## ---- initialization------------------------------------------------------- library('RGMQL') ## ---- initialization_RGMQLlib---------------------------------------------- library('RGMQLlib') ## ---- init----------------------------------------------------------------- init_gmql() ## ---- read GMQL dataset---------------------------------------------------- gmql_dataset_path <- system.file("example", "EXON", package = "RGMQL") data_out = read_gmql(gmql_dataset_path) ## ---- read GRangesList----------------------------------------------------- library("GenomicRanges") # Granges Object with one region: chr2 and two metadata columns: score = 5 # and GC = 0.45 gr1 <- GRanges(seqnames = "chr2", ranges = IRanges(103, 106), strand = "+", score = 5L, GC = 0.45) # Granges Object with two regions both chr1 and two metadata columns: score = 3 # for the fist region and score = 4 for the second one, GC = 0.3 and 0.5 # for the first and second region, respectively gr2 <- GRanges(seqnames = c("chr1", "chr1"), ranges = IRanges(c(107, 113), width = 3), strand = c("+", "-"), score = 3:4, GC = c(0.3, 0.5)) grl <- GRangesList("txA" = gr1, "txB" = gr2) data_out <- read_GRangesList(grl) ## ---- query---------------------------------------------------------------- # These statements define the paths to the folders "EXON" and "MUT" in the # subdirectory "example" of the package "RGMQL" exon_path <- system.file("example", "EXON", package = "RGMQL") mut_path <- system.file("example", "MUT", package = "RGMQL") # Read EXON folder as a GMQL dataset named "exon_ds" containing a single # sample with exon regions, and MUT folder as a GMQL dataset named "mut_ds" # containing multiple samples with mutation regions exon_ds <- read_gmql(exon_path) mut_ds <- read_gmql(mut_path) # Filter out mut_ds based on predicate mut = filter(mut_ds, manually_curated__dataType == 'dnaseq' & clinical_patient__tumor_tissue_site == 'breast') # Filter out exon_ds based on predicate exon = filter(exon_ds, annotation_type == 'exons' & original_provider == 'RefSeq') # For each mutation sample, count mutations within each exon while # mapping the mutations to the exon regions using the map() function exon1 <- map(exon, mut) # Remove exons in each sample that do not contain mutations exon2 <- filter(exon1, count_left_right >= 1) # Using the extend() function, count how many exons remain in each sample and # store the result in the sample metadata as a new attribute-value pair, # with exon_count as attribute name exon3 <- extend(exon2, exon_count = COUNT()) # Order samples in descending order of the added metadata exon_count exon_res = arrange(exon3, list(DESC("exon_count"))) ## ---- materialize---------------------------------------------------------- # Materialize the result dataset on disk collect(exon_res) ## ---- execute, eval = FALSE------------------------------------------------ # execute() ## ---- take,eval=FALSE------------------------------------------------------ # g <- take(exon_res, rows = 45) ## ---- init with login------------------------------------------------------ test_url = "http://www.gmql.eu/gmql-rest/" login_gmql(test_url) ## ---- run, eval = FALSE---------------------------------------------------- # # job <- run_query(test_url, "query_1", "DNA = SELECT() Example_Dataset_1; # MATERIALIZE DNA INTO RESULT_DS;", output_gtf = FALSE) # ## ---- run_from_file, eval = FALSE------------------------------------------ # query_path <- system.file("example", "query1.txt", package = "RGMQL") # job <- run_query_fromfile(test_url, query_path, output_gtf = FALSE) ## ---- trace, eval = FALSE-------------------------------------------------- # jod_id <- job$id # trace_job(test_url, jod_id) ## ---- download, eval = FALSE----------------------------------------------- # name_dataset <- job$datasets[[1]]$name # download_dataset(test_url, name_dataset) ## ---- logout--------------------------------------------------------------- logout_gmql(test_url) ## ---- download_as_GRangesList, eval=FALSE---------------------------------- # name_dataset <- job$datasets[[1]]$name # grl = download_as_GRangesList(remote_url, name_dataset) # ## ---- login remote, eval = FALSE------------------------------------------- # login_gmql(test_url) ## ---- initialize remote---------------------------------------------------- init_gmql(url = test_url) ## ---- change processing mode----------------------------------------------- remote_processing(TRUE) ## ---- init remote processing----------------------------------------------- init_gmql(url = test_url, remote_processing = TRUE) ## ---- remote query--------------------------------------------------------- ## Read the remote dataset Example_Dataset_1 ## Read the remote dataset Example_Dataset_2 TCGA_dnaseq <- read_gmql("public.Example_Dataset_1", is_local = FALSE) HG19_bed_ann <- read_gmql("public.Example_Dataset_2", is_local = FALSE) ## Filter out TCGA_dnaseq based on predicate mut = filter(TCGA_dnaseq, manually_curated__dataType == 'dnaseq' & clinical_patient__tumor_tissue_site == 'breast') # Filter out HG19_bed_ann based on predicate exon = filter(HG19_bed_ann, annotation_type == 'exons' & original_provider == 'RefSeq') # For each mutation sample, count mutations within each exon while # mapping the mutations to the exon regions using the map() function exon1 <- map(exon, mut) # Remove exons in each sample that do not contain mutations exon2 <- filter(exon1, count_left_right >= 1) # Using the extend() function, count how many exons remain in each sample and # store the result in the sample metadata as a new attribute-value pair, # with exon_count as attribute name exon3 <- extend(exon2, exon_count = COUNT()) # Order samples in descending order of the added metadata exon_count exon_res = arrange(exon3, list(DESC("exon_count"))) ## ---- remote materialize, eval = FALSE------------------------------------- # collect(exon_res,"exon_res") ## ---- remote execute, eval = FALSE----------------------------------------- # execute() ## ---- download_as_GRangesList_2, eval=FALSE-------------------------------- # name_dataset <- job$datasets[[1]]$name # grl = download_as_GRangesList(remote_url, name_dataset) ## ---- download_2, eval = FALSE--------------------------------------------- # name_dataset <- job$datasets[[1]]$name # download_dataset(test_url, name_dataset) ## ---- logout_2, eval=FALSE------------------------------------------------- # logout_gmql(test_url) ## ---- switch mode---------------------------------------------------------- remote_processing(TRUE) ## ---- mixed query---------------------------------------------------------- # This statement defines the path to the folder "MUT" in the subdirectory # "example" of the package "RGMQL" mut_path <- system.file("example", "MUT", package = "RGMQL") # Read MUT folder as a GMQL dataset named "mut_ds" containing a single # sample with mutation regions mut_ds <- read_gmql(mut_path, is_local = TRUE) # Read the remote dataset Example_Dataset_2 HG19_bed_ann <- read_gmql("public.Example_Dataset_2", is_local = FALSE) # Filter out based on predicate mut = filter(mut_ds, manually_curated__dataType == 'dnaseq' & clinical_patient__tumor_tissue_site == 'breast') exon = filter(HG19_bed_ann, annotation_type == 'exons' & original_provider == 'RefSeq') # For each mutation sample, count mutations within each exon while # mapping the mutations to the exon regions using the map() function exon1 <- map(exon, mut) # Remove exons in each sample that do not contain mutations exon2 <- filter(exon1, count_left_right >= 1) # Using the extend() function, count how many exons remain in each sample and # store the result in the sample metadata as a new attribute-value pair, # with exon_count as attribute name exon3 <- extend(exon2, exon_count = COUNT()) # Order samples in descending order of the added metadata exon_count exon_res = arrange(exon3, list(DESC("exon_count"))) ## ---- mixed materialize, eval = FALSE-------------------------------------- # collect(exon_res,"exon_res") ## ---- mixed execute, eval = FALSE------------------------------------------ # execute() ## ---- import--------------------------------------------------------------- # This statement defines the path to the folder "EXON" in the subdirectory # "example" of the package "RGMQL" dataset_path <- system.file("example", "EXON", package = "RGMQL") # Import the GMQL dataset EXON as GRangesList imported_data <- import_gmql(dataset_path, is_gtf = FALSE) imported_data # and its metadata metadata(imported_data) ## ---- export--------------------------------------------------------------- # This statement defines the path to the subdirectory "example" of the # package "RGMQL" dir_out <- system.file("example", package = "RGMQL") # Export the GRangesList 'data' as GMQL dataset called 'example' at destination # path export_gmql(imported_data, dir_out, is_gtf = TRUE) ## ---- filter_extract------------------------------------------------------- # This statement defines the path to the folder "TEAD" in the subdirectory # "example" of the package "RGMQL" data_in <- system.file("example", "TEAD", package = "RGMQL") matrix <- filter_and_extract(data_in, metadata = NULL, region_attributes = c("count")) matrix ## ---- metadata------------------------------------------------------------- # This statement defines the path to the folder "DATASET_META" in the # subdirectory "example" of the package "RGMQL" dataset_path <- system.file("example", "DATASET_META", package = "RGMQL") # Import the GMQL dataset DATASET_META as GRangesList grl_data <- import_gmql(dataset_path, is_gtf = FALSE) grl_data # and its metadata metadata(grl_data) ## ---- retrieve_value------------------------------------------------------- # store metadata on variable a a = metadata(grl_data) # get disease value of sample S_00000 a$S_00000$disease ## ---- retrieve_values------------------------------------------------------ # get all disease values of sample S_00000 a$S_00000[which(names(a$S_00000) %in% "disease")]