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-## ---- include=FALSE-----------------------------------------------------------

-options(tinytex.verbose = TRUE)

-

-

-## ---- 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 = 5, 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" 

-

-exon_ds <- read_gmql(exon_path)

-mut_ds <- read_gmql(mut_path)

-

-# Filter out mut_ds based on a metadata predicate to keep breast cancer 

-# mutations only

-

-mut = filter(mut_ds, manually_curated__dataType == 'dnaseq' & 

-                clinical_patient__tumor_tissue_site == 'breast')

-

-# Filter out exon_ds based on a metadata predicate to keep Refseq exons only

-

-exon = filter(exon_ds, annotation_type == 'exons' & 

-                    original_provider == 'RefSeq')

-

-# For each mutation sample, map the mutations to the exon regions using 

-# the map() function and count mutations within each exon storing the value

-# in the default region attribute 'count_left_right'

-

-exon1 <- map(exon, mut)

-

-# Remove exons in each sample that do not contain mutations

-

-exon2 <- filter(exon1, r_predicate = 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)

-

-## ---- materializeElsewhere----------------------------------------------------

-# Materialize the result dataset into a specific folder on disk

-collect(exon_res, dir_out = "./WD_R", name = "dataset") #, 

-

-## ---- execute, eval = FALSE---------------------------------------------------

-#  execute()

-

-## ---- take,eval=FALSE---------------------------------------------------------

-#  g <- take(exon_res, rows = 45)

-

-## ---- init with guest login---------------------------------------------------

-test_url = "http://www.gmql.eu/gmql-rest"

-login_gmql(test_url)

-

-## ---- init with login---------------------------------------------------------

-test_url = "http://www.gmql.eu/gmql-rest"

-login_gmql(test_url, username = 'myname', password = 'mypassword')

-

-## ---- 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-----------------------------------------------------

-#  job_id <- job$id

-#  trace_job(test_url, job_id)

-

-## ---- download, eval = FALSE--------------------------------------------------

-#  name_dataset <- job$datasets[[1]]$name

-#  download_dataset(test_url, name_dataset)

-#  

-

-## ---- download_as_GRangesList, eval=FALSE-------------------------------------

-#  name_dataset <- job$datasets[[1]]$name

-#  grl = download_as_GRangesList(test_url, name_dataset)

-

-## ---- logout------------------------------------------------------------------

-logout_gmql(test_url)

-

-## ---- login remote, eval = FALSE----------------------------------------------

-#  test_url = "http://www.gmql.eu/gmql-rest"

-#  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 HG19_TCGA_dnaseq

-## Read the remote dataset HG19_BED_ANNOTATION

-

-TCGA_dnaseq <- read_gmql("public.HG19_TCGA_dnaseq", is_local = FALSE)

-HG19_bed_ann <- read_gmql("public.HG19_BED_ANNOTATION", is_local = FALSE)

-

-# Filter out mut_ds based on a metadata predicate to keep breast cancer 

-# mutations only

-

-mut = filter(TCGA_dnaseq, manually_curated__dataType == 'dnaseq' & 

-                clinical_patient__tumor_tissue_site == 'breast')

-

-# Filter out exon_ds based on a metadata predicate to keep Refseq exons only 

-

-exon = filter(HG19_bed_ann, annotation_type == 'exons' & 

-                    original_provider == 'RefSeq')

-

-# For each mutation sample, map the mutations to the exon regions using 

-# the map() function and count mutations within each exon storing the value

-# in the default region attribute 'count_left_right'

-

-exon1 <- map(exon, mut)

-

-# Remove exons in each sample that do not contain mutations

-

-exon2 <- filter(exon1, r_predicate = 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, name="exon_res_data")

-

-## ---- remote execute, eval = FALSE--------------------------------------------

-#  job<-execute()

-

-## ---- download_2, eval = FALSE------------------------------------------------

-#  name_dataset <- job$datasets[[1]]$name

-#  download_dataset(test_url, name_dataset)

-

-## ---- download_as_GRangesList_2, eval=FALSE-----------------------------------

-#  name_dataset <- job$datasets[[1]]$name

-#  grl = download_as_GRangesList(test_url, name_dataset)

-

-## ---- logout_2, eval=FALSE----------------------------------------------------

-#  logout_gmql(test_url)

-

-## ---- switch mode-------------------------------------------------------------

-test_url = "http://www.gmql.eu/gmql-rest"

-init_gmql(url = test_url)

-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" 

-

-mut_ds <- read_gmql(mut_path, is_local = TRUE)

-

-# Read the remote dataset HG19_BED_ANNOTATION

-

-HG19_bed_ann <- read_gmql("public.HG19_BED_ANNOTATION", is_local = FALSE)

-

-# Filter out mut_ds based on a metadata predicate to keep breast cancer 

-# mutations only

-

-mut = filter(mut_ds, manually_curated__dataType == 'dnaseq' & 

-                clinical_patient__tumor_tissue_site == 'breast')

-

-# Filter out exon_ds based on a metadata predicate to keep Refseq exons only 

-

-exon = filter(HG19_bed_ann, annotation_type == 'exons' & 

-                    original_provider == 'RefSeq')

-

-# For each mutation sample, map the mutations to the exon regions using 

-# the map() function and count mutations within each exon storing the value

-# in the default region attribute 'count_left_right'

-

-exon1 <- map(exon, mut)

-

-# Remove exons in each sample that do not contain mutations

-

-exon2 <- filter(exon1, r_predicate = 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_result_dataset")

-

-## ---- mixed execute, eval = FALSE---------------------------------------------

-#  job<-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

-

-imported_data@metadata

-

-

-## ---- export------------------------------------------------------------------

-# This statement defines the path to the subdirectory "exp" of the 

-# package "RGMQL"

-

-dir_out <- paste(system.file("example", package = "RGMQL"), 'exp', sep='/')

-

-# Export the GRangesList 'imported_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 "TCGA-ACC" in the subdirectory 

-# "example" of the package "RGMQL"

-

-data_in <- system.file("example", "TCGA-ACC", package = "RGMQL")

-

-matrix <- filter_and_extract(data_in, metadata= NULL,

-                             region_attributes = 

-                               FULL(except = c('fpkm_uq','fpkm')))

-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

-

-grl_data@metadata

-

-

-## ---- retrieve_value----------------------------------------------------------

-

-# store metadata on variable a

-

-a = grl_data@metadata

-

-# 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")]

-

-