## ----load_data from package, echo=TRUE, results='hide', message=FALSE--------- library(RITANdata) library(RITAN) require(knitr) ## ----citation, echo=TRUE------------------------------------------------------ kable( attr(network_list, 'network_data_sources') ) ## ----example1, echo=TRUE------------------------------------------------------ my_genes <- geneset_list$MSigDB_C7[['GSE6269_HEALTHY_VS_FLU_INF_PBMC_DN']] net <- network_overlap( my_genes, resources = 'CCSB' ) ## ----example1.1, echo=TRUE---------------------------------------------------- head(unique(net)) ## ----example2, echo=TRUE, eval=FALSE------------------------------------------ # net2 <- network_overlap( my_genes, resources = c('CCSB','STRING'), minStringScore = 700 ) # str(net2) ## ----check_input1, echo = TRUE------------------------------------------------ my_genes <- geneset_list$MSigDB_C2[['VERNOCHET_ADIPOGENESIS']] i <- check_any_net_input( my_genes ) table(i) ## ----check_input2, echo = TRUE------------------------------------------------ i <- check_net_input( my_genes, network_list[['dPPI']] ) table(i) names(i)[i == 'no'] ## ----example3_1, echo=TRUE---------------------------------------------------- my_genes <- geneset_list$MSigDB_C7[['GOLDRATH_NAIVE_VS_MEMORY_CD8_TCELL_UP']] net3.1 <- network_overlap( my_genes, resources = 'PID', include_neighbors = FALSE, dedup = TRUE ) nets2use <- c('PID','dPPI','TFe','HumanNet','CCSB') net3.2 <- network_overlap( my_genes, resources = nets2use, include_neighbors = FALSE, dedup = TRUE ) net3.3 <- network_overlap( my_genes, resources = 'PID', include_neighbors = TRUE, dedup = TRUE ) ## ----example4, fig.height = 5, fig.width = 5, fig.align = 'center'------------ require(igraph) net4 <- network_overlap( my_genes, resources = c('PID','dPPI','TFe'), include_neighbors = FALSE, dedup = TRUE ) edges <- as.matrix( net4[, c(1,3)] ) G <- igraph::make_undirected_graph( c(t(edges)) ) par(mar=rep(0,4)) plot(G, vertex.size = 20, vertex.frame.color = 'white' ) ## ----example5, fig.height = 5, fig.width = 5, fig.align = 'center'------------ require(igraph) G <- as.graph( network_list$PID ) all( c("EP300", "SFN", "TP53", "CCNB1") %in% names(V(G)) ) get.shortest.paths(G, "TP53" , to="CCNB1", mode = "all" )$vpath get.shortest.paths(G, "EP300", to="SFN" , mode = "all" )$vpath G <- as.graph( network_list$HumanNet ) all( c("EP300", "SFN", "TP53", "CCNB1") %in% names(V(G)) ) get.shortest.paths(G, "TP53" , to="CCNB1", mode = "all" )$vpath get.shortest.paths(G, "EP300", to="SFN" , mode = "all" )$vpath ## ----example6, echo=TRUE, eval=FALSE------------------------------------------ # my_genes <- geneset_list$MSigDB_C2[['VERNOCHET_ADIPOGENESIS']] # net5 <- network_overlap( my_genes ) # g <- unique(c( net5$p1, net5$p2 )) # # tab <- data.frame( gene = c('FABP4', 'CEBPA','PPARG','ADRB3','RETN','AGT','HP', # 'RARRES2','PANK3','FFAR2','LUM', 'MC2R','ADCYAP1R1'), # TrogRatio = c( 1.8, 1.7, 0.6, 0.3 , 0.3 , 0.4 ,0.2, # 0.3, 0.1, 0.5, 0.3 , 0.5 , 0.1), # WAT_BAT = c( 0.8, 1.0, 0.6, 10.0, 21.6, 215.4,2.4, # 9.5, 3.9, 4.6, 4.0 , 7.3 , 2.6), # initial = g %in% my_genes # ) # # write_simple_table(net3.1, 'net_example.sif') # write_simple_table(tab, 'net_example.tab') ## ----example7, echo=TRUE, eval=FALSE------------------------------------------ # # ## Add a new resource to "network_list" # ### For brevity, we # network_list[['BioGRID_Mouse']] <- readSIF( 'BIOGRID-ORGANISM-Mus_musculus-3.4.136.symbols.sif.gz', header = TRUE ) # # > str(network_list[['BioGRID_Mouse']]) # # 'data.frame': 38322 obs. of 3 variables: # # $ p1 : chr "SMAD2" "SMAD2" "SMAD2" "SMAD2" ... # # $ edge_type: chr "physical" "physical" "physical" "physical" ... # # $ p2 : chr "Rasd2" "Rab34" "Rhebl1" "Rab38" ... # # ## Short example from Tang's 2010 Nature paper # my_mouse <- c('Sost','Fxyd4','Tmprss6','Crtap','Thpo','Kcnn4','Osm','Slc29a3','ALB') # # ## First, check if these genes appear in the BioGRID network. # check_net_input( my_mouse, network_list[['BioGRID_Mouse']] ) # # Sost Fxyd4 Tmprss6 Crtap Thpo Kcnn4 Osm Slc29a3 ALB # # "yes" "no" "no" "no" "no" "no" "no" "no" "no" # # ## After correcting a few gene names, get the induced subnetwork from mouse data. # my_mouse <- c('Sost','Fxyd4','Tmprss6','CRTAP','Thpo','KCNN4','Osm','Slc29a3','ALB') # net.m <- network_overlap( my_mouse, include_neighbors = TRUE, resources = c('BioGRID_Mouse') ) # str(net.m) # # Generating undirected subnetwork... # # Total induced subnetwork from 9 genes has 17 nodes and 17 edges (17 unique). # # 'data.frame': 17 obs. of 3 variables: # # $ p1 : chr "Sf3a1" "Nphp1" "Iqcb1" "Invs" ... # # $ edge_type: chr "physical" "physical" "physical" "physical" ... # # $ p2 : chr "CRTAP" "Invs" "Nphp1" "ALB" ... # # ## Also, check within BioGRD's human network # check_net_input( my_mouse, network_list[['BioGRID_Human']] ) # # Sost Fxyd4 Tmprss6 CRTAP Thpo KCNN4 Osm Slc29a3 ALB # # "no" "no" "no" "yes" "no" "yes" "no" "no" "yes" # # ## Note that gene symbols are case sensitive # my_mouse <- c('SOST','Fxyd4','Tmprss6','CRTAP','THPO','KCNN4','OSM','Slc29a3','ALB') # check_net_input( my_mouse, network_list[['BioGRID_Human']] ) # # SOST Fxyd4 Tmprss6 CRTAP THPO KCNN4 OSM Slc29a3 ALB # # "yes" "no" "no" "yes" "no" "yes" "yes" "no" "yes" # # ## Get the induced subnetowrk from human data # net.h <- network_overlap( my_mouse, include_neighbors = TRUE, resources = c('BioGRID_Human') ) # str(net.h) # # Generating undirected subnetwork... # # Total induced subnetwork from 9 genes has 224 nodes and 755 edges (634 unique). # # 'data.frame': 755 obs. of 3 variables: # # $ p1 : chr "MBIP" "SH3GL1" "TNNT1" "GFAP" ... # # $ edge_type: chr "physical" "physical" "physical" "physical" ... # # $ p2 : chr "MBIP" "SH3GL1" "TNNT1" "GRAP2" ... # ## ----example8, echo=TRUE, eval=TRUE------------------------------------------- net <- network_overlap( 'FOXP3', include_neighbors = TRUE, resources = c("PID","dPPI","CCSB" ) ) genes <- unique(c( net$p1, net$p2 )) e1 <- term_enrichment( genes, "Blood_Translaiton_Modules", verbose=FALSE, all_symbols = cached_coding_genes ) summary(e1) e2 <- term_enrichment( genes, "ReactomePathways", verbose=FALSE, all_symbols = cached_coding_genes ) summary(e2)