1(从day_2_cardio_cell_sample数据集加载#命名为day2)图书馆(scGPS)day2 < -day_2_cardio_cell_samplemixedpop1 < -new_scGPS_object（ExpressionMatrix =day2＄dat2_counts,GeneMetadata =day2＄dat2geneInfo,CellMetadata =day2＄dat2_clusters)2(从day_5_cardio_cell_sample数据集加载#命名为第5天)day5 < -day_5_cardio_cell_samplemixedpop2 < -new_scGPS_object（ExpressionMatrix =day5＄dat5_counts,GeneMetadata =day5＄dat5geneInfo,CellMetadata =day5＄dat5_clusters)

#选择一个子种群c_selectID < -1#加载基因列表(可以是用户选择的任何基因列表)基因< -training_gene_sample基因< -基因＄Merged_unique#加载集群信息cluster_mixedpop1 < -colData(mixedpop1),1］cluster_mixedpop2 < -colData(mixedpop2),1］#跑步训练(这里使用nboots = 3，但建议使用nboots = 50-100)LSOLDA_dat < -bootstrap_prediction（nboots =3.，mixedpop1 =mixedpop1,mixedpop2 =mixedpop2,基因=的基因,c_selectID =c_selectID,listData =列表(),cluster_mixedpop1 =cluster_mixedpop1,cluster_mixedpop2 =cluster_mixedpop2,trainset_ratio =0.7）的名字(LSOLDA_dat)#>[1]“准确性”“ElasticNetGenes”“偏差”#> [4] "ElasticNetFit" "LDAFit" "predictor_S1"#> [7] "ElasticNetPredict" " ldpredict " "cell_results"

#总结结果LDAsum_pred_lda < -summary_prediction_lda（LSOLDA_dat =LSOLDA_dat,nPredSubpop =4）套索显示细胞的百分比#分类为单元格所属sum_pred_lasso < -summary_prediction_lasso（LSOLDA_dat =LSOLDA_dat,nPredSubpop =4）#情节总结结果plot_sum < -函数(sum_dat) {sum_dat_tf < -t(sum_dat)sum_dat_tf < -na.omit(sum_dat_tf)sum_dat_tf < -应用(sum_dat [,-ncol(sum_dat)),1，函数(x) {as.numeric（as.vector(x))})sum_dat＄< -名字gsub（“ElasticNet for subpop”，“sp”, sum_dat＄名)sum_dat＄< -名字gsub（"在目标mixedpop"，“p”, sum_dat＄名)sum_dat＄< -名字gsub（“subpop的LDA”，“sp”, sum_dat＄名)sum_dat＄< -名字gsub（"在目标mixedpop"，“p”, sum_dat＄名)colnames(sum_dat_tf) < -sum_dat＄的名字箱线图(sum_dat_tf拉斯维加斯=2）｝plot_sum(sum_pred_lasso)

plot_sum(sum_pred_lda)

# summary accuracy检查遗漏测试集中的模型精度summary_accuracy（对象=LSOLDA_dat)#> [1] 61.39535 68.57143 60.74766# summary模型解释的最大偏差summary_deviance（对象=LSOLDA_dat)# > allDeviance美元#>[1]“10.55”“5.84”“11.36”# ># > DeviMax美元#> dat_DE$Dfd偏差DEgenes#> 1 0 5.84 genes_cluster1#> 2 1 5.84 genes_cluster1#> 3 2 5.84 genes_cluster1 . ##> 4 3 5.84 genes_cluster1#> 5个剩余DEgenes剩余DEgenes剩余DEgenes# ># > LassoGenesMax美元# >零

#使用CORE在表达式数据中查找集群信息day5 < -day_5_cardio_cell_samplecellname < -colnames(day5＄dat5_counts)集群< -day5＄dat5_clusterscellname < -data.frame（“集群”=集群,“cellBarcodes”＝cellname)mixedpop2 < -new_scGPS_object（ExpressionMatrix =day5＄dat5_counts,GeneMetadata =day5＄dat5geneInfo,CellMetadata =cellname)CORE_cluster < -CORE_clustering(mixedpop2remove_outlier =c（0)，PCA =假）#更新集群信息，用户可以…key_height < -CORE_cluster＄optimalClust＄KeyStats＄高度optimal_res < -CORE_cluster＄optimalClust＄OptimalResoptimal_index =哪一个(key_height= =optimal_res)clustering_after_outlier_removal < -unname（unlist（CORE_cluster＄集群[[optimal_index]]))corresponding_cells_after_outlier_removal < -CORE_cluster＄cellsForClusteringoriginal_cells_before_removal < -colData(mixedpop2),2］corresponding_index < -匹配(corresponding_cells_after_outlier_removaloriginal_cells_before_removal)#检查匹配相同的（as.character(original_cells_before_removal [corresponding_index]),corresponding_cells_after_outlier_removal)#>[1]是真的#用移除异常值后的新聚类创建新对象mixedpop2_post_clustering < -mixedpop2 [, corresponding_index]colData(mixedpop2_post_clustering),1) < -clustering_after_outlier_removal

#使用SCORE在表达式数据中查找聚类信息day5 < -day_5_cardio_cell_samplecellname < -colnames(day5＄dat5_counts)集群< -day5＄dat5_clusterscellname < -data.frame（“集群”=集群,“cellBarcodes”＝cellname)mixedpop2 < -new_scGPS_object（ExpressionMatrix =day5＄dat5_counts,GeneMetadata =day5＄dat5geneInfo,CellMetadata =cellname)SCORE_test < -CORE_bagging(mixedpop2remove_outlier =c（0)，PCA =假，bagging_run =20.，subsample_proportion =。8）

dev.off（）#>空设备# > 1##3.2.1情节核心集群p1 < -plot_CORE(CORE_cluster＄树,CORE_cluster＄集群,color_branch =c（“# 208 eb7”，“# 6 ce9d3”，“# 1 c5e39”，“# 8 fca40”，“154975 #”，“# b1c8eb”）)p1# > 3美元#> [1] 1 5 0 1#提取CORE识别的最优索引key_height < -CORE_cluster＄optimalClust＄KeyStats＄高度optimal_res < -CORE_cluster＄optimalClust＄OptimalResoptimal_index =哪一个(key_height= =optimal_res)#绘制一个最佳聚类条plot_optimal_CORE（original_tree =CORE_cluster＄树,optimal_cluster =unlist(CORE_cluster＄集群[optimal_index]),改变=-2000年）#>排序和分配标签…# > 2# > 162335 na# > 3# > 162335423#>绘制彩色树状图现在....#>绘制下面的酒吧现在....3.2.2 plot SCORE聚类#绘制所有集群条plot_CORE(SCORE_test＄树,list_clusters =SCORE_test＄集群)#绘制一个稳定的最佳聚类条plot_optimal_CORE（original_tree =SCORE_test＄树,optimal_cluster =unlist(SCORE_test＄集群(SCORE_test＄optimal_index]),改变=-100年）#>排序和分配标签…# > 2# > 162335 na# > 3# > 162335423#>绘制彩色树状图现在....#>绘制下面的酒吧现在....

t < -tSNE（expression.mat =分析(mixedpop2))使用前1500个基因准备PCA输入…计算PCA值…#>运行tSNE…p2 < -plot_reduced(t,color_fac =因素（colData(mixedpop2),1]),托盘=1：长度（独特的（colData(mixedpop2),1))))#>警告:不鼓励使用' reduced_dat_toPlot$Dim1 '。请改用“Dim1”。#>警告:不鼓励使用' reduced_dat_toPlot$Dim2 '。请改用“Dim2”。p2

#加载基因列表(可以是用户选择的任何基因列表)基因< -training_gene_sample<基因的基因＄Merged_unique基因表也可以通过差异表达进行客观鉴定#analysis需要find_markers的集群信息。这里，我们使用#核心的结果。#colData(mixedpop2)[，1] <- unlist(SCORE_test$Cluster[SCORE_test$optimal_index])suppressMessages（图书馆(locfit))德根< -find_markers（expression_matrix =分析(mixedpop2),集群=colData(mixedpop2),1]，selected_cluster =独特的（colData(mixedpop2),1)))#输出包含每个集群的数据帧。#数据帧包含所有基因，按p值排序的名字(德根)#> [1] "baseMean" "log2FoldChange" "lfcSE" "stat"#>[5]“pvalue”“padj”“id”#您可以注释标识的集群DEgeneList_1vsOthers < -德根＄DE_Subpop1vsRemaining＄id#用户需要检查基因输入的格式，以确保它们是#与表达矩阵中的基因名一致#以下命令将“PathwayEnrichment.xlsx”文件保存到# dir工作#使用500个顶级DE基因suppressMessages（图书馆(剂量))suppressMessages（图书馆(ReactomePA))suppressMessages（图书馆(clusterProfiler))genes500 < -as.factor(DEgeneList_1vsOthers [seq_len（500)))enrichment_test < -annotate_clusters(基因,pvalueCutoff =0.05，gene_symbol =真正的）#浓缩输出可以通过运行显示clusterProfiler：：dotplot(enrichment_testshowCategory =10，字体。大小=6）

#选择一个亚群体，输入基因列表c_selectID < -1#注意确保这里输入的基因格式与此格式相同mixedpop1和mixedpop2中的基因#基因=德根＄id (1：500］#运行nboots = 2次的测试引导cluster_mixedpop1 < -colData(mixedpop1),1］cluster_mixedpop2 < -colData(mixedpop2),1］LSOLDA_dat < -bootstrap_prediction（nboots =2，mixedpop1 =mixedpop1,mixedpop2 =mixedpop2,基因=的基因,c_selectID =c_selectID,listData =列表(),cluster_mixedpop1 =cluster_mixedpop1,cluster_mixedpop2 =cluster_mixedpop2)

#获取汇总矩阵的行数row_cluster < -长度（独特的（colData(mixedpop2),1)))LDA显示被分类为单元格的单元格百分比#归属LDA分类器summary_prediction_lda（LSOLDA_dat =LSOLDA_dat,nPredSubpop =row_cluster)#> V1 V2的名称#> 1 21.3903743315508 41.7112299465241 LDA的子pop 1在目标mixedpop2#> 2 95.7142857142857 75 LDA的subpop 2在目标mixedpop2#> 3 23.3082706766917 37.593984962406 LDA的子pop 3在目标mixedpop2#> 4 50 52.5 LDA子pop 4在目标mixedpop2套索显示被分类为单元格的单元格的百分比#归属套索分类器summary_prediction_lasso（LSOLDA_dat =LSOLDA_dat,nPredSubpop =row_cluster)#> V1 V2的名称#> 1 9.62566844919786 64.7058823529412为目标mixedpop2的子pop1添加弹性网#> 2 99.2857142857143 98.5714285714286在目标mixedpop2的子pop2的ElasticNet#> 3 86.4661654135338 75.187969924812在目标mixedpop2中为subpop3执行ElasticNet#> 4 90 87.5用于目标mixedpop2中的subpop4的ElasticNet模型训练过程中模型解释的最大偏差summary_deviance（对象=LSOLDA_dat)# > allDeviance美元#>[1]“39.71”“47.21”# ># > DeviMax美元#> dat_DE$Dfd偏差DEgenes#> 1 0 47.21 genes_cluster1#> 21 47.21 genes_cluster1#> 3 2 47.21 genes_cluster1 . ##> 4 3 47.21 genes_cluster1 . ##> 5 4 47.21 genes_cluster1#> 6 5 47.21 genes_cluster1#> 7 8 47.21 genes_cluster1 . ##> 8 9 47.21 genes_cluster1#> 9 11 47.21 genes_cluster1#> 10 14 47.21 genes_cluster1#> 11 18 47.21 genes_cluster1#> 12 20 47.21 genes_cluster1 . ##> 13 23 47.21 genes_cluster1#> 14剩余的DEgenes剩余的DEgenes剩余的DEgenes# ># > LassoGenesMax美元# >零# summary accuracy检查遗漏测试集中的模型精度summary_accuracy（对象=LSOLDA_dat)#> [1] 72.76786 70.53571

#运行预测3个集群cluster_mixedpop1 < -colData(mixedpop1),1］cluster_mixedpop2 < -colData(mixedpop2),1］#cluster_mixedpop2 <- as.numeric(as.vector(colData(mixedpop2)[，1]))c_selectID < -1#前200个基因标记区分簇1基因=德根＄id (1：200］LSOLDA_dat1 < -bootstrap_prediction（nboots =2，mixedpop1 =mixedpop2,mixedpop2 =mixedpop2,基因=基因,c_selectID,listData =列表(),cluster_mixedpop1 =cluster_mixedpop2,cluster_mixedpop2 =cluster_mixedpop2)c_selectID < -2基因=德根＄id (1：200］LSOLDA_dat2 < -bootstrap_prediction（nboots =2，mixedpop1 =mixedpop2,mixedpop2 =mixedpop2,基因=基因,c_selectID,listData =列表(),cluster_mixedpop1 =cluster_mixedpop2,cluster_mixedpop2 =cluster_mixedpop2)c_selectID < -3.基因=德根＄id (1：200］LSOLDA_dat3 < -bootstrap_prediction（nboots =2，mixedpop1 =mixedpop2,mixedpop2 =mixedpop2,基因=基因,c_selectID,listData =列表(),cluster_mixedpop1 =cluster_mixedpop2,cluster_mixedpop2 =cluster_mixedpop2)c_selectID < -4基因=德根＄id (1：200］LSOLDA_dat4 < -bootstrap_prediction（nboots =2，mixedpop1 =mixedpop2,mixedpop2 =mixedpop2,基因=基因,c_selectID,listData =列表(),cluster_mixedpop1 =cluster_mixedpop2,cluster_mixedpop2 =cluster_mixedpop2)#准备sankey图的表输入LASSO_C1S2 < -reformat_LASSO（c_selectID =1，mp_selectID =2，LSOLDA_dat =LSOLDA_dat1,nPredSubpop =长度（独特的（colData(mixedpop2)(,1))),Nodes_group =“# 7570 b3”）LASSO_C2S2 < -reformat_LASSO（c_selectID =2，mp_selectID =2，LSOLDA_dat =LSOLDA_dat2,nPredSubpop =长度（独特的（colData(mixedpop2)(,1))),Nodes_group =“# 1 b9e77”）LASSO_C3S2 < -reformat_LASSO（c_selectID =3.，mp_selectID =2，LSOLDA_dat =LSOLDA_dat3,nPredSubpop =长度（独特的（colData(mixedpop2)(,1))),Nodes_group =“# e7298a”）LASSO_C4S2 < -reformat_LASSO（c_selectID =4，mp_selectID =2，LSOLDA_dat =LSOLDA_dat4,nPredSubpop =长度（独特的（colData(mixedpop2)(,1))),Nodes_group =“# 00飞行符”）结合< -rbind(lasso_c1s2, lasso_c2s2, lasso_c3s2, lasso_c4s2)结合< -结合(is.na(结合＄值)! =真正的，]nboots =2#links:源，目标，值#来源:节点，节点组combined_D3obj < -列表（节点=[(nboots相结合+3.）：(nboots+4)),链接=(相结合,c((nboots+2）：(nboots+1)，ncol(联合))))图书馆(networkD3)Node_source < -as.vector（排序（独特的(combined_D3obj＄链接＄源)))Node_target < -as.vector（排序（独特的(combined_D3obj＄链接＄目标)))Node_all < -独特的（c(Node_source Node_target))#为Source分配id(从0开始)< -combined_D3obj来源＄链接＄源目标< -combined_D3obj＄链接＄目标为(我在1：长度(Node_all)) {来源(来源= =Node_all[我]]< -我－1目标(目标= =Node_all[我]]< -我－1｝＃combined_D3obj＄链接＄源< -as.numeric(源)combined_D3obj＄链接＄目标< -as.numeric(目标)combined_D3obj＄链接＄LinkColor < -结合＄节点组#准备节点信息node_df < -data.frame（节点=Node_all)node_df＄id < -as.numeric（c（0，1：（长度(Node_all)-1）））suppressMessages（图书馆(dplyr))颜色< -结合% > %数(节点,颜色=节点组)% > %选择（2）node_df＄颜色< -颜色＄颜色suppressMessages（图书馆(networkD3))p1 < -sankeyNetwork（链接=combined_D3obj＄链接,节点=node_df,值=“价值”，节点组=“颜色”，LinkGroup =“LinkColor”，NodeID =“节点”，源=“源”，目标=“目标”，字形大小=22）p1

#运行预测3个集群cluster_mixedpop1 < -colData(mixedpop1),1］cluster_mixedpop2 < -colData(mixedpop2),1］row_cluster < -长度（独特的（colData(mixedpop2),1)))c_selectID < -1#前200个基因标记区分簇1基因=德根＄id (1：200］LSOLDA_dat1 < -bootstrap_prediction（nboots =2，mixedpop1 =mixedpop1,mixedpop2 =mixedpop2,基因=基因,c_selectID,listData =列表(),cluster_mixedpop1 =cluster_mixedpop1,cluster_mixedpop2 =cluster_mixedpop2)c_selectID < -2基因=德根＄id (1：200］LSOLDA_dat2 < -bootstrap_prediction（nboots =2，mixedpop1 =mixedpop1,mixedpop2 =mixedpop2,基因=基因,c_selectID,listData =列表(),cluster_mixedpop1 =cluster_mixedpop1,cluster_mixedpop2 =cluster_mixedpop2)c_selectID < -3.基因=德根＄id (1：200］LSOLDA_dat3 < -bootstrap_prediction（nboots =2，mixedpop1 =mixedpop1,mixedpop2 =mixedpop2,基因=基因,c_selectID,listData =列表(),cluster_mixedpop1 =cluster_mixedpop1,cluster_mixedpop2 =cluster_mixedpop2)#准备sankey图的表输入LASSO_C1S1 < -reformat_LASSO（c_selectID =1，mp_selectID =1，LSOLDA_dat =LSOLDA_dat1,nPredSubpop =row_cluster,Nodes_group =“# 7570 b3”）LASSO_C2S1 < -reformat_LASSO（c_selectID =2，mp_selectID =1，LSOLDA_dat =LSOLDA_dat2,nPredSubpop =row_cluster,Nodes_group =“# 1 b9e77”）LASSO_C3S1 < -reformat_LASSO（c_selectID =3.，mp_selectID =1，LSOLDA_dat =LSOLDA_dat3,nPredSubpop =row_cluster,Nodes_group =“# e7298a”）结合< -rbind(LASSO_C2S1 LASSO_C1S1 LASSO_C3S1)nboots =2#links:源，目标，值#来源:节点，节点组combined_D3obj < -列表（节点=[(nboots相结合+3.）：(nboots+4)),链接=(相结合,c((nboots+2）：(nboots+1)，ncol(联合))))结合< -结合(is.na(结合＄值)! =真正的，]图书馆(networkD3)Node_source < -as.vector（排序（独特的(combined_D3obj＄链接＄源)))Node_target < -as.vector（排序（独特的(combined_D3obj＄链接＄目标)))Node_all < -独特的（c(Node_source Node_target))#为Source分配id(从0开始)< -combined_D3obj来源＄链接＄源目标< -combined_D3obj＄链接＄目标为(我在1：长度(Node_all)) {来源(来源= =Node_all[我]]< -我－1目标(目标= =Node_all[我]]< -我－1｝combined_D3obj＄链接＄源< -as.numeric(源)combined_D3obj＄链接＄目标< -as.numeric(目标)combined_D3obj＄链接＄LinkColor < -结合＄节点组#准备节点信息node_df < -data.frame（节点=Node_all)node_df＄id < -as.numeric（c（0，1：（长度(Node_all)-1）））suppressMessages（图书馆(dplyr))n < -长度（独特的(node_df＄节点)getPalette =colorRampPalette(RColorBrewer：：brewer.pal（9，“set2”中的）)颜色=getPalette(n)node_df＄颜色< -颜色suppressMessages（图书馆(networkD3))p1 < -sankeyNetwork（链接=combined_D3obj＄链接,节点=node_df,值=“价值”，节点组=“颜色”，LinkGroup =“LinkColor”，NodeID =“节点”，源=“源”，目标=“目标”，字形大小=22）p1

devtools：：session_info（）# >会话信息────────────────────────────────────────────────────────────────#>设置值#>版本R正在开发中(不稳定)(2022-10-25 r83175)#> os Ubuntu 22.04.1 LTS#>系统x86_64, linux-gnu#> ui X11#>语言(EN)C .选项C#> ctype en_US。utf - 8# >tz美国/纽约#>日期2022-11-012.9.2.1 @ /usr/bin/(通过rmarkdown)# >包# >────────────────────────────────────────────────────────────────────#>包的版本日期(UTC[2]生物导体> Bioconductor Bioconductor>猿5.6-2 2022-03-02 [2]CRAN (R 4.3.0)> aplot 0.1.8 2022-10-09 [2] CRAN (R 4.3.0)[2] CRAN (R 4.3.0)[2] Bioconductor Bioconductor#> BiocGenerics * 0.45.0 2022-11-01 [2] Bioconductor[2]生物导体[2]生物导体[2] CRAN (R 4.3.0)> bit64 4.0.5 2020-08-30 [2] CRAN (R 4.3.0)[2] CRAN (R 4.3.0)[2] CRAN (R 4.3.0)#> bslib 0.4.0 2022-07-16 [2] CRAN (R 4.3.0)>缓存1.0.6 2021-08-19 [2]CRAN (R 4.3.0)> callr 3.7.2 2022-08-22 [2] CRAN (R 4.3.0)#>插入* 6 -93 2022-08-09 [2]CRAN (R 4.3.0)[2] CRAN (R 4.3.0)> cli 3.4.1 2022-09-23 [2] CRAN (R 4.3.0)#> clusterProfiler * 4.7.0 2022-11-01 [2] Bioconductor[2] CRAN (R 4.3.0)[2] CRAN (R 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