## ----load--------------------------------------------------------------------- library(ReactomeContentService4R) ## ----class-species, warnings=FALSE, rownames.print=FALSE---------------------- # Fetch all Human Pathways pathways <- getSchemaClass(class = "Pathway", species = "human", all = TRUE) head(pathways, 5) # Filter Disease Pathways from results disease.pathways <- pathways[pathways$isInDisease == TRUE, ] head(disease.pathways, 5) ## ----queryID------------------------------------------------------------------ # Fetch the Reactome object with all attributes using a given id hsa.2033519 <- query(id = "R-HSA-2033519") str(hsa.2033519, max.level = 1) ## ----diseaseSlot, rownames.print=FALSE---------------------------------------- hsa.2033519[["disease"]] ## ----searchQuery, rownames.print=FALSE---------------------------------------- # Search for a human disease name bdd.search <- searchQuery(query = "bone development disease", species = "human") bdd.search[["results"]] # the entries dataframe for the first row, with typeName 'Pathway' (bdd.search[["results"]])[[1]][[1]] ## ----rles, rownames.print=FALSE----------------------------------------------- # Get sub-Events of an Event fgfr1.signal.reactions <- getParticipants("R-HSA-5655302", retrieval = "EventsInPathways") head(fgfr1.signal.reactions, 5) ## ----allInstances, rownames.print=FALSE--------------------------------------- # Get all Entities of a ReactionLikeEvent instances.1839098 <- getParticipants("R-HSA-1839098", retrieval = "AllInstances") instances.1839098 ## ----image, eval=FALSE-------------------------------------------------------- # # Visualize above Reaction # exportImage("R-HSA-1839098", output = "reaction", format = "jpg", quality = 10) ## ---- echo=FALSE-------------------------------------------------------------- # to prevent weird warnings in the windows check knitr::include_graphics('img/R-HSA-1839098.jpg') ## ----getPathways, rownames.print=FALSE---------------------------------------- # get lower-level pathways (default) getPathways("R-HSA-176949") # get top-level pathways getPathways("R-HSA-176949", top.level = TRUE) ## ----ref-all------------------------------------------------------------------ # Get the Reactome ReferenceEntity of id "TP53" tp53.re <- map2RefEntities("TP53") str(tp53.re) ## ----ref-pe, rownames.print=FALSE--------------------------------------------- # Extract PhysicalEntities of "TP53" tp53.all.info <- query(tp53.re$dbId) head(tp53.all.info$physicalEntity, 5) ## ----map2Events, rownames.print=FALSE----------------------------------------- # Get Pathways associated with "TP53" tp53.pathways <- map2Events("TP53", resource = "HGNC", species = "human", mapTo = "pathways") head(tp53.pathways, 5) ## ----------------------------------------------------------------------------- str(tp53.all.info, max.level = 1) ## ----event2Ids---------------------------------------------------------------- # Find all non-Reactome ids for this Event ids <- event2Ids("R-HSA-176942") str(ids) ## ---- rownames.print=FALSE---------------------------------------------------- # Look into orthologousEvents of this pathway stab.p53 <- query("R-HSA-69541") stab.p53[["orthologousEvent"]] ## ----------------------------------------------------------------------------- stab.p53[["isInferred"]] ## ----getOrthology------------------------------------------------------------- # Fetch Human orthologous instance getOrthology("R-SSC-69541", species = "human") ## ----ReactomeGSA, eval=FALSE-------------------------------------------------- # # Install GSA packages # # devtools::install_github("reactome/ReactomeGSA") # # devtools::install_github("reactome/ReactomeGSA.data") # library(ReactomeGSA) # library(ReactomeGSA.data) # data("griss_melanoma_proteomics") # # # Create an analysis request and set parameters # my_request <- ReactomeAnalysisRequest(method = "Camera") # my_request <- set_parameters(request = my_request, max_missing_values = 0.5) # my_request <- add_dataset(request = my_request, # expression_values = griss_melanoma_proteomics, # name = "Proteomics", # type = "proteomics_int", # comparison_factor = "condition", # comparison_group_1 = "MOCK", # comparison_group_2 = "MCM", # additional_factors = c("cell.type", "patient.id")) # # # Run analysis # result <- perform_reactome_analysis(request = my_request, compress = F) # # # Retrieve the fold-change data for the proteomics dataset # proteomics_fc <- get_result(result, type = "fold_changes", name = "Proteomics") # # # Merge the pathway level data for all result sets # combined_pathways <- pathways(result) # # # Get the analysis token # token <- gsub(".*=", "", result@reactome_links[[1]][["url"]]) # # select the id of the pathway with highest foldchange # id <- rownames(combined_pathways[1,]) ## ----diagram, eval=FALSE------------------------------------------------------ # exportImage(id = id, output = "diagram", format = "png", token = token, quality = 8) ## ---- echo=FALSE-------------------------------------------------------------- # to prevent weird warnings in the windows check knitr::include_graphics('img/R-HSA-163200.png') ## ----fireworks, eval=FALSE---------------------------------------------------- # # Fireworks of Human Pathways # exportImage(species = "9606", output = "fireworks", format = "jpg", # token = token, fireworksCoverage = TRUE, quality = 7) ## ---- echo=FALSE-------------------------------------------------------------- # to prevent weird warnings in the windows check knitr::include_graphics('img/covered-fireworks.jpg') ## ----getSpecies, rownames.print=FALSE----------------------------------------- # List main species getSpecies(main = TRUE) ## ----person------------------------------------------------------------------- getPerson(name = "Justin Cook") ## ----sessioninfo-------------------------------------------------------------- sessionInfo()