Introduction_1_loadmetadata
2024-06-16
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:
typeA string specifying output data type. Available options are “biosample”, “individual”, “variant” or “frequency”.
filtersIdentifiers 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.filterLogicA 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_idIdentifiers used in Progenetix database for identifying individuals.biosample_idIdentifiers used in Progenetix database for identifying biosamples.codematchesA 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 whenfiltersinclude ontology-irrelevant filters such as PMID and cohort identifiers. Default is FALSE.limitInteger to specify the number of returned samples/individuals/coverage profiles for each filter. Default is 0 (return all).skipInteger 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).datasetA 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:C28077"
#> [6] "NCIT:C28078"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 biosample_label biosample_legacy_id individual_id
#> 1700 pgxbs-kftvj3y5 NA NA pgxind-kftx4x5b
#> 1701 pgxbs-kftvkvef NA NA pgxind-kftx7204
#> 1702 pgxbs-kftvkwfd NA NA pgxind-kftx73ah
#> 1703 pgxbs-kftvl99a NA NA pgxind-kftx7jcp
#> 1704 pgxbs-kftvl9tn NA NA pgxind-kftx7k2d
#> 1705 pgxbs-kftvj6tn NA NA pgxind-kftx50oe
#> callset_ids group_id group_label pubmed_id
#> 1700 pgxcs-kftweo97 NA NA PMID:20215515
#> 1701 pgxcs-kftwuxdr NA NA PMID:28336552
#> 1702 pgxcs-kftwv8r2 NA NA
#> 1703 pgxcs-kftx0s1n NA NA PMID:28481359
#> 1704 pgxcs-kftx0yfl NA NA PMID:28481359
#> 1705 pgxcs-kftwfk6p NA NA PMID:21521776
#> pubmed_label
#> 1700 Rothenberg SM, Mohapatra G et al. (2010): A genome-wide screen for microdeletions reveals...
#> 1701 Jordan EJ, Kim HR et al. (2017): Prospective Comprehensive Molecular Characterization of Lung...
#> 1702
#> 1703 Zehir A, Benayed R et al. (2017): Mutational landscape of metastatic cancer revealed...
#> 1704 Zehir A, Benayed R et al. (2017): Mutational landscape of metastatic cancer revealed...
#> 1705 Broët P, Dalmasso C et al. (2011): Genomic profiles specific to patient ethnicity...
#> cellosaurus_id cellosaurus_label cbioportal_id
#> 1700 cellosaurus:CVCL_1475 NCI-H1563
#> 1701 cbioportal:lung_msk_2017
#> 1702 cbioportal:lung_msk_pdx
#> 1703 cbioportal:msk_impact_2017
#> 1704 cbioportal:msk_impact_2017
#> 1705
#> cbioportal_label tcgaproject_id tcgaproject_label
#> 1700 NA
#> 1701 NA
#> 1702 NA
#> 1703 NA
#> 1704 NA
#> 1705 NA
#> external_references_id___arrayexpress
#> 1700
#> 1701
#> 1702
#> 1703
#> 1704
#> 1705
#> external_references_label___arrayexpress cohort_ids
#> 1700 NA
#> 1701 NA
#> 1702 NA
#> 1703 NA
#> 1704 NA
#> 1705 NA
#> legacy_ids
#> 1700 PGX_AM_BS_GSM827536
#> 1701 PGX_AM_BS_LUNG_MSK_2017-P_0002091_T02_IM5
#> 1702 PGX_AM_BS_LUNG_MSK_PDX-P_0012001_T01_IM5
#> 1703 PGX_AM_BS_MSK_IMPACT_2017-P_0011030_T01_IM5
#> 1704 PGX_AM_BS_MSK_IMPACT_2017-P_0011548_T01_IM5
#> 1705 PGX_AM_BS_GSM837804
#> notes histological_diagnosis_id
#> 1700 Lung adenocarcinoma [cell line NCI-H1563] NCIT:C3512
#> 1701 Lung Adenocarcinoma NCIT:C3512
#> 1702 Lung Adenocarcinoma NCIT:C3512
#> 1703 Lung Adenocarcinoma NCIT:C3512
#> 1704 Lung Adenocarcinoma NCIT:C3512
#> 1705 lung adenocarcinoma [Western European] NCIT:C3512
#> histological_diagnosis_label icdo_morphology_id icdo_morphology_label
#> 1700 Lung Adenocarcinoma pgx:icdom-81403 Adenocarcinoma, NOS
#> 1701 Lung Adenocarcinoma pgx:icdom-81403 Adenocarcinoma, NOS
#> 1702 Lung Adenocarcinoma pgx:icdom-81403 Adenocarcinoma, NOS
#> 1703 Lung Adenocarcinoma pgx:icdom-81403 Adenocarcinoma, NOS
#> 1704 Lung Adenocarcinoma pgx:icdom-81403 Adenocarcinoma, NOS
#> 1705 Lung Adenocarcinoma pgx:icdom-81403 Adenocarcinoma, NOS
#> icdo_topography_id icdo_topography_label pathological_stage_id
#> 1700 pgx:icdot-C34.9 Lung, NOS
#> 1701 pgx:icdot-C34.9 Lung, NOS NCIT:C92207
#> 1702 pgx:icdot-C34.9 Lung, NOS NCIT:C92207
#> 1703 pgx:icdot-C34.9 Lung, NOS NCIT:C92207
#> 1704 pgx:icdot-C34.9 Lung, NOS NCIT:C92207
#> 1705 pgx:icdot-C34.9 Lung, NOS NCIT:C92207
#> pathological_stage_label biosample_status_id biosample_status_label
#> 1700 EFO:0030035 cancer cell line sample
#> 1701 Stage Unknown EFO:0009656 neoplastic sample
#> 1702 Stage Unknown EFO:0009656 neoplastic sample
#> 1703 Stage Unknown EFO:0009656 neoplastic sample
#> 1704 Stage Unknown EFO:0009656 neoplastic sample
#> 1705 Stage Unknown EFO:0009656 neoplastic sample
#> sampled_tissue_id sampled_tissue_label tnm stage grade age_iso sex_id
#> 1700 UBERON:0002048 lung NA NA NA NA
#> 1701 UBERON:0002048 lung NA NA NA P38Y NA
#> 1702 UBERON:0002048 lung NA NA NA P69Y NA
#> 1703 UBERON:0002048 lung NA NA NA P69Y NA
#> 1704 UBERON:0002048 lung NA NA NA P69Y NA
#> 1705 UBERON:0002048 lung NA NA NA NA
#> sex_label followup_state_id followup_state_label followup_time
#> 1700 NA EFO:0030039 no followup status NA
#> 1701 NA EFO:0030039 no followup status NA
#> 1702 NA EFO:0030039 no followup status NA
#> 1703 NA EFO:0030039 no followup status NA
#> 1704 NA EFO:0030039 no followup status NA
#> 1705 NA EFO:0030039 no followup status NA
#> geoprov_city geoprov_country geoprov_iso_alpha3 geoprov_long_lat
#> 1700 Charlestown United States of America USA -71.06::42.38
#> 1701 New York City United States of America USA -74.01::40.71
#> 1702 New York City United States of America USA -74.01::40.71
#> 1703 New York City United States of America USA -74.01::40.71
#> 1704 New York City United States of America USA -74.01::40.71
#> 1705 Evry France FRA 2.45::48.63
#> cnv_fraction cnv_del_fraction cnv_dup_fraction cell_line
#> 1700 NA NA NA
#> 1701 NA NA NA
#> 1702 NA NA NA
#> 1703 NA NA NA
#> 1704 NA NA NA
#> 1705 NA NA NAThe 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","followup_state_label","followup_time")
biosamples_2[metainfo]
#> biosample_id individual_id pubmed_id followup_state_label
#> 1 pgxbs-kftvgioe pgxind-kftx28pu PMID:24174329 alive (follow-up status)
#> 2 pgxbs-kftvgiom pgxind-kftx28q5 PMID:24174329 dead (follow-up status)
#> followup_time
#> 1 NA
#> 2 NAIt’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 49print(dim(biosamples_3))
#> [1] 1000 492.5 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 45052.6 Parameter codematches use
The NCIt code of retrieved samples doesn’t only contain specified filters but contains child terms.
unique(biosamples$histological_diagnosis_id)
#> [1] "NCIT:C3512" "NCIT:C5649" "NCIT:C5650" "NCIT:C7270" "NCIT:C2923"
#> [6] "NCIT:C7269" "NCIT:C7268"Setting codematches as TRUE allows this function to only return biosamples with exact match to the filter.
biosamples_4 <- pgxLoader(type="biosample", filters = "NCIT:C3512",codematches = TRUE)
unique(biosamples_4$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 25# data looks like this
individuals[c(36:40),]
#> individual_id individual_legacy_id
#> 36 pgxind-kftx49h3 NA
#> 37 pgxind-kftx7kn0 NA
#> 38 pgxind-kftx27yo NA
#> 39 pgxind-kftx49ik NA
#> 40 pgxind-kftx3xon NA
#> legacy_ids sex_id
#> 36 PGX_IND_GSM135032 PATO:0020000
#> 37 PGX_IND_NBL_TARGET_2018_PUB-TARGET_30_PAKADI_01 PATO:0020002
#> 38 PGX_IND_9006325_NB-pla-11 PATO:0020000
#> 39 PGX_IND_GSM135069 PATO:0020000
#> 40 PGX_IND_GSM313917 PATO:0020000
#> sex_label age_iso age_days data_use_conditions_id
#> 36 genotypic sex NA NA
#> 37 female genotypic sex P4Y 1460.970 NA
#> 38 genotypic sex P1Y5M 517.326 NA
#> 39 genotypic sex NA NA
#> 40 genotypic sex P10Y4M 3774.092 NA
#> data_use_conditions_label histological_diagnosis_id
#> 36 NA NCIT:C3270
#> 37 NA NCIT:C3270
#> 38 NA NCIT:C3270
#> 39 NA NCIT:C3270
#> 40 NA NCIT:C3270
#> histological_diagnosis_label index_disease_notes index_disease_followup_time
#> 36 Neuroblastoma NA None
#> 37 Neuroblastoma NA None
#> 38 Neuroblastoma NA P28M
#> 39 Neuroblastoma NA None
#> 40 Neuroblastoma NA P42M
#> index_disease_followup_state_id index_disease_followup_state_label
#> 36 EFO:0030039 no followup status
#> 37 EFO:0030039 no followup status
#> 38 EFO:0030041 alive (follow-up status)
#> 39 EFO:0030039 no followup status
#> 40 EFO:0030039 no followup 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
#> geoprov_id geoprov_city geoprov_country geoprov_iso_alpha3
#> 36 NA Chiba Japan NA
#> 37 NA Bethesda United States of America NA
#> 38 NA San Francisco United States NA
#> 39 NA Chiba Japan NA
#> 40 NA Tokyo Japan NA
#> geoprov_long_lat cell_line_donation_id cell_line_donation_label
#> 36 140.12::35.6 NA NA
#> 37 -77.1::38.98 NA NA
#> 38 -122.42::37.77 NA NA
#> 39 140.12::35.6 NA NA
#> 40 139.69::35.69 NA NA3.3 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 individual_legacy_id legacy_ids sex_id
#> 1 pgxind-kftx3565 NA PGX_IND_EpTu-N270 PATO:0020000
#> 2 pgxind-kftx26ml NA PGX_IND_AdSqLu-bjo-01 PATO:0020001
#> sex_label age_iso age_days data_use_conditions_id
#> 1 genotypic sex NA NA NA
#> 2 male genotypic sex NA NA NA
#> data_use_conditions_label histological_diagnosis_id
#> 1 NA NCIT:C3697
#> 2 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
#> geoprov_id geoprov_city geoprov_country geoprov_iso_alpha3 geoprov_long_lat
#> 1 NA Nijmegen The Netherlands NA 5.84::51.81
#> 2 NA Helsinki Finland NA 24.94::60.17
#> cell_line_donation_id cell_line_donation_label
#> 1 NA NA
#> 2 NA NA4 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 bypgxLoaderfunction.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 ifgroup_idis 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 includepval,pval.coord,pval.method,conf.int,linetype, andpalette(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 4.4.0 (2024-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> 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
#>
#> other attached packages:
#> [1] pgxRpi_1.0.2 BiocStyle_2.32.1
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.5 xfun_0.44 bslib_0.7.0
#> [4] ggplot2_3.5.1 rstatix_0.7.2 lattice_0.22-6
#> [7] vctrs_0.6.5 tools_4.4.0 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_4.4.0
#> [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_2.3.8 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_2.8.3
#> [34] abind_1.4-5 km.ci_0.5-6 tidyselect_1.2.1
#> [37] digest_0.6.35 dplyr_1.1.4 purrr_1.0.2
#> [40] bookdown_0.39 labeling_0.4.3 splines_4.4.0
#> [43] fastmap_1.2.0 grid_4.4.0 colorspace_2.1-0
#> [46] cli_3.6.2 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_1.47 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