Introduction_1_load_metadata
2025-02-13
Contents
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 "https://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.
1 Load library
library(pgxRpi)1.1 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", "PMID:22824167"). 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 PMID and cohort identifiers. Default isFALSE.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).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"http://progenetix.org".entry_point: A string specifying the entry point of the Beacon v2 API. Default is"beacon", resulting in the endpoint being"http://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.
2 Retrieve biosamples information
2.1 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 EDAM:operation_3227 EDAM:operation_3227 ontologyTerm NA
#> 2 EDAM:operation_3961 EDAM:operation_3961 ontologyTerm NA
#> 3 labelSeg-based calibration labelSeg-based calibration alphanumeric NA
#> 4 NCIT:C28076 Disease Grade Qualifier ontologyTerm NA
#> 5 NCIT:C18000 Histologic Grade ontologyTerm NA
#> 6 NCIT:C14158 High Grade ontologyTerm NAThe following query retrieves information about all retinoblastoma samples in Progenetix, utilizing a specific filter based on an NCIt code as a disease identifier.
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-kftvh1n1 pgxind-kftx2vtw EFO:0009656 neoplastic sample
#> 2 pgxbs-kftvh1n3 pgxind-kftx2vty EFO:0009656 neoplastic sample
#> 3 pgxbs-kftvh1n4 pgxind-kftx2vu0 EFO:0009656 neoplastic sample
#> 4 pgxbs-kftvh1n6 pgxind-kftx2vu2 EFO:0009656 neoplastic sample
#> 5 pgxbs-kftvh1n8 pgxind-kftx2vu4 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 info
#> 1 NCIT:C92207 Stage Unknown NA NA <NA> NA
#> 2 NCIT:C92207 Stage Unknown NA NA <NA> NA
#> 3 NCIT:C92207 Stage Unknown NA NA <NA> NA
#> 4 NCIT:C92207 Stage Unknown NA NA <NA> NA
#> 5 NCIT:C92207 Stage Unknown NA NA <NA> NA
#> notes icdo_morphology_id icdo_morphology_label icdo_topography_id
#> 1 Retinoblastoma pgx:icdom-95103 Retinoblastoma, NOS pgx:icdot-C69.2
#> 2 Retinoblastoma pgx:icdom-95103 Retinoblastoma, NOS pgx:icdot-C69.2
#> 3 Retinoblastoma pgx:icdom-95103 Retinoblastoma, NOS pgx:icdot-C69.2
#> 4 Retinoblastoma pgx:icdom-95103 Retinoblastoma, NOS pgx:icdot-C69.2
#> 5 Retinoblastoma pgx:icdom-95103 Retinoblastoma, NOS pgx:icdot-C69.2
#> icdo_topography_label
#> 1 Retina
#> 2 Retina
#> 3 Retina
#> 4 Retina
#> 5 Retina
#> external_references_description
#> 1 Zielinski B, Gratias S et al. (2005): Detection of chromosomal imbalances in retinoblastoma...
#> 2 Zielinski B, Gratias S et al. (2005): Detection of chromosomal imbalances in retinoblastoma...
#> 3 Zielinski B, Gratias S et al. (2005): Detection of chromosomal imbalances in retinoblastoma...
#> 4 Zielinski B, Gratias S et al. (2005): Detection of chromosomal imbalances in retinoblastoma...
#> 5 Zielinski B, Gratias S et al. (2005): Detection of chromosomal imbalances in retinoblastoma...
#> external_references_id external_references_reference
#> 1 PMID:15834944 https://europepmc.org/article/MED/15834944
#> 2 PMID:15834944 https://europepmc.org/article/MED/15834944
#> 3 PMID:15834944 https://europepmc.org/article/MED/15834944
#> 4 PMID:15834944 https://europepmc.org/article/MED/15834944
#> 5 PMID:15834944 https://europepmc.org/article/MED/15834944
#> analysis_info cohorts_id cohorts_label
#> 1 NA pgx:cohort-2021progenetix Version at Progenetix Update 2021
#> 2 NA pgx:cohort-2021progenetix Version at Progenetix Update 2021
#> 3 NA pgx:cohort-2021progenetix Version at Progenetix Update 2021
#> 4 NA pgx:cohort-2021progenetix Version at Progenetix Update 2021
#> 5 NA pgx:cohort-2021progenetix Version at Progenetix Update 2021
#> geo_location_geometry_coordinates geo_location_geometry_type
#> 1 8.69,49.41 Point
#> 2 8.69,49.41 Point
#> 3 8.69,49.41 Point
#> 4 8.69,49.41 Point
#> 5 8.69,49.41 Point
#> geo_location_properties_iso3166alpha3 geo_location_properties_city
#> 1 DEU Heidelberg
#> 2 DEU Heidelberg
#> 3 DEU Heidelberg
#> 4 DEU Heidelberg
#> 5 DEU Heidelberg
#> geo_location_properties_country geo_location_properties_label
#> 1 Germany Heidelberg, Germany
#> 2 Germany Heidelberg, Germany
#> 3 Germany Heidelberg, Germany
#> 4 Germany Heidelberg, Germany
#> 5 Germany Heidelberg, Germany
#> geo_location_properties_latitude geo_location_properties_longitude
#> 1 49.41 8.69
#> 2 49.41 8.69
#> 3 49.41 8.69
#> 4 49.41 8.69
#> 5 49.41 8.69
#> geo_location_properties_precision geo_location_type
#> 1 city Feature
#> 2 city Feature
#> 3 city Feature
#> 4 city Feature
#> 5 city Feature
#> updated analysis_info_experiment_id
#> 1 2020-09-10 17:44:29.148000 <NA>
#> 2 2020-09-10 17:44:29.150000 <NA>
#> 3 2020-09-10 17:44:29.151000 <NA>
#> 4 2020-09-10 17:44:29.152000 <NA>
#> 5 2020-09-10 17:44:29.154000 <NA>
#> analysis_info_platform_id analysis_info_series_id geo_location
#> 1 <NA> <NA> NA
#> 2 <NA> <NA> NA
#> 3 <NA> <NA> NA
#> 4 <NA> <NA> NA
#> 5 <NA> <NA> NAThe data contains many columns representing different aspects of sample information.
2.2 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 PMID:19607727 https://europepmc.org/article/MED/19607727
#> 2 PMID: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_iso3166alpha3 geo_location_properties_city
#> 1 USA New York City
#> 2 USA New York City
#> geo_location_properties_country geo_location_properties_label
#> 1 United States of America New York City, United States
#> 2 United States of America New York City, United States
#> geo_location_properties_latitude geo_location_properties_longitude
#> 1 40.71 -74.01
#> 2 40.71 -74.01
#> geo_location_properties_precision geo_location_type
#> 1 city Feature
#> 2 city Feature
#> updated
#> 1 2020-09-10 17:46:45.105000
#> 2 2020-09-10 17:46:45.115000It’s also possible to query by a combination of filters, biosample id, and individual id.
2.3 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.
biosamples_3 <- pgxLoader(type="biosamples", filters = "NCIT:C7541",skip=0, limit = 10)
# Dimension: Number of samples * features
print(dim(biosamples))
#> [1] 256 42
print(dim(biosamples_3))
#> [1] 10 382.4 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.
unique(biosamples$histological_diagnosis_id)
#> [1] "NCIT:C7541" "NCIT:C8714" "NCIT:C8713"Setting codematches as TRUE allows this function to only return biosamples that exactly match the specified filter, excluding child terms.
biosamples_4 <- pgxLoader(type="biosamples", filters = "NCIT:C7541",codematches = TRUE)
unique(biosamples_4$histological_diagnosis_id)
#> [1] "NCIT:C7541"2.5 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.
biosamples_5 <- pgxLoader(type="biosamples",filters = "NCIT:C7541",domain=c("progenetix.org","cancercelllines.org"), entry_point="beacon") # both resources use the same entry point
biosamples_5[["cancercelllines.org"]][1:2,1:10]
#> biosample_id individual_id biosample_status_id biosample_status_label
#> 1 cellzbs-75b73A5d cellzind-bB32Bb4d EFO:0001639 cancer cell line
#> 2 cellzbs-7D8289ed cellzind-bcBE35Aa EFO:0001639 cancer cell line
#> sample_origin_type_id sample_origin_type_label histological_diagnosis_id
#> 1 OBI:0001479 specimen from organism NCIT:C42596
#> 2 OBI:0001479 specimen from organism NCIT:C7541
#> histological_diagnosis_label sampled_tissue_id sampled_tissue_label
#> 1 Sporadic retinoblastoma UBERON:0000966 retina
#> 2 Retinoblastoma UBERON:0000966 retina3 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
#> 254 pgxind-m3io67j5 NCIT:C16576 female <NA> NCIT:C7541
#> 255 pgxind-kftx26ml NCIT:C20197 male <NA> NCIT:C3493
#> histological_diagnosis_label followup_time followup_state_id
#> 254 Retinoblastoma <NA> EFO:0030039
#> 255 Squamous Cell Lung Carcinoma <NA> EFO:0030039
#> followup_state_label diseases_notes info
#> 254 no followup status <NA> <NA>
#> 255 no followup status <NA> PGX_IND_AdSqLu-bjo-01
#> updated info_legacy_ids info_provenance
#> 254 2024-11-19T03:41:20.977857 <NA> <NA>
#> 255 2018-09-26 09:50:52.800000 <NA> <NA>4 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
#> 1 pgxcs-kftw8qme pgxbs-kftvik5i pgxind-kftx4963 progenetix
#> 2 pgxcs-kftw8rrh pgxbs-kftvik96 pgxind-kftx49ao progenetix
#> analysis_info_experiment_id analysis_info_experiment_title
#> 1 geo:GSM115217 GSM115217
#> 2 geo:GSM120460 GSM120460
#> analysis_info_operation_id analysis_info_operation_label
#> 1 EDAM:operation_3961 Copy number variation detection
#> 2 EDAM:operation_3961 Copy number variation detection
#> analysis_info_series_id platform_id platform_label
#> 1 geo:GSE5051 geo:GPL2826 VUMC MACF human 30K oligo v31
#> 2 geo:GSE5359 geo:GPL3960 MPIMG Homo sapiens 44K aCGH3_MPIMG_BERLIN
#> updated
#> 1 2024-11-20T07:24:51.839782
#> 2 2024-11-20T07:24:53.4966125 Retrieve 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.
pgxLoader(type="counts",filters = "NCIT:C7541")
#> filter entity count
#> 1 NCIT:C7541 individuals 254
#> 2 NCIT:C7541 biosamples 256
#> 3 NCIT:C7541 analyses 2766 Visualization 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.
6.1 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)
6.1.1 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)
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.
7 Session Info
#> R Under development (unstable) (2025-01-20 r87609)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
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#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB 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: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> [1] pgxRpi_1.3.3 BiocStyle_2.35.0
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#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 xfun_0.50 bslib_0.9.0
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#> [52] survival_3.8-3 broom_1.0.7 future.apply_1.11.3
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#> [67] knitr_1.49 KMsurv_0.1-5 survMisc_0.5.6
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#> [76] jsonlite_1.8.9 R6_2.6.0