## ----suppress-progressbars, echo=FALSE---------------------------------------- options(progress_enabled = FALSE, width = 80) ## ----workflow, results='hide'------------------------------------------------- library(wppi) # example gene set genes_interest <- c( 'ERCC8', 'AKT3', 'NOL3', 'TTK', 'GFI1B', 'CDC25A', 'TPX2', 'SHE' ) scores <- score_candidate_genes_from_PPI(genes_interest) scores # # A tibble: 295 x 3 # score gene_symbol uniprot # # 1 0.247 KNL1 Q8NG31 # 2 0.247 HTRA2 O43464 # 3 0.247 KAT6A Q92794 # 4 0.247 BABAM1 Q9NWV8 # 5 0.247 SKI P12755 # 6 0.247 FOXA2 Q9Y261 # 7 0.247 CLK2 P49760 # 8 0.247 HNRNPA1 P09651 # 9 0.247 HK1 P19367 # 10 0.180 SH3RF1 Q7Z6J0 # # . with 285 more rows ## ----database-knowledge-omnipath---------------------------------------------- omnipath_data <- wppi_omnipath_data(datasets = 'omnipath') ## ----database-knowledge, results='hide'--------------------------------------- db <- wppi_data(datasets = c('omnipath', 'kinaseextra')) names(db) # [1] "hpo" "go" "omnipath" "uniprot" ## ----database-knowledge-hpo--------------------------------------------------- # example HPO annotations set HPO_data <- wppi_hpo_data() HPO_interest <- unique(dplyr::filter(HPO_data, grepl('Diabetes', Name))$Name) ## ----omnipath-graph----------------------------------------------------------- graph_op <- graph_from_op(db$omnipath) ## ----neighborhood-subnetwork, results='hide'---------------------------------- graph_op_1 <- subgraph_op(graph_op, genes_interest) igraph::vcount(graph_op_1) # [1] 256 ## ----weighted-adjacency-matrix------------------------------------------------ w_adj <- weighted_adj(graph_op_1, db$go, db$hpo) ## ----random-walk-------------------------------------------------------------- w_rw <- random_walk(w_adj) ## ----scoring-proteins--------------------------------------------------------- scores <- prioritization_genes(graph_op_1, w_rw, genes_interest) scores # # A tibble: 249 x 3 # score gene_symbol uniprot # # 1 0.251 HTRA2 O43464 # 2 0.251 KAT6A Q92794 # 3 0.251 BABAM1 Q9NWV8 # 4 0.251 SKI P12755 # 5 0.251 CLK2 P49760 # 6 0.248 TUBB P07437 # 7 0.248 KNL1 Q8NG31 # 8 0.189 SH3RF1 Q7Z6J0 # 9 0.189 SRPK2 P78362 # 10 0.150 CSNK1D P48730 # # . with 239 more rows ## ----fig_knitr,echo=TRUE,eval = FALSE----------------------------------------- # library(knitr) # knitr::include_graphics("../figures/fig1.png") ## ----sessionInfo, echo=FALSE-------------------------------------------------- sessionInfo()