Introduction_1_loadmetadata
2024-10-13
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 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: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-kftvjjhx pgxind-kftx5fyd lung adenocarcinoma
#> 1701 pgxbs-kftvjjhz pgxind-kftx5fyf lung adenocarcinoma
#> 1702 pgxbs-kftvjji1 pgxind-kftx5fyh lung adenocarcinoma
#> 1703 pgxbs-kftvjjn2 pgxind-kftx5g4r lung adenocarcinoma [cell line PC-9/GR4]
#> 1704 pgxbs-kftvjjn4 pgxind-kftx5g4t lung adenocarcinoma [cell line PC-9/WZR10]
#> 1705 pgxbs-kftvjjn5 pgxind-kftx5g4v lung adenocarcinoma [cell line PC-9/WZR11]
#> 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:C27975 Stage Ia EFO:0009656
#> 1701 NCIT:C27976 Stage Ib EFO:0009656
#> 1702 NCIT:C27976 Stage Ib EFO:0009656
#> 1703 NCIT:C92207 Stage Unknown EFO:0030035
#> 1704 NCIT:C92207 Stage Unknown EFO:0030035
#> 1705 NCIT:C92207 Stage Unknown EFO:0030035
#> 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 cancer cell line sample OBI:0001876 cell culture
#> 1704 cancer cell line sample OBI:0001876 cell culture
#> 1705 cancer cell line sample OBI:0001876 cell culture
#> sampled_tissue_id sampled_tissue_label tnm tumor_grade_id
#> 1700 UBERON:0002048 lung NA
#> 1701 UBERON:0002048 lung NA
#> 1702 UBERON:0002048 lung NA
#> 1703 UBERON:0002048 lung NA
#> 1704 UBERON:0002048 lung NA
#> 1705 UBERON:0002048 lung NA
#> tumor_grade_label age_iso biosample_label icdo_morphology_id
#> 1700 NA NA pgx:icdom-81403
#> 1701 NA NA pgx:icdom-81403
#> 1702 NA NA pgx:icdom-81403
#> 1703 NA NA pgx:icdom-81403
#> 1704 NA NA pgx:icdom-81403
#> 1705 NA NA 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
#> pubmed_id
#> 1700 PMID:26444668
#> 1701 PMID:26444668
#> 1702 PMID:26444668
#> 1703 PMID:22961667
#> 1704 PMID:22961667
#> 1705 PMID:22961667
#> pubmed_label
#> 1700 Aramburu A, Zudaire I, Pajares MJ, Agorreta J, Orta et al. (2015): Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy ...
#> 1701 Aramburu A, Zudaire I, Pajares MJ, Agorreta J, Orta et al. (2015): Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy ...
#> 1702 Aramburu A, Zudaire I, Pajares MJ, Agorreta J, Orta et al. (2015): Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy ...
#> 1703 Ercan D, Xu C, Yanagita M et al. (2012): Reactivation of ERK signaling causes resistance...
#> 1704 Ercan D, Xu C, Yanagita M et al. (2012): Reactivation of ERK signaling causes resistance...
#> 1705 Ercan D, Xu C, Yanagita M et al. (2012): Reactivation of ERK signaling causes resistance...
#> cellosaurus_id cellosaurus_label cbioportal_id cbioportal_label
#> 1700 NA
#> 1701 NA
#> 1702 NA
#> 1703 cellosaurus:CVCL_DH34 PC-9/GR4 NA
#> 1704 cellosaurus:CVCL_DG31 PC-9/WZR10 NA
#> 1705 cellosaurus:CVCL_DG32 PC-9/WZR11 NA
#> tcgaproject_id tcgaproject_label cohort_ids biosample_name geoprov_city
#> 1700 NA GSM1857292 San Sebastian
#> 1701 NA GSM1857293 San Sebastian
#> 1702 NA GSM1857294 San Sebastian
#> 1703 NA GSM925738 Boston
#> 1704 NA GSM925740 Boston
#> 1705 NA GSM925741 Boston
#> geoprov_country geoprov_iso_alpha3 geoprov_long_lat group_id
#> 1700 Spain ESP -1.97::43.31 NA
#> 1701 Spain ESP -1.97::43.31 NA
#> 1702 Spain ESP -1.97::43.31 NA
#> 1703 United States of America USA -71.06::42.36 NA
#> 1704 United States of America USA -71.06::42.36 NA
#> 1705 United States of America USA -71.06::42.36 NA
#> group_label
#> 1700 NA
#> 1701 NA
#> 1702 NA
#> 1703 NA
#> 1704 NA
#> 1705 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","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 KoelnIt’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 38
print(dim(biosamples_3))
#> [1] 1000 382.5 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$histological_diagnosis_id)
#> [1] "NCIT:C3512" "NCIT:C5649" "NCIT:C7269" "NCIT:C2923" "NCIT:C7268"
#> [6] "NCIT:C5650" "NCIT:C7270"Setting codematches as TRUE allows this function to only return biosamples with exact match to the filter.
biosamples_4 <- pgxLoader(type="biosample", filters = "NCIT:C3512",codematches = TRUE)
unique(biosamples_4$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-kftx27zb PATO:0020000 genotypic sex P5Y7M 2039.1294
#> 37 pgxind-kftx27zd PATO:0020000 genotypic sex P0Y5M 152.0835
#> 38 pgxind-kftx27zf PATO:0020000 genotypic sex P0Y4M 121.6668
#> 39 pgxind-kftx27zh PATO:0020000 genotypic sex P0Y6M 182.5002
#> 40 pgxind-kftx27zj PATO:0020000 genotypic sex P0Y4M 121.6668
#> 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 P20M
#> 37 Neuroblastoma NA P41M
#> 38 Neuroblastoma NA P18M
#> 39 Neuroblastoma NA P8M
#> 40 Neuroblastoma NA P19M
#> index_disease_followup_state_id index_disease_followup_state_label
#> 36 EFO:0030049 alive (follow-up status)
#> 37 EFO:0030049 alive (follow-up status)
#> 38 EFO:0030049 alive (follow-up status)
#> 39 EFO:0030049 alive (follow-up 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 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 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 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 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.5 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_1.0.5 BiocStyle_2.32.1
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.5 xfun_0.48 bslib_0.8.0
#> [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_5.2.3 tibble_3.2.1 fansi_1.0.6
#> [13] highr_0.11 pkgconfig_2.0.3 Matrix_1.7-0
#> [16] data.table_1.16.2 lifecycle_1.0.4 compiler_4.4.1
#> [19] farver_2.1.2 munsell_0.5.1 tinytex_0.53
#> [22] carData_3.0-5 htmltools_0.5.8.1 sass_0.4.9
#> [25] yaml_2.3.10 Formula_1.2-5 pillar_1.9.0
#> [28] car_3.1-3 ggpubr_0.6.0 jquerylib_0.1.4
#> [31] tidyr_1.3.1 cachem_1.1.0 survminer_0.4.9
#> [34] magick_2.8.5 abind_1.4-8 km.ci_0.5-6
#> [37] tidyselect_1.2.1 digest_0.6.37 dplyr_1.1.4
#> [40] purrr_1.0.2 bookdown_0.40 labeling_0.4.3
#> [43] splines_4.4.1 fastmap_1.2.0 grid_4.4.1
#> [46] colorspace_2.1-1 cli_3.6.3 magrittr_2.0.3
#> [49] survival_3.7-0 utf8_1.2.4 broom_1.0.7
#> [52] withr_3.0.1 scales_1.3.0 backports_1.5.0
#> [55] lubridate_1.9.3 timechange_0.3.0 rmarkdown_2.28
#> [58] httr_1.4.7 gridExtra_2.3 ggsignif_0.6.4
#> [61] zoo_1.8-12 evaluate_1.0.1 knitr_1.48
#> [64] KMsurv_0.1-5 survMisc_0.5.6 rlang_1.1.4
#> [67] Rcpp_1.0.13 xtable_1.8-4 glue_1.8.0
#> [70] BiocManager_1.30.25 attempt_0.3.1 jsonlite_1.8.9
#> [73] R6_2.5.1 plyr_1.8.9