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: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.
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$histological_diagnosis_id)
#> [1] "NCIT:C3512" "NCIT:C2923" "NCIT:C7270" "NCIT:C7269" "NCIT:C5649"
#> [6] "NCIT:C5650" "NCIT:C7268"
Setting codematches
as TRUE allows this function to only return biosamples with exact match to the filter.
biosamples_4 <- pgxLoader(type="biosample", filters = "NCIT:C3512",codematches = TRUE)
unique(biosamples_4$histological_diagnosis_id)
#> [1] "NCIT:C3512"
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 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
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
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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