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 cancer-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-kftvjjhx NA NA pgxind-kftx5fyd
#> 1701 pgxbs-kftvjjhz NA NA pgxind-kftx5fyf
#> 1702 pgxbs-kftvjji1 NA NA pgxind-kftx5fyh
#> 1703 pgxbs-kftvjjn2 NA NA pgxind-kftx5g4r
#> 1704 pgxbs-kftvjjn4 NA NA pgxind-kftx5g4t
#> 1705 pgxbs-kftvjjn5 NA NA pgxind-kftx5g4v
#> callset_ids group_id group_label pubmed_id
#> 1700 pgxcs-kftwjevi NA NA PMID:26444668
#> 1701 pgxcs-kftwjew0 NA NA PMID:26444668
#> 1702 pgxcs-kftwjewi NA NA PMID:26444668
#> 1703 pgxcs-kftwjg5r NA NA PMID:22961667
#> 1704 pgxcs-kftwjg6q NA NA PMID:22961667
#> 1705 pgxcs-kftwjg78 NA NA 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 external_references_id___arrayexpress
#> 1700
#> 1701
#> 1702
#> 1703
#> 1704
#> 1705
#> external_references_label___arrayexpress external_references_id___PMID
#> 1700 PMID:26444668
#> 1701 PMID:26444668
#> 1702 PMID:26444668
#> 1703 PMID:22961667
#> 1704 PMID:22961667
#> 1705 PMID:22961667
#> external_references_label___PMID external_references_id___cbioportal
#> 1700 NA
#> 1701 NA
#> 1702 NA
#> 1703 NA
#> 1704 NA
#> 1705 NA
#> external_references_label___cbioportal
#> 1700 NA
#> 1701 NA
#> 1702 NA
#> 1703 NA
#> 1704 NA
#> 1705 NA
#> external_references_id___cellosaurus
#> 1700
#> 1701
#> 1702
#> 1703 cellosaurus:CVCL_DH34
#> 1704 cellosaurus:CVCL_DG31
#> 1705 cellosaurus:CVCL_DG32
#> external_references_label___cellosaurus cohort_ids legacy_ids
#> 1700 NA PGX_AM_BS_GSM1857292
#> 1701 NA PGX_AM_BS_GSM1857293
#> 1702 NA PGX_AM_BS_GSM1857294
#> 1703 NA PGX_AM_BS_GSM925738
#> 1704 NA PGX_AM_BS_GSM925740
#> 1705 NA PGX_AM_BS_GSM925741
#> notes histological_diagnosis_id
#> 1700 lung adenocarcinoma NCIT:C3512
#> 1701 lung adenocarcinoma NCIT:C3512
#> 1702 lung adenocarcinoma NCIT:C3512
#> 1703 lung adenocarcinoma [cell line PC-9/GR4] NCIT:C3512
#> 1704 lung adenocarcinoma [cell line PC-9/WZR10] NCIT:C3512
#> 1705 lung adenocarcinoma [cell line PC-9/WZR11] 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 NCIT:C27975
#> 1701 pgx:icdot-C34.9 Lung, NOS NCIT:C27976
#> 1702 pgx:icdot-C34.9 Lung, NOS NCIT:C27976
#> 1703 pgx:icdot-C34.9 Lung, NOS
#> 1704 pgx:icdot-C34.9 Lung, NOS
#> 1705 pgx:icdot-C34.9 Lung, NOS
#> pathological_stage_label biosample_status_id biosample_status_label
#> 1700 Stage Ia EFO:0009656 neoplastic sample
#> 1701 Stage Ib EFO:0009656 neoplastic sample
#> 1702 Stage Ib EFO:0009656 neoplastic sample
#> 1703 EFO:0030035 cancer cell line sample
#> 1704 EFO:0030035 cancer cell line sample
#> 1705 EFO:0030035 cancer cell line 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 NA
#> 1702 UBERON:0002048 lung NA NA NA NA
#> 1703 UBERON:0002048 lung NA NA NA NA
#> 1704 UBERON:0002048 lung NA NA NA 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 San Sebastian Spain ESP -1.97::43.31
#> 1701 San Sebastian Spain ESP -1.97::43.31
#> 1702 San Sebastian Spain ESP -1.97::43.31
#> 1703 Boston United States of America USA -71.06::42.36
#> 1704 Boston United States of America USA -71.06::42.36
#> 1705 Boston United States of America USA -71.06::42.36
#> cnv_fraction cnv_del_fraction cnv_dup_fraction cell_line experiment_id
#> 1700 NA NA NA geo:GSM1857292
#> 1701 NA NA NA geo:GSM1857293
#> 1702 NA NA NA geo:GSM1857294
#> 1703 NA NA NA geo:GSM925738
#> 1704 NA NA NA geo:GSM925740
#> 1705 NA NA NA geo:GSM925741
#> series_id platform_id cell_line_id cell_line_label
#> 1700 geo:GSE72192 geo:GPL3720 NA NA
#> 1701 geo:GSE72192 geo:GPL3720 NA NA
#> 1702 geo:GSE72192 geo:GPL3720 NA NA
#> 1703 geo:GSE37698 geo:GPL3720 NA NA
#> 1704 geo:GSE37698 geo:GPL3720 NA NA
#> 1705 geo:GSE37698 geo:GPL3720 NA NAThe 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","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.
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 60
print(dim(biosamples_3))
#> [1] 1000 60The 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 4505codematches useThe NCIt code of retrieved samples doesn’t only contain specified filters but contains child terms.
unique(biosamples$histological_diagnosis_id)
#> [1] "NCIT:C3512" "NCIT:C5649" "NCIT:C7269" "NCIT:C2923" "NCIT:C7268"
#> [6] "NCIT:C5650" "NCIT:C7270"Setting codematches as TRUE allows this function to only return biosamples with exact match to the filter.
biosamples_4 <- pgxLoader(type="biosample", filters = "NCIT:C3512",codematches = TRUE)
unique(biosamples_4$histological_diagnosis_id)
#> [1] "NCIT:C3512"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] 1989 60
# data looks like this
individuals[c(36:40),]
#> biosample_id biosample_label biosample_legacy_id individual_id
#> 36 pgxbs-kftvgi2m NA NA pgxind-kftx27zb
#> 37 pgxbs-kftvgi2n NA NA pgxind-kftx27zd
#> 38 pgxbs-kftvgi2p NA NA pgxind-kftx27zf
#> 39 pgxbs-kftvgi2q NA NA pgxind-kftx27zh
#> 40 pgxbs-kftvgi2s NA NA pgxind-kftx27zj
#> callset_ids group_id group_label pubmed_id
#> 36 pgxcs-kftvlyoe NA NA PMID:9006325
#> 37 pgxcs-kftvlyov NA NA PMID:9006325
#> 38 pgxcs-kftvlypc NA NA PMID:9006325
#> 39 pgxcs-kftvlypt NA NA PMID:9006325
#> 40 pgxcs-kftvlyqa NA NA PMID:9006325
#> pubmed_label
#> 36 Plantaz D, Mohapatra G et al. (1997): Gain of chromosome 17 is the...
#> 37 Plantaz D, Mohapatra G et al. (1997): Gain of chromosome 17 is the...
#> 38 Plantaz D, Mohapatra G et al. (1997): Gain of chromosome 17 is the...
#> 39 Plantaz D, Mohapatra G et al. (1997): Gain of chromosome 17 is the...
#> 40 Plantaz D, Mohapatra G et al. (1997): Gain of chromosome 17 is the...
#> cellosaurus_id cellosaurus_label cbioportal_id cbioportal_label
#> 36 NA
#> 37 NA
#> 38 NA
#> 39 NA
#> 40 NA
#> tcgaproject_id tcgaproject_label external_references_id___arrayexpress
#> 36 NA NA
#> 37 NA NA
#> 38 NA NA
#> 39 NA NA
#> 40 NA NA
#> external_references_label___arrayexpress external_references_id___PMID
#> 36 PMID:9006325
#> 37 PMID:9006325
#> 38 PMID:9006325
#> 39 PMID:9006325
#> 40 PMID:9006325
#> external_references_label___PMID external_references_id___cbioportal
#> 36 NA
#> 37 NA
#> 38 NA
#> 39 NA
#> 40 NA
#> external_references_label___cbioportal external_references_id___cellosaurus
#> 36 NA
#> 37 NA
#> 38 NA
#> 39 NA
#> 40 NA
#> external_references_label___cellosaurus cohort_ids
#> 36 NA
#> 37 NA
#> 38 NA
#> 39 NA
#> 40 NA
#> legacy_ids notes histological_diagnosis_id
#> 36 PGX_AM_BS_9006325_NB-pla-22 Neuroblastoma NCIT:C3270
#> 37 PGX_AM_BS_9006325_NB-pla-23 Neuroblastoma NCIT:C3270
#> 38 PGX_AM_BS_9006325_NB-pla-24 Neuroblastoma NCIT:C3270
#> 39 PGX_AM_BS_9006325_NB-pla-25 Neuroblastoma NCIT:C3270
#> 40 PGX_AM_BS_9006325_NB-pla-26 Neuroblastoma NCIT:C3270
#> histological_diagnosis_label icdo_morphology_id icdo_morphology_label
#> 36 Neuroblastoma pgx:icdom-95003 Neuroblastoma, NOS
#> 37 Neuroblastoma pgx:icdom-95003 Neuroblastoma, NOS
#> 38 Neuroblastoma pgx:icdom-95003 Neuroblastoma, NOS
#> 39 Neuroblastoma pgx:icdom-95003 Neuroblastoma, NOS
#> 40 Neuroblastoma pgx:icdom-95003 Neuroblastoma, NOS
#> icdo_topography_id icdo_topography_label pathological_stage_id
#> 36 pgx:icdot-C72.9 Nervous system, NOS NCIT:C27971
#> 37 pgx:icdot-C72.9 Nervous system, NOS NCIT:C27971
#> 38 pgx:icdot-C72.9 Nervous system, NOS NCIT:C27971
#> 39 pgx:icdot-C72.9 Nervous system, NOS NCIT:C27971
#> 40 pgx:icdot-C72.9 Nervous system, NOS NCIT:C27971
#> pathological_stage_label biosample_status_id biosample_status_label
#> 36 Stage IV EFO:0009656 neoplastic sample
#> 37 Stage IV EFO:0009656 neoplastic sample
#> 38 Stage IV EFO:0009656 neoplastic sample
#> 39 Stage IV EFO:0009656 neoplastic sample
#> 40 Stage IV EFO:0009656 neoplastic sample
#> sampled_tissue_id sampled_tissue_label tnm stage grade age_iso sex_id
#> 36 UBERON:0001016 nervous system NA NA NA P5Y7M NA
#> 37 UBERON:0001016 nervous system NA NA NA P0Y5M NA
#> 38 UBERON:0001016 nervous system NA NA NA P0Y4M NA
#> 39 UBERON:0001016 nervous system NA NA NA P0Y6M NA
#> 40 UBERON:0001016 nervous system NA NA NA P0Y4M NA
#> sex_label followup_state_id followup_state_label followup_time
#> 36 NA EFO:0030049 dead (follow-up status) NA
#> 37 NA EFO:0030049 dead (follow-up status) NA
#> 38 NA EFO:0030049 dead (follow-up status) NA
#> 39 NA EFO:0030049 dead (follow-up status) NA
#> 40 NA EFO:0030041 alive (follow-up status) NA
#> geoprov_city geoprov_country geoprov_iso_alpha3 geoprov_long_lat
#> 36 San Francisco United States of America USA -122.42::37.77
#> 37 San Francisco United States of America USA -122.42::37.77
#> 38 San Francisco United States of America USA -122.42::37.77
#> 39 San Francisco United States of America USA -122.42::37.77
#> 40 San Francisco United States of America USA -122.42::37.77
#> cnv_fraction cnv_del_fraction cnv_dup_fraction cell_line experiment_id
#> 36 NA NA NA
#> 37 NA NA NA
#> 38 NA NA NA
#> 39 NA NA NA
#> 40 NA NA NA
#> series_id platform_id cell_line_id cell_line_label
#> 36 NA NA
#> 37 NA NA
#> 38 NA NA
#> 39 NA NA
#> 40 NA NAYou can get the id from the query of samples
individual <- pgxLoader(type="individual",individual_id = "pgxind-kftx26ml", biosample_id="pgxbs-kftvh94d")
individual
#> biosample_id biosample_label biosample_legacy_id individual_id
#> 1 pgxbs-kftvh94d NA NA pgxind-kftx3565
#> 2 pgxbs-kftva6d4 NA NA pgxind-kftx26ml
#> callset_ids group_id group_label pubmed_id
#> 1 pgxcs-kftvu6cg NA NA PMID:12015749
#> 2 pgxcs-kftvlmn8 NA NA PMID:9591638
#> pubmed_label
#> 1 Jeuken JW, Sprenger SH et al. (2002): Correlation between localization, age, and chromosomal...
#> 2 Björkqvist AM, Husgafvel-Pursiainen K et al. (1998): DNA gains in 3q occur frequently...
#> cellosaurus_id cellosaurus_label cbioportal_id cbioportal_label
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> tcgaproject_id tcgaproject_label external_references_id___arrayexpress
#> 1 NA NA NA
#> 2 NA NA NA
#> external_references_label___arrayexpress external_references_id___PMID
#> 1 NA PMID:12015749
#> 2 NA PMID:9591638
#> external_references_label___PMID external_references_id___cbioportal
#> 1 NA NA
#> 2 NA NA
#> external_references_label___cbioportal external_references_id___cellosaurus
#> 1 NA NA
#> 2 NA NA
#> external_references_label___cellosaurus cohort_ids legacy_ids
#> 1 NA NA PGX_AM_BS_EpTu-N270
#> 2 NA NA PGX_AM_BS_AdSqLu-bjo-01
#> notes histological_diagnosis_id
#> 1 myxopapillary ependymoma [WHO grade I, Spinal] NCIT:C3697
#> 2 squamous cell carcinoma [lung] NCIT:C3493
#> histological_diagnosis_label icdo_morphology_id icdo_morphology_label
#> 1 Myxopapillary Ependymoma pgx:icdom-93941 Myxopapillary ependymoma
#> 2 Squamous Cell Lung Carcinoma pgx:icdom-80703 Squamous cell carcinoma, NOS
#> icdo_topography_id icdo_topography_label pathological_stage_id
#> 1 pgx:icdot-C72.0 Spinal cord NCIT:C92207
#> 2 pgx:icdot-C34.9 Lung, NOS NCIT:C92207
#> pathological_stage_label biosample_status_id biosample_status_label
#> 1 Stage Unknown EFO:0009656 neoplastic sample
#> 2 Stage Unknown EFO:0009656 neoplastic sample
#> sampled_tissue_id sampled_tissue_label tnm stage grade age_iso sex_id
#> 1 UBERON:0002240 spinal cord NA NA NA NA NA
#> 2 UBERON:0002048 lung NA NA NA NA NA
#> sex_label followup_state_id followup_state_label followup_time geoprov_city
#> 1 NA EFO:0030039 no followup status NA Nijmegen
#> 2 NA EFO:0030039 no followup status NA Helsinki
#> geoprov_country geoprov_iso_alpha3 geoprov_long_lat cnv_fraction
#> 1 Netherlands NLD 5.84::51.81 NA
#> 2 Finland FIN 24.94::60.17 NA
#> cnv_del_fraction cnv_dup_fraction cell_line experiment_id series_id
#> 1 NA NA NA NA NA
#> 2 NA NA NA NA NA
#> platform_id cell_line_id cell_line_label
#> 1 NA NA NA
#> 2 NA NA NA#> R version 4.4.0 RC (2024-04-16 r86468)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.20-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
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#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> other attached packages:
#> [1] pgxRpi_1.1.2 BiocStyle_2.33.0
#>
#> loaded via a namespace (and not attached):
#> [1] digest_0.6.35 R6_2.5.1 bookdown_0.39
#> [4] fastmap_1.1.1 xfun_0.43 cachem_1.0.8
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#> [19] curl_5.2.1 evaluate_0.23 bslib_0.7.0
#> [22] Rcpp_1.0.12 yaml_2.3.8 BiocManager_1.30.22
#> [25] jsonlite_1.8.8 rlang_1.1.3