/count_data_',i,'.csv')
+            write.csv(df,file=file.name,quote=F)
+        }
+        #保存经过第一步预处理的基因表达矩阵
+        
+        for (i in 1:N){
+            df = label.list[[i]]
+            if (!dir.exists(paste0(outputdir,'/label_data'))){dir.create(paste0(outputdir,'/label_data'))}
+            file.name=paste0(outputdir,'/label_data/label_data_',i,'.csv')
+            write.csv(df,file=file.name,quote=F)
+        }
+        #保存label信息
+        for (i in 1:N){
+            df = norm.list[[i]]
+            if (!dir.exists(paste0(outputdir,'/norm_data'))){dir.create(paste0(outputdir,'/norm_data'))}
+            file.name=paste0(outputdir,'/norm_data/norm_data_',i,'.csv')
+            write.csv(df,file=file.name,quote=F)
+        }
+        #保存标准化的基因表达矩阵
+        for (i in 1:N){
+            df = scale.list[[i]]
+            if (!dir.exists(paste0(outputdir,'/scale_data'))){dir.create(paste0(outputdir,'/scale_data'))}
+            file.name=paste0(outputdir,'/scale_data/scale_data_',i,'.csv')
+            write.csv(df,file=file.name,quote=F)
+        }
+        #保存scale后的基因表达矩阵
+    }
+}

From 2facd9b731bd6fb2bbe07ad2045c3a77f89cef64 Mon Sep 17 00:00:00 2001
From: Dee-chen <371761145@qq.com>
Date: Tue, 15 Mar 2022 18:07:31 +0800
Subject: [PATCH 09/17] =?UTF-8?q?=E4=BC=98=E5=8C=96=E7=BB=93=E6=9E=9C?=
 =?UTF-8?q?=E8=BE=93=E5=87=BA?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main.py        | 2 +-
 scGCN/data.py  | 5 +++--
 scGCN/train.py | 9 ++++++++-
 scGCN/utils.py | 2 +-
 4 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/main.py b/main.py
index ff00622..7466fe9 100644
--- a/main.py
+++ b/main.py
@@ -1,5 +1,5 @@
 import argparse
-#import threading
+import threading
 import time
 import os
 import configparser
diff --git a/scGCN/data.py b/scGCN/data.py
index cf2fe58..d5384ab 100644
--- a/scGCN/data.py
+++ b/scGCN/data.py
@@ -32,6 +32,7 @@ def input_data(DataDir,Rgraph=True):
     lab_label2.columns = ['type']
     ##！！！此处通过物种1的数据中获得了type ，也许可以使用list(lab_label1['type']，lab_label2['type'])
     # 来合并两个数据集的细胞类型。但也许会影响之后运行？
+
     types = np.unique(lab_label1['type']).tolist()
     #label1=lab_label1['type'].tolist()
     #label2=lab_label2['type'].tolist()
@@ -89,11 +90,11 @@ def input_data(DataDir,Rgraph=True):
     #lab_train2 = pd.concat([label_train1, lab_label2])
 
     #' save objects
-
+    types_all = np.unique([*lab_label1['type'], *lab_label2['type']]).tolist()
     PIK = "{}/datasets.dat".format(DataDir)
     res = [
         data_train1, data_test1, data_val1, label_train1, label_test1,
-        label_val1, lab_data2, lab_label2, types
+        label_val1, lab_data2, lab_label2, types_all
     ]
     #此处原来的types仅仅是Reference的types
     with open(PIK, "wb") as f:
diff --git a/scGCN/train.py b/scGCN/train.py
index 86f74a0..0dc912d 100644
--- a/scGCN/train.py
+++ b/scGCN/train.py
@@ -38,7 +38,7 @@
 flags.DEFINE_integer('max_degree', 3, 'Maximum Chebyshev polynomial degree.')
 
 # Load data
-adj, features, labels_binary_train, labels_binary_val, labels_binary_test, train_mask, pred_mask, val_mask, test_mask, new_label, true_label, index_guide = load_data(
+adj, features, labels_binary_train, labels_binary_val, labels_binary_test, train_mask, pred_mask, val_mask, test_mask, new_label, true_label, index_guide, rename  = load_data(
     FLAGS.dataset,rgraph=FLAGS.graph)
     #pred_mask 仅仅是ref
     #new_label new_label是所有的真实的二进制的值
@@ -164,6 +164,7 @@ def evaluate(features, support, labels, mask, placeholders):
 #' save the predicted labels of query data
 os.mkdir(FLAGS.output)
 scGCN_all_labels = true_label.values.flatten()  #' ground truth
+
 np.savetxt(FLAGS.output + '/scGCN_all_input_labels.csv',scGCN_all_labels,delimiter=',',comments='',fmt='%s')
 np.savetxt(FLAGS.output+'/scGCN_query_mask.csv',pred_mask,delimiter=',',comments='',fmt='%s')           
 ab = sess.run(tf.nn.softmax(predict_output))
@@ -173,3 +174,9 @@ def evaluate(features, support, labels, mask, placeholders):
 np.savetxt(FLAGS.output+'/scGCN_index_guide.csv',index_guide,delimiter=',',comments='',fmt='%f')
 np.savetxt(FLAGS.output+'/scGCN_all_binary_input_labels.csv',all_binary_labels,delimiter=',',comments='',fmt='%f')
 
+scGCN_data2_labels = scGCN_all_labels[np.where(index_guide<0)]
+data2_binary_prediction = all_binary_prediction[np.where(index_guide<0)]
+new_rename = dict(zip(rename.values(),rename.keys()))
+data2_labels_prediction = data2_binary_prediction.replace(new_rename)
+
+np.savetxt(FLAGS.output+'scGCN_data2_labels_2_prediction.csv',np.column_stack((scGCN_data2_labels,data2_labels_prediction )),delimiter=',',comments='',fmt='%s %s')
\ No newline at end of file
diff --git a/scGCN/utils.py b/scGCN/utils.py
index b5052d9..8bc30c5 100644
--- a/scGCN/utils.py
+++ b/scGCN/utils.py
@@ -198,7 +198,7 @@ def load_data(datadir,rgraph=True):
     adj = nx.adjacency_matrix(nx.from_dict_of_lists(adj))
 
     print("assign input coordinatly....")
-    return adj, features, labels_binary_train, labels_binary_val, labels_binary_test, train_mask, pred_mask, val_mask, test_mask, new_label, true_label, index_guide
+    return adj, features, labels_binary_train, labels_binary_val, labels_binary_test, train_mask, pred_mask, val_mask, test_mask, new_label, true_label, index_guide, rename 
 
 
 def preprocess_features(features):

From 63ba77603d6c3b7216393848b141a2d79ee2fbaa Mon Sep 17 00:00:00 2001
From: ianutin <yanyuting@genomics.cn>
Date: Wed, 16 Mar 2022 15:31:13 +0800
Subject: [PATCH 10/17] 03-16add_sankey

---
 sankey.py      | 129 +++++++++++++++++++++++++++++++++++++++++++++++++
 scGCN/train.py |   7 ++-
 2 files changed, 135 insertions(+), 1 deletion(-)
 create mode 100644 sankey.py

diff --git a/sankey.py b/sankey.py
new file mode 100644
index 0000000..1175c0c
--- /dev/null
+++ b/sankey.py
@@ -0,0 +1,129 @@
+import pandas as pd
+from pyecharts.charts import Sankey
+from pyecharts import options as opts
+# pd.set_option('display.max_columns',20)
+# pd.set_option('display.max.rows',20000)
+
+def createSankey(dir_in,rel_species="original",qur_species="predicted",species="species"):
+    # data_in=pd.read_csv(dir_in,header=None)
+    data_in=dir_in
+    data=pd.DataFrame(columns=["original","predicted","number"])
+    aa=data_in[1].groupby(data_in[0]).value_counts()
+    # print(aa)
+    cc=[]
+    dd=[]
+    for i in range(len(aa.index)):
+        str_a=str(aa.index[i][0])
+        cc.append(str_a)
+        str_b=str(aa.index[i][1])
+        dd.append(str_b)
+    bb=aa.values.tolist()
+    data["original"]=cc
+    data["predicted"]=dd
+    data["number"]=bb
+    # print(data_out)
+
+    data.columns=["rel","pre","num"]
+    data.dropna(axis=0,how="any",inplace=True) #生成一个没有空值的一对一同源的pd表格
+    # print(data.head())
+    def changeNames1(dataa):
+        a=[]
+        a= rel_species+"_"+dataa['rel']
+        return a
+    def changeNames2(dataa):
+        a=[]
+        a=qur_species+"_"+dataa['pre']
+        return a
+        # dataa["num"]=dataa["num"]
+    print("not ok")
+    data["rel"]=data.apply(lambda r:changeNames1(r),axis=1)
+    data["pre"]=data.apply(lambda r:changeNames2(r),axis=1)
+    ###################################################################
+    def createNode(a,b):
+        nodes=[]
+        for i in range(0, len(a)):
+            dic = {}
+            if(len(a[i])!=0):
+
+                dic["name"] = a[i]
+                nodes.append(dic)
+            else:
+                pass
+        for j in range(0, len(b)):
+            dic={}
+            if(len(b[j])!=0):
+
+                dic["name"] = b[j]
+                nodes.append(dic)
+            else:
+                pass
+        return nodes
+    sig_rel=list(set(data["rel"].tolist()))
+    sig_pre=list(set(data["pre"].tolist()))
+    # sig_rel=sig_data["rel"].tolist()
+    # sig_pre=sig_data["pre"].tolist()
+    sig_nodes=createNode(sig_rel,sig_pre)
+    # print(sig_nodes)
+    def createLinks(dataa):
+        links = []
+        for i in dataa.values:
+            dic = {}
+            if(len(i[0])!=0):
+                dic["source"]=i[0]
+                dic["target"]=i[1]
+                dic["value"]=i[2]
+                links.append(dic)
+            else:
+                pass
+        return links
+    sig_links=createLinks(data)
+    # print(sig_links)
+    ################################################################
+    c=Sankey()
+    c.add(
+        series_name="the number of cells",
+        nodes=sig_nodes,
+        links=sig_links,
+        pos_top="10%",  #设置图片离标题的高度
+        #pos_right="5%",
+        #pos_bottom="5%",
+        pos_left="5%",
+        is_draggable=False, #设置是否可以拖动节点
+        focus_node_adjacency=True,
+        linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color="source",type_="dotted"),
+        label_opts=opts.LabelOpts(position="right",),
+    )
+    c.set_global_opts(
+        title_opts=opts.TitleOpts(title="left:"+rel_species+";"+"right:"+qur_species),
+        # toolbox_opts=opts.ToolboxOpts(
+        #     pos_right="20%",
+        #)
+        # legend_opts=opts.LegendOpts(
+        #     is_show=True,
+        # )
+        # tooltip_opts=opts.TooltipOpts(
+        #
+        # ),
+        # visualmap_opts=opts.VisualMapOpts(
+        #     is_calculable=True,
+        # )
+        # datazoom_opts=opts.DataZoomOpts(
+        #     is_show=True,
+        # )
+
+    )
+    # c = (
+    #         Sankey()
+    #         .add(
+    #             series_name="the number of cells",
+    #             nodes=sig_nodes,
+    #             links=sig_links,
+    #             linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color="source",type_="dotted"),
+    #             label_opts=opts.LabelOpts(position="right",),
+    #         )
+    #         .set_global_opts(title_opts=opts.TitleOpts(title="left:"+rel_species+";"+"right:"+qur_species))
+    #     )
+    # 输出html可视化结果
+    dir_out="./"+species+"_sankey.html"
+    c.render(dir_out)
+# createGraph("./datain/data_in.txt")
\ No newline at end of file
diff --git a/scGCN/train.py b/scGCN/train.py
index 0dc912d..b5464a8 100644
--- a/scGCN/train.py
+++ b/scGCN/train.py
@@ -7,6 +7,7 @@
 from utils import *
 from tensorflow.python.saved_model import tag_constants
 from models import scGCN
+from sankey import *
 sys.stdout = open("output_log.txt", "w")
 
 import warnings
@@ -179,4 +180,8 @@ def evaluate(features, support, labels, mask, placeholders):
 new_rename = dict(zip(rename.values(),rename.keys()))
 data2_labels_prediction = data2_binary_prediction.replace(new_rename)
 
-np.savetxt(FLAGS.output+'scGCN_data2_labels_2_prediction.csv',np.column_stack((scGCN_data2_labels,data2_labels_prediction )),delimiter=',',comments='',fmt='%s %s')
\ No newline at end of file
+np.savetxt(FLAGS.output+'scGCN_data2_labels_2_prediction.csv',np.column_stack((scGCN_data2_labels,data2_labels_prediction )),delimiter=',',comments='',fmt='%s %s')
+resule_lables=pd.DataFrame(columns=["original","predicted"])
+resule_lables["original"]=scGCN_data2_labels
+resule_lables["predicted"]=data2_labels_prediction
+createSankey(resule_lables)
\ No newline at end of file

From 1812998be3ff3a0dd0dd378f8ef5874009c87bd0 Mon Sep 17 00:00:00 2001
From: Dee-chen <371761145@qq.com>
Date: Fri, 18 Mar 2022 18:27:24 +0800
Subject: [PATCH 11/17] Update train.py

---
 scGCN/train.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/scGCN/train.py b/scGCN/train.py
index 0dc912d..aaa1bfe 100644
--- a/scGCN/train.py
+++ b/scGCN/train.py
@@ -177,6 +177,8 @@ def evaluate(features, support, labels, mask, placeholders):
 scGCN_data2_labels = scGCN_all_labels[np.where(index_guide<0)]
 data2_binary_prediction = all_binary_prediction[np.where(index_guide<0)]
 new_rename = dict(zip(rename.values(),rename.keys()))
-data2_labels_prediction = data2_binary_prediction.replace(new_rename)
+data2_binary_prediction_tab=pd.DataFrame(data2_binary_prediction)
+data2_labels_prediction = data2_binary_prediction_tab.replace(new_rename)
 
-np.savetxt(FLAGS.output+'scGCN_data2_labels_2_prediction.csv',np.column_stack((scGCN_data2_labels,data2_labels_prediction )),delimiter=',',comments='',fmt='%s %s')
\ No newline at end of file
+
+np.savetxt(FLAGS.output+'/scGCN_data2_labels_2_prediction.csv',np.column_stack((scGCN_data2_labels,data2_labels_prediction )),delimiter=',',comments='',fmt='%s %s')
\ No newline at end of file

From 65e97e41c6bcde7b094e511924359415f7c124f6 Mon Sep 17 00:00:00 2001
From: Dee-chen <371761145@qq.com>
Date: Fri, 18 Mar 2022 18:37:37 +0800
Subject: [PATCH 12/17] =?UTF-8?q?=E9=A2=84=E5=A4=84=E7=90=86=E4=BF=AE?=
 =?UTF-8?q?=E5=A4=8Dbug?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 scGCN/.Rhistory         |   0
 scGCN/data_preprocess.R | 293 ++++++++++++++++++++++++++++++++++++++--
 2 files changed, 283 insertions(+), 10 deletions(-)
 create mode 100644 scGCN/.Rhistory

diff --git a/scGCN/.Rhistory b/scGCN/.Rhistory
new file mode 100644
index 0000000..e69de29
diff --git a/scGCN/data_preprocess.R b/scGCN/data_preprocess.R
index 4778719..e4e1f4e 100644
--- a/scGCN/data_preprocess.R
+++ b/scGCN/data_preprocess.R
@@ -1,23 +1,296 @@
-#' This functions takes raw counts and labels of reference/query set to generate scGCN training input
-#' @param count.list list of reference data and query data; rows are genes and columns are cells
-#' @param label.list list of reference label and query label (if any), both are data frames with rownames identical with colnames of data; the first column is cell type
-#' @return This function returns files saved in folders "input" & "process_data"
-#' @export: all files are saved in current path
-#' @examples: load count.list and label.list from folder "example_data"
-#' save_processed_data(count.list,label.list)
+#' This functions provides the optional way to generate scGCN labels
+#' @param Dat1  reference data; rows are genes and columns are cells
+#' @param Dat2  query data; rows are genes and columns are cells
+#' @param Lab1  label of reference data; rows are genes and columns are cells
+#' @param Lab2  query data; rows are genes and columns are cells
+#' @param inter_graph  inter-dataset graph in input folder
+
+#' @return This function returns a list of two statistical scores: entropy score and enrichment score that unknown cells have higher entropy score and lower enrichment score
+#' @examples: first load count.list and label.list from folder "example_data" and run 'data_preprocess.R'
+#' then run 'metrics(dat1,lab1,dat2,lab2,inter_graph,cluster)'
+
+suppressMessages(library(Seurat)); suppressMessages(library(entropy))
 
-source('data_preprocess_utility.R')
 
 args=commandArgs(T)
 
 ref_c=read.table(args[1],header=T)
-qur_c=read.table(args[2],header=T)
+qur_c=read.table(args[3],header=T)
 count.list=list(ref_c,qur_c)
-ref_t=read.table(args[3],header=T)
+ref_t=read.table(args[2],header=T)
 qur_t=read.table(args[4],header=T)
 label.list=list(ref_t,qur_t)
 
 
+#save_processed_data(count.list,label.list)
+
+
+
+metrics <- function(lab1,inter_graph,clusters){
+    inter.graph <- inter_graph; hc <- clusters
+    
+    #' ------------ check cluster enrichment in cell types ------------    
+    #' Examine in clusters
+    
+    #' check enrichment
+    eval1 <- sapply(names(table(hc)),function(name){
+        ps1 <- which(hc==name)
+        foo <- table(lab1[inter_graph[which(inter_graph[,2]%in%ps1),1],1])
+        tem <- table(lab1)/sum(table(lab1))
+        if (length(foo)>0){
+            temp <- unlist(sapply(1:length(foo),function(i){
+                ind <- grep(names(foo)[i],names(tem))
+                r1 <- foo[i]/sum(foo)
+                return (r1/tem[ind])}))
+            return (max(temp)/sum(temp))
+        } else {return (0)}
+        })    
+    
+    #' check mixtureness
+    eval2 <- sapply(names(table(hc)),function(name){
+        ps1 <- which(hc==name)
+        foo <- table(lab1[inter_graph[which(inter_graph[,2]%in%ps1),1],1])
+        tem <- table(lab1)/sum(table(lab1))
+        if (length(foo)>0){
+            temp <- unlist(sapply(1:length(foo),function(i){
+                ind <- grep(names(foo)[i],names(tem))
+                r1 <- foo[i]/sum(foo)
+                return (r1/tem[ind])}))
+            temp2 <- entropy.empirical(temp,unit='log')
+            return (temp2)
+        } else {return (0) }
+    })
+    return (list(eval1=eval1,eval2=eval2))
+}
+
+
+#' This functions provides the optional way to generate scGCN labels
+#' @param Dat1  reference data; rows are genes and columns are cells
+#' @param Dat2  query data; rows are genes and columns are cells
+#' @param Lab1  label of reference data; rows are genes and columns are cells
+#' @param Lab2  query data; rows are genes and columns are cells
+
+#' @return This function returns files saved in folders "input" & "process_data"
+#' @export: all files are saved in current path
+#' @examples: load count.list and label.list from folder "example_data"
+#' save_processed_data(count.list,label.list)
+
+GenerateGraph <- function(Dat1,Dat2,Lab1,K,check.unknown){
+    object1 <- CreateSeuratObject(counts=Dat1,project = "1",assay = "Data1",
+                                  min.cells = 0,min.features = 0,
+                                  names.field = 1,names.delim = "_")
+        
+    object2 <- CreateSeuratObject(counts=Dat2,project = "2",assay = "Data2",
+                                  min.cells = 0,min.features =0,names.field = 1,
+                                  names.delim = "_")
+                                  
+    objects <- list(object1,object2)    
+    objects1 <- lapply(objects,function(obj){
+        obj <- NormalizeData(obj,verbose=F)
+        obj <- FindVariableFeatures(obj,
+                                       selection.method = "vst",
+                                       nfeatures = 2000,verbose=F)
+          obj <- ScaleData(obj,features=rownames(obj),verbose=FALSE)
+          obj <- RunPCA(obj, features=rownames(obj), verbose = FALSE)
+        return(obj)})
+    #'  Inter-data graph  
+    object.nn <- FindIntegrationAnchors(object.list = objects1,k.anchor=K,verbose=F)
+    arc=object.nn@anchors
+    d1.arc1=cbind(arc[arc[,4]==1,1],arc[arc[,4]==1,2],arc[arc[,4]==1,3]) 
+    grp1=d1.arc1[d1.arc1[,3]>0,1:2]-1
+    
+    if (check.unknown){
+        obj <- objects1[[2]]
+        obj <- RunPCA(obj, features = VariableFeatures(object = obj),npcs=30,verbose=F)
+        obj <- FindNeighbors(obj,verbose=F)
+        obj <- FindClusters(obj, resolution = 0.5,verbose=F)
+        hc <- Idents(obj); inter.graph=grp1+1
+        scores <- metrics(lab1=Lab1,inter_graph=inter.graph,clusters=hc)
+        saveRDS(scores,file='./input/statistical_scores.RDS')
+    }
+    #'  Intra-data graph  
+    d2.list <- list(objects1[[2]],objects1[[2]])
+    d2.nn <- FindIntegrationAnchors(object.list =d2.list,k.anchor=K,verbose=F)    
+    d2.arc=d2.nn@anchors
+    d2.arc1=cbind(d2.arc[d2.arc[,4]==1,1],d2.arc[d2.arc[,4]==1,2],d2.arc[d2.arc[,4]==1,3])
+    d2.grp=d2.arc1[d2.arc1[,3]>0,1:2]-1
+    final <- list(inteG=grp1,intraG=d2.grp)
+    return (final)
+}
+
+#' This function normalize count data matrix from refrence and query set
+#' @param count.list list of (1) reference data and (2) query data, rows are genes and columns are cells
+#' @return This function returns normalized data list 
+#' @export
+#' @examples
+#' normalize_data(count.list)
+normalize_data <- function(count.list){
+    norm.list <- vector('list')
+    var.features <- vector('list')
+    for ( i in 1:length(count.list)){
+        norm.list[[i]] <- as.matrix(Seurat:::NormalizeData.default(count.list[[i]],verbose=F))
+        #' select variable features
+        hvf.info <- Seurat:::FindVariableFeatures.default(count.list[[i]],selection.method='vst',verbose=F)
+        hvf.info <- hvf.info[which(x = hvf.info[, 1, drop = TRUE] != 0), ]
+        hvf.info <- hvf.info[order(hvf.info$vst.variance.standardized, decreasing = TRUE), , drop = FALSE]
+        var.features[[i]] <- head(rownames(hvf.info), n = 2000)
+    }
+    #' select variable features
+    sel.features <- selectHVFeature(count.list,var.features)
+    return (list(norm.list,sel.features))}
+
+#' This function returns scaled  data matrix from refrence and query set
+#' @param count.list list of raw counts of (1) reference data and (2) query data, rows are genes and columns are cells
+#' @param norm.list list of normalized (1) reference data and (2) query data, rows are genes and columns are cells
+#' @param hvg.features variable features of data list
+#' @return This function returns scaled data list 
+#' @export
+#' @examples
+#' scale_data(count.list,norm.list,hvg.features)
+scale_data <- function(count.list,norm.list,hvg.features){
+    scale.list <- lapply(norm.list,function(mat){
+        Seurat:::ScaleData.default(object = mat, features = hvg.features,verbose=F)})
+    scale.list <- lapply(1:length(count.list),function(i){
+        return (scale.list[[i]][na.omit(match(rownames(count.list[[i]]),rownames(scale.list[[i]]))),])})
+    return (scale.list)}
+
+#' This function returns a set of highly variable features
+#' @param count.list list of raw counts of (1) reference data and (2) query data, rows are genes and columns are cells
+#' @param var.features variable features of data list
+#' @return This function returns a common set of highly variable features
+#' @export
+#' @examples
+#' selectHVFeature(count.list,var.features)
+selectHVFeature <- function(count.list,var.features,nfeatures = 2000){
+    var.features1 <- unname(unlist(var.features))
+    var.features2 <- sort(table(var.features1), decreasing = TRUE)
+    for (i in 1:length(count.list)) {
+        var.features3 <- var.features2[names(var.features2) %in% rownames(count.list[[i]])]}    
+    tie.val <- var.features3[min(nfeatures, length(var.features3))]
+    features <- names(var.features3[which(var.features3 > tie.val)])
+    if (length(features) > 0) {
+        feature.ranks <- sapply(features, function(x) {
+            ranks <- sapply(var.features, function(y) {
+                if (x %in% y) {
+                    return(which(x == y))
+                }
+                return(NULL)
+            })
+            median(unlist(ranks))
+        })
+        features <- names(sort(feature.ranks))
+    }
+    features.tie <- var.features3[which(var.features3 == tie.val)]
+    tie.ranks <- sapply(names(features.tie), function(x) {
+        ranks <- sapply(var.features, function(y) {
+            if (x %in% y) {return(which(x == y))}
+            return(NULL)
+        })
+        median(unlist(ranks))
+    })
+    features <- c(features, names(head(sort(tie.ranks), nfeatures - length(features))))
+    return(features)
+}
+
+#' This functions takes refrence data and labels to identify variable gene features
+#' @param data reference data; rows are genes and columns are cells
+#' @param label data frame with rownames identical with colnames of data; the first column is cell type
+#' @param nf number of variable features
+#' @return This function returns variable gene features using ANOVA
+#' @export
+#' @examples
+#' select_feature(data=reference.data,label=reference.label)
+select_feature <- function(data,label,nf=2000){
+    M <- nrow(data); new.label <- label[,1]
+    pv1 <- sapply(1:M, function(i){
+        mydataframe <- data.frame(y=as.numeric(data[i,]), ig=new.label)
+        fit <- aov(y ~ ig, data=mydataframe)
+        summary(fit)[[1]][["Pr(>F)"]][1]})
+    names(pv1) <- rownames(data)
+    pv1.sig <- names(pv1)[order(pv1)[1:nf]]
+    egen <- unique(pv1.sig)
+    return (egen)
+}
+
+#' This functions takes refrence data and labels to identify variable gene features
+#' @param count_list list of reference data and query data; rows are genes and columns are cells
+#' @param label_list list of reference label and query label (if any), both are data frames with rownames identical with colnames of data; the first column is cell type
+#' @return This function returns four elements, including the normalized data, scaled data, hvg features, and selected features
+#' @export
+#' @examples
+#' pre_process(count_list,label_list)
+pre_process <- function(count_list,label_list){
+    sel.features <- select_feature(count_list[[1]],label_list[[1]])
+    count_list_new <- list(count_list[[1]][sel.features,],count_list[[2]][sel.features,])
+    return (count_list_new)
+}
+
+#' This functions takes raw counts and labels of reference/query set to generate scGCN training input
+#' @param count.list list of reference data and query data; rows are genes and columns are cells
+#' @param label.list list of reference label and query label (if any), both are data frames with rownames identical with colnames of data; the first column is cell type
+#' @return This function returns files saved in folders "input" & "process_data"
+#' @export: all files are saved in current path
+#' @examples: load count.list and label.list from folder "example_data"
+#' save_processed_data(count.list,label.list)
+save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=FALSE,outdir="input"){
+    count.list <- pre_process(count_list=count.list,label_list=label.list)
+    #' save counts data to certain path: 'input'
+    dir.create(outdir); 
+    write.csv(t(count.list[[1]]),file='input/Data1.csv',quote=F,row.names=T)
+    write.csv(t(count.list[[2]]),file='input/Data2.csv',quote=F,row.names=T)
+
+    #' optional graph: R genreated graph has minor differnce with python, user can choose the one with better performance
+    if (Rgraph){
+        #' use R generated graph
+        new.dat1 <- count.list[[1]]; new.dat2 <- count.list[[2]]
+        new.lab1 <- label.list[[1]]; new.lab2 <- label.list[[2]]
+        graphs <- suppressWarnings(GenerateGraph(Dat1=new.dat1,Dat2=new.dat2,
+                                                 Lab1=new.lab1,K=5,
+                                                 check.unknown=check_unknown))
+        write.csv(graphs[[1]],file='input/inter_graph.csv',quote=F,row.names=T)
+        write.csv(graphs[[2]],file='input/intra_graph.csv',quote=F,row.names=T)
+        write.csv(new.lab1,file='input/Label1.csv',quote=F,row.names=F)
+        write.csv(new.lab2,file='input/Label2.csv',quote=F,row.names=F)        
+    } else {
+        #' use python generated graph
+        dir.create('results')
+        #' @param norm.list normalized data
+        res1 <- normalize_data(count.list)
+        norm.list <- res1[[1]]; hvg.features <- res1[[2]];
+        #' @param scale.list scaled data
+        scale.list <- scale_data(count.list,norm.list,hvg.features)
+        outputdir <- 'process_data'; dir.create(outputdir)
+        write.csv(hvg.features,file=paste0(outputdir,'/sel_features.csv'),quote=F,row.names=F)
+        N <- length(count.list)
+        for (i in 1:N){
+            df = count.list[[i]]
+            if (!dir.exists(paste0(outputdir,'/count_data'))){dir.create(paste0(outputdir,'/count_data'))}
+            file.name=paste0(outputdir,'/count_data/count_data_',i,'.csv')
+            write.csv(df,file=file.name,quote=F)
+        }
+        
+        for (i in 1:N){
+            df = label.list[[i]]
+            if (!dir.exists(paste0(outputdir,'/label_data'))){dir.create(paste0(outputdir,'/label_data'))}
+            file.name=paste0(outputdir,'/label_data/label_data_',i,'.csv')
+            write.csv(df,file=file.name,quote=F)
+        }
+        
+        for (i in 1:N){
+            df = norm.list[[i]]
+            if (!dir.exists(paste0(outputdir,'/norm_data'))){dir.create(paste0(outputdir,'/norm_data'))}
+            file.name=paste0(outputdir,'/norm_data/norm_data_',i,'.csv')
+            write.csv(df,file=file.name,quote=F)
+        }
+        
+        for (i in 1:N){
+            df = scale.list[[i]]
+            if (!dir.exists(paste0(outputdir,'/scale_data'))){dir.create(paste0(outputdir,'/scale_data'))}
+            file.name=paste0(outputdir,'/scale_data/scale_data_',i,'.csv')
+            write.csv(df,file=file.name,quote=F)
+        }
+    }
+}
 save_processed_data(count.list,label.list)
 
 

From 52e53607dd514af6c923e325f7a395d886dde38b Mon Sep 17 00:00:00 2001
From: ianutin <yanyuting@genomics.cn>
Date: Mon, 21 Mar 2022 10:15:28 +0800
Subject: [PATCH 13/17] Update sankey.py

---
 sankey.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sankey.py b/sankey.py
index 1175c0c..5bbc3ab 100644
--- a/sankey.py
+++ b/sankey.py
@@ -8,7 +8,7 @@ def createSankey(dir_in,rel_species="original",qur_species="predicted",species="
     # data_in=pd.read_csv(dir_in,header=None)
     data_in=dir_in
     data=pd.DataFrame(columns=["original","predicted","number"])
-    aa=data_in[1].groupby(data_in[0]).value_counts()
+    aa=data_in["predicted"].groupby(data_in["original"]).value_counts()
     # print(aa)
     cc=[]
     dd=[]

From 5499756303bd97d024a62d158fcedabcc3deaa1a Mon Sep 17 00:00:00 2001
From: Dee-chen <371761145@qq.com>
Date: Mon, 21 Mar 2022 16:15:44 +0800
Subject: [PATCH 14/17] 3.21

---
 sankey.py => scGCN/sankey.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename sankey.py => scGCN/sankey.py (98%)

diff --git a/sankey.py b/scGCN/sankey.py
similarity index 98%
rename from sankey.py
rename to scGCN/sankey.py
index 1175c0c..5bbc3ab 100644
--- a/sankey.py
+++ b/scGCN/sankey.py
@@ -8,7 +8,7 @@ def createSankey(dir_in,rel_species="original",qur_species="predicted",species="
     # data_in=pd.read_csv(dir_in,header=None)
     data_in=dir_in
     data=pd.DataFrame(columns=["original","predicted","number"])
-    aa=data_in[1].groupby(data_in[0]).value_counts()
+    aa=data_in["predicted"].groupby(data_in["original"]).value_counts()
     # print(aa)
     cc=[]
     dd=[]

From 89e9eff41f81e2a65eab3e1c73427837f541e3e7 Mon Sep 17 00:00:00 2001
From: Dee-chen <371761145@qq.com>
Date: Mon, 21 Mar 2022 16:22:57 +0800
Subject: [PATCH 15/17] 3.21.new

---
 scGCN/data_preprocess_utility.R |  7 +++
 scGCN/main.py                   | 94 +++++++++++++++++++++++++++++++++
 scGCN/main.tmp.py               | 94 +++++++++++++++++++++++++++++++++
 3 files changed, 195 insertions(+)
 create mode 100644 scGCN/main.py
 create mode 100644 scGCN/main.tmp.py

diff --git a/scGCN/data_preprocess_utility.R b/scGCN/data_preprocess_utility.R
index e272f4a..0d02f45 100644
--- a/scGCN/data_preprocess_utility.R
+++ b/scGCN/data_preprocess_utility.R
@@ -234,8 +234,15 @@ save_processed_data <- function(count.list,label.list,Rgraph=TRUE,check_unknown=
                                                  check.unknown=check_unknown))
         write.csv(graphs[[1]],file='input/inter_graph.csv',quote=F,row.names=T)
         write.csv(graphs[[2]],file='input/intra_graph.csv',quote=F,row.names=T)
+<<<<<<< Updated upstream
         write.csv(new.lab1,file='input/Label1.csv',quote=F,row.names=F)
         write.csv(new.lab2,file='input/Label2.csv',quote=F,row.names=F)        
+=======
+        #数据集之间锚点
+        write.csv(new.lab1,file='input/Label1.csv',quote=F,row.names=F)
+        write.csv(new.lab2,file='input/Label2.csv',quote=F,row.names=F)
+        #到此未对label2做处理        
+>>>>>>> Stashed changes
     } else {
         #' use python generated graph
         dir.create('results')
diff --git a/scGCN/main.py b/scGCN/main.py
new file mode 100644
index 0000000..aaf81b9
--- /dev/null
+++ b/scGCN/main.py
@@ -0,0 +1,94 @@
+import argparse
+#import threading
+import time
+import os
+import configparser
+import logging
+import coloredlogs
+import sys
+import subprocess
+import multiprocessing
+import pandas as pd
+
+class NewConfigParser(configparser.ConfigParser):
+    def optionxform(self, optionstr):
+        return optionstr
+
+def err_exit():
+    sys.exit('\033[1;31;47m!!The program exited abnormally, please check the log file !!\033[0m')
+
+def main():
+    pwd= os.getcwd()
+    parser = argparse.ArgumentParser(description='scGCN pipline')
+    parser.add_argument('-i', type=str, required=True, metavar='input_list',help='The count matix and cell type.')
+    parser.add_argument('-o', type=str, metavar='outputdir', default=os.sep.join([pwd, 'output']),
+                        help='The output dir.default:' + os.sep.join([pwd, 'output']))
+    parser.add_argument('--log', type=str, metavar='logfile',help='log file name,or log will print on stdout')
+    parser.add_argument('--debug', action='store_true', default=False, help='The log file will contain the output of each software itself, which is convenient for finding errors (-log is required)')
+
+    args = parser.parse_args()
+
+    if not args.log:
+        coloredlogs.install(
+                fmt='%(asctime)s: %(levelname)s\t%(message)s',
+                evel='info'.upper(), stream=sys.stdout
+            )
+    else:
+        if args.debug:
+            l='debug'
+        else:
+            l='info'
+        print('Logs will be written to {}'.format(args.log))
+        if os.path.exists(args.log):
+            os.remove(args.log)
+        logging.basicConfig(
+                    filename=args.log,
+                    filemode='a',
+                    format='%(asctime)s: %(levelname)s\t%(message)s',
+                    datefmt='%H:%M:%S',
+                    level=l.upper()
+                    )
+    home_dir=os.path.dirname(os.path.abspath(__file__))
+
+    logging.info("Start checking the input file..")
+    try:
+        input_list=pd.read_csv(args.i, sep='\t', header=0, index_col='Species')
+    except FileNotFoundError:
+        logging.error('Checking input file :[ERR] -- The input file :%s  does not exist' %(args.input_list))
+        err_exit()
+
+    if input_list.columns.tolist()!=["specie","count","type","dbclass"]:
+        logging.error('The input file :%s format error.' % (args.input_list))
+        err_exit()
+    for f in input_list.count:
+        if not os.path.exists(f):
+            logging.error('The input count file :%s does not exist.'%f)
+            err_exit()
+    for f in input_list.type:
+        if not os.path.exists(f):
+            logging.error('The cell type count file :%s does not exist.'%f)
+            err_exit()
+
+    os.mkdir(args.o)
+    currentPath = os.path.realpath(__file__)
+    for ref in input_list[input_list.dbclass=="ref"].itertuples():
+        for query in input_list[input_list.dbclass=="query"].itertuples():
+            os.mkdir(os.sep.join([args.o,ref.Species,query.Species]))
+            logging.info('Start processing %s to %s  type predictions.' % (ref.Species, query.Species))
+            logging.info('Data pre-processing...')
+            os.chdir(os.sep.join([args.o,ref.Species,query.Species]))
+            stdout=subprocess.run(['Rscript',os.sep.join([currentPath,'data_preprocess.R']),ref.count,ref.type,query.count,query.type],stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            logging.debug('{} stdout:\n'.format('Data pre-processing') +stdout.stdout.decode('utf-8'))
+            logging.debug('{} stderr:\n'.format('Data pre-processing') +stdout.stderr.decode('utf-8'))
+            logging.info("Data pre-processing finish.")
+
+            logging.info("Cell type predictions...")
+            stdout = subprocess.run(['python',os.sep.join([currentPath,'train.py'])],stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            logging.debug('{} stdout:\n'.format('Data pre-processing') +stdout.stdout.decode('utf-8'))
+            logging.debug('{} stderr:\n'.format('Data pre-processing') +stdout.stderr.decode('utf-8'))
+            logging.info("Cell type predictions finish.")
+
+    logging.info("ALL finish.")
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/scGCN/main.tmp.py b/scGCN/main.tmp.py
new file mode 100644
index 0000000..75061a1
--- /dev/null
+++ b/scGCN/main.tmp.py
@@ -0,0 +1,94 @@
+import argparse
+#import threading
+import time
+import os
+import configparser
+import logging
+import coloredlogs
+import sys
+import subprocess
+import multiprocessing
+import pandas as pd
+
+class NewConfigParser(configparser.ConfigParser):
+    def optionxform(self, optionstr):
+        return optionstr
+
+def err_exit():
+    sys.exit('\033[1;31;47m!!The program exited abnormally, please check the log file !!\033[0m')
+
+def main():
+    pwd= os.getcwd()
+    parser = argparse.ArgumentParser(description='scGCN pipline')
+    parser.add_argument('-i', type=str, required=True, metavar='input_list',help='The count matix and cell type.')
+    parser.add_argument('-o', type=str, metavar='outputdir', default=os.sep.join([pwd, 'output']),
+                        help='The output dir.default:' + os.sep.join([pwd, 'output']))
+    parser.add_argument('--log', type=str, metavar='logfile',help='log file name,or log will print on stdout')
+    parser.add_argument('--debug', action='store_true', default=False, help='The log file will contain the output of each software itself, which is convenient for finding errors (-log is required)')
+
+    args = parser.parse_args()
+
+
+    if not args.log:
+        coloredlogs.install(
+                fmt='%(asctime)s: %(levelname)s\t%(message)s',
+                evel='info'.upper(), stream=sys.stdout
+            )
+    else:
+        if args.debug:
+            l='debug'
+        else:
+            l='info'
+        print('Logs will be written to {}'.format(args.log))
+        if os.path.exists(args.log):
+            os.remove(args.log)
+        logging.basicConfig(
+                    filename=args.log,
+                    filemode='a',
+                    format='%(asctime)s: %(levelname)s\t%(message)s',
+                    datefmt='%H:%M:%S',
+                    level=l.upper()
+                    )
+    home_dir=os.path.dirname(os.path.abspath(__file__))
+
+    logging.info("Start checking the input file..")
+    try:
+        input_list=pd.read_csv(args.i, sep='\t', header=0)
+    except FileNotFoundError:
+        logging.error('Checking input file :[ERR] -- The input file :%s  does not exist' %(args.input_list))
+        err_exit()
+    if input_list.columns.tolist()!=["Species","count","type","dbclass"]:
+        logging.error('The input file :%s format error.' % (args.i))
+        err_exit()
+    for f in input_list['count']:
+        if not os.path.exists(f):
+            logging.error('The input count file :%s does not exist.'%f)
+            err_exit()
+    for f in input_list['type']:
+        if not os.path.exists(f):
+            logging.error('The cell type count file :%s does not exist.'%f)
+            err_exit()
+
+    os.mkdir(args.o)
+    currentPath = os.path.realpath(__file__)
+    for ref in input_list[input_list.dbclass=="ref"].itertuples():
+        for query in input_list[input_list.dbclass=="query"].itertuples():
+            os.mkdir(os.sep.join([args.o,ref.Species,query.Species]))
+            logging.info('Start processing %s to %s  type predictions.' % (ref.Species, query.Species))
+            logging.info('Data pre-processing...')
+            os.chdir(os.sep.join([args.o,ref.Species,query.Species]))
+            stdout=subprocess.run(['Rscript',os.sep.join([currentPath,'data_preprocess.R']),ref.count,ref.type,query.count,query.type],stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            logging.debug('{} stdout:\n'.format('Data pre-processing') +stdout.stdout.decode('utf-8'))
+            logging.debug('{} stderr:\n'.format('Data pre-processing') +stdout.stderr.decode('utf-8'))
+            logging.info("Data pre-processing finish.")
+
+            logging.info("Cell type predictions...")
+            stdout = subprocess.run(['python',os.sep.join([currentPath,'train.py'])],stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            logging.debug('{} stdout:\n'.format('Data pre-processing') +stdout.stdout.decode('utf-8'))
+            logging.debug('{} stderr:\n'.format('Data pre-processing') +stdout.stderr.decode('utf-8'))
+            logging.info("Cell type predictions finish.")
+
+    logging.info("ALL finish.")
+
+if __name__ == '__main__':
+    main()

From 2a8d30ec9c4531c8168570805be33cbf2a18438f Mon Sep 17 00:00:00 2001
From: ianutin <yanyuting@genomics.cn>
Date: Tue, 22 Mar 2022 16:51:22 +0800
Subject: [PATCH 16/17] update all

update all
---
 .../inspectionProfiles/profiles_settings.xml  |    6 +
 .idea/misc.xml                                |    4 +
 .idea/modules.xml                             |    8 +
 .idea/scGCN.iml                               |   12 +
 .idea/vcs.xml                                 |    6 +
 .idea/workspace.xml                           |   63 +
 scGCN/__pycache__/data.cpython-38.pyc         |  Bin 0 -> 2395 bytes
 scGCN/__pycache__/graph.cpython-38.pyc        |  Bin 0 -> 2022 bytes
 scGCN/__pycache__/layers.cpython-38.pyc       |  Bin 0 -> 3677 bytes
 scGCN/__pycache__/models.cpython-38.pyc       |  Bin 0 -> 4354 bytes
 scGCN/__pycache__/sankey.cpython-38.pyc       |  Bin 0 -> 2823 bytes
 scGCN/__pycache__/utility.cpython-38.pyc      |  Bin 0 -> 7647 bytes
 scGCN/__pycache__/utils.cpython-38.pyc        |  Bin 0 -> 9015 bytes
 .../best_validation.data-00000-of-00001       |  Bin 0 -> 771080 bytes
 scGCN/checkpoints/best_validation.index       |  Bin 0 -> 386 bytes
 scGCN/checkpoints/best_validation.meta        |  Bin 0 -> 86140 bytes
 scGCN/checkpoints/checkpoint                  |    2 +
 scGCN/data.py                                 |   24 +-
 scGCN/data.txt                                |  105 +
 scGCN/err.txt                                 |   14 +
 scGCN/graph.py                                |   22 +-
 scGCN/input/Data1.csv                         | 7265 +++++++++++++
 scGCN/input/Data2.csv                         | 1842 ++++
 scGCN/input/Label1.csv                        | 7265 +++++++++++++
 scGCN/input/Label2.csv                        | 1842 ++++
 scGCN/input/inter_graph.csv                   | 3641 +++++++
 scGCN/input/intra_graph.csv                   | 5005 +++++++++
 scGCN/layers.py                               |    7 +-
 scGCN/models.py                               |    4 +-
 scGCN/output_log.txt                          |  230 +
 .../results/scGCN_all_binary_input_labels.csv | 9105 +++++++++++++++++
 .../scGCN_all_binary_predicted_labels.csv     | 9105 +++++++++++++++++
 scGCN/results/scGCN_all_input_labels.csv      | 9105 +++++++++++++++++
 .../scGCN_data2_labels_2_prediction.csv       | 1841 ++++
 scGCN/results/scGCN_index_guide.csv           | 9105 +++++++++++++++++
 scGCN/results/scGCN_query_mask.csv            | 9105 +++++++++++++++++
 scGCN/species_sankey.html                     |  284 +
 scGCN/train.py                                |   18 +-
 scGCN/utility.py                              |   17 +-
 scGCN/utils.py                                |   61 +-
 40 files changed, 75022 insertions(+), 91 deletions(-)
 create mode 100644 .idea/inspectionProfiles/profiles_settings.xml
 create mode 100644 .idea/misc.xml
 create mode 100644 .idea/modules.xml
 create mode 100644 .idea/scGCN.iml
 create mode 100644 .idea/vcs.xml
 create mode 100644 .idea/workspace.xml
 create mode 100644 scGCN/__pycache__/data.cpython-38.pyc
 create mode 100644 scGCN/__pycache__/graph.cpython-38.pyc
 create mode 100644 scGCN/__pycache__/layers.cpython-38.pyc
 create mode 100644 scGCN/__pycache__/models.cpython-38.pyc
 create mode 100644 scGCN/__pycache__/sankey.cpython-38.pyc
 create mode 100644 scGCN/__pycache__/utility.cpython-38.pyc
 create mode 100644 scGCN/__pycache__/utils.cpython-38.pyc
 create mode 100644 scGCN/checkpoints/best_validation.data-00000-of-00001
 create mode 100644 scGCN/checkpoints/best_validation.index
 create mode 100644 scGCN/checkpoints/best_validation.meta
 create mode 100644 scGCN/checkpoints/checkpoint
 create mode 100644 scGCN/data.txt
 create mode 100644 scGCN/err.txt
 create mode 100644 scGCN/input/Data1.csv
 create mode 100644 scGCN/input/Data2.csv
 create mode 100644 scGCN/input/Label1.csv
 create mode 100644 scGCN/input/Label2.csv
 create mode 100644 scGCN/input/inter_graph.csv
 create mode 100644 scGCN/input/intra_graph.csv
 create mode 100644 scGCN/output_log.txt
 create mode 100644 scGCN/results/scGCN_all_binary_input_labels.csv
 create mode 100644 scGCN/results/scGCN_all_binary_predicted_labels.csv
 create mode 100644 scGCN/results/scGCN_all_input_labels.csv
 create mode 100644 scGCN/results/scGCN_data2_labels_2_prediction.csv
 create mode 100644 scGCN/results/scGCN_index_guide.csv
 create mode 100644 scGCN/results/scGCN_query_mask.csv
 create mode 100644 scGCN/species_sankey.html

diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..ac8300d
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (tensorflow)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..8a3509e
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/scGCN.iml" filepath="$PROJECT_DIR$/.idea/scGCN.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/scGCN.iml b/.idea/scGCN.iml
new file mode 100644
index 0000000..38f76ec
--- /dev/null
+++ b/.idea/scGCN.iml
@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (tensorflow)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..94a25f7
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
new file mode 100644
index 0000000..5466975
--- /dev/null
+++ b/.idea/workspace.xml
@@ -0,0 +1,63 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ChangeListManager">
+    <list default="true" id="8a2e3ba0-3983-4619-8968-85fc8a66c364" name="Default Changelist" comment="" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
+  </component>
+  <component name="ProjectId" id="26jYUNe3TmZTkRIWBpzbaDAs4U9" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent">
+    <property name="RunOnceActivity.OpenProjectViewOnStart" value="true" />
+    <property name="RunOnceActivity.ShowReadmeOnStart" value="true" />
+    <property name="last_opened_file_path" value="$PROJECT_DIR$" />
+    <property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
+  </component>
+  <component name="RunManager">
+    <configuration name="train" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+      <module name="scGCN" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/scGCN" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/scGCN/train.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <recent_temporary>
+      <list>
+        <item itemvalue="Python.train" />
+      </list>
+    </recent_temporary>
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="8a2e3ba0-3983-4619-8968-85fc8a66c364" name="Default Changelist" comment="" />
+      <created>1647938476993</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1647938476993</updated>
+    </task>
+    <servers />
+  </component>
+</project>
\ No newline at end of file
diff --git a/scGCN/__pycache__/data.cpython-38.pyc b/scGCN/__pycache__/data.cpython-38.pyc
new file mode 100644
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diff --git a/scGCN/__pycache__/graph.cpython-38.pyc b/scGCN/__pycache__/graph.cpython-38.pyc
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GIT binary patch
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diff --git a/scGCN/checkpoints/best_validation.meta b/scGCN/checkpoints/best_validation.meta
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diff --git a/scGCN/checkpoints/checkpoint b/scGCN/checkpoints/checkpoint
new file mode 100644
index 0000000..fce2a8c
--- /dev/null
+++ b/scGCN/checkpoints/checkpoint
@@ -0,0 +1,2 @@
+model_checkpoint_path: "best_validation"
+all_model_checkpoint_paths: "best_validation"
diff --git a/scGCN/data.py b/scGCN/data.py
index d5384ab..705f3c9 100644
--- a/scGCN/data.py
+++ b/scGCN/data.py
@@ -27,21 +27,14 @@ def input_data(DataDir,Rgraph=True):
 
     lab_data1 = data1.reset_index(drop=True)  #.transpose()
     lab_data2 = data2.reset_index(drop=True)  #.transpose()
-    #重新给一个索引，将原来的索引赋值给第一列，将现在的索引设置为0/1/2/3、、、、、、
     lab_label1.columns = ['type']
     lab_label2.columns = ['type']
-    ##！！！此处通过物种1的数据中获得了type ，也许可以使用list(lab_label1['type']，lab_label2['type'])
-    # 来合并两个数据集的细胞类型。但也许会影响之后运行？
-
+    
     types = np.unique(lab_label1['type']).tolist()
-    #label1=lab_label1['type'].tolist()
-    #label2=lab_label2['type'].tolist()
-    #types=np.unique(label1.extend(label2))
+
     random.seed(123)
     p_data = []
     p_label = []
-
-    ##此处循环类型列表从而从各个细胞类型中拆分训练、测试集
     for i in types:
         tem_index = lab_label1[lab_label1['type'] == i].index
         tem_label = lab_label1[lab_label1['type'] == i]
@@ -85,19 +78,20 @@ def input_data(DataDir,Rgraph=True):
     label_train1 = pd.concat(label_train)
     label_test1 = pd.concat(label_test)
     label_val1 = pd.concat(label_val)
-    #原来划分训练集、测试集、验证集的只有reference数据集
-    #train2 = pd.concat([data_train1, lab_data2])
-    #lab_train2 = pd.concat([label_train1, lab_label2])
+
+    train2 = pd.concat([data_train1, lab_data2])
+    lab_train2 = pd.concat([label_train1, lab_label2])
 
     #' save objects
-    types_all = np.unique([*lab_label1['type'], *lab_label2['type']]).tolist()
+    types_all = np.unique([*lab_label1['type'],*lab_label2['type']]).tolist()
     PIK = "{}/datasets.dat".format(DataDir)
     res = [
         data_train1, data_test1, data_val1, label_train1, label_test1,
         label_val1, lab_data2, lab_label2, types_all
     ]
-    #此处原来的types仅仅是Reference的types
+
     with open(PIK, "wb") as f:
         pkl.dump(res, f)
-
+    with open("data.txt","w") as out:
+        out.write(str(res))
     print('load data succesfully....')
diff --git a/scGCN/data.txt b/scGCN/data.txt
new file mode 100644
index 0000000..8f34b83
--- /dev/null
+++ b/scGCN/data.txt
@@ -0,0 +1,105 @@
+[      ABCC3  ABI3  ACADVL  ACKR3  ACTB  ...  PEPD  VIL1  TMEM37  CA2  PGRMC2
+722       0     0       1      0    24  ...     0     4       0    0       2
+241       0     0       0      0    22  ...     0     0       0    0       3
+5925      0     0       0      0     3  ...     0     0       0    0       0
+2037      0     0       1      0     9  ...     0     0       0    0       0
+3496      0     0       2      0    24  ...     0     0       0    0       2
+...     ...   ...     ...    ...   ...  ...   ...   ...     ...  ...     ...
+1052      0     0       2      0     0  ...     0     0       0    0       0
+4638      0     0       0      0     0  ...     0     0       0    0       0
+2228      0     0       0      0    18  ...     1     0       0    0       0
+402       0     0       0      0     4  ...     0     0       0    0       0
+975       0     0       0      0    14  ...     0     0       0    1       2
+
+[5877 rows x 2000 columns],       ABCC3  ABI3  ACADVL  ACKR3  ACTB  ...  PEPD  VIL1  TMEM37  CA2  PGRMC2
+4436      0     0       2      0     1  ...     0     0       0    0       0
+5086      0     0       0      0    14  ...     1     0       0    0       2
+386       0     0       1      0     1  ...     0     0       0    0       1
+4657      0     0       0      0     5  ...     0     0       0    0       0
+4323      0     0       3      1     7  ...     0     0       0    0       0
+...     ...   ...     ...    ...   ...  ...   ...   ...     ...  ...     ...
+2806      0     0       2      0     7  ...     0     0       0    0       1
+1073      0     0       0      0     1  ...     0     0       0    0       0
+1081      0     0       2      0     2  ...     1     0       0    0       1
+1050      0     0       1      0     3  ...     0     0       0    0       0
+1059      0     0       3      0     1  ...     0     0       0    0       0
+
+[731 rows x 2000 columns],       ABCC3  ABI3  ACADVL  ACKR3  ACTB  ...  PEPD  VIL1  TMEM37  CA2  PGRMC2
+2786      0     0       1      0     3  ...     0     1       0    0       0
+4371      0     0       0      0    13  ...     0     0       0    0       0
+3963      0     0       0      0     6  ...     0     0       0    0       0
+3690      0     0       0      0    32  ...     0     0       0    0       0
+1993      0     0       0      0     2  ...     0     2       0    0       1
+...     ...   ...     ...    ...   ...  ...   ...   ...     ...  ...     ...
+230       0     0       4      0     5  ...     0     0       0    0       2
+2789      0     0       1      0    12  ...     0     0       0    0       0
+4452      0     0       1      0     5  ...     0     0       0    0       0
+1058      0     0       0      0     2  ...     0     0       0    0       0
+6569      0     0       0      0    11  ...     0     0       0    1       0
+
+[656 rows x 2000 columns],                     type
+722                alpha
+241                alpha
+5925               alpha
+2037               alpha
+3496               alpha
+...                  ...
+1052  quiescent_stellate
+4638  quiescent_stellate
+2228  quiescent_stellate
+402   quiescent_stellate
+975   quiescent_stellate
+
+[5877 rows x 1 columns],                     type
+4436               alpha
+5086               alpha
+386                alpha
+4657               alpha
+4323               alpha
+...                  ...
+2806  quiescent_stellate
+1073  quiescent_stellate
+1081  quiescent_stellate
+1050  quiescent_stellate
+1059  quiescent_stellate
+
+[731 rows x 1 columns],                     type
+2786               alpha
+4371               alpha
+3963               alpha
+3690               alpha
+1993               alpha
+...                  ...
+230   quiescent_stellate
+2789  quiescent_stellate
+4452  quiescent_stellate
+1058  quiescent_stellate
+6569  quiescent_stellate
+
+[656 rows x 1 columns],       ABCC3  ABI3  ACADVL  ACKR3  ACTB  ...  PEPD  VIL1  TMEM37  CA2  PGRMC2
+0         0     0       0      0    17  ...     0     0       0    0      10
+1         1     0       0      0    82  ...     1     0       0    0       1
+2         0     0       0      0     8  ...     0     0       0    0       1
+3         0     0       0      0     2  ...     0     0       0    0       1
+4         0     0       0      0     8  ...     0     0       0    0       8
+...     ...   ...     ...    ...   ...  ...   ...   ...     ...  ...     ...
+1836      0     0       0      0     0  ...     0     0       0    0       3
+1837      0     0       0      0     0  ...     0     0       0    0       1
+1838      0     0       0      0     9  ...     0     0       0    0       0
+1839      0     0       0      0    22  ...     0     0       0    0       0
+1840      0     0       0      0    10  ...     0     0       0    0       0
+
+[1841 rows x 2000 columns],                     type
+0                   beta
+1                 ductal
+2                  delta
+3                  delta
+4                   beta
+...                  ...
+1836                beta
+1837                beta
+1838              ductal
+1839  quiescent_stellate
+1840              ductal
+
+[1841 rows x 1 columns], ['alpha', 'beta', 'delta', 'ductal', 'endothelial', 'gamma', 'macrophage', 'quiescent_stellate']]
\ No newline at end of file
diff --git a/scGCN/err.txt b/scGCN/err.txt
new file mode 100644
index 0000000..3962baa
--- /dev/null
+++ b/scGCN/err.txt
@@ -0,0 +1,14 @@
+                       0
+0                  alpha
+1                  alpha
+2                  alpha
+3                  alpha
+4                  alpha
+...                  ...
+9100  quiescent_stellate
+9101  quiescent_stellate
+9102  quiescent_stellate
+9103  quiescent_stellate
+9104  quiescent_stellate
+
+[9105 rows x 1 columns]['alpha', 'beta', 'delta', 'ductal', 'endothelial', 'gamma', 'macrophage', 'quiescent_stellate']
\ No newline at end of file
diff --git a/scGCN/graph.py b/scGCN/graph.py
index 2931759..c5488a8 100644
--- a/scGCN/graph.py
+++ b/scGCN/graph.py
@@ -8,49 +8,46 @@ def graph_construct(outputdir):
 
     #' import processed data 
     files1 = glob.glob(path0 + "/count_data/*.csv")
-    #获取该路径下的所有csv文件
     files1.sort()
     count_list = []
     for df in files1:
         print(df)
         count_list.append(pd.read_csv(df, index_col=0))
-    #得到所有的原始基因表达矩阵
+
     files2 = glob.glob(path0 + "/norm_data/*.csv")
     files2.sort()
     norm_list = []
     for df in files2:
         print(df)
         norm_list.append(pd.read_csv(df, index_col=0))
-    #得到所有的标准化基因表达矩阵
+
     files3 = glob.glob(path0 + "/scale_data/*.csv")
     files3.sort()
     scale_list = []
     for df in files3:
         print(df)
         scale_list.append(pd.read_csv(df, index_col=0))
-    #得到所有的scale后的基因表达矩阵
+
     files4 = glob.glob(path0 + "/label_data/*.csv")
     files4.sort()
     label_list = []
     for df in files4:
         print(df)
         label_list.append(pd.read_csv(df, index_col=0))
-    #得到label数据
+
     fpath = os.path.join(path0, 'sel_features.csv')
     features = pd.read_csv(fpath, index_col=False).values.flatten()
-    #得到高变基因信息
+
     #' graph construction    
     import itertools
-    #该模块处理迭代
+
     N = len(count_list)
     if (N == 1):
         combine = pd.Series([(0, 0)])
-        #combine的值是一个列，该列只有一行一个值为0 (0,0)
     else:
         combin = list(itertools.product(list(range(N)), list(range(N))))
         index = [i for i, x in enumerate([i[0] < i[1] for i in combin]) if x]
         combine = pd.Series(combin)[index]
-        #1 (0,1)
 
     pairss1 = generate_graph(count_list=count_list,
                              norm_list=norm_list,
@@ -72,14 +69,13 @@ def graph_construct(outputdir):
 
     graph1 = pairss1[0].iloc[:, 0:2].reset_index()
     graph1.to_csv('./input/inter_graph.csv')
-    #数据集间
+
     #'@param graph2: intra-dataset graph 
     graph2 = pairss2[0].iloc[:, 0:2].reset_index()
     graph2.to_csv('./input/intra_graph.csv')
-    #数据集内
+
     label1 = label_list[0]
     label1.to_csv('./input/Label1.csv', index=False)
-    #reference label
+
     label2 = label_list[1]
     label2.to_csv('./input/Label2.csv', index=False)
-    #query label
\ No newline at end of file
diff --git a/scGCN/input/Data1.csv b/scGCN/input/Data1.csv
new file mode 100644
index 0000000..3ec9242
--- /dev/null
+++ b/scGCN/input/Data1.csv
@@ -0,0 +1,7265 @@
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N1,DHRS7,FLII,IRF2BP2,DDR1,PLP2,POMP,ZBTB20,TIMP1,HNRNPK,TRIM8,PPFIBP1,CX3CL1,DOCK10,CIITA,EIF2S2,FAM105A,NPDC1,MFI2,SPRYD3,RHOBTB3,FES,INA,CDK17,RETSAT,NDFIP1,CNRIP1,POLE4,SMAGP,RAPGEF4,PDP1,RPL35A,DOCK8,DTX2,AGPAT2,HSP90AB1,CAMK2B,ALDOA,ZHX2,NFIC,CANX,EHF,WSCD2,ABHD15,INPP5F,PDE4DIP,LRPAP1,TNFRSF11A,NBEA,EPN3,FKBP2,GLIS3,RELA,HSP90B1,ACAA2,LGALS3BP,MISP,MAGED1,TTC9,SHMT2,RPS11,KYNU,GPC6,RASA3,ATP1B1,REG1A,PAIP2B,COX4I1,EBF2,TPI1,INPP5D,ASAP1,ARL6IP1,MEX3A,ALDOC,RPS2,RDX,TSPAN3,FGFR3,LMTK3,HVCN1,FGF14,ATP6V1F,KCNJ8,ZDHHC7,ID1,PCBP1,TAGLN3,PLAU,TLR3,CTNNA2,MAFK,TMC4,ANK1,RPL10A,GIMAP8,GCGR,ERLEC1,LZTS2,CCNI,ANKRD9,RNPEPL1,IL6ST,EFTUD2,CTRB2,MAP1S,MAPRE3,IFRD2,METRN,SPTB,GNA14,HDLBP,HBEGF,PDE3A,A1CF,MEST,ANXA6,NPEPPS,ST8SIA4,FANCF,KCTD11,TMEM167A,PAQR4,MIF,WBP2,FAM212A,SPOCK1,HECW2,CAPN5,DFNA5,FCGR1A,ILK,EXOC3L1,IRX1,RPS27A,NRIP1,FAM159B,FOS,MKNK2,ZDHHC14,MAPKAPK2,KCNIP1,RNF44,NLRC4,CD300C,TMED10,NFASC,MYH14,USP11,CD36,TXN,STC2,RPS10,RALA,GATA2,SMPD1,ADD3,HSD3B7,MAF,PCDH1,RAB5C,FAM65A,PLXNC1,EIF4G2,CORO1C,NR3C1,HMP19,PDIA6,ATP6V0E2,CPNE5,ZNF503,ARHGEF10,FFAR4,PCSK5,CACNA2D3,SERPINE2,KLF4,PCDH17,PLXDC1,SRD5A1,RPS13,SLCO3A1,GUCD1,ECE1,CTNNAL1,ATRNL1,LMAN1,MYD88,NFE2L3,CHST8,PDGFA,RTN1,FAM84B,STK38L,TRAM2,ZCCHC24,AP1M2,SSH3,ACSF2,ANKRD11,ERBB4,DTX4,BLVRB,GALE,ADPGK,TSPYL2,ENO2,FMNL2,ITIH5,PI15,JDP2,SYT8,SERTAD2,MSRB3,PPP2R1A,TSHZ2,PNPLA2,FAM222A,CAMK2N1,CAPZB,HEY2,RPL12,PPP1R14A,HMGCS1,ACTR3,GATAD2A,MTDH,PRMT2,DLST,TMEM8A,GPR65,ATP2A3,SNN,RPS26,AHRR,MMP15,WWC1,SDHA,COMT,RCN2,TNIP2,MAP1LC3A,NLRP3,TOM1,PDXK,PLCD3,CXCL3,MYO6,CSF1,PKIG,F2RL3,ARL6IP5,FRZB,PRRC2C,HNRNPF,ARG2,VWA5A,SNRPB,GIMAP1,RHOG,RFX6,ANTXR2,SLC7A14,UBE2I,CLINT1,EIF3D,ARRDC4,RBBP8,RPS20,ACPP,ITPKC,TMEM158,PIR,SYPL1,FGFR2,ATP6V1A,VASN,TLN1,MBOAT7,SDC2,NFIA,CLN8,PSIP1,SLC7A5,SLCO2A1,FAM49A,ARHGAP25,DDAH1,ANO1,JUNB,PARVG,CBX6,HK3,PICALM,RHBDF2,ACOT9,CD200,ARHGDIA,PPM1E,UBE2H,LIFR,HOXB2,DNM2,ARRDC1,SUN2,BAX,SERP1,SNAP23,TRPM3,SPON2,TMEM164,COX17,MLXIPL,RAB11FIP4,ADCY4,PIGP,FOXC1,NDRG4,HAND2,PRKCD,TMEM130,MAN1A1,CLUH,NUTF2,CD300LF,TMEM86B,CACNA2D2,KCNC1,HS3ST3B1,EIF3H,SH3GL2,PTPRG,KDM5B,TXNRD1,AMIGO2,RNPEP,FAM229B,HOXA4,SH3KBP1,ATF4,RIPK2,KCNAB2,CFLAR,RASSF7,BAMBI,IGF2R,FAM162A,ADARB1,LYPLA2,FAM131C,PTPN14,SBK1,FRMD6,TERF2IP,RUNDC3A,RSU1,RUNX2,ESYT1,PEPD,VIL1,TMEM37,CA2,PGRMC2
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diff --git a/scGCN/input/Data2.csv b/scGCN/input/Data2.csv
new file mode 100644
index 0000000..8c99864
--- /dev/null
+++ b/scGCN/input/Data2.csv
@@ -0,0 +1,1842 @@
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P,MAFF,PARVB,TNFAIP3,CSPG4,SPNS2,CASP4,TMEM51,CARD11,BCL3,RPL18A,CDH3,NEURL3,RAB3B,BTG1,RCAN1,PARD3,SMOX,DOCK4,CLEC5A,FBLIM1,NPTX2,RRAS,LSR,BARX2,NFKBIA,ZEB1,BCL10,TRIOBP,MXRA8,PKM,METRNL,FA2H,EMILIN2,CLEC4A,RPLP2,CTNNA1,GJA1,RAPGEF5,MAPT,VWA1,HMOX1,CAMK2G,RAP2B,SVIL,NID2,RPS18,AKR1B1,TOMM7,AMBP,RCC2,TMEM88,CHGA,FGF2,MERTK,TLR4,DCN,CGN,ITM2A,RAB25,FOXF1,CHRM3,APLP1,PROSER2,RPL23,ASAP2,CMTM7,SLC2A13,TSC22D1,EPHA2,RPS6,TGFBR3,PTGS1,PAQR5,ARHGAP29,LYPD1,ANGPT1,DLK1,NDST1,QKI,LIMS1,SMTN,TMEM98,CXCL2,DPP4,TSLP,A4GALT,RHBDF1,FILIP1L,HEY1,PPP1CA,AMOTL2,MFNG,HES1,RPL7,SAMD11,S1PR3,CMKLR1,PPP1R13L,ACE,RPL27,PLS3,LDB2,SPRY1,AHR,LBH,TPGS2,DDR2,LIPH,NEK6,RNF24,CD82,CARD10,PLEC,CCL4,ALDH2,ELOVL1,MUC13,DCDC2,ARHGAP18,CHST2,CDC42EP5,PEBP1,FGR,SEZ6L2,MMP9,GJB1,DSC2,SERPINB1,THBD,TNKS1BP1,PTP4A3,PFKFB2,TIMP3,TWIST1,GPRC5A,SLC9A3R1,VEGFA,VAMP2,SNRPN,PTP4A1,DUOX2,GAS6,HMHA1,PLEK2,GCH1,PROCR,MAFB,ZFP36,RPSA,SHROOM3,ENTPD3,RPL14,NRN1,IL4I1,TUBB2B,MDK,CTTN,TNS1,TTC3,CDCP1,CDK5R2,SELPLG,BIRC3,STX1A,CHST1,EEF1B2,F11R,VIM,LATS2,ZNF385A,TPD52,HAVCR2,USH1C,PGD,CSF2RA,RHOH,LMO7,RPS17,SPTAN1,GNB4,TAGAP,LILRA5,PDGFB,LRG1,FSTL3,RND3,GPR146,GATA6,DNAJB9,NOS3,GNG12,PPP1R16B,BSG,TRPC6,RPL17,SNAP25,NENF,SLC7A8,CPLX2,DUSP4,RPS16,GBP4,DUSP26,RPS6KA2,RAB11FIP1,SLITRK6,TRIB1,STAT6,FLI1,GABRB3,TSPAN1,UNC93B1,RPL13,SLC6A17,PIGR,THBS1,RAMP3,RPL28,MSR1,SYK,SH2D3C,FAM83H,TRPM2,ARF6,DLC1,FGFRL1,INSM1,CTGF,ARHGEF2,BMP2,ITPR3,SUSD4,LIF,RPS25,LAMA3,OCIAD2,OLFM1,GRIA3,MAP4K4,ADAM8,ELL2,GJD2,RPS23,BNIP2,IQGAP2,CNIH2,MPZL2,LPL,THY1,RGMA,HDAC7,ARHGAP9,PPP2R2B,ENTPD2,UCN3,RASD1,RPL26,CPD,CAP1,HIGD1A,TWF2,CHST15,NDRG2,FAM89B,F2RL1,SRC,P4HB,BCL2L1,PPP1R15A,SH2B3,SLC25A23,MEIS2,GATM,PTGIR,VTCN1,DOCK9,ZNF521,TRIP10,ISL1,MSI2,EBF1,CDC42EP2,ZNF217,TMED3,TREM2,C15orf48,GALNT2,SQSTM1,KRT23,BTG2,FBXO7,MPRIP,ARHGEF18,RPL6,HNMT,RHOF,PDLIM1,MAP3K1,LIMS2,PLEKHB1,NFKBIE,PTPRN2,PFN2,BICC1,RPS14,SORBS2,NETO2,LLGL2,RNF152,CIRBP,PXN,RPS6KA1,GALNT3,CPNE2,PIK3IP1,SPTSSB,SCARF1,SGK223,RPL15,CSNK1E,CLDN3,ATP13A3,PLOD1,CAPNS1,RPLP0,NREP,PERP,C2CD4B,CREB3L1,MYL6,ETV1,FOXO3,HEBP1,FOXF2,GIMAP7,PFKP,ENPP2,TDO2,RGS9,ENTPD1,MICAL2,LACC1,RAB42,SLC25A5,CD99L2,SYP,LGR4,DPP6,SLFN5,CLIC1,AGTRAP,NEUROD1,SLC44A4,STXBP1,DUSP23,OAZ1,RPL35,LAT2,FKBP10,SYT4,AQP1,GNAI2,SOD3,APOBEC2,YWHAB,FOXS1,LOXL2,RPS28,CHN2,NLRC3,RPH3AL,NPNT,SCTR,IGF1,PLXNB2,MPZL1,RAP1GAP,SGK1,RPL31,SH2D4A,ABTB2,TTYH1,CFP,LAMB1,PPM1F,CDKN1A,VCL,RGS19,RFTN1,RPS21,ENC1,SLC39A14,GRB10,PHLDB1,ABCA1,NPL,C1orf54,NAA20,SHB,PTPRT,NUCB1,MPP1,SLC29A4,DKK3,IFNAR2,KIRREL,INSIG2,TSPAN7,RASSF3,AOAH,CXXC5,CYYR1,SYNE2,LRRC59,SVIP,GNB2L1,VMP1,HSD17B2,KCNK13,ALCAM,CALB1,GCNT3,CTNNB1,CDV3,YBX1,LIMA1,SDC3,TNFRSF21,SYT16,LRFN5,YWHAQ,TCF15,SIPA1,ORAI1,TMEM26,RASL12,LTBP1,BCL9L,ST6GALNAC2,SLC1A5,KCNN3,MLPH,KIFC3,SCD,RPL5,SULF2,RPL27A,TUBB,KIAA1217,CASP7,RPS8,AHCYL1,UACA,HSPB6,APBB2,EPS8L1,ASS1,IVNS1ABP,PEA15,HK2,CFI,SLC3A1,RASGRF1,SNCB,VPS37B,GPX2,VCAM1,RASGRP4,CARTPT,TJP3,CYSLTR2,PROM2,ACTN1,TP53I13,RHBDD2,KLF3,AKAP7,CA9,LURAP1L,PACSIN2,MAN1C1,MYO10,VDAC2,FAM107B,SPRED1,PRRX1,POSTN,CNP,KCNE3,SH3RF1,PDE4A,TGFB2,CDH13,KANK2,IKZF1,CTSA,UBC,TACC1,RPL11,CHP1,RPS3,ACSL1,RRAGD,FDFT1,JAM2,LDLRAP1,SLC39A1,MYADM,STAU1,SEMA4C,MOB1A,TXNIP,NECAP2,CPM,LAPTM4B,EIF4EBP1,RPL13A,APP,SEC14L1,TCF7L1,LDLR,KCNK5,RAB9A,ACSL5,GAPDH,PGK1,DDAH2,RPL8,ISG20,TMOD3,SYT5,FUT4,KIF12,CD63,ITGA9,LXN,NRROS,NAMPT,LGMN,RPS9,CAST,MYC,RNASE1,CYFIP1,RBMS2,TBC1D22A,PKP3,SFT2D1,RGS16,ARRDC3,FBP1,RPS5,FAM102A,ATP6V1G1,VANGL1,MGST1,DAZAP2,USP53,DOK4,PON3,RPL18,AJUBA,FRMD8,VAV1,CYP2U1,FBXO16,PDLIM4,EFNA4,SIL1,MYO1D,SLAMF9,MEDAG,RPL7A,ADI1,SLC34A2,MOB3B,NUCB2,IGSF3,CASP1,PRKG1,IGF2BP2,B3GNT3,ARHGAP21,TMEM45B,PARD6B,FERMT1,TJP1,PTAFR,FPGS,CMTM3,CYP2S1,AVPR1A,TPBG,SNAI2,PREPL,VWA5B2,TBC1D2,ZNF395,HMGN1,RPL19,PAK3,CD38,TRAM1,MDFI,TNC,NFATC4,SLPI,SERINC1,AREG,SLC31A2,WFDC1,EPHB6,RPL21,SFXN3,ADA,KCNH2,LASP1,FRMD4A,EFHD2,TNFAIP2,SOCS2,BTG3,GNGT2,RALY,ELAVL4,RASGRF2,SQRDL,PSD,CLTB,HN1L,SCAMP5,SPSB1,FAM110C,RBPMS2,GIPC1,HDAC9,STEAP4,MAPK8IP2,WFDC2,RCC1,FOSL1,CHRD,SLC45A3,ARPC1A,CYP26B1,WFS1,CELF3,RPS24,NACA,KIF5C,SET,LMNA,EDN3,CAPN6,PPP1R1B,TCIRG1,SOWAHC,WDR25,AMOTL1,MFSD10,ANKH,NRARP,EDIL3,CD47,ZNF703,ST3GAL1,GALK1,PSMA7,ZNF704,FERMT2,CTBP2,SLC36A1,RPS29,PLXNA2,PACS1,CYTH4,SLC2A5,GRB2,KCNJ6,MRC2,TKT,PPP1CB,MAP2K3,ZMYND15,RAPGEF1,TMBIM6,TICAM1,NFKB1,RAP1GAP2,PRKACB,RPS15A,PDE4B,BCL2L12,QPCT,SLC22A18,SLC16A7,SLC38A4,GALNT1,RNF19A,TMED6,MAP4,CDC37,ERP29,SMURF1,IQGAP1,CLCF1,EDARADD,RBM47,NEURL1,SHE,PCDH18,INF2,PREX1,NBEAL2,C1QTNF1,MICALL1,TNFAIP8,SLC38A2,TPM3,PON2,PID1,TMEM132A,ATP6AP1,TUBB2A,RPL37A,ID2,JAG2,ENPEP,KDELR1,CERS2,ITGB8,CCDC6,PTBP1,APRT,LPAR6,FAM171A1,TPD52L1,BTF3,GSTO1,FAM110D,MMP12,TNFAIP1,SEPW1,RAB13,ICAM1,SHANK2,CYP1B1,RCSD1,CUTA,TRIB2,PPP2R3A,TRAF4,GGT1,SLC16A10,GLIPR1,LPCAT2,GNAO1,SSR4,CADPS,SSX2IP,TIPARP,STXBP2,RNF130,VAMP5,HOXA5,MEFV,CST7,WEE1,TSPAN6,PTPN22,ACAA1,CALR,WNT4,RALB,ITGAV,ADM,OSTC,WWC2,HOXB7,BMPR2,CASR,ITPRIPL2,RPS7,ANXA1,F3,IL15RA,KIF1A,SDCBP,SPRY4,TCEA3,RASSF2,NUAK2,SRGAP1,PKP4,ANXA9,ARHGEF15,KCNH6,CHPF,CCRL2,FJX1,HEG1,CRTAP,CRIM1,AGTR1,MOB1B,MAN1A2,COX8A,SLC16A6,TUSC3,CNT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diff --git a/scGCN/input/Label1.csv b/scGCN/input/Label1.csv
new file mode 100644
index 0000000..7ca0c78
--- /dev/null
+++ b/scGCN/input/Label1.csv
@@ -0,0 +1,7265 @@
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diff --git a/scGCN/input/Label2.csv b/scGCN/input/Label2.csv
new file mode 100644
index 0000000..d37b473
--- /dev/null
+++ b/scGCN/input/Label2.csv
@@ -0,0 +1,1842 @@
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diff --git a/scGCN/input/inter_graph.csv b/scGCN/input/inter_graph.csv
new file mode 100644
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diff --git a/scGCN/input/intra_graph.csv b/scGCN/input/intra_graph.csv
new file mode 100644
index 0000000..186dbda
--- /dev/null
+++ b/scGCN/input/intra_graph.csv
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diff --git a/scGCN/layers.py b/scGCN/layers.py
index d580e98..d2573c8 100644
--- a/scGCN/layers.py
+++ b/scGCN/layers.py
@@ -1,5 +1,5 @@
 from utils import *
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 flags = tf.app.flags
 FLAGS = flags.FLAGS
@@ -72,18 +72,19 @@ def __init__(self, input_dim, output_dim, placeholders, dropout=0.,
                  sparse_inputs=False, act=tf.nn.relu, bias=False,
                  featureless=False, **kwargs):
         super(GraphConvolution, self).__init__(**kwargs)
-        #这里是调用父类的__init__()
+
         if dropout:
             self.dropout = placeholders['dropout']
         else:
             self.dropout = 0.
+
         self.act = act
         self.support = placeholders['support']
         self.sparse_inputs = sparse_inputs
         self.featureless = featureless
         self.bias = bias
 
-        # helper variable for sparse dropout 辅助变量
+        # helper variable for sparse dropout
         self.num_features_nonzero = placeholders['num_features_nonzero']
 
         with tf.variable_scope(self.name + '_vars'):
diff --git a/scGCN/models.py b/scGCN/models.py
index 1460b86..2b79bb2 100644
--- a/scGCN/models.py
+++ b/scGCN/models.py
@@ -1,4 +1,5 @@
 from layers import *
+import tensorflow.compat.v1 as tf
 from utils import *
 
 flags = tf.app.flags
@@ -88,10 +89,9 @@ def __init__(self, placeholders, input_dim, **kwargs):
         self.inputs = placeholders['features']
         self.input_dim = input_dim
         # self.input_dim = self.inputs.get_shape().as_list()[1]  # To be supported in future Tensorflow versions
-        #input_dim=features[2][1]
         self.output_dim = placeholders['labels'].get_shape().as_list()[1]
-        ####!!!!!!!!!!!标签的处理
         self.placeholders = placeholders
+
         self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
 
         self.build()
diff --git a/scGCN/output_log.txt b/scGCN/output_log.txt
new file mode 100644
index 0000000..1322bd0
--- /dev/null
+++ b/scGCN/output_log.txt
@@ -0,0 +1,230 @@
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+True
+load data succesfully....
+assign input coordinatly....
+Epoch: 0001 train_loss= 2.08189 train_acc= 0.07504 val_loss= 2.06602 val_acc= 0.66159 time= 1.17427
+Epoch: 0002 train_loss= 2.06592 train_acc= 0.66020 val_loss= 2.05035 val_acc= 0.73933 time= 1.11924
+Epoch: 0003 train_loss= 2.05022 train_acc= 0.73728 val_loss= 2.03366 val_acc= 0.74695 time= 1.09725
+Epoch: 0004 train_loss= 2.03348 train_acc= 0.74409 val_loss= 2.01463 val_acc= 0.75000 time= 1.07624
+Epoch: 0005 train_loss= 2.01440 train_acc= 0.74545 val_loss= 1.99291 val_acc= 0.74695 time= 1.11725
+Epoch: 0006 train_loss= 1.99261 train_acc= 0.74732 val_loss= 1.96871 val_acc= 0.75000 time= 1.13328
+Epoch: 0007 train_loss= 1.96834 train_acc= 0.74970 val_loss= 1.94223 val_acc= 0.74848 time= 1.08324
+Epoch: 0008 train_loss= 1.94181 train_acc= 0.74987 val_loss= 1.91341 val_acc= 0.74848 time= 1.14326
+Epoch: 0009 train_loss= 1.91292 train_acc= 0.74936 val_loss= 1.88261 val_acc= 0.74848 time= 1.12225
+Epoch: 0010 train_loss= 1.88200 train_acc= 0.74783 val_loss= 1.84960 val_acc= 0.75152 time= 1.03823
+Epoch: 0011 train_loss= 1.84891 train_acc= 0.74783 val_loss= 1.81422 val_acc= 0.74848 time= 1.12725
+Epoch: 0012 train_loss= 1.81342 train_acc= 0.74681 val_loss= 1.77652 val_acc= 0.74848 time= 1.10225
+Epoch: 0013 train_loss= 1.77563 train_acc= 0.74647 val_loss= 1.73665 val_acc= 0.75152 time= 1.09525
+Epoch: 0014 train_loss= 1.73563 train_acc= 0.74902 val_loss= 1.69478 val_acc= 0.75305 time= 1.16926
+Epoch: 0015 train_loss= 1.69362 train_acc= 0.75004 val_loss= 1.65115 val_acc= 0.75305 time= 1.14826
+Epoch: 0016 train_loss= 1.64980 train_acc= 0.75225 val_loss= 1.60580 val_acc= 0.75457 time= 1.11725
+Epoch: 0017 train_loss= 1.60426 train_acc= 0.75498 val_loss= 1.55886 val_acc= 0.75457 time= 1.12125
+Epoch: 0018 train_loss= 1.55712 train_acc= 0.75906 val_loss= 1.51058 val_acc= 0.75457 time= 1.09826
+Epoch: 0019 train_loss= 1.50865 train_acc= 0.76365 val_loss= 1.46137 val_acc= 0.75915 time= 1.11324
+Epoch: 0020 train_loss= 1.45923 train_acc= 0.76842 val_loss= 1.41164 val_acc= 0.76372 time= 1.37831
+Epoch: 0021 train_loss= 1.40928 train_acc= 0.77403 val_loss= 1.36176 val_acc= 0.76524 time= 1.31630
+Epoch: 0022 train_loss= 1.35917 train_acc= 0.77727 val_loss= 1.31211 val_acc= 0.77744 time= 1.44332
+Epoch: 0023 train_loss= 1.30930 train_acc= 0.78390 val_loss= 1.26319 val_acc= 0.78354 time= 1.85442
+Epoch: 0024 train_loss= 1.26011 train_acc= 0.79071 val_loss= 1.21515 val_acc= 0.79116 time= 1.11125
+Epoch: 0025 train_loss= 1.21182 train_acc= 0.79785 val_loss= 1.16823 val_acc= 0.80031 time= 1.09025
+Epoch: 0026 train_loss= 1.16464 train_acc= 0.80551 val_loss= 1.12268 val_acc= 0.81098 time= 1.08525
+Epoch: 0027 train_loss= 1.11882 train_acc= 0.81351 val_loss= 1.07868 val_acc= 0.81860 time= 1.05724
+Epoch: 0028 train_loss= 1.07456 train_acc= 0.81895 val_loss= 1.03633 val_acc= 0.82927 time= 1.07825
+Epoch: 0029 train_loss= 1.03193 train_acc= 0.82678 val_loss= 0.99572 val_acc= 0.83994 time= 1.14125
+Epoch: 0030 train_loss= 0.99104 train_acc= 0.83393 val_loss= 0.95689 val_acc= 0.84451 time= 1.13126
+Epoch: 0031 train_loss= 0.95193 train_acc= 0.83903 val_loss= 0.91980 val_acc= 0.84756 time= 1.11025
+Epoch: 0032 train_loss= 0.91455 train_acc= 0.84584 val_loss= 0.88437 val_acc= 0.85213 time= 1.12426
+Epoch: 0033 train_loss= 0.87880 train_acc= 0.85128 val_loss= 0.85049 val_acc= 0.86281 time= 1.17026
+Epoch: 0034 train_loss= 0.84460 train_acc= 0.85537 val_loss= 0.81804 val_acc= 0.86585 time= 1.16526
+Epoch: 0035 train_loss= 0.81182 train_acc= 0.86013 val_loss= 0.78693 val_acc= 0.86738 time= 1.13726
+Epoch: 0036 train_loss= 0.78038 train_acc= 0.86336 val_loss= 0.75708 val_acc= 0.86890 time= 1.19327
+Epoch: 0037 train_loss= 0.75018 train_acc= 0.86609 val_loss= 0.72841 val_acc= 0.87043 time= 1.17026
+Epoch: 0038 train_loss= 0.72116 train_acc= 0.86745 val_loss= 0.70085 val_acc= 0.87195 time= 1.07624
+Epoch: 0039 train_loss= 0.69328 train_acc= 0.87017 val_loss= 0.67439 val_acc= 0.87195 time= 1.08524
+Epoch: 0040 train_loss= 0.66648 train_acc= 0.87187 val_loss= 0.64899 val_acc= 0.87195 time= 1.13329
+Epoch: 0041 train_loss= 0.64076 train_acc= 0.87357 val_loss= 0.62463 val_acc= 0.87195 time= 1.09424
+Epoch: 0042 train_loss= 0.61606 train_acc= 0.87442 val_loss= 0.60130 val_acc= 0.87500 time= 1.14126
+Epoch: 0043 train_loss= 0.59239 train_acc= 0.87902 val_loss= 0.57896 val_acc= 0.87805 time= 1.19027
+Epoch: 0044 train_loss= 0.56972 train_acc= 0.88208 val_loss= 0.55761 val_acc= 0.88415 time= 1.09625
+Epoch: 0045 train_loss= 0.54804 train_acc= 0.88344 val_loss= 0.53720 val_acc= 0.88567 time= 1.12225
+Epoch: 0046 train_loss= 0.52731 train_acc= 0.88565 val_loss= 0.51771 val_acc= 0.89024 time= 1.08524
+Epoch: 0047 train_loss= 0.50749 train_acc= 0.88770 val_loss= 0.49910 val_acc= 0.89024 time= 1.12430
+Epoch: 0048 train_loss= 0.48858 train_acc= 0.89110 val_loss= 0.48132 val_acc= 0.89177 time= 1.17826
+Epoch: 0049 train_loss= 0.47052 train_acc= 0.89246 val_loss= 0.46436 val_acc= 0.89482 time= 1.10925
+Epoch: 0050 train_loss= 0.45328 train_acc= 0.89382 val_loss= 0.44818 val_acc= 0.89787 time= 1.14326
+Epoch: 0051 train_loss= 0.43684 train_acc= 0.89586 val_loss= 0.43276 val_acc= 0.90091 time= 1.13326
+Epoch: 0052 train_loss= 0.42117 train_acc= 0.89740 val_loss= 0.41805 val_acc= 0.90091 time= 1.10624
+Epoch: 0053 train_loss= 0.40624 train_acc= 0.89978 val_loss= 0.40405 val_acc= 0.90396 time= 1.08225
+Epoch: 0054 train_loss= 0.39202 train_acc= 0.90284 val_loss= 0.39074 val_acc= 0.91159 time= 1.13124
+Epoch: 0055 train_loss= 0.37850 train_acc= 0.90607 val_loss= 0.37806 val_acc= 0.91463 time= 1.11425
+Epoch: 0056 train_loss= 0.36563 train_acc= 0.90914 val_loss= 0.36599 val_acc= 0.91768 time= 1.09725
+Epoch: 0057 train_loss= 0.35340 train_acc= 0.91526 val_loss= 0.35451 val_acc= 0.91768 time= 1.22934
+Epoch: 0058 train_loss= 0.34179 train_acc= 0.91883 val_loss= 0.34360 val_acc= 0.92378 time= 1.11025
+Epoch: 0059 train_loss= 0.33075 train_acc= 0.92105 val_loss= 0.33323 val_acc= 0.92683 time= 1.14026
+Epoch: 0060 train_loss= 0.32026 train_acc= 0.92377 val_loss= 0.32338 val_acc= 0.92835 time= 1.16526
+Epoch: 0061 train_loss= 0.31028 train_acc= 0.92700 val_loss= 0.31401 val_acc= 0.92988 time= 1.14026
+Epoch: 0062 train_loss= 0.30080 train_acc= 0.92938 val_loss= 0.30511 val_acc= 0.92988 time= 1.12025
+Epoch: 0063 train_loss= 0.29179 train_acc= 0.93228 val_loss= 0.29666 val_acc= 0.93140 time= 1.14626
+Epoch: 0064 train_loss= 0.28323 train_acc= 0.93432 val_loss= 0.28862 val_acc= 0.93445 time= 1.18127
+Epoch: 0065 train_loss= 0.27509 train_acc= 0.93738 val_loss= 0.28098 val_acc= 0.93445 time= 1.16525
+Epoch: 0066 train_loss= 0.26735 train_acc= 0.94061 val_loss= 0.27371 val_acc= 0.94055 time= 1.13826
+Epoch: 0067 train_loss= 0.25998 train_acc= 0.94453 val_loss= 0.26679 val_acc= 0.94360 time= 1.16226
+Epoch: 0068 train_loss= 0.25298 train_acc= 0.94793 val_loss= 0.26021 val_acc= 0.94970 time= 1.17226
+Epoch: 0069 train_loss= 0.24631 train_acc= 0.95236 val_loss= 0.25394 val_acc= 0.95122 time= 1.19427
+Epoch: 0070 train_loss= 0.23997 train_acc= 0.95508 val_loss= 0.24799 val_acc= 0.95274 time= 1.19127
+Epoch: 0071 train_loss= 0.23392 train_acc= 0.95814 val_loss= 0.24231 val_acc= 0.95579 time= 1.11626
+Epoch: 0072 train_loss= 0.22816 train_acc= 0.95933 val_loss= 0.23689 val_acc= 0.95579 time= 1.17026
+Epoch: 0073 train_loss= 0.22266 train_acc= 0.96103 val_loss= 0.23172 val_acc= 0.95732 time= 1.10226
+Epoch: 0074 train_loss= 0.21740 train_acc= 0.96205 val_loss= 0.22678 val_acc= 0.96037 time= 1.11525
+Epoch: 0075 train_loss= 0.21238 train_acc= 0.96342 val_loss= 0.22205 val_acc= 0.96341 time= 1.11125
+Epoch: 0076 train_loss= 0.20758 train_acc= 0.96478 val_loss= 0.21754 val_acc= 0.96341 time= 1.10024
+Epoch: 0077 train_loss= 0.20298 train_acc= 0.96563 val_loss= 0.21322 val_acc= 0.96341 time= 1.15026
+Epoch: 0078 train_loss= 0.19858 train_acc= 0.96614 val_loss= 0.20909 val_acc= 0.96341 time= 1.20427
+Epoch: 0079 train_loss= 0.19437 train_acc= 0.96733 val_loss= 0.20513 val_acc= 0.96494 time= 1.12725
+Epoch: 0080 train_loss= 0.19032 train_acc= 0.96801 val_loss= 0.20135 val_acc= 0.96646 time= 1.20127
+Epoch: 0081 train_loss= 0.18644 train_acc= 0.96818 val_loss= 0.19772 val_acc= 0.96646 time= 1.16126
+Epoch: 0082 train_loss= 0.18271 train_acc= 0.96852 val_loss= 0.19424 val_acc= 0.96646 time= 1.09125
+Epoch: 0083 train_loss= 0.17913 train_acc= 0.96869 val_loss= 0.19091 val_acc= 0.96646 time= 1.20527
+Epoch: 0084 train_loss= 0.17568 train_acc= 0.96903 val_loss= 0.18771 val_acc= 0.96646 time= 1.13025
+Epoch: 0085 train_loss= 0.17237 train_acc= 0.96920 val_loss= 0.18464 val_acc= 0.96799 time= 1.15626
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+Epoch: 0096 train_loss= 0.14306 train_acc= 0.97550 val_loss= 0.15787 val_acc= 0.97104 time= 1.11526
+Epoch: 0097 train_loss= 0.14093 train_acc= 0.97584 val_loss= 0.15594 val_acc= 0.97409 time= 1.09846
+Epoch: 0098 train_loss= 0.13886 train_acc= 0.97601 val_loss= 0.15408 val_acc= 0.97409 time= 1.17725
+Epoch: 0099 train_loss= 0.13687 train_acc= 0.97635 val_loss= 0.15228 val_acc= 0.97409 time= 1.12125
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+Epoch: 0122 train_loss= 0.10445 train_acc= 0.98026 val_loss= 0.12284 val_acc= 0.97561 time= 1.11025
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+Epoch: 0172 train_loss= 0.07408 train_acc= 0.98451 val_loss= 0.09525 val_acc= 0.97713 time= 1.06624
+Epoch: 0173 train_loss= 0.07371 train_acc= 0.98451 val_loss= 0.09491 val_acc= 0.97713 time= 1.09624
+Epoch: 0174 train_loss= 0.07334 train_acc= 0.98468 val_loss= 0.09458 val_acc= 0.97713 time= 1.16426
+Epoch: 0175 train_loss= 0.07297 train_acc= 0.98485 val_loss= 0.09425 val_acc= 0.97713 time= 1.17026
+Epoch: 0176 train_loss= 0.07261 train_acc= 0.98485 val_loss= 0.09392 val_acc= 0.97713 time= 1.12926
+Epoch: 0177 train_loss= 0.07225 train_acc= 0.98485 val_loss= 0.09361 val_acc= 0.97713 time= 1.18327
+Epoch: 0178 train_loss= 0.07190 train_acc= 0.98485 val_loss= 0.09329 val_acc= 0.97713 time= 1.18627
+Epoch: 0179 train_loss= 0.07155 train_acc= 0.98485 val_loss= 0.09298 val_acc= 0.97713 time= 1.10225
+Epoch: 0180 train_loss= 0.07121 train_acc= 0.98502 val_loss= 0.09267 val_acc= 0.97713 time= 1.20027
+Epoch: 0181 train_loss= 0.07087 train_acc= 0.98502 val_loss= 0.09237 val_acc= 0.97713 time= 1.13326
+Epoch: 0182 train_loss= 0.07053 train_acc= 0.98519 val_loss= 0.09207 val_acc= 0.97713 time= 1.14926
+Epoch: 0183 train_loss= 0.07020 train_acc= 0.98519 val_loss= 0.09178 val_acc= 0.97713 time= 1.13126
+Epoch: 0184 train_loss= 0.06987 train_acc= 0.98537 val_loss= 0.09149 val_acc= 0.97713 time= 1.24928
+Epoch: 0185 train_loss= 0.06955 train_acc= 0.98554 val_loss= 0.09120 val_acc= 0.97713 time= 1.12625
+Epoch: 0186 train_loss= 0.06923 train_acc= 0.98554 val_loss= 0.09092 val_acc= 0.97713 time= 1.18926
+Epoch: 0187 train_loss= 0.06891 train_acc= 0.98554 val_loss= 0.09064 val_acc= 0.97713 time= 1.12725
+Epoch: 0188 train_loss= 0.06859 train_acc= 0.98571 val_loss= 0.09036 val_acc= 0.97713 time= 1.11125
+Epoch: 0189 train_loss= 0.06828 train_acc= 0.98571 val_loss= 0.09009 val_acc= 0.97713 time= 1.10425
+Epoch: 0190 train_loss= 0.06798 train_acc= 0.98571 val_loss= 0.08982 val_acc= 0.97713 time= 1.08424
+Epoch: 0191 train_loss= 0.06767 train_acc= 0.98588 val_loss= 0.08955 val_acc= 0.97713 time= 1.16426
+Epoch: 0192 train_loss= 0.06737 train_acc= 0.98588 val_loss= 0.08929 val_acc= 0.97713 time= 1.07425
+Epoch: 0193 train_loss= 0.06707 train_acc= 0.98588 val_loss= 0.08903 val_acc= 0.97713 time= 1.10124
+Epoch: 0194 train_loss= 0.06678 train_acc= 0.98605 val_loss= 0.08877 val_acc= 0.97713 time= 1.07024
+Epoch: 0195 train_loss= 0.06649 train_acc= 0.98605 val_loss= 0.08852 val_acc= 0.97713 time= 1.14326
+Epoch: 0196 train_loss= 0.06620 train_acc= 0.98605 val_loss= 0.08827 val_acc= 0.97713 time= 1.17727
+Epoch: 0197 train_loss= 0.06591 train_acc= 0.98605 val_loss= 0.08802 val_acc= 0.97561 time= 1.08925
+Epoch: 0198 train_loss= 0.06563 train_acc= 0.98605 val_loss= 0.08777 val_acc= 0.97561 time= 1.10526
+Epoch: 0199 train_loss= 0.06535 train_acc= 0.98605 val_loss= 0.08753 val_acc= 0.97561 time= 1.12925
+Epoch: 0200 train_loss= 0.06507 train_acc= 0.98605 val_loss= 0.08729 val_acc= 0.97561 time= 1.08925
+Finished Training....
+Checking train/test/val set accuracy: 0.9860473030457717, 0.9822161422708618, 0.975609756097561
+Checking pred set accuracy: 0.9521998913633895
+not ok
diff --git a/scGCN/results/scGCN_all_binary_input_labels.csv b/scGCN/results/scGCN_all_binary_input_labels.csv
new file mode 100644
index 0000000..4d61d1d
--- /dev/null
+++ b/scGCN/results/scGCN_all_binary_input_labels.csv
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diff --git a/scGCN/results/scGCN_all_binary_predicted_labels.csv b/scGCN/results/scGCN_all_binary_predicted_labels.csv
new file mode 100644
index 0000000..3e4efc2
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new file mode 100644
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diff --git a/scGCN/results/scGCN_data2_labels_2_prediction.csv b/scGCN/results/scGCN_data2_labels_2_prediction.csv
new file mode 100644
index 0000000..4eb9c46
--- /dev/null
+++ b/scGCN/results/scGCN_data2_labels_2_prediction.csv
@@ -0,0 +1,1841 @@
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diff --git a/scGCN/results/scGCN_index_guide.csv b/scGCN/results/scGCN_index_guide.csv
new file mode 100644
index 0000000..ca300a0
--- /dev/null
+++ b/scGCN/results/scGCN_index_guide.csv
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diff --git a/scGCN/results/scGCN_query_mask.csv b/scGCN/results/scGCN_query_mask.csv
new file mode 100644
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diff --git a/scGCN/species_sankey.html b/scGCN/species_sankey.html
new file mode 100644
index 0000000..836d68a
--- /dev/null
+++ b/scGCN/species_sankey.html
@@ -0,0 +1,284 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="UTF-8">
+    <title>Awesome-pyecharts</title>
+            <script type="text/javascript" src="https://assets.pyecharts.org/assets/echarts.min.js"></script>
+
+</head>
+<body>
+    <div id="ba55a3ac21c6498baf558a4a09d32a53" class="chart-container" style="width:900px; height:500px;"></div>
+    <script>
+        var chart_ba55a3ac21c6498baf558a4a09d32a53 = echarts.init(
+            document.getElementById('ba55a3ac21c6498baf558a4a09d32a53'), 'white', {renderer: 'canvas'});
+        var option_ba55a3ac21c6498baf558a4a09d32a53 = {
+    "animation": true,
+    "animationThreshold": 2000,
+    "animationDuration": 1000,
+    "animationEasing": "cubicOut",
+    "animationDelay": 0,
+    "animationDurationUpdate": 300,
+    "animationEasingUpdate": "cubicOut",
+    "animationDelayUpdate": 0,
+    "color": [
+        "#c23531",
+        "#2f4554",
+        "#61a0a8",
+        "#d48265",
+        "#749f83",
+        "#ca8622",
+        "#bda29a",
+        "#6e7074",
+        "#546570",
+        "#c4ccd3",
+        "#f05b72",
+        "#ef5b9c",
+        "#f47920",
+        "#905a3d",
+        "#fab27b",
+        "#2a5caa",
+        "#444693",
+        "#726930",
+        "#b2d235",
+        "#6d8346",
+        "#ac6767",
+        "#1d953f",
+        "#6950a1",
+        "#918597"
+    ],
+    "series": [
+        {
+            "type": "sankey",
+            "name": "the number of cells",
+            "data": [
+                {
+                    "name": "original_alpha"
+                },
+                {
+                    "name": "original_quiescent_stellate"
+                },
+                {
+                    "name": "original_delta"
+                },
+                {
+                    "name": "original_endothelial"
+                },
+                {
+                    "name": "original_beta"
+                },
+                {
+                    "name": "original_ductal"
+                },
+                {
+                    "name": "original_gamma"
+                },
+                {
+                    "name": "original_macrophage"
+                },
+                {
+                    "name": "predicted_ductal"
+                },
+                {
+                    "name": "predicted_delta"
+                },
+                {
+                    "name": "predicted_quiescent_stellate"
+                },
+                {
+                    "name": "predicted_gamma"
+                },
+                {
+                    "name": "predicted_endothelial"
+                },
+                {
+                    "name": "predicted_macrophage"
+                },
+                {
+                    "name": "predicted_alpha"
+                },
+                {
+                    "name": "predicted_beta"
+                }
+            ],
+            "links": [
+                {
+                    "source": "original_alpha",
+                    "target": "predicted_alpha",
+                    "value": 181
+                },
+                {
+                    "source": "original_alpha",
+                    "target": "predicted_beta",
+                    "value": 5
+                },
+                {
+                    "source": "original_alpha",
+                    "target": "predicted_gamma",
+                    "value": 4
+                },
+                {
+                    "source": "original_alpha",
+                    "target": "predicted_ductal",
+                    "value": 1
+                },
+                {
+                    "source": "original_beta",
+                    "target": "predicted_beta",
+                    "value": 871
+                },
+                {
+                    "source": "original_beta",
+                    "target": "predicted_alpha",
+                    "value": 18
+                },
+                {
+                    "source": "original_beta",
+                    "target": "predicted_gamma",
+                    "value": 4
+                },
+                {
+                    "source": "original_beta",
+                    "target": "predicted_delta",
+                    "value": 1
+                },
+                {
+                    "source": "original_delta",
+                    "target": "predicted_delta",
+                    "value": 177
+                },
+                {
+                    "source": "original_delta",
+                    "target": "predicted_beta",
+                    "value": 22
+                },
+                {
+                    "source": "original_delta",
+                    "target": "predicted_alpha",
+                    "value": 9
+                },
+                {
+                    "source": "original_delta",
+                    "target": "predicted_gamma",
+                    "value": 6
+                },
+                {
+                    "source": "original_delta",
+                    "target": "predicted_ductal",
+                    "value": 4
+                },
+                {
+                    "source": "original_ductal",
+                    "target": "predicted_ductal",
+                    "value": 274
+                },
+                {
+                    "source": "original_ductal",
+                    "target": "predicted_beta",
+                    "value": 1
+                },
+                {
+                    "source": "original_endothelial",
+                    "target": "predicted_endothelial",
+                    "value": 128
+                },
+                {
+                    "source": "original_endothelial",
+                    "target": "predicted_beta",
+                    "value": 11
+                },
+                {
+                    "source": "original_gamma",
+                    "target": "predicted_gamma",
+                    "value": 40
+                },
+                {
+                    "source": "original_gamma",
+                    "target": "predicted_alpha",
+                    "value": 1
+                },
+                {
+                    "source": "original_macrophage",
+                    "target": "predicted_macrophage",
+                    "value": 36
+                },
+                {
+                    "source": "original_quiescent_stellate",
+                    "target": "predicted_quiescent_stellate",
+                    "value": 46
+                },
+                {
+                    "source": "original_quiescent_stellate",
+                    "target": "predicted_gamma",
+                    "value": 1
+                }
+            ],
+            "left": "5%",
+            "top": "10%",
+            "right": "20%",
+            "bottom": "5%",
+            "nodeWidth": 20,
+            "nodeGap": 8,
+            "nodeAlign": "justify",
+            "layoutIteration": 32,
+            "orient": "horizontal",
+            "draggable": false,
+            "focusNodeAdjacency": true,
+            "label": {
+                "show": true,
+                "position": "right",
+                "margin": 8
+            },
+            "lineStyle": {
+                "show": true,
+                "width": 1,
+                "opacity": 0.2,
+                "curveness": 0.5,
+                "type": "dotted",
+                "color": "source"
+            }
+        }
+    ],
+    "legend": [
+        {
+            "data": [
+                "the number of cells"
+            ],
+            "selected": {
+                "the number of cells": true
+            },
+            "show": true,
+            "padding": 5,
+            "itemGap": 10,
+            "itemWidth": 25,
+            "itemHeight": 14
+        }
+    ],
+    "tooltip": {
+        "show": true,
+        "trigger": "item",
+        "triggerOn": "mousemove|click",
+        "axisPointer": {
+            "type": "line"
+        },
+        "showContent": true,
+        "alwaysShowContent": false,
+        "showDelay": 0,
+        "hideDelay": 100,
+        "textStyle": {
+            "fontSize": 14
+        },
+        "borderWidth": 0,
+        "padding": 5
+    },
+    "title": [
+        {
+            "text": "left:original;right:predicted",
+            "padding": 5,
+            "itemGap": 10
+        }
+    ]
+};
+        chart_ba55a3ac21c6498baf558a4a09d32a53.setOption(option_ba55a3ac21c6498baf558a4a09d32a53);
+    </script>
+</body>
+</html>
diff --git a/scGCN/train.py b/scGCN/train.py
index f219bec..9b14232 100644
--- a/scGCN/train.py
+++ b/scGCN/train.py
@@ -3,11 +3,12 @@
 import time
 import numpy as np
 import pickle as pkl
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+#import tensorflow as tf
 from utils import *
+from sankey import *
 from tensorflow.python.saved_model import tag_constants
 from models import scGCN
-from sankey import *
 sys.stdout = open("output_log.txt", "w")
 
 import warnings
@@ -39,18 +40,15 @@
 flags.DEFINE_integer('max_degree', 3, 'Maximum Chebyshev polynomial degree.')
 
 # Load data
-adj, features, labels_binary_train, labels_binary_val, labels_binary_test, train_mask, pred_mask, val_mask, test_mask, new_label, true_label, index_guide, rename  = load_data(
+adj, features, labels_binary_train, labels_binary_val, labels_binary_test, train_mask, pred_mask, val_mask, test_mask, new_label, true_label, index_guide,rename = load_data(
     FLAGS.dataset,rgraph=FLAGS.graph)
-    #pred_mask 仅仅是ref
-    #new_label new_label是所有的真实的二进制的值
-    #true_label 这个变量中存储了所有的原本真实标签label
-    #index_guide 这里就是给未来的结果分配index,依次是ref_train/qur/ref_val/ref_test
 
 support = [preprocess_adj(adj)]
 num_supports = 1
 model_func = scGCN
 
 # Define placeholders
+tf.compat.v1.disable_eager_execution()
 placeholders = {
     'support':
     [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
@@ -65,7 +63,6 @@
     tf.placeholder_with_default(0., shape=()),
     'num_features_nonzero':
     tf.placeholder(tf.int32)  # helper variable for sparse dropout
-    #placeholder()在神经网络构建graph的时候在模型中的占位，此时并没有把要输入的数据传入模型，它只会分配必要的内存
 }
 
 # Create model
@@ -165,7 +162,6 @@ def evaluate(features, support, labels, mask, placeholders):
 #' save the predicted labels of query data
 os.mkdir(FLAGS.output)
 scGCN_all_labels = true_label.values.flatten()  #' ground truth
-
 np.savetxt(FLAGS.output + '/scGCN_all_input_labels.csv',scGCN_all_labels,delimiter=',',comments='',fmt='%s')
 np.savetxt(FLAGS.output+'/scGCN_query_mask.csv',pred_mask,delimiter=',',comments='',fmt='%s')           
 ab = sess.run(tf.nn.softmax(predict_output))
@@ -174,10 +170,11 @@ def evaluate(features, support, labels, mask, placeholders):
 np.savetxt(FLAGS.output+'/scGCN_all_binary_predicted_labels.csv',all_binary_prediction,delimiter=',',comments='',fmt='%f')
 np.savetxt(FLAGS.output+'/scGCN_index_guide.csv',index_guide,delimiter=',',comments='',fmt='%f')
 np.savetxt(FLAGS.output+'/scGCN_all_binary_input_labels.csv',all_binary_labels,delimiter=',',comments='',fmt='%f')
-
+# new process
 scGCN_data2_labels = scGCN_all_labels[np.where(index_guide<0)]
 data2_binary_prediction = all_binary_prediction[np.where(index_guide<0)]
 new_rename = dict(zip(rename.values(),rename.keys()))
+#更换字典rename中的key与value
 data2_binary_prediction_tab=pd.DataFrame(data2_binary_prediction)
 data2_labels_prediction = data2_binary_prediction_tab.replace(new_rename)
 
@@ -188,4 +185,3 @@ def evaluate(features, support, labels, mask, placeholders):
 resule_lables["original"]=scGCN_data2_labels
 resule_lables["predicted"]=data2_labels_prediction
 createSankey(resule_lables)
-
diff --git a/scGCN/utility.py b/scGCN/utility.py
index 7e62d8f..094c012 100644
--- a/scGCN/utility.py
+++ b/scGCN/utility.py
@@ -11,7 +11,7 @@ def svd1(mat, num_cc):
     u = U[:, 0:int(num_cc)]
     v = V[0:int(num_cc), :].transpose()
     return u, v, d
-#？
+
 def pls(x, y, num_cc):
     random.seed(42)
     plsca = PLSCanonical(n_components=int(num_cc), algorithm='svd')
@@ -39,10 +39,8 @@ def runcca(data1, data2, num_cc=20):
     object1 = scale2(data1)
     object2 = scale2(data2)
     mat3 = np.matmul(np.matrix(object1).transpose(), np.matrix(object2))
-    #返回两个数组的矩阵乘积
     a = svd1(mat=mat3, num_cc=int(num_cc))
     cca_data = np.concatenate((a[0], a[1]))
-    #数组的拼接函数
     ind = np.where(
         [cca_data[:, col][0] < 0 for col in range(cca_data.shape[1])])[0]
     cca_data[:, ind] = cca_data[:, ind] * (-1)
@@ -68,16 +66,10 @@ def l2norm(mat):
 #' @rdname runCCA
 #' @export feature loadings and embeddings
 def runCCA(data_use1, data_use2, features, count_names, num_cc):
-    #runCCA(data_use1=scale_data1,
-    #  data_use2=scale_data2,
-    #  features=features,
-    #  count_names=rowname,
-    #  num_cc=30)
     features = checkFeature(data_use1, features)
     features = checkFeature(data_use2, features)
     data1 = data_use1.loc[features, ]
     data2 = data_use2.loc[features, ]
-    #features是非零方差的数据
     cca_results = runcca(data1=data1, data2=data2, num_cc=num_cc)
     cell_embeddings = np.matrix(cca_results[0])
     combined_data = data1.merge(data2,
@@ -91,9 +83,9 @@ def runCCA(data_use1, data_use2, features, count_names, num_cc):
     return cca_results, loadings
 
 
-# Check if features have zero variance 零方差
+# Check if features have zero variance
 # @return Returns a vector of features that is the subset of features
-# that have non-zero variance 非零方差
+# that have non-zero variance
 #' @param data_use pandas data frame
 def checkFeature(data_use, features):
     data1 = data_use.loc[features, ]
@@ -231,8 +223,6 @@ def generate_graph(count_list, norm_list, scale_list, features, combine, k_filte
         scale_data1 = scale_list[i]
         scale_data2 = scale_list[j]
         rowname = counts1.index
-        #如果combine是1 (0,1) 2(0,2) 5(1,2);i 是0/0/1，j是1/2/2
-
         #' @param data_use1 pandas data frame
         #' @param data_use2 pandas data frame
         #' @export feature loadings and embeddings (pandas data frame)
@@ -241,7 +231,6 @@ def generate_graph(count_list, norm_list, scale_list, features, combine, k_filte
                                          features=features,
                                          count_names=rowname,
                                          num_cc=30)
-        #return cca_results, loadings
         norm_embedding = l2norm(mat=cell_embedding[0])
         #' identify nearest neighbor
         cells1 = counts1.columns
diff --git a/scGCN/utils.py b/scGCN/utils.py
index 8bc30c5..28ffea4 100644
--- a/scGCN/utils.py
+++ b/scGCN/utils.py
@@ -7,7 +7,7 @@
 from data import *
 from collections import defaultdict
 from scipy.stats import uniform
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 #' -------- convert graph to specific format -----------
 
@@ -29,7 +29,6 @@ def graph(matrix):
 def sample_mask(idx, l):
     """Create mask."""
     mask = np.zeros(l)
-    # 给定性状和类型的用0填充的数组
     mask[idx] = 1
     return np.array(mask, dtype=np.bool)
 
@@ -48,38 +47,37 @@ def load_data(datadir,rgraph=True):
     PIK = "{}/datasets.dat".format(datadir)
     with open(PIK, "rb") as f:
         objects = pkl.load(f)
+
     data_train1, data_test1, data_val1, label_train1, label_test1, label_val1, lab_data2, lab_label2, types = tuple(
         objects)
-    # data_train1 data_test1 data_val1只有ref,label相对应
-    # lab_data2与lab_label2是qur
-    #此处原来的types仅仅是ref中的types
+
     train2 = pd.concat([data_train1, lab_data2])
     lab_train2 = pd.concat([label_train1, lab_label2])
-    #train2包含了两个数据集的数据，包含了从Ref中随机得到的train以及所有的qur
+
     datas_train = np.array(train2)
     datas_test = np.array(data_test1)
     datas_val = np.array(data_val1)
-    ####!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-    ####!!!!!!!!!!!!!!!!!!!!这里就是给未来的结果分配index,依次是ref_train/qur/ref_val/ref_test
+
     index_guide = np.concatenate(
         (label_train1.index, lab_label2.index * (-1) - 1, label_val1.index,
          label_test1.index))
-    #numpy提供了numpy.concatenate((a1,a2,...), axis=0)函数。能够一次完成多个数组的拼接
+
     labels_train = np.array(lab_train2).flatten()
     labels_test = np.array(label_test1).flatten()
     labels_val = np.array(label_val1).flatten()
-    #同上，labels_train包含了两个数据集
+
     #' convert pandas data frame to csr_matrix format
-    datas_tr = scipy.sparse.csr_matrix(datas_train.astype('Float64'))
-    datas_va = scipy.sparse.csr_matrix(datas_val.astype('Float64'))
-    datas_te = scipy.sparse.csr_matrix(datas_test.astype('Float64'))
+    datas_tr = scipy.sparse.csr_matrix(datas_train.astype('float64'))
+    datas_va = scipy.sparse.csr_matrix(datas_val.astype('float64'))
+    datas_te = scipy.sparse.csr_matrix(datas_test.astype('float64'))
 
     #' 3) set the unlabeled data in training set
+
     #' @param N; the number of labeled samples in training set
     M = len(data_train1)
 
-    #这里是有改动？
     #' 4) get the feature object by combining training, test, valiation sets
+
     features = sp.vstack((sp.vstack((datas_tr, datas_va)), datas_te)).tolil()
     features = preprocess_features(features)
 
@@ -93,46 +91,44 @@ def load_data(datadir,rgraph=True):
     Labels = pd.DataFrame(labels)
 
     true_label = Labels
-    #这个变量中存储了所有的原本真实标签label
     #' convert list to binary matrix
-    uniq = np.unique(Labels.values)
-
+    f = open("./err.txt", "a")
+    f.write(str(Labels))
+    f.close
+    uniq = np.unique(Labels.to_numpy())
     rename = {}
-    #这里把类型转换成了数字，而此处的类型只有ref的;原始types变量里面只存储了唯一的Ref的label
+
     for line in range(0, len(types)):
         key = types[line]
         rename[key] = int(line)
-    #！！！！这里把类型数字进行了替换，所以会导致data2内新类型仍然是文字而报错
-    Label1 = Labels.replace(rename)
-    #Labels是原始的所有的真实标签label
-    #因此Lanel1是所有转换为数字？的真实标签
-    indices = np.array(Label1.values, dtype='int').tolist()
 
+    Label1 = Labels.replace(rename)
+    Label1 = Label1.where((Label1.notna()),0)
+    indices = np.array(Label1.values,dtype="int").tolist()
     indice = [item for sublist in indices for item in sublist]
 
     #' convert list to binary matrix
     indptr = range(len(indice) + 1)
+    f = open("./err.txt", "a")
+    f.write(str(types))
+    f.close
     dat = np.ones(len(indice))
     binary_label = scipy.sparse.csr_matrix((dat, indice, indptr))
-    #binary_label是所有的二进制标签
+
     #' new label with binary values
     new_label = np.array(binary_label.todense())
-    #new_label是所有的二进制的值
     idx_train = range(M)
-    #M = len(data_train1) data_train1是从ref中提取出来的train
     idx_pred = range(M, len(labels_tr))
-    #这个区间相当于是所有的qur
     idx_val = range(len(labels_tr), len(labels_tr) + len(labels_va))
-    #这个区间仅仅是从ref中提取出来的val
     idx_test = range(
         len(labels_tr) + len(labels_va),
         len(labels_tr) + len(labels_va) + len(labels_te))
-    #这个区间仅仅是从ref中提取出来的test
+
     train_mask = sample_mask(idx_train, new_label.shape[0])
     pred_mask = sample_mask(idx_pred, new_label.shape[0])
     val_mask = sample_mask(idx_val, new_label.shape[0])
     test_mask = sample_mask(idx_test, new_label.shape[0])
-    #给定形状和类型的用0填充的数组，也就是所有的标签做01处理
+
     labels_binary_train = np.zeros(new_label.shape)
     labels_binary_val = np.zeros(new_label.shape)
     labels_binary_test = np.zeros(new_label.shape)
@@ -157,7 +153,7 @@ def load_data(datadir,rgraph=True):
     fake2 = np.array([-1] * len(data_train1))
     fake3 = np.array([-1] * (len(data_val1) + len(data_test1)))
     find1 = np.concatenate((fake2, np.array(lab_data2.index), fake3)).flatten()
-    #这里是什么意思啊？师兄？
+
     #' ---------------------------------------------
     #'  intra-graph
     #' ---------------------------------------------
@@ -198,7 +194,7 @@ def load_data(datadir,rgraph=True):
     adj = nx.adjacency_matrix(nx.from_dict_of_lists(adj))
 
     print("assign input coordinatly....")
-    return adj, features, labels_binary_train, labels_binary_val, labels_binary_test, train_mask, pred_mask, val_mask, test_mask, new_label, true_label, index_guide, rename 
+    return adj, features, labels_binary_train, labels_binary_val, labels_binary_test, train_mask, pred_mask, val_mask, test_mask, new_label, true_label, index_guide,rename
 
 
 def preprocess_features(features):
@@ -247,7 +243,6 @@ def uniform(shape, scale=0.05, name=None):
 
 
 def glorot(shape, name=None):
-    #Glorot深度学习中的参数初始化方法
     """Glorot & Bengio (AISTATS 2010) init."""
     init_range = np.sqrt(6.0 / (shape[0] + shape[1]))
     initial = tf.random_uniform(shape,

From 670b420916740ec67cf6e0cbb284f1e2dbafa211 Mon Sep 17 00:00:00 2001
From: ianutin <83384997+ianutin@users.noreply.github.com>
Date: Thu, 14 Apr 2022 20:08:58 +0800
Subject: [PATCH 17/17] Update README.md

---
 README.md | 65 ++++++++++++++++++++-----------------------------------
 1 file changed, 23 insertions(+), 42 deletions(-)

diff --git a/README.md b/README.md
index dc85a41..86f0459 100644
--- a/README.md
+++ b/README.md
@@ -1,75 +1,56 @@
-scGCN is a Graph Convolutional Networks Algorithm for Knowledge Transfer in Single Cell Omics
+scRNA_GCN流程是在scGCN流程的基础上进行的升级和完善，支持单细胞RNA基因表达矩阵作为输入，用以跨物种之间的细胞类型比较。
+## 概述
 
-This is a TensorFlow implementation of scGCN for leveraging and label transfer across differnt single cell datasets.
+基于卷积神经网络（Convolutional Neural Networks）的跨物种单细胞卷积神经网络细胞类型注释软件是一个linux平台的python软件，旨在帮助没有过多的编程知识的用户在下游分析单细胞转录组测序（scRNA-seq）及相关组学数据。软件提供了一种方便的算法来对不同物种的基因表达相似性进行分析，从而在无需知道跨物种之间的物种特异性marker的情况下，根据参考物种的细胞类型注释对待测物种的细胞类型进行注释。并且，在细胞类型注释完成后，还能简单地比较分析不同物种细胞类型的基因表达差异。软件操作难度低，利于没有编程基础的研究人员学习并使用。
 
-[![DOI](https://zenodo.org/badge/294531199.svg)](https://zenodo.org/badge/latestdoi/294531199)
- 
-## Overview
-
-Single-cell omics represent the fastest-growing genomics data type in the literature and the public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of the single-cell omics. The current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Benchmarked with other label transfer methods on different single cell omics datasets, scGCN has consistently demonstrated superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow and provided here. 
-
-## Requirements
+## 需要安装的包
 * setuptools >= 40.6.3
 * numpy >= 1.15.4
 * tensorflow >= 1.15.0
 * networkx >= 2.2
 * scipy >= 1.1.0
 
-## Installation
+## 流程的安装
 
-Download scGCN:
+下载流程：
 ```
-git clone https://github.com/QSong-github/scGCN
+git clone https://github.com/Dee-chen/scGCN/tree/cdy
 ```
-Install requirements and scGCN:
+安装依赖:
 
 ```bash
 python setup.py install
 ```
-The general installation time is less than 10 seconds, and have been tested on mac OS and linux system. 
 
-## Run the demo
 
-load the example data using the data_preprocess.R script
-In the example data, we include the data from Mouse (reference) and Human (query) of GSE84133 dataset. The reference dataset contains 1,841 cells and the query dataset contains more cells (N=7,264) and 12,182 genes. 
+## 运行示例
+
+在示例文档中，有两个物种的相关基因表达矩阵数据
 ```bash
 cd scGCN
-Rscript data_preprocess.R # load example data 
-python train.py # run scGCN
+Rscript data_preprocess.R # 加载和预处理示例数据
+python train.py # 运行主程序
 ```
-All output will be shown in the output_log.txt file. Performance will be shown at the bottom. 
-We also provide the Seurat performance on this reference-qeury set (as in Figure 4), by run 
-
 ```
 Rscript Seurat_result.R
 ```
 
-## Input data
-
-When using your own data, you have to provide 
-* the raw data matrix of reference data and cell labels
-* the raw data matrix of query data
-
-## Output
-
-The output files with scGCN predicted labels will be stored in the results folder.
-
-## Model options 
-
-We also provide other GCN models includidng GAT (Veličković et al., ICLR 2018), HyperGCN (Chami et al., NIPS 2019) and GWNN (Xu et al., ICLR 2019) for optional use.
-
-## Detecting unknown cells
+## 输入数据
 
-For the query data that have cell types not appearing in reference data, we provide a screening step in our scGCN model using two statistical metrics, entropy score and enrichment score. If certain cells in query data have higher entropy and lower enrichment, these cells should be assigned as unknown cells. Specifically, choose check_unknown=TRUE in the function 'save_processed_data' to detect unknown cells.
+当使用自定义数据时，需要提供一个待测物种的基因表达矩阵，和一个参考物种的基因表达矩阵以及对应的细胞类型标签数据
+* 基因表达矩阵是数据框结构，行是细胞，列是基因
+* 细胞类型标签是单列数据框结构，以"type"作为单列数据的表头
 
-## Reproduction instructions
+## 输出
 
-The above scripts can reproduce the quantitative results in our manuscript based on our provided data.
+输出的数据信息会在results文件夹中，可视化结果则在当前运行的主程序目录下
 
-## Cite
+## 差异分析
+在原始scGCN的基础上，增加了初步的差异分析功能，用以可视化跨物种同一细胞类型的主要差异基因
 
-Please cite our paper and the related GCN papers if you use this code in your own work:
+## 引用
 
+原始scGCN的文章引用信息：
 ```
 Song, Q., Su, J., & Zhang, W. (2020). scGCN: a Graph Convolutional Networks Algorithm for Knowledge Transfer in Single Cell Omics. bioRxiv.
 ```