Introduction_1_load_metadata
Progenetix is an open data resource that provides curated individual cancer copy number variation (CNV) profiles along with associated metadata sourced from published oncogenomic studies and various data repositories. This vignette provides a comprehensive guide on accessing and utilizing metadata for samples or their corresponding individuals within the Progenetix database.
If your focus lies in cancer cell lines, you can access data from cancercelllines.org by
setting the domain parameter to
"cancercelllines.org" in pgxLoader function.
This data repository originates from CNV profiling data of cell lines
initially collected as part of Progenetix and currently includes
additional types of genomic mutations.
Load library
pgxLoader function
This function loads various data from Progenetix
database via the Beacon v2 API with some extensions (BeaconPlus).
The parameters of this function used in this tutorial:
type: A string specifying output data type."individuals","biosamples","analyses","filtering_terms", and"counts"are used in this tutorial.filters: Identifiers used in public repositories, bio-ontology terms, or custom terms such asc("NCIT:C7376", "pgx:icdom-85003"). When multiple filters are used, they are combined using AND logic when the parametertypeis"individuals","biosamples", or"analyses"; OR logic when the parametertypeis"counts".individual_id: Identifiers used in the query database for identifying individuals.biosample_id: Identifiers used in the query database for identifying biosamples.codematches: A logical value determining whether to exclude samples from child concepts of specified filters in the ontology tree. IfTRUE, only samples exactly matching the specified filters will be included. Do not use this parameter whenfiltersinclude ontology-irrelevant filters such as pubmed or cohort identifiers. Default isFALSE.filter_pattern: Optional string pattern to match against thelabelfield of available filters. Only used when the parameter type is"filtering_terms". Default isNULL, which includes all filters.limit: Integer to specify the number of returned profiles. Default is0(return all).skip: Integer to specify the number of skipped profiles. E.g. ifskip = 2, limit=500, the first 2*500=1000 profiles are skipped and the next 500 profiles are returned. Default is0(no skip).use_https: A logical value indicating whether to use the HTTPS protocol. IfTRUE, the domain will be prefixed with"https://"; otherwise,"http://"will be used. Default isTRUE.dataset: A string specifying the dataset to query from the Beacon response. Default isNULL, which includes results from all datasets.domain: A string specifying the domain of the query data resource. Default is"progenetix.org".entry_point: A string specifying the entry point of the Beacon v2 API. Default is"beacon", resulting in the endpoint being"https://progenetix.org/beacon".num_cores: An integer specifying the number of cores to use for parallel processing during Beacon v2 phenotypic/meta-data queries from multiple domains. Default is1.
Retrieve biosamples information
Search by filters
Filters are a significant enhancement to the Beacon query API, providing a mechanism for specifying rules to select records based on their field values. To learn more about how to utilize filters in Progenetix, please refer to the documentation.
The following example demonstrates how to access all available filters in Progenetix:
all_filters <- pgxLoader(type="filtering_terms")
head(all_filters)
#> id label type scopes
#> 1 labelSeg-based calibration labelSeg-based calibration alphanumeric NA
#> 2 NCIT:C28076 Disease Grade Qualifier ontologyTerm NA
#> 3 NCIT:C18000 Histologic Grade ontologyTerm NA
#> 4 NCIT:C14158 High Grade ontologyTerm NA
#> 5 NCIT:C14161 Low Grade ontologyTerm NA
#> 6 NCIT:C14167 Poorly Differentiated ontologyTerm NAIf you’re interested in filters related to a specific disease or
phenotype, you can use the filter_pattern argument to
narrow down the list. For example, to search for filters related to
retinoblastoma:
query_filter <- pgxLoader(type="filtering_terms",filter_pattern="retinoblastoma")
query_filter
#> id label type scopes
#> 1 NCIT:C7541 Retinoblastoma ontologyTerm NA
#> 2 NCIT:C8713 Bilateral Retinoblastoma ontologyTerm NA
#> 3 NCIT:C8714 Unilateral Retinoblastoma ontologyTerm NA
#> 4 pgx:icdom-95103 Retinoblastoma, NOS ontologyTerm NATo retrieve biosamples associated with a specific disease, use
appropriate filter terms. In this example, we use an NCIt code corresponding to
retinoblastoma (NCIT:C7541):
biosamples <- pgxLoader(type="biosamples", filters = "NCIT:C7541")
# data looks like this
biosamples[1:5,]
#> biosample_id individual_id biosample_status_id biosample_status_label
#> 1 pgxbs-kftvh7yo pgxind-kftx33q4 EFO:0009656 neoplastic sample
#> 2 pgxbs-m84d025w pgxind-m84cza9r EFO:0010942 primary tumor sample
#> 3 pgxbs-m3io5l72 pgxind-m3io5l72 EFO:0009656 neoplastic sample
#> 4 pgxbs-m84dbk0v pgxind-m84d7hbi EFO:0010942 primary tumor sample
#> 5 pgxbs-kftvhcom pgxind-kftx39mg EFO:0009656 neoplastic sample
#> sample_origin_type_id sample_origin_type_label histological_diagnosis_id
#> 1 OBI:0001479 specimen from organism NCIT:C7541
#> 2 OBI:0001479 specimen from organism NCIT:C7541
#> 3 OBI:0001479 specimen from organism NCIT:C7541
#> 4 OBI:0001479 specimen from organism NCIT:C7541
#> 5 OBI:0001479 specimen from organism NCIT:C7541
#> histological_diagnosis_label sampled_tissue_id sampled_tissue_label
#> 1 Retinoblastoma UBERON:0000966 retina
#> 2 Retinoblastoma UBERON:0000966 retina
#> 3 Retinoblastoma UBERON:0000966 retina
#> 4 Retinoblastoma UBERON:0000966 retina
#> 5 Retinoblastoma UBERON:0000966 retina
#> pathological_stage_id pathological_stage_label tnm tumor_grade age_iso
#> 1 NCIT:C92207 Stage Unknown NA NA P0Y6M
#> 2 <NA> <NA> NA NA P17Y12M30D
#> 3 NCIT:C92207 Stage Unknown NA NA <NA>
#> 4 <NA> <NA> NA NA P17Y12M30D
#> 5 NCIT:C92207 Stage Unknown NA NA <NA>
#> info notes icdo_morphology_id icdo_morphology_label
#> 1 NA retinoblastoma pgx:icdom-95103 Retinoblastoma, NOS
#> 2 NA Retinoblastoma pgx:icdom-95103 Retinoblastoma, NOS
#> 3 NA Retinoblastoma pgx:icdom-95103 Retinoblastoma, NOS
#> 4 NA Retinoblastoma pgx:icdom-95103 Retinoblastoma, NOS
#> 5 NA Retinoblastoma [76.2 months] pgx:icdom-95103 Retinoblastoma, NOS
#> icdo_topography_id icdo_topography_label
#> 1 pgx:icdot-C69.2 Retina
#> 2 pgx:icdot-C69.2 Retina
#> 3 pgx:icdot-C69.2 Retina
#> 4 pgx:icdot-C69.2 Retina
#> 5 pgx:icdot-C69.2 Retina
#> external_references_description
#> 1 Lillington DM, Goff LK et al. (2002): High level amplification of N-MYC is...
#> 2 <NA>
#> 3 Francis et al. Cancers 2021,Targeted sequencing of 83 Retinoblastoma tumor-normal pairs via MSK-IMPACT. Genomic data provided is limited to somatic alterations.
#> 4 <NA>
#> 5 Herzog S, Lohmann DR et al. (2001): Marked differences in unilateral isolated retinoblastomas...
#> external_references_id
#> 1 pubmed:12232763
#> 2 <NA>
#> 3 pubmed:33466343,cbioportal:rbl_mskcc_2020
#> 4 <NA>
#> 5 pubmed:11281459
#> external_references_reference
#> 1 https://europepmc.org/article/MED/12232763
#> 2 <NA>
#> 3 https://europepmc.org/article/MED/33466343,https://www.cbioportal.org/study/summary?id=rbl_mskcc_2020
#> 4 <NA>
#> 5 https://europepmc.org/article/MED/11281459
#> analysis_info cohorts_id
#> 1 NA pgx:cohort-2021progenetix
#> 2 NA pgx:cohort-GENIE-UCSF,pgx:cohort-GENIE
#> 3 NA cbioportal:rbl_mskcc_2020,PMID:33466343
#> 4 NA pgx:cohort-GENIE-MSK,pgx:cohort-GENIE
#> 5 NA pgx:cohort-2021progenetix
#> cohorts_label
#> 1 Version at Progenetix Update 2021
#> 2 GENIE samples, University of California, San Francisco,AACR GENIE project samples
#> 3 Targeted sequencing of 83 Retinoblastoma tumor-normal pairs via MSK-IMPACT. Genomic data provided is limited to somatic alterations.,Francis et al. Cancers 2021
#> 4 GENIE samples, Memorial Sloan Kettering Cancer Center,AACR GENIE project samples
#> 5 Version at Progenetix Update 2021
#> geo_location_geometry_coordinates geo_location_geometry_type
#> 1 -0.13,51.51 Point
#> 2 -122.42,37.77 Point
#> 3 <NA> <NA>
#> 4 -74.01,40.71 Point
#> 5 8.77,50.81 Point
#> geo_location_properties_iso3166alpha2 geo_location_properties_city
#> 1 UK London
#> 2 US San Francisco
#> 3 <NA> <NA>
#> 4 US New York City
#> 5 DE Marburg An Der Lahn
#> geo_location_properties_continent geo_location_properties_country
#> 1 Europe United Kingdom
#> 2 North America United States of America
#> 3 <NA> <NA>
#> 4 North America United States of America
#> 5 Europe Germany
#> geo_location_properties_geoprov_id geo_location_type
#> 1 london::unitedkingdom Feature
#> 2 sanfrancisco::unitedstates Feature
#> 3 <NA> <NA>
#> 4 newyorkcity::unitedstates Feature
#> 5 marburganderlahn::germany Feature
#> updated pathological_stage external_references
#> 1 2020-09-10 17:44:36.144000 NA NA
#> 2 2025-06-12T13:37:59.821628 NA NA
#> 3 2025-04-07T11:07:20.661404 NA NA
#> 4 2025-06-12T13:43:03.813500 NA NA
#> 5 2020-09-10 17:44:41.343000 NA NA
#> geo_location_properties_iso3166alpha3 geo_location
#> 1 <NA> NA
#> 2 USA NA
#> 3 <NA> NA
#> 4 USA NA
#> 5 DEU NA
#> analysis_info_experiment_id analysis_info_platform_id analysis_info_series_id
#> 1 <NA> <NA> <NA>
#> 2 <NA> <NA> <NA>
#> 3 <NA> <NA> <NA>
#> 4 <NA> <NA> <NA>
#> 5 <NA> <NA> <NA>The data contains many columns representing different aspects of sample information.
Search by biosample id and individual id
In the Beacon v2 specification, biosample id and individual id are unique identifiers for biosamples and their corresponding individuals, respectively. These identifiers can be obtained through metadata searches using filters as described above or by querying the Progenetix search interface, which provides access to the IDs used in the Progenetix database.
biosamples_2 <- pgxLoader(type="biosamples", biosample_id = "pgxbs-kftvki7h",individual_id = "pgxind-kftx6ltu")
biosamples_2
#> biosample_id individual_id biosample_status_id biosample_status_label
#> 1 pgxbs-kftvki7h pgxind-kftx6ltd EFO:0009656 neoplastic sample
#> 2 pgxbs-kftvki7v pgxind-kftx6ltu EFO:0009656 neoplastic sample
#> sample_origin_type_id sample_origin_type_label histological_diagnosis_id
#> 1 OBI:0001479 specimen from organism NCIT:C3512
#> 2 OBI:0001479 specimen from organism NCIT:C3512
#> histological_diagnosis_label sampled_tissue_id sampled_tissue_label
#> 1 Lung Adenocarcinoma UBERON:0002048 lung
#> 2 Lung Adenocarcinoma UBERON:0002048 lung
#> pathological_stage_id pathological_stage_label
#> 1 NCIT:C27976 Stage Ib
#> 2 NCIT:C27977 Stage IIIa
#> tnm_id
#> 1 NCIT:C48706,NCIT:C48714,NCIT:C48724
#> 2 NCIT:C48706,NCIT:C48714,NCIT:C48728
#> tnm_label tumor_grade age_iso info
#> 1 N1 Stage Finding,N3 Stage Finding,T2 Stage Finding NA P56Y NA
#> 2 N1 Stage Finding,N3 Stage Finding,T3 Stage Finding NA P75Y NA
#> notes icdo_morphology_id icdo_morphology_label
#> 1 adenocarcinoma [lung] pgx:icdom-81403 Adenocarcinoma, NOS
#> 2 adenocarcinoma [lung] pgx:icdom-81403 Adenocarcinoma, NOS
#> icdo_topography_id icdo_topography_label
#> 1 pgx:icdot-C34.9 Lung, NOS
#> 2 pgx:icdot-C34.9 Lung, NOS
#> external_references_description
#> 1 Kang JU, Koo SH et al. (2009): Identification of novel candidate target genes,...
#> 2 Kang JU, Koo SH et al. (2009): Identification of novel candidate target genes,...
#> external_references_id external_references_reference
#> 1 pubmed:19607727 https://europepmc.org/article/MED/19607727
#> 2 pubmed:19607727 https://europepmc.org/article/MED/19607727
#> analysis_info_experiment_id analysis_info_platform_id analysis_info_series_id
#> 1 geo:GSM417055 geo:GPL8690 geo:GSE16597
#> 2 geo:GSM417063 geo:GPL8690 geo:GSE16597
#> cohorts_id
#> 1 pgx:cohort-arraymap,pgx:cohort-2021progenetix,pgx:cohort-carriocordo2021heterogeneity
#> 2 pgx:cohort-arraymap,pgx:cohort-2021progenetix
#> cohorts_label
#> 1 arrayMap collection,Version at Progenetix Update 2021,Carrio-Cordo and Baudis - Genomic Heterogeneity in Cancer Types (2021)
#> 2 arrayMap collection,Version at Progenetix Update 2021
#> geo_location_geometry_coordinates geo_location_geometry_type
#> 1 -74.01,40.71 Point
#> 2 -74.01,40.71 Point
#> geo_location_properties_iso3166alpha2 geo_location_properties_iso3166alpha3
#> 1 US USA
#> 2 US USA
#> geo_location_properties_city geo_location_properties_continent
#> 1 New York City North America
#> 2 New York City North America
#> geo_location_properties_country geo_location_properties_geoprov_id
#> 1 United States of America newyorkcity::unitedstates
#> 2 United States of America newyorkcity::unitedstates
#> geo_location_type updated
#> 1 Feature 2020-09-10 17:46:45.105000
#> 2 Feature 2020-09-10 17:46:45.115000It’s also possible to query by a combination of filters, biosample id, and individual id.
Access a subset of samples
By default, it returns all related samples (limit=0). You can access
a subset of them via the parameter limit and
skip. For example, if you want to access the first 10
samples , you can set limit = 10, skip =
0.
Parameter codematches use
Some filters, such as NCIt codes, are hierarchical. As a result, retrieved samples may include not only the specified filters but also their child terms.
Setting codematches as TRUE allows this function to only
return biosamples that exactly match the specified filter, excluding
child terms.
Retrieve individuals information
If you want to query details of individuals (e.g. clinical data)
where the samples of interest come from, set the parameter
type to “individuals” and follow the same steps as
above.
individuals <- pgxLoader(type="individuals",individual_id = "pgxind-kftx26ml",filters="NCIT:C7541")
# data looks like this
tail(individuals,2)
#> individual_id sex_id sex_label age_iso histological_diagnosis_id
#> 328 pgxind-kftx32rc NCIT:C17998 unknown P0Y5M NCIT:C7541
#> 329 pgxind-kftx26ml NCIT:C20197 male <NA> NCIT:C3493
#> histological_diagnosis_label followup_time followup_state_id
#> 328 Retinoblastoma P97M EFO:0030049
#> 329 Lung Squamous Cell Carcinoma <NA> EFO:0030039
#> followup_state_label diseases_notes info
#> 328 dead (follow-up status) <NA> PGX_IND_RetBl-lil-021
#> 329 no followup status <NA> PGX_IND_AdSqLu-bjo-01
#> updated info_legacy_ids info_provenance
#> 328 2018-09-26 09:51:32.561000 <NA> <NA>
#> 329 2025-07-11T16:57:08.312131 <NA> <NA>Retrieve analyses information
If you want to know more details about data analyses, set the
parameter type to “analyses”. The other steps are the same,
except the parameter codematches is not available because
analyses data do not include filter information, even though it can be
searched by filters.
analyses <- pgxLoader(type="analyses",biosample_id = c("pgxbs-kftvik5i","pgxbs-kftvik96"))
analyses
#> analysis_id biosample_id individual_id calling_pipeline analysis_info
#> 1 pgxcs-kftw8rrh pgxbs-kftvik96 pgxind-kftx49ao progenetix NA
#> 2 pgxcs-kftw8qme pgxbs-kftvik5i pgxind-kftx4963 progenetix NA
#> platform_id platform_label
#> 1 geo:GPL3960 MPIMG Homo sapiens 44K aCGH3_MPIMG_BERLIN
#> 2 geo:GPL2826 VUMC MACF human 30K oligo v31
#> updated
#> 1 2025-07-13T18:28:43.540286
#> 2 2025-07-13T18:28:42.839469Retrieve the number of results for a specific filter
To retrieve the number of results for a specific filter in
Progenetix, set the type parameter to
"counts". You can query different Beacon v2 resources by
setting the domain and entry_point parameters
accordingly.
Query from multiple data resources
You can query data from multiple resources via the Beacon v2 API by
setting the domain and entry_point parameters
accordingly. To speed up the process, use the num_cores
parameter to enable parallel processing across different domains. For
resources that only support http (e.g., local or internal
network instances), set use_https = FALSE to avoid
connection issues.
record_counts <- pgxLoader(type="counts",filters = "NCIT:C9245",domain=c("progenetix.org","cancercelllines.org"), entry_point=c("beacon","beacon"))
record_counts
#> $progenetix.org
#> filter entity count
#> 1 NCIT:C9245 individuals 8267
#> 2 NCIT:C9245 biosamples 9862
#> 3 NCIT:C9245 analyses 12001
#>
#> $cancercelllines.org
#> filter entity count
#> 1 NCIT:C9245 individuals 42
#> 2 NCIT:C9245 biosamples 1005
#> 3 NCIT:C9245 analyses 929Visualization of survival data
Suppose you want to investigate whether there are survival
differences associated with a particular disease, for example, between
younger and older patients, or based on other variables. You can query
and visualize the relevant information using the
pgxMetaplot function.
pgxMetaplot function
This function generates a survival plot using metadata of individuals
obtained by the pgxLoader function.
The parameters of this function:
data: The data frame returned by thepgxLoaderfunction, containing survival data for individuals. The survival state is represented by Experimental Factor Ontology in the “followup_state_id” column, and the survival time is represented in ISO 8601 duration format in the “followup_time” column.group_id: A string specifying which column is used for grouping in the Kaplan-Meier plot.condition: A string for splitting individuals into younger and older groups, following the ISO 8601 duration format. Only used ifgroup_idis “age_iso”.return_data: A logical value determining whether to return the metadata used for plotting. Default is FALSE....: Other parameters relevant to KM plot. These includepval,pval.coord,pval.method,conf.int,linetype, andpalette(see ggsurvplot function from survminer package)
Example usage
# query metadata of individuals with lung adenocarcinoma
luad_inds <- pgxLoader(type="individuals",filters="NCIT:C3512")
# use 70 years old as the splitting condition
pgxMetaplot(data=luad_inds, group_id="age_iso", condition="P70Y", pval=TRUE)
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the ggpubr package.
#> Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
It’s noted that not all individuals have available survival data. If
you set return_data to TRUE, the function will return the
metadata of individuals used for the plot.
Session Info
#> R version 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] future_1.67.0 pgxRpi_1.7.0 BiocStyle_2.39.0
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 xfun_0.54 bslib_0.9.0
#> [4] ggplot2_4.0.0 rstatix_0.7.3 lattice_0.22-7
#> [7] vctrs_0.6.5 tools_4.5.2 generics_0.1.4
#> [10] curl_7.0.0 parallel_4.5.2 tibble_3.3.0
#> [13] pkgconfig_2.0.3 Matrix_1.7-4 data.table_1.17.8
#> [16] RColorBrewer_1.1-3 S7_0.2.0 lifecycle_1.0.4
#> [19] compiler_4.5.2 farver_2.1.2 codetools_0.2-20
#> [22] carData_3.0-5 htmltools_0.5.8.1 sys_3.4.3
#> [25] buildtools_1.0.0 sass_0.4.10 yaml_2.3.10
#> [28] Formula_1.2-5 crayon_1.5.3 pillar_1.11.1
#> [31] car_3.1-3 ggpubr_0.6.2 jquerylib_0.1.4
#> [34] tidyr_1.3.1 cachem_1.1.0 survminer_0.5.1
#> [37] abind_1.4-8 km.ci_0.5-6 parallelly_1.45.1
#> [40] tidyselect_1.2.1 digest_0.6.38 dplyr_1.1.4
#> [43] purrr_1.2.0 listenv_0.10.0 labeling_0.4.3
#> [46] maketools_1.3.2 splines_4.5.2 fastmap_1.2.0
#> [49] grid_4.5.2 cli_3.6.5 magrittr_2.0.4
#> [52] survival_3.8-3 broom_1.0.10 future.apply_1.20.0
#> [55] withr_3.0.2 scales_1.4.0 backports_1.5.0
#> [58] lubridate_1.9.4 timechange_0.3.0 rmarkdown_2.30
#> [61] httr_1.4.7 globals_0.18.0 gridExtra_2.3
#> [64] ggsignif_0.6.4 zoo_1.8-14 evaluate_1.0.5
#> [67] knitr_1.50 KMsurv_0.1-6 survMisc_0.5.6
#> [70] rlang_1.1.6 xtable_1.8-4 glue_1.8.0
#> [73] BiocManager_1.30.26 attempt_0.3.1 jsonlite_2.0.0
#> [76] R6_2.6.1