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.
library(pgxRpi)
pgxLoader
functionThis 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”.type, filters, filterLogic, individual_id, biosample_id, codematches, limit, skip, dataset
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-kftvkvg7 NA NA pgxind-kftx722b
#> 1701 pgxbs-kftvkvn8 NA NA pgxind-kftx72b4
#> 1702 pgxbs-kftvgjjq NA NA pgxind-kftx29ry
#> 1703 pgxbs-kftvgjo2 NA NA pgxind-kftx29x9
#> 1704 pgxbs-kftvl6p6 NA NA pgxind-kftx7g6p
#> 1705 pgxbs-kftvk3je NA NA pgxind-kftx63u8
#> callset_ids group_id group_label pubmed_id
#> 1700 pgxcs-kftwuxwh NA NA PMID:28336552
#> 1701 pgxcs-kftwv02y NA NA PMID:28336552
#> 1702 pgxcs-kftvmemh NA NA PMID:24174329
#> 1703 pgxcs-kftvmfwt NA NA PMID:24174329
#> 1704 pgxcs-kftwzlf0 NA NA PMID:28481359
#> 1705 pgxcs-kftwomky NA NA
#> pubmed_label
#> 1700 Jordan EJ, Kim HR et al. (2017): Prospective Comprehensive Molecular Characterization of Lung...
#> 1701 Jordan EJ, Kim HR et al. (2017): Prospective Comprehensive Molecular Characterization of Lung...
#> 1702 Clinical Lung Cancer Genome Project (CLCGP), Network Genomic Medicine (NGM). (2013): A genomics-based classification of human lung...
#> 1703 Clinical Lung Cancer Genome Project (CLCGP), Network Genomic Medicine (NGM). (2013): A genomics-based classification of human lung...
#> 1704 Zehir A, Benayed R et al. (2017): Mutational landscape of metastatic cancer revealed...
#> 1705
#> cellosaurus_id cellosaurus_label cbioportal_id
#> 1700 cbioportal:lung_msk_2017
#> 1701 cbioportal:lung_msk_2017
#> 1702
#> 1703
#> 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 notes
#> 1700 PGX_AM_BS_LUNG_MSK_2017-P_0002523_T01_IM3 Lung Adenocarcinoma
#> 1701 PGX_AM_BS_LUNG_MSK_2017-P_0003964_T01_IM3 Lung Adenocarcinoma
#> 1702 PGX_AM_BS_24174329-clc-S01201 adenocarcinoma [lung]
#> 1703 PGX_AM_BS_24174329-clc-S01363 adenocarcinoma [lung]
#> 1704 PGX_AM_BS_MSK_IMPACT_2017-P_0008577_T02_IM5 Lung Adenocarcinoma
#> 1705 PGX_AM_BS_GSM1018726 lung adenocarcinoma
#> histological_diagnosis_id histological_diagnosis_label icdo_morphology_id
#> 1700 NCIT:C3512 Lung Adenocarcinoma pgx:icdom-81403
#> 1701 NCIT:C3512 Lung Adenocarcinoma pgx:icdom-81403
#> 1702 NCIT:C3512 Lung Adenocarcinoma pgx:icdom-81403
#> 1703 NCIT:C3512 Lung Adenocarcinoma pgx:icdom-81403
#> 1704 NCIT:C3512 Lung Adenocarcinoma pgx:icdom-81403
#> 1705 NCIT:C3512 Lung Adenocarcinoma pgx:icdom-81403
#> icdo_morphology_label icdo_topography_id icdo_topography_label
#> 1700 Adenocarcinoma, NOS pgx:icdot-C34.9 Lung, NOS
#> 1701 Adenocarcinoma, NOS pgx:icdot-C34.9 Lung, NOS
#> 1702 Adenocarcinoma, NOS pgx:icdot-C34.9 Lung, NOS
#> 1703 Adenocarcinoma, NOS pgx:icdot-C34.9 Lung, NOS
#> 1704 Adenocarcinoma, NOS pgx:icdot-C34.9 Lung, NOS
#> 1705 Adenocarcinoma, NOS pgx:icdot-C34.9 Lung, NOS
#> 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 sampled_tissue_id sampled_tissue_label tnm stage
#> 1700 neoplastic sample UBERON:0002048 lung NA NA
#> 1701 neoplastic sample UBERON:0002048 lung NA NA
#> 1702 neoplastic sample UBERON:0002048 lung NA NA
#> 1703 neoplastic sample UBERON:0002048 lung NA NA
#> 1704 neoplastic sample UBERON:0002048 lung NA NA
#> 1705 neoplastic sample UBERON:0002048 lung NA NA
#> grade age_iso geoprov_city geoprov_country geoprov_iso_alpha3
#> 1700 NA P70Y New York City United States of America USA
#> 1701 NA P72Y New York City United States of America USA
#> 1702 NA P73Y Koeln Germany DEU
#> 1703 NA P58Y Koeln Germany DEU
#> 1704 NA P69Y New York City United States of America USA
#> 1705 NA Tokyo Japan JPN
#> geoprov_long_lat cnv_fraction cnv_del_fraction cnv_dup_fraction cell_line
#> 1700 -74.01::40.71 NA NA NA
#> 1701 -74.01::40.71 NA NA NA
#> 1702 6.95::50.93 NA NA NA
#> 1703 6.95::50.93 NA NA NA
#> 1704 -74.01::40.71 NA NA NA
#> 1705 139.69::35.69 NA NA NA
The data contains many columns representing different aspects of sample information.
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.
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
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
codematches
useThe NCIt code of retrieved samples doesn’t only contain specified filters but contains child terms.
unique(biosamples$histological_diagnosis_id)
#> [1] "NCIT:C3512" "NCIT:C2923" "NCIT:C5650" "NCIT:C7270" "NCIT:C5649"
#> [6] "NCIT:C7269" "NCIT:C7268"
Setting codematches
as TRUE allows this function to only return biosamples with exact match to the filter.
biosamples_4 <- pgxLoader(type="biosample", filters = "NCIT:C3512",codematches = TRUE)
unique(biosamples_4$histological_diagnosis_id)
#> [1] "NCIT:C3512"
filterLogic
useThis 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")
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.
type, filters, filterLogic, individual_id, biosample_id, codematches, limit, skip, dataset
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 legacy_ids sex_id
#> 36 pgxind-kftx2ovj NA PGX_IND_Nbl-mic-AH-2 PATO:0020000
#> 37 pgxind-kftx3xqn NA PGX_IND_GSM313952 PATO:0020000
#> 38 pgxind-kftx2y30 NA PGX_IND_NB2p-sta-33 PATO:0020000
#> 39 pgxind-kftx5dye NA PGX_IND_GSM634062 PATO:0020000
#> 40 pgxind-kftx3zwx NA PGX_IND_GSM365862 PATO:0020000
#> sex_label age_iso age_days data_use_conditions_id
#> 36 genotypic sex P1Y2M 426.0759 NA
#> 37 genotypic sex P2Y8M 973.8186 NA
#> 38 genotypic sex P2Y 730.4850 NA
#> 39 genotypic sex NA NA
#> 40 genotypic sex NA 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 P63.6M
#> 37 Neuroblastoma NA P88M
#> 38 Neuroblastoma NA None
#> 39 Neuroblastoma NA None
#> 40 Neuroblastoma NA None
#> index_disease_followup_state_id index_disease_followup_state_label
#> 36 EFO:0030041 dead (follow-up status)
#> 37 EFO:0030039 no followup status
#> 38 EFO:0030039 no followup 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 Gent Belgium NA
#> 37 NA Tokyo Japan NA
#> 38 NA Dublin Ireland NA
#> 39 NA Genoa Italy NA
#> 40 NA Chicago United States NA
#> geoprov_long_lat cell_line_donation_id cell_line_donation_label
#> 36 3.7199999999999998::51.05 NA NA
#> 37 139.69::35.69 NA NA
#> 38 -6.25::53.33 NA NA
#> 39 8.92::44.43 NA NA
#> 40 -87.65::41.85 NA NA
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 NCIT:C20197
#> sex_label age_iso age_days data_use_conditions_id
#> 1 genotypic sex NA NA NA
#> 2 male 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 NA
pgxMetaplot
functionThis 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.
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