r-dag-notebook-pack_deal.ipynb

{"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"4.4.0"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30749,"isInternetEnabled":true,"language":"r","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# This notebook to apply some causal inference methods data of Direct acyclic graph (DAG)","metadata":{}},{"cell_type":"code","source":"##install necessary packages\ninstall.packages(\"sem\")\ninstall.packages(\"meta\")\ninstall.packages(\"igraph\")\ninstall.packages(\"deal\")","metadata":{"execution":{"iopub.status.busy":"2024-07-29T18:40:18.924874Z","iopub.execute_input":"2024-07-29T18:40:18.926732Z","iopub.status.idle":"2024-07-29T18:40:28.982057Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stderr","text":"Installing package into ‘/usr/local/lib/R/site-library’\n(as ‘lib’ is unspecified)\n\n","output_type":"stream"}]},{"cell_type":"code","source":"library(sem)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-07-29T18:33:46.066057Z","iopub.execute_input":"2024-07-29T18:33:46.068172Z","iopub.status.idle":"2024-07-29T18:33:46.099890Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"library(meta)","metadata":{"execution":{"iopub.status.busy":"2024-07-29T18:33:47.843332Z","iopub.execute_input":"2024-07-29T18:33:47.845103Z","iopub.status.idle":"2024-07-29T18:33:47.860977Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"library(igraph)","metadata":{"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"library(deal)","metadata":{"execution":{"iopub.status.busy":"2024-07-29T18:34:30.909143Z","iopub.execute_input":"2024-07-29T18:34:30.910851Z","iopub.status.idle":"2024-07-29T18:34:30.939219Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"## Five different models of DAG are applied on simulated data representing OMICS data. S represents the SNP genotype data which can represent 0,1,2. G represents the quantitative trait such as gene expression or any other traits that related with SNP. P is the phenotype. The below graphs are the cases we considered in the applications.","metadata":{}},{"cell_type":"code","source":"# DAG type a\ng <- make_empty_graph() + vertices(c(\"S\",\"P\",\"G\"))\ng <- g + path(c(\"S\",\"P\"), color = \"red\")\ng <- g + path(c(\"S\",\"G\"), color = \"red\")\nplot(g, layout=layout.circle, main = \"(a)\")\n# DAG type b\ng <- make_empty_graph() + vertices(c(\"S\",\"P\",\"G\"))\ng <- g + path(c(\"S\",\"G\"), color = \"red\")\ng <- g + path(c(\"G\",\"P\"), color = \"red\")\nplot(g, layout=layout.circle, main = \"(b)\")\n# DAG type c\ng <- make_empty_graph() + vertices(c(\"S\",\"P\",\"G\"))\ng <- g + path(c(\"S\",\"P\"), color = \"red\")\ng <- g + path(c(\"P\",\"G\"), color = \"red\")\nplot(g, layout=layout.circle, main = \"(c)\")\n# DAG type d\ng <- make_empty_graph() + vertices(c(\"S\",\"P\",\"G\"))\ng <- g + path(c(\"S\",\"P\"), color = \"red\")\ng <- g + path(c(\"S\",\"G\"), color = \"red\")\ng <- g + path(c(\"G\",\"P\"), color = \"red\")\nplot(g, layout=layout.circle, main = \"(d)\")\n# DAG type f\ng <- make_empty_graph() + vertices(c(\"S\",\"P\",\"G\"))\ng <- g + path(c(\"S\",\"P\"), color = \"red\")\ng <- g + path(c(\"S\",\"G\"), color = \"red\")\ng <- g + path(c(\"P\",\"G\"), color = \"red\")\nplot(g, layout=layout.circle, main = \"(f)\")\n","metadata":{"execution":{"iopub.status.busy":"2024-07-29T18:34:44.483980Z","iopub.execute_input":"2024-07-29T18:34:44.485670Z","iopub.status.idle":"2024-07-29T18:34:45.130819Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\n+Wa//Wd2ceM/vaQBRo2Nl96Mry5Z2rVPnxg+PMaNa45VAYDVC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AwBUm7Djc0888eVxPh9jxix3ZRYAaPSEHZ974onIZJb75Kmn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EOR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\nAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMAS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jo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\nTIQdUDySDqBZCTugGCQdQBkQdkDjSDqAsiHsgI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/bYeO3feO21YfTdPHP3n/70q1/9qq5ujS8powDCDgCIiMjl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"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":"## We use deal packages to claculate network score\n\n### References\n### Bottcher, S.G. (2001). Learning Bayesian Networks with Mixed Variables, Artificial Intelligence and Statistics 2001, Morgan Kaufmann, San Francisco, CA, USA, 149-156. Further information about deal can be found at:\n### http://www.math.aau.dk/~dethlef/novo/deal.\n","metadata":{}},{"cell_type":"code","source":"\nalph<-1;bett<-1;delt<-1\nmug<-10;mup<-10;gam<-1;zet<-1\nsigg<-0.3;sigp<-0.3;sige<-0.3;bet<-1\n\npar<-function(y){\nif (y==\"a\"){u1<-c(1,1,0,0);u2<-c(1,1,0,0)}\nelse if (y==\"b\"){u1<-c(1,1,0,0);u2<-c(1,0,1,0)}\nelse if (y==\"c\"){u1<-c(1,0,1,0);u2<-c(1,1,0,0)}\nelse if (y==\"d\"){u1<-c(1,1,0,0);u2<-c(1,1,1,0)}\nelse if (y==\"f\"){u1<-c(1,1,1,0);u2<-c(1,0,0,0)}\nuu<-rbind(u1,u2)\nreturn (uu)\n}\n\n\n\n\nmer<-function(v1,v2) paste(c(as.character(v1),as.character(v2)),collapse=\"\")\ngenr<-function(count,maf){\na<-rbinom(count,1,1-maf)\nb<-rbinom(count,1,1-maf)\nd<-c()\nfor (i in 1:count){d<-cbind(d,mer(a[i],b[i]))}\ns<-d\nfor (i in 1:count){\n if (s[i]==\"00\"){s[i]<-\"2\"}\nelse if (s[i]==\"11\"){s[i]<-\"0\"}\nelse {s[i]<-\"1\"}}\nreturn(s)\n}\n\n\n\n\ndatset<-function(z,u){\n  A<-par(u)\n  g<-c();p<-c();ee<-c();SS<-c()\n  for (i in 1:length(z)){\n if (A[1,4]==0){E<-0}\n  else {E<-rnorm(1,0,0.09)}\n  sv<-as.numeric(z[i])\n  G<-0;P<-0\n  if (A[2,3]==1){\n  G<-rnorm(1,A[1,1]*mug+alph*A[1,2]*sv+delt*A[1,3]*P+zet*A[1,4]*E,0.09)\n  P<-rnorm(1,A[2,1]*mup+bet*A[2,2]*sv+gam*A[2,3]*G+zet*A[2,4]*E,0.09)\n  }\n  else if (A[1,3]==1){\n  P<-rnorm(1,A[2,1]*mup+bet*A[2,2]*sv+gam*A[2,3]*G+zet*A[2,4]*E,0.09)\n  G<-rnorm(1,A[1,1]*mug+alph*A[1,2]*sv+delt*A[1,3]*P+zet*A[1,4]*E,0.09)\n  }\n  else {\n  P<-rnorm(1,A[2,1]*mup+bet*A[2,2]*sv+gam*A[2,3]*G+zet*A[2,4]*E,0.09)\n  G<-rnorm(1,A[1,1]*mug+alph*A[1,2]*sv+delt*A[1,3]*P+zet*A[1,4]*E,0.09)\n  }\n  SS<-cbind(SS,round(sv))\n  g<-cbind(g,round(G,digits = 4))\n  p<-cbind(p,round(P,digits = 4))\n  ee<-cbind(ee,round(E,digits = 4))\n  }\n  SET<-as.array(SS,dim=c(1,length(SS)))\n  SET<-rbind(SET,g)\n  SET<-rbind(SET,p)\n  SET<-rbind(SET,ee)\n  #matrix(c(SS,g,p,ee),nrow=4,ncol=length(SS),byrow=T)\n  rownames(SET)<-c(\"S\",\"G\",\"P\",\"E\")\n SET<-t(SET)\n  return (SET)\n  }\n\n\n\n\n\nreadcov<- function(xx){\nte<-c(\"\")\ncols<-colnames(xx)\nfor (i in 1:length(xx[1,])){\nfor (j in 1:length(xx[,1])){\nif (j<=i){te<-paste(te,as.character(xx[i,j]),collapse=\"\")}\nif (j==i && j!=length(xx[1,])){te<-paste(te,\"\\n\",collapse=\"\")}\n}}\nreturn(te)\n}\n\n\nspeciEq<-function(ZZ){\neqtext<-c(\"\")\nif (ZZ[2,3]==0 && ZZ[1,3]==1){\nif (ZZ[2,2]==1){eqtext<-paste(eqtext,\"P=bett*S\\n\")}\nif (ZZ[2,4]==1){eqtext<-paste(eqtext,\"P=zett*E\\n\")}\nif (ZZ[1,2]==1){eqtext<-paste(eqtext,\"G=alphh*S\\n\")}\neqtext<-paste(eqtext,\"G=deltt*P\\n\")\nif (ZZ[1,4]==1){eqtext<-paste(eqtext,\"G=zett*E\\n\")}\n}\nif (ZZ[1,3]==0 && ZZ[2,3]==1){\nif (ZZ[1,2]==1){eqtext<-paste(eqtext,\"G=alphh*S\\n\")}\nif (ZZ[1,4]==1){eqtext<-paste(eqtext,\"G=zett*E\\n\")}\nif (ZZ[2,2]==1){eqtext<-paste(eqtext,\"P=bett*S\\n\")}\neqtext<-paste(eqtext,\"P=gamm*G\\n\")\nif (ZZ[2,4]==1){eqtext<-paste(eqtext,\"P=zett*E\\n\")}\n}\nif (ZZ[1,3]==0 && ZZ[2,3]==0 && ZZ[1,1]!=0 && ZZ[2,1]!=0){\nif (ZZ[1,2]==1){eqtext<-paste(eqtext,\"G=alphh*S\\n\")}\nif (ZZ[1,4]==1){eqtext<-paste(eqtext,\"G=zett*E\\n\")}\nif (ZZ[2,2]==1){eqtext<-paste(eqtext,\"P=bett*S\\n\")}\nif (ZZ[2,4]==1){eqtext<-paste(eqtext,\"P=zett*E\\n\")}\n}\nif (ZZ[1,1]==0 && ZZ[2,2]==1){eqtext<-paste(eqtext,\"P=bett*S\\n\")}\nif (ZZ[2,1]==0 && ZZ[1,2]==1){eqtext<-paste(eqtext,\"G=alphh*S\\n\")}\nreturn(eqtext)\n}\n\n\n\nallscores<-function(N1,N2,model_used,model_sim,mf){\nalph<-1;bet<-1;delt<-1\nmug<-10;mup<-10;gam<-1;zet<-1\nmer<-function(v1,v2) paste(c(as.character(v1),as.character(v2)),collapse=\"\")\ngenr<-function(count,maf){\na<-rbinom(count,1,1-maf)\nb<-rbinom(count,1,1-maf)\nd<-c()\nfor (i in 1:count){d<-cbind(d,mer(a[i],b[i]))}\ns<-d\nfor (i in 1:count){\n if (s[i]==\"00\"){s[i]<-\"2\"}\nelse if (s[i]==\"11\"){s[i]<-\"0\"}\nelse {s[i]<-\"1\"}}\nreturn(s)\n}\ndatset<-function(z,u){\n  A<-par(u)\n  g<-c();p<-c();ee<-c();SS<-c()\n  for (i in 1:length(z)){\n if (A[1,4]==0){E<-0}\n  else {E<-rnorm(1,0,0.09)}\n  sv<-as.numeric(z[i])\n  G<-0;P<-0\n  if (A[2,3]==1){\n  G<-rnorm(1,A[1,1]*mug+alph*A[1,2]*sv+delt*A[1,3]*P+zet*A[1,4]*E,0.09)\n  P<-rnorm(1,A[2,1]*mup+bet*A[2,2]*sv+gam*A[2,3]*G+zet*A[2,4]*E,0.09)\n  }\n  else if (A[1,3]==1){\n  P<-rnorm(1,A[2,1]*mup+bet*A[2,2]*sv+gam*A[2,3]*G+zet*A[2,4]*E,0.09)\n  G<-rnorm(1,A[1,1]*mug+alph*A[1,2]*sv+delt*A[1,3]*P+zet*A[1,4]*E,0.09)\n  }\n  else {\n  P<-rnorm(1,A[2,1]*mup+bet*A[2,2]*sv+gam*A[2,3]*G+zet*A[2,4]*E,0.09)\n  G<-rnorm(1,A[1,1]*mug+alph*A[1,2]*sv+delt*A[1,3]*P+zet*A[1,4]*E,0.09)\n  }\n  SS<-cbind(SS,round(sv))\n  g<-cbind(g,round(G,digits = 4))\n  p<-cbind(p,round(P,digits = 4))\n  ee<-cbind(ee,round(E,digits = 4))\n  }\n  SET<-as.array(SS,dim=c(1,length(SS)))\n  SET<-rbind(SET,g)\n  SET<-rbind(SET,p)\n  SET<-rbind(SET,ee)\n  #matrix(c(SS,g,p,ee),nrow=4,ncol=length(SS),byrow=T)\n  rownames(SET)<-c(\"S\",\"G\",\"P\",\"E\")\n SET<-t(SET)\n  return (SET)\n  }\nall_sco<-c()\nfor (k in 1:length(model_sim)){\nset_sco<-c()\nfor (j in 1:N1){\nsnp<-genr(N2,mf)\ndataq<-datset(snp,model_sim[k])\nif (model_sim[k]%in%c(\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\")){dataq<-dataq[,1:3]}\nelse if  (model_sim[k]==\"m\"){dataq<-dataq[,1:2]}\nelse if  (model_sim[k]==\"n\"){dataq<-dataq[,c(1,3)]}\nq<-as.data.frame(dataq)\nq[,1]<-as.factor(q[,1])\nnet1<-network(q)\nnet1.prior<-jointprior(net1)\ncol_sco<-c()\nfor (v in 1:length(model_used)){\nnet2<-network(q)\nmodel_par<-par(model_used[v])\nif (model_par[2,3]==1){\nnet2<-getnetwork(insert(net2,2,3,q,net1.prior))\nif (model_par[2,2]==1){net2<-getnetwork(insert(net2,1,3,q,net1.prior))}\nif (model_par[2,4]==1){net2<-getnetwork(insert(net2,4,3,q,net1.prior))}\nif (model_par[1,2]==1){net2<-getnetwork(insert(net2,1,2,q,net1.prior))}\nif (model_par[1,4]==1){net2<-getnetwork(insert(net2,4,2,q,net1.prior))}\n}\nelse if (model_par[1,3]==1){\nnet2<-getnetwork(insert(net2,3,2,q,net1.prior))\nif (model_par[1,2]==1){net2<-getnetwork(insert(net2,1,2,q,net1.prior))}\nif (model_par[1,4]==1){net2<-getnetwork(insert(net2,4,2,q,net1.prior))}\nif (model_par[2,2]==1){net2<-getnetwork(insert(net2,1,3,q,net1.prior))}\nif (model_par[1,4]==1){net2<-getnetwork(insert(net2,4,3,q,net1.prior))}\n}\nelse {\nif (model_par[1,2]==1){net2<-getnetwork(insert(net2,1,2,q,net1.prior))}\nif (model_par[1,4]==1){net2<-getnetwork(insert(net2,4,2,q,net1.prior))}\nif (model_par[2,2]==1){net2<-getnetwork(insert(net2,1,3,q,net1.prior))}\nif (model_par[2,4]==1){net2<-getnetwork(insert(net2,4,3,q,net1.prior))}\n}\nnet2.nw<-getnetwork(learn(net2,q,net1.prior))\ncol_sco<-cbind(col_sco,net2.nw$score)\n }\nset_sco<-rbind(set_sco,col_sco)\n }\ncol_av<-apply(set_sco,2,mean)\nall_sco<-rbind(all_sco,col_av)\n}\nall_sco<-t(all_sco)\nfor (i in 1:length(model_used)){\nfor (j in 1:length(model_sim)){all_sco[i,j]<-all_sco[i,j]}}\ncolnames(all_sco)<-model_sim\nrownames(all_sco)<-model_used\nreturn(all_sco)\n}\n\n\n\n\n\n\n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-07-29T18:44:28.438235Z","iopub.execute_input":"2024-07-29T18:44:28.439947Z","iopub.status.idle":"2024-07-29T18:44:28.480061Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":"### the code above is to calculate the Network score by the use of \"deal\" packages of each model (a,b,c,d,f) that have been used in simulated data and compared to each others\n","metadata":{}},{"cell_type":"code","source":"Netscores<-allscores(100,100,c(\"a\",\"b\",\"c\",\"d\",\"f\"),c(\"a\",\"b\",\"c\",\"d\",\"f\"),0.05)","metadata":{"execution":{"iopub.status.busy":"2024-07-29T18:45:11.010984Z","iopub.execute_input":"2024-07-29T18:45:11.012623Z","iopub.status.idle":"2024-07-29T18:45:36.215595Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"Imaginary sample size: 6 \nImaginary sample size: 6 \nImaginary sample size: 4 \nImaginary sample size: 6 \nImaginary sample size: 4 \nImaginary sample size: 4 \nImaginary sample size: 6 \nImaginary sample size: 4 \nImaginary sample size: 6 \nImaginary sample size: 4 \nImaginary sample size: 6 \nImaginary sample size: 4 \nImaginary sample size: 4 \nImaginary sample size: 4 \nImaginary sample size: 4 \nImaginary sample size: 6 \nImaginary sample size: 4 \nImaginary sample size: 4 \nImaginary sample size: 6 \nImaginary sample size: 4 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\n","output_type":"stream"}]},{"cell_type":"code","source":"Netscores","metadata":{"execution":{"iopub.status.busy":"2024-07-29T18:45:50.410634Z","iopub.execute_input":"2024-07-29T18:45:50.412267Z","iopub.status.idle":"2024-07-29T18:45:50.432603Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A matrix: 5 × 5 of type dbl</caption>\n<thead>\n\t<tr><th></th><th scope=col>a</th><th scope=col>b</th><th scope=col>c</th><th scope=col>d</th><th scope=col>f</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>a</th><td>112.85512</td><td> 82.30791</td><td> 84.04449</td><td> 69.18546</td><td>101.46367</td></tr>\n\t<tr><th scope=row>b</th><td> 93.25372</td><td>114.94358</td><td> 91.03529</td><td> 70.35397</td><td>105.52345</td></tr>\n\t<tr><th scope=row>c</th><td> 93.23780</td><td> 90.26642</td><td>116.95154</td><td> 94.67435</td><td> 24.24188</td></tr>\n\t<tr><th scope=row>d</th><td>121.20811</td><td>116.89917</td><td>118.92376</td><td>100.99182</td><td>128.24787</td></tr>\n\t<tr><th scope=row>f</th><td> 17.68976</td><td> 38.18385</td><td> 14.07935</td><td>-25.68264</td><td>129.73568</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA matrix: 5 × 5 of type dbl\n\n| <!--/--> | a | b | c | d | f |\n|---|---|---|---|---|---|\n| a | 112.85512 |  82.30791 |  84.04449 |  69.18546 | 101.46367 |\n| b |  93.25372 | 114.94358 |  91.03529 |  70.35397 | 105.52345 |\n| c |  93.23780 |  90.26642 | 116.95154 |  94.67435 |  24.24188 |\n| d | 121.20811 | 116.89917 | 118.92376 | 100.99182 | 128.24787 |\n| f |  17.68976 |  38.18385 |  14.07935 | -25.68264 | 129.73568 |\n\n","text/latex":"A matrix: 5 × 5 of type dbl\n\\begin{tabular}{r|lllll}\n  & a & b & c & d & f\\\\\n\\hline\n\ta & 112.85512 &  82.30791 &  84.04449 &  69.18546 & 101.46367\\\\\n\tb &  93.25372 & 114.94358 &  91.03529 &  70.35397 & 105.52345\\\\\n\tc &  93.23780 &  90.26642 & 116.95154 &  94.67435 &  24.24188\\\\\n\td & 121.20811 & 116.89917 & 118.92376 & 100.99182 & 128.24787\\\\\n\tf &  17.68976 &  38.18385 &  14.07935 & -25.68264 & 129.73568\\\\\n\\end{tabular}\n","text/plain":"  a         b         c         d         f        \na 112.85512  82.30791  84.04449  69.18546 101.46367\nb  93.25372 114.94358  91.03529  70.35397 105.52345\nc  93.23780  90.26642 116.95154  94.67435  24.24188\nd 121.20811 116.89917 118.92376 100.99182 128.24787\nf  17.68976  38.18385  14.07935 -25.68264 129.73568"},"metadata":{}}]}]}