devtools::install_github("AndrewC160/ROMOPomics",force=T) #for installation reference and not runROMOPOmics standardizes metadata of high throughput assays with associated patient clinical data. Our package ROMOPOmics provides a framework to standardize these datasets and a pipeline to convert this information into a SQL-friendly database that is easily accessed by users. After installation of our R package from the github repository, users specify a data directory and a mask file describing how to map their data’s fields into a common data model. The resulting standardized data tables are then formatted into a SQLite database for easily interoperating and sharing the dataset.
Flow chart of essential functions for a basic ROMOPOmics implementation using two different input datasets.
See our vignette ROMOPOmics
library(ROMOPOmics)dm_file <- system.file("extdata","OMOP_CDM_v6_0_custom.csv",package="ROMOPOmics",mustWork = TRUE)
dm <- loadDataModel(master_table_file = dm_file)
tcga_files <-
list(
"brca_clinical" = system.file("extdata","brca_clinical.csv",package="ROMOPOmics",mustWork = TRUE),
"brca_mutation" = system.file("extdata","brca_mutation.csv",package="ROMOPOmics",mustWork = TRUE)
)
msks <- list(brca_clinical=loadModelMasks(system.file("extdata","brca_clinical_mask.csv",package="ROMOPOmics",mustWork = TRUE)),
brca_mutation=loadModelMasks(system.file("extdata","brca_mutation_mask.csv",package="ROMOPOmics",mustWork = TRUE)))
omop_inputs <- list(brca_clinical=readInputFile(input_file = tcga_files$brca_clinical,
data_model = dm,
mask_table = msks$brca_clinical),
brca_mutation=readInputFile(input_file = tcga_files$brca_mutation,
data_model = dm,
mask_table = msks$brca_mutation))
db_inputs <- combineInputTables(input_table_list = omop_inputs)
omop_db <- buildSQLDBR(omop_tables = db_inputs,file.path(tempdir(),"TCGA.sqlite"))
DBI::dbListTables(omop_db)## [1] "COHORT" "MEASUREMENT" "OBSERVATION" "PERSON" "PROVIDER"
## [6] "SEQUENCING" "SPECIMEN" "sqlite_stat1" "sqlite_stat4"
dm_file <- system.file("extdata","OMOP_CDM_v6_0_custom.csv",package="ROMOPOmics",mustWork = TRUE)
dm <- loadDataModel(master_table_file = dm_file)
msk_file <- system.file("extdata","GSE60682_standard_mask.csv",package="ROMOPOmics",mustWork = TRUE)
msks <- loadModelMasks(msk_file)
in_file <- system.file("extdata","GSE60682_standard.csv",package="ROMOPOmics",mustWork = TRUE)
omop_inputs <- readInputFile(input_file=in_file,data_model=dm,mask_table=msks,transpose_input_table = TRUE)
db_inputs <- combineInputTables(input_table_list = omop_inputs)
omop_db <- buildSQLDBR(omop_tables = db_inputs, sql_db_file=file.path(tempdir(),"GSE60682_sqlDB.sqlite"))
DBI::dbListTables(omop_db)## [1] "CONDITION_OCCURRENCE" "DRUG_EXPOSURE" "PERSON"
## [4] "PROVIDER" "SEQUENCING" "SPECIMEN"
## [7] "sqlite_stat1" "sqlite_stat4"
library(Biobase)
gse_ids <- c("GSE9006", "GSE26440", "GSE11504", "TABM666", "GSE6011", "GSE37721", "GSE20307", "GSE20436")
stevens_gse_lst <- fetch_geo_series(gse_ids,data_dir = tempdir())
stevens_gse_lst$merged_metadata## # A tibble: 683 x 96
## sample gse_channel gse_channel_cou… gse_contact_add… gse_contact_city
## <chr> <chr> <chr> <chr> <chr>
## 1 GSM13… 1 1 Westplein 11 rotterdam
## 2 GSM13… 1 1 Westplein 11 rotterdam
## 3 GSM13… 1 1 Westplein 11 rotterdam
## 4 GSM13… 1 1 Westplein 11 rotterdam
## 5 GSM13… 1 1 Westplein 11 rotterdam
## 6 GSM13… 1 1 Westplein 11 rotterdam
## 7 GSM13… 1 1 Westplein 11 rotterdam
## 8 GSM13… 1 1 Westplein 11 rotterdam
## 9 GSM13… 1 1 Westplein 11 rotterdam
## 10 GSM13… 1 1 Westplein 11 rotterdam
## # … with 673 more rows, and 91 more variables: gse_contact_country <chr>,
## # gse_contact_email <chr>, gse_contact_institute <chr>,
## # gse_contact_name <chr>, `gse_contact_zip/postal_code` <chr>,
## # gse_data_processing <chr>, gse_data_row_count <chr>, gse_description <chr>,
## # gse_extract_protocol <chr>, gse_hyb_protocol <chr>, gse_label <chr>,
## # gse_label_protocol <chr>, gse_last_update_date <chr>, gse_molecule <chr>,
## # gse_organism <chr>, gse_platform_id <chr>, gse_scan_protocol <chr>,
## # gse_source_name <chr>, gse_status <chr>, gse_submission_date <chr>,
## # gse_supplementary_file <chr>, gse_taxid <chr>, gse_title <chr>,
## # gse_type <chr>, gse_id <chr>, gpl_contact_address <chr>,
## # gpl_contact_city <chr>, gpl_contact_country <chr>, gpl_contact_email <chr>,
## # gpl_contact_institute <chr>, gpl_contact_name <chr>,
## # gpl_contact_phone <chr>, gpl_contact_state <chr>,
## # gpl_contact_web_link <chr>, `gpl_contact_zip/postal_code` <chr>,
## # gpl_data_row_count <chr>, gpl_distribution <chr>, gpl_geo_accession <chr>,
## # gpl_last_update_date <chr>, gpl_manufacturer <chr>, gpl_organism <chr>,
## # gpl_status <chr>, gpl_submission_date <chr>, gpl_taxid <chr>,
## # gpl_technology <chr>, gpl_title <chr>, gds_id <chr>,
## # gse_contact_department <chr>, gse_contact_phone <chr>,
## # gse_contact_laboratory <chr>, gse_tissue <chr>,
## # gse_treatment_protocol <chr>, gse_age <chr>, gse_contact_state <chr>,
## # gse_relation <chr>, gse_group <chr>, gse_sex <chr>, gse_Sex <chr>,
## # gse_Age <chr>, gse_contact_fax <chr>, gse_Ethnicity <chr>,
## # gse_Gender <chr>, gse_Illness <chr>,
## # `gse_pH<7.30_at_time_of_diagnosis` <chr>, gse_Race <chr>,
## # gse_Treatment <chr>, `gse_age_(years)` <chr>, gse_disease_state <chr>,
## # gse_outcome <chr>, `gse_age_(in_years)` <chr>,
## # gse_number_of_replicates <chr>, `gse_race/ethnicity` <chr>,
## # gpl_manufacture_protocol <chr>, gpl_relation <chr>,
## # gpl_supplementary_file <chr>, gse_age_at_onset <chr>, gse_cell_type <chr>,
## # `gse_fstl-1_(ng/ml)` <chr>, gse_original_diagnosis <chr>,
## # gse_prototype <chr>, gse_race <chr>, gse_ttf <chr>,
## # gse_updated_diagnosis <chr>, gse_disease <chr>,
## # gse_equivalent_who_simplified_trachoma_score <chr>, gse_ethnicity <chr>,
## # gse_fpc_trachoma_score <chr>, gse_gender <chr>, gse_growth_protocol <chr>,
## # gse_ompa_copies_per_swab <chr>, `gse_roche_ct/ng_pcr_amplicor_value` <chr>