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 specifying the dataset parameter as “cancercelllines”. 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.

The parameters of this function used in this tutorial:

  • type A string specifying output data type. Available options are “biosample”, “individual”, “variant” or “frequency”.
  • filters Identifiers for cancer type, literature, cohorts, and age such as c(“NCIT:C7376”, “pgx:icdom-98353”, “PMID:22824167”, “pgx:cohort-TCGAcancers”, “age:>=P50Y”). For more information about filters, see the documentation.
  • filterLogic A string specifying logic for combining multiple filters when query metadata. Available options are “AND” and “OR”. Default is “AND”. An exception is filters associated with age that always use AND logic when combined with any other filter, even if filterLogic = “OR”, which affects other filters.
  • individual_id Identifiers used in Progenetix database for identifying individuals.
  • biosample_id Identifiers used in Progenetix database for identifying biosamples.
  • codematches A logical value determining whether to exclude samples from child concepts of specified filters that belong to cancer type/tissue encoding system (NCIt, icdom/t, Uberon). If TRUE, retrieved samples only keep samples exactly encoded by specified filters. Do not use this parameter when filters include ontology-irrelevant filters such as PMID and cohort identifiers. Default is FALSE.
  • limit Integer to specify the number of returned samples/individuals/coverage profiles for each filter. Default is 0 (return all).
  • skip Integer to specify the number of skipped samples/individuals/coverage profiles for each filter. E.g. if skip = 2, limit=500, the first 2*500 =1000 profiles are skipped and the next 500 profiles are returned. Default is NULL (no skip).
  • dataset A string specifying the dataset to query. Default is “progenetix”. Other available options are “cancercelllines”.

2 Retrieve meatdata of samples

2.1 Relevant parameters

type, filters, filterLogic, individual_id, biosample_id, codematches, limit, skip, dataset

2.2 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 pgxFilter function helps access available filters used in Progenetix. Here is the example use:

# access all filters
all_filters <- pgxFilter()
# get all prefix
all_prefix <- pgxFilter(return_all_prefix = TRUE)
# access specific filters based on prefix
ncit_filters <- pgxFilter(prefix="NCIT")
head(ncit_filters)
#> [1] "NCIT:C28076" "NCIT:C18000" "NCIT:C14158" "NCIT:C14161" "NCIT:C14167"
#> [6] "NCIT:C28077"

The following query is designed to retrieve metadata in Progenetix related to all samples of lung adenocarcinoma, utilizing a specific type of filter based on an NCIt code as an ontology identifier.

biosamples <- pgxLoader(type="biosample", filters = "NCIT:C3512")
# data looks like this
biosamples[c(1700:1705),]
#>        biosample_id   individual_id                 notes
#> 1700 pgxbs-kftvgj01 pgxind-kftx293w adenocarcinoma [lung]
#> 1701 pgxbs-kftvk3j9 pgxind-kftx63u2   lung adenocarcinoma
#> 1702 pgxbs-kftvkvan pgxind-kftx71vb   Lung Adenocarcinoma
#> 1703 pgxbs-kftvkwq2 pgxind-kftx73nx   Lung Adenocarcinoma
#> 1704 pgxbs-kftvihws pgxind-kftx46ef   lung adenocarcinoma
#> 1705 pgxbs-kftvl9fn pgxind-kftx7jkq   Lung Adenocarcinoma
#>      histological_diagnosis_id histological_diagnosis_label
#> 1700                NCIT:C3512          Lung Adenocarcinoma
#> 1701                NCIT:C3512          Lung Adenocarcinoma
#> 1702                NCIT:C3512          Lung Adenocarcinoma
#> 1703                NCIT:C3512          Lung Adenocarcinoma
#> 1704                NCIT:C3512          Lung Adenocarcinoma
#> 1705                NCIT:C3512          Lung Adenocarcinoma
#>      pathological_stage_id pathological_stage_label biosample_status_id
#> 1700           NCIT:C92207            Stage Unknown         EFO:0009656
#> 1701           NCIT:C92207            Stage Unknown         EFO:0009656
#> 1702           NCIT:C92207            Stage Unknown         EFO:0009656
#> 1703           NCIT:C92207            Stage Unknown         EFO:0009656
#> 1704           NCIT:C92207            Stage Unknown         EFO:0009656
#> 1705           NCIT:C92207            Stage Unknown         EFO:0009656
#>      biosample_status_label sample_origin_type_id sample_origin_type_label
#> 1700      neoplastic sample           OBI:0001479   specimen from organism
#> 1701      neoplastic sample           OBI:0001479   specimen from organism
#> 1702      neoplastic sample           OBI:0001479   specimen from organism
#> 1703      neoplastic sample           OBI:0001479   specimen from organism
#> 1704      neoplastic sample           OBI:0001479   specimen from organism
#> 1705      neoplastic sample           OBI:0001479   specimen from organism
#>      sampled_tissue_id sampled_tissue_label tnm_id tnm_label    stage_id
#> 1700    UBERON:0002048                 lung     NA        NA NCIT:C92207
#> 1701    UBERON:0002048                 lung     NA        NA NCIT:C92207
#> 1702    UBERON:0002048                 lung     NA        NA NCIT:C92207
#> 1703    UBERON:0002048                 lung     NA        NA NCIT:C92207
#> 1704    UBERON:0002048                 lung     NA        NA NCIT:C92207
#> 1705    UBERON:0002048                 lung     NA        NA NCIT:C92207
#>        stage_label tumor_grade_id tumor_grade_label age_iso biosample_label
#> 1700 Stage Unknown             NA                NA    P51Y              NA
#> 1701 Stage Unknown             NA                NA                      NA
#> 1702 Stage Unknown             NA                NA    P64Y              NA
#> 1703 Stage Unknown             NA                NA    P69Y              NA
#> 1704 Stage Unknown             NA                NA                      NA
#> 1705 Stage Unknown             NA                NA    P69Y              NA
#>      icdo_morphology_id icdo_morphology_label icdo_topography_id
#> 1700    pgx:icdom-81403   Adenocarcinoma, NOS    pgx:icdot-C34.9
#> 1701    pgx:icdom-81403   Adenocarcinoma, NOS    pgx:icdot-C34.9
#> 1702    pgx:icdom-81403   Adenocarcinoma, NOS    pgx:icdot-C34.9
#> 1703    pgx:icdom-81403   Adenocarcinoma, NOS    pgx:icdot-C34.9
#> 1704    pgx:icdom-81403   Adenocarcinoma, NOS    pgx:icdot-C34.9
#> 1705    pgx:icdom-81403   Adenocarcinoma, NOS    pgx:icdot-C34.9
#>      icdo_topography_label     pubmed_id
#> 1700             Lung, NOS PMID:24174329
#> 1701             Lung, NOS              
#> 1702             Lung, NOS PMID:28336552
#> 1703             Lung, NOS PMID:28481359
#> 1704             Lung, NOS PMID:18632575
#> 1705             Lung, NOS PMID:28481359
#>                                                                                                                               pubmed_label
#> 1700 Clinical Lung Cancer Genome Project (CLCGP), Network Genomic Medicine (NGM). (2013): A genomics-based classification of human lung...
#> 1701                                                                                                                                      
#> 1702                                      Jordan EJ, Kim HR et al. (2017): Prospective Comprehensive Molecular Characterization of Lung...
#> 1703                                               Zehir A, Benayed R et al. (2017): Mutational landscape of metastatic cancer revealed...
#> 1704                                                  Aviel-Ronen S, Coe BP et al. (2008): Genomic markers for malignant progression in...
#> 1705                                               Zehir A, Benayed R et al. (2017): Mutational landscape of metastatic cancer revealed...
#>      cellosaurus_id cellosaurus_label              cbioportal_id
#> 1700                                                            
#> 1701                                                            
#> 1702                                    cbioportal:lung_msk_2017
#> 1703                                  cbioportal:msk_impact_2017
#> 1704                                                            
#> 1705                                  cbioportal:msk_impact_2017
#>      cbioportal_label tcgaproject_id tcgaproject_label cohort_ids
#> 1700               NA                                          NA
#> 1701               NA                                          NA
#> 1702               NA                                          NA
#> 1703               NA                                          NA
#> 1704               NA                                          NA
#> 1705               NA                                          NA
#>                         biosample_name  geoprov_city          geoprov_country
#> 1700               24174329-clc-S00308         Koeln                  Germany
#> 1701                        GSM1018721         Tokyo                    Japan
#> 1702   LUNG_MSK_2017-P_0001303_T03_IM5 New York City United States of America
#> 1703 MSK_IMPACT_2017-P_0000231_T01_IM3 New York City United States of America
#> 1704                         GSM302276     Vancouver                   Canada
#> 1705 MSK_IMPACT_2017-P_0011192_T01_IM5 New York City United States of America
#>      geoprov_iso_alpha3 geoprov_long_lat group_id group_label cnv_fraction
#> 1700                DEU      6.95::50.93       NA          NA           NA
#> 1701                JPN    139.69::35.69       NA          NA           NA
#> 1702                USA    -74.01::40.71       NA          NA           NA
#> 1703                USA    -74.01::40.71       NA          NA           NA
#> 1704                CAN   -123.12::49.25       NA          NA           NA
#> 1705                USA    -74.01::40.71       NA          NA           NA
#>      cnv_del_fraction cnv_dup_fraction
#> 1700               NA               NA
#> 1701               NA               NA
#> 1702               NA               NA
#> 1703               NA               NA
#> 1704               NA               NA
#> 1705               NA               NA

The data contains many columns representing different aspects of sample information.

2.3 Search by biosample id and individual id

In Progenetix, biosample id and individual id serve as unique identifiers for biosamples and the corresponding individuals. You can obtain these IDs through metadata search with filters as described above, or through website interface query.

biosamples_2 <- pgxLoader(type="biosample", biosample_id = "pgxbs-kftvgioe",individual_id = "pgxind-kftx28q5")

metainfo <- c("biosample_id","individual_id","pubmed_id","histological_diagnosis_id","geoprov_city")
biosamples_2[metainfo]
#>     biosample_id   individual_id     pubmed_id histological_diagnosis_id
#> 1 pgxbs-kftvgioe pgxind-kftx28pu PMID:24174329                NCIT:C3512
#> 2 pgxbs-kftvgiom pgxind-kftx28q5 PMID:24174329                NCIT:C3512
#>   geoprov_city
#> 1        Koeln
#> 2        Koeln

It’s also possible to query by a combination of filters, biosample id, and individual id.

2.4 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 1000 samples , you can set limit = 1000, skip = 0.

biosamples_3 <- pgxLoader(type="biosample", filters = "NCIT:C3512",skip=0, limit = 1000)
# Dimension: Number of samples * features
print(dim(biosamples))
#> [1] 4641   44
print(dim(biosamples_3))
#> [1] 1000   44

2.5 Query the number of samples in Progenetix

The number of samples in specific group can be queried by pgxCount function.

pgxCount(filters = "NCIT:C3512")
#>      filters               label total_count exact_match_count
#> 1 NCIT:C3512 Lung Adenocarcinoma        4641              4505

2.6 Parameter codematches use

The NCIt code of retrieved samples doesn’t only contain specified filters but contains child terms.

unique(biosamples$histological_diagnosis_id)
#> [1] "NCIT:C3512" "NCIT:C2923" "NCIT:C7270" "NCIT:C7269" "NCIT:C5649"
#> [6] "NCIT:C5650" "NCIT:C7268"

Setting codematches as TRUE allows this function to only return biosamples with exact match to the filter.

biosamples_4 <- pgxLoader(type="biosample", filters = "NCIT:C3512",codematches = TRUE)

unique(biosamples_4$histological_diagnosis_id)
#> [1] "NCIT:C3512"

2.7 Parameter filterLogic use

This function supports querying samples that belong to multiple filters. For example, If you want to retrieve information about lung adenocarcinoma samples from the literature PMID:24174329, you can specify multiple matching filters and set filterLogic to “AND”.

biosamples_5 <- pgxLoader(type="biosample", filters = c("NCIT:C3512","PMID:24174329"), 
                          filterLogic = "AND")

3 Retrieve meatdata of individuals

If you want to query metadata (e.g. survival data) of individuals where the samples of interest come from, you can follow the tutorial below.

3.1 Relevant parameters

type, filters, filterLogic, individual_id, biosample_id, codematches, limit, skip, dataset

3.2 Search by filters

individuals <- pgxLoader(type="individual",filters="NCIT:C3270")
# Dimension: Number of individuals * features
print(dim(individuals))
#> [1] 2001   17
# data looks like this
individuals[c(36:40),]
#>      individual_id       sex_id     sex_label age_iso  age_days
#> 36 pgxind-kftx49ng PATO:0020000 genotypic sex                NA
#> 37 pgxind-kftx7f5s  NCIT:C16576        female    P69Y 25201.733
#> 38 pgxind-kftx3y25  NCIT:C20197          male     P6Y  2191.455
#> 39 pgxind-kftx7gr5  NCIT:C16576        female    P69Y 25201.733
#> 40 pgxind-kftx382b PATO:0020000 genotypic sex                NA
#>    data_use_conditions_id data_use_conditions_label histological_diagnosis_id
#> 36                     NA                        NA                NCIT:C3270
#> 37                     NA                        NA                NCIT:C3270
#> 38                     NA                        NA                NCIT:C3270
#> 39                     NA                        NA                NCIT:C3270
#> 40                     NA                        NA                NCIT:C3270
#>    histological_diagnosis_label index_disease_notes index_disease_followup_time
#> 36                Neuroblastoma                  NA                        None
#> 37                Neuroblastoma                  NA                        None
#> 38                Neuroblastoma                  NA                        None
#> 39                Neuroblastoma                  NA                        None
#> 40                Neuroblastoma                  NA                        P48M
#>    index_disease_followup_state_id index_disease_followup_state_label
#> 36                     EFO:0030039                 no followup status
#> 37                     EFO:0030039                 no followup status
#> 38                     EFO:0030039                 no followup status
#> 39                     EFO:0030039                 no followup status
#> 40                     EFO:0030041            dead (follow-up status)
#>    auxiliary_disease_id auxiliary_disease_label auxiliary_disease_notes
#> 36                   NA                      NA                      NA
#> 37                   NA                      NA                      NA
#> 38                   NA                      NA                      NA
#> 39                   NA                      NA                      NA
#> 40                   NA                      NA                      NA
#>    individual_legacy_id
#> 36                   NA
#> 37                   NA
#> 38                   NA
#> 39                   NA
#> 40                   NA

3.3 Search by biosample id and individual id

You can get the id from the query of samples

individual <- pgxLoader(type="individual",individual_id = "pgxind-kftx26ml", biosample_id="pgxbs-kftvh94d")

individual
#>     individual_id       sex_id     sex_label age_iso age_days
#> 1 pgxind-kftx3565 PATO:0020000 genotypic sex      NA       NA
#> 2 pgxind-kftx26ml  NCIT:C20197          male      NA       NA
#>   data_use_conditions_id data_use_conditions_label histological_diagnosis_id
#> 1                     NA                        NA                NCIT:C3697
#> 2                     NA                        NA                NCIT:C3493
#>   histological_diagnosis_label index_disease_notes index_disease_followup_time
#> 1     Myxopapillary Ependymoma                  NA                        None
#> 2 Squamous Cell Lung Carcinoma                  NA                        None
#>   index_disease_followup_state_id index_disease_followup_state_label
#> 1                     EFO:0030039                 no followup status
#> 2                     EFO:0030039                 no followup status
#>   auxiliary_disease_id auxiliary_disease_label auxiliary_disease_notes
#> 1                   NA                      NA                      NA
#> 2                   NA                      NA                      NA
#>   individual_legacy_id
#> 1                   NA
#> 2                   NA

4 Visualization of survival data

4.1 pgxMetaplot function

This function generates a survival plot using metadata of individuals obtained by the pgxLoader function.

The parameters of this function:

  • data: The meatdata of individuals returned by pgxLoader function.
  • group_id: A string specifying which column is used for grouping in the Kaplan-Meier plot.
  • condition: Condition for splitting individuals into younger and older groups. Only used if group_id is age related.
  • return_data: A logical value determining whether to return the metadata used for plotting. Default is FALSE.
  • ...: Other parameters relevant to KM plot. These include pval, pval.coord, pval.method, conf.int, linetype, and palette (see ggsurvplot function from survminer package)

Suppose you want to investigate whether there are survival differences between younger and older patients with a particular disease, you can query and visualize the relevant information as follows:

# query metadata of individuals with lung adenocarcinoma
luad_inds <- pgxLoader(type="individual",filters="NCIT:C3512")
# use 65 years old as the splitting condition
pgxMetaplot(data=luad_inds, group_id="age_iso", condition="P65Y", 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.

5 Session Info

#> R version 4.4.1 (2024-06-14 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
#> 
#> Matrix products: default
#> 
#> 
#> locale:
#> [1] LC_COLLATE=C                          
#> [2] LC_CTYPE=English_United States.utf8   
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C                          
#> [5] LC_TIME=English_United States.utf8    
#> 
#> time zone: America/New_York
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] pgxRpi_1.1.4     BiocStyle_2.33.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.5        xfun_0.45           bslib_0.7.0        
#>  [4] ggplot2_3.5.1       rstatix_0.7.2       lattice_0.22-6     
#>  [7] vctrs_0.6.5         tools_4.4.1         generics_0.1.3     
#> [10] curl_5.2.1          tibble_3.2.1        fansi_1.0.6        
#> [13] highr_0.11          pkgconfig_2.0.3     Matrix_1.7-0       
#> [16] data.table_1.15.4   lifecycle_1.0.4     compiler_4.4.1     
#> [19] farver_2.1.2        munsell_0.5.1       tinytex_0.51       
#> [22] carData_3.0-5       htmltools_0.5.8.1   sass_0.4.9         
#> [25] yaml_2.3.9          pillar_1.9.0        car_3.1-2          
#> [28] ggpubr_0.6.0        jquerylib_0.1.4     tidyr_1.3.1        
#> [31] cachem_1.1.0        survminer_0.4.9     magick_2.8.4       
#> [34] abind_1.4-5         km.ci_0.5-6         tidyselect_1.2.1   
#> [37] digest_0.6.36       dplyr_1.1.4         purrr_1.0.2        
#> [40] bookdown_0.40       labeling_0.4.3      splines_4.4.1      
#> [43] fastmap_1.2.0       grid_4.4.1          colorspace_2.1-0   
#> [46] cli_3.6.3           magrittr_2.0.3      survival_3.7-0     
#> [49] utf8_1.2.4          broom_1.0.6         withr_3.0.0        
#> [52] scales_1.3.0        backports_1.5.0     lubridate_1.9.3    
#> [55] timechange_0.3.0    rmarkdown_2.27      httr_1.4.7         
#> [58] gridExtra_2.3       ggsignif_0.6.4      zoo_1.8-12         
#> [61] evaluate_0.24.0     knitr_1.48          KMsurv_0.1-5       
#> [64] survMisc_0.5.6      rlang_1.1.4         Rcpp_1.0.12        
#> [67] xtable_1.8-4        glue_1.7.0          BiocManager_1.30.23
#> [70] attempt_0.3.1       jsonlite_1.8.8      R6_2.5.1           
#> [73] plyr_1.8.9