| Title: | Curated Prostate Cancer Data |
|---|---|
| Description: | The package curatedPCaData offers a selection of annotated prostate cancer datasets featuring multiple omics, manually curated metadata, and derived downstream variables. The studies are offered as MultiAssayExperiment (MAE) objects via ExperimentHub, and comprise of clinical characteristics tied to gene expression, copy number alteration and somatic mutation data. Further, downstream features computed from these multi-omics data are offered. Multiple vignettes help grasp characteristics of the various studies and provide example exploratory and meta-analysis of leveraging the multiple studies provided here-in. |
| Authors: | Teemu Daniel Laajala [aut, cre] (ORCID: <https://orcid.org/0000-0002-7016-7354>), Jordan Creed [ctb], Christelle Colin Leitzinger [ctb], Varsha Sreekanth [ctb], Federico Calboli [ctb], Kalaimathy Singaravelu [ctb], Michael Orman [ctb], Alex Soupir [ctb], Anni Halkola [ctb] |
| Maintainer: | Teemu Daniel Laajala <[email protected]> |
| License: | CC BY 4.0 |
| Version: | 1.8.0 |
| Built: | 2026-10-10 11:20:37 UTC |
| Source: | https://github.com/bioc/curatedPCaData |
MultiAssayExperiment object containing gene expression (gex), copy number alteration (cna), mutations (mut) and immune cell estimates for Abida et al.
A MAE object spanning 444 castrate resistant prostate cancer samples.
matrix with 20291 rows and 444 columns, from GISTIC discretized copy number alteration calls.
matrix with 18971 rows and 266 columns, from z-score normalized expression in relative to paired normals.
RaggedExperiment with 63184 rows and 444 columns, mutation data from cbioportal
matrix with 22 rows and 261 columns, of cibersort based deconvolution data
matrix with 39 rows and 261 columns, of xcell based deconvolution data
matrix with 8 rows and 261 columns, of epic based deconvolution data
matrix with 11 rows and 261 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 261 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 261 columns, of risk scores and AR scores
mmatrix with 11 rows and 261 columns, of mcp-counter based deconvolution data
the clinical data refers to a sample of 444 tumors collected in 429 patients. The tissue was collected primarily at metastatic sites rather than from the prostate.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.cbioportal.org/study/summary?id=prad_su2c_2019
(PubMed)
mae_abida <- getPCa('abida')mae_abida <- getPCa('abida')
MultiAssayExperiment object containing copy number alteration (cna) and mutations for Baca et al.
A MAE object spanning 56 prostate cancer samples.
matrix with 20124 rows and 56 columns, from GISTIC discretized copy number alteration calls.
RaggedExperiment with 2734 rows and 57 columns, mutation data from cbioportal
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.cbioportal.org/study/summary?id=prad_broad_2013
mae_baca <- getPCa('baca')mae_baca <- getPCa('baca')
MultiAssayExperiment object containing gene expression (gex), copy number alteration (cna), mutations (mut) and immune cell estimates for Barbieri et al.
A MAE object spanning prostate adenocarcinomas from Barbieri et. al
matrix with 20844 rows and 109 columns, from GISTIC discretized copy number alteration calls
matrix with 17917 rows and 31 columns, from z-score normalized expression in relative to paired normals.
RaggedExperiment with 5737 rows and 112 columns, mutation data from cbioportal
matrix with 22 rows and 31 columns, of cibersort based deconvolution data
matrix with 39 rows and 31 columns, of xcell based deconvolution data
matrix with 8 rows and 31 columns, of epic based deconvolution data
matrix with 11 rows and 31 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 31 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 31 columns, of risk scores and AR scores
matrix with 11 rows and 31 columns, of mcp-counter based deconvolution data
The data comprises of primary localised prostate tumors from two cohorts, the Weill Cornell Medical College (WCMC; New York, NY), and the Uropath (Perth, Australia), which commercially provides banked urological tissues. None of the samples comes from patients who had received prior treatment for prostate cancer.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.cbioportal.org/study/summary?id=prad_broad
(PubMed)
mae_barbieri <- getPCa('barbieri')mae_barbieri <- getPCa('barbieri')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates for Barwick et al.
A MAE object spanning 146 prostate cancer samples.
matrix with 482 rows and 146 columns, for the log-quantile normalized gene expression data
matrix with 22 rows and 139 columns, of cibersort based deconvolution data
matrix with 11 rows and 139 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 139 columns, of cell types based on ESTIMATE method
matrix with 3 rows and 139 columns, of risk scores and AR scores
matrix with 4 rows and 139 columns, of mcp-counter based deconvolution data
Barwick et al. uses an older customized DASL array; therefore its gene coverage is lower, and many downstream methods fail due to lack of gene overlap.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18655
(PubMed)
mae_barwick <- getPCa('barwick')mae_barwick <- getPCa('barwick')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates for Chandran et al., Yu et al.
An MAE object spanning 171 men with prostate cancer
matrix with 9007 rows and 171 columns, for the gene expression data
matrix with 22 rows and 171 columns, of cibersort based deconvolution data
matrix with 39 rows and 171 columns, of xcell based deconvolution data
matrix with 8 rows and 171 columns, of epic based deconvolution data
matrix with 11 rows and 171 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 171 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 171 columns, of risk scores and AR scores
matrix with 11 rows and 171 columns, of mcp-counter based deconvolution data
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6919
mae_chandran <- getPCa('chandran')mae_chandran <- getPCa('chandran')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates for Friedrich et al.
An MAE object spanning 255 men with prostate cancer
matrix with 23097 rows and 255 columns, for the log-quantile normalized gene expression data
matrix with 22 rows and 255 columns, of cibersort based deconvolution data
matrix with 39 rows and 255 columns, of xcell based deconvolution data
matrix with 8 rows and 255 columns, of epic based deconvolution data
matrix with 11 rows and 255 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 255 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 255 columns, of risk scores and AR scores
matrix with 11 rows and 255 columns, of mcp-counter based deconvolution data
The data comprises of 255 samples, with 164 primary tumors samples, 52 adjacent normal samples, and 39 benign prostate hyperplasia samples. This dataset includes in its totality the 164 samples analysed in Kreutz et al. 2020.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134051
mae_friedrich <- getPCa('friedrich')mae_friedrich <- getPCa('friedrich')
MultiAssayExperiment object containing copy number alteration (cna) for Hieronymus et al.
A MAE object spanning 104 tumor samples
matrix with 18026 rows and 104 columns, from GISTIC discretized copy number alteration calls
The data comprises of 104 samples, for which are available clinical data and Copy Number Alteration, but no gene expression data – thus no deconvolution results are available.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54691
(PubMed)
mae_hieronymus <- getPCa('hieronymus')mae_hieronymus <- getPCa('hieronymus')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates for the ICGC CA (Canadian) cohort.
An MAE object spanning 213 men with prostate cancer
matrix with 17208 rows and 213 columns, of gene expression data
matrix with 22 rows and 213 columns, of cibersort based deconvolution data
matrix with 39 rows and 213 columns, of xcell based deconvolution data
matrix with 8 rows and 213 columns, of epic based deconvolution data
matrix with 11 rows and 213 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 213 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 213 columns, of risk scores and AR scores
matrix with 11 rows and 213 columns, of mcp-counter based deconvolution data
The data refers to samples from the ICGC Canadian Prostate Cancer Genome Network (ICGC-PRAD-CA)
A MultiAssayExperiment corresponding to the study and its available omics.
https://dcc.icgc.org/projects/PRAD-CA
(PubMed)
mae_icgcca <- getPCa('icgcca')mae_icgcca <- getPCa('icgcca')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates from IGC's Expression Project for Oncology (expO) with a subset for prostate cancer
An MAE object spanning 83 men with prostate cancer
matrix with 12798 rows and 83 columns, of gene expression data
matrix with 22 rows and 83 columns, of cibersort based deconvolution data
matrix with 39 rows and 83 columns, of xcell based deconvolution data
matrix with 8 rows and 83 columns, of epic based deconvolution data
matrix with 11 rows and 83 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 83 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 83 columns, of risk scores and AR scores
matrix with 11 rows and 83 columns, of mcp-counter based deconvolution data
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse2109
mae_igc <- getPCa('igc')mae_igc <- getPCa('igc')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates from Kim et al.
An MAE object spanning 266 men with prostate cancer
matrix with 17638 rows and 266 columns, of gene expression data
matrix with 22 rows and 266 columns, of cibersort based deconvolution data
matrix with 39 rows and 266 columns, of xcell based deconvolution data
matrix with 8 rows and 266 columns, of epic based deconvolution data
matrix with 11 rows and 266 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 266 columns, of cell types based on ESTIMATE method
matrix with 2 rows and 266 columns, of risk scores and AR scores
matrix with 11 rows and 266 columns, of mcp-counter based deconvolution data
The dataset consists of 266 NCCN very low/low or favorable-intermediate risk PCa patients
A MultiAssayExperiment corresponding to the study and its available
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119616
(PubMed)
mae_kim <- getPCa('kim')mae_kim <- getPCa('kim')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates from Kunderfranco et al.
An MAE object spanning 67 samples of normal prostate samples and prostate cancer samples
matrix with 16546 rows and 67 columns, of gene expression data
matrix with 22 rows and 67 columns, of cibersort based deconvolution data
matrix with 39 rows and 67 columns, of xcell based deconvolution data
matrix with 8 rows and 67 columns, of epic based deconvolution data
matrix with 11 rows and 67 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 67 columns, of cell types based on ESTIMATE method
matrix with 2 rows and 67 columns, of risk scores and AR scores
matrix with 11 rows and 67 columns, of mcp-counter based deconvolution data
The data contains 14 disease free benign prostate hyperplasia samples and 53 prostate cancer samples.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14206
(PubMed)
mae_kunderfranco <- getPCa('kunderfranco')mae_kunderfranco <- getPCa('kunderfranco')
MultiAssayExperiment object containing gene expression (gex), mutations and immune cell estimates from Ren et al.
An MAE object spanning 65 men with prostate cancer
matrix with 21046 rows and 65 columns, of gene expression data
RaggedExperiment with 50625 rows and 65 columns, of mutation data from cbioportal
matrix with 22 rows and 65 columns, of cibersort based deconvolution data
matrix with 39 rows and 65 columns, of xcell based deconvolution data
matrix with 8 rows and 65 columns, of epic based deconvolution data
matrix with 11 rows and 65 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 65 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 65 columns, of risk scores and AR scores
matrix with 11 rows and 65 columns, of mcp-counter based deconvolution data
The data contains 14 disease free benign prostate hyperplasia samples and 53 prostate cancer samples.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.cbioportal.org/study/summary?id=prad_eururol_2017
(PubMed)
mae_ren <- getPCa('ren')mae_ren <- getPCa('ren')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates from Sun et al.
An MAE object spanning 79 men with prostate cancer
matrix with 12798 rows and 79 columns, of gene expression data
matrix with 22 rows and 79 columns, of cibersort based deconvolution data
matrix with 39 rows and 79 columns, of xcell based deconvolution data
matrix with 8 rows and 79 columns, of epic based deconvolution data
matrix with 11 rows and 79 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 79 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 79 columns, of risk scores and AR scores
matrix with 11 rows and 79 columns, of mcp-counter based deconvolution data
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25136
(PubMed)
mae_sun <- getPCa('sun')mae_sun <- getPCa('sun')
MultiAssayExperiment object containing gene expression (gex), copy number alteration (cna), mutations and immune cell estimates from Taylor et al.
An MAE object spanning 218 men with prostate cancer
matrix with 17832 rows and 194 columns, of gistic values for copy number alteration data
matrix with 18062 rows and 218 columns, of log-ratios for copy number alteration data
matrix with 17410 rows and 179 columns, of gene expression data
RaggedExperiment with 90 rows and 43 columns, of mutation data
matrix with 22 rows and 179 columns, of cibersort based deconvolution data
matrix with 39 rows and 179 columns, of xcell based deconvolution data
matrix with 8 rows and 179 columns, of epic based deconvolution data
matrix with 11 rows and 179 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 179 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 179 columns, of risk scores and AR scores
matrix with 11 rows and 179 columns, of mcp-counter based deconvolution data
Note that there is lack of overlap between the omics provided for each sample.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21035
(PubMed)
mae_taylor <- getPCa('taylor')mae_taylor <- getPCa('taylor')
MultiAssayExperiment object containing gene expression (gex), copy number alteration (cna), mutations and immune cell estimates from GDC TCGA.
An MAE object spanning 369 men with prostate cancer
matrix with 23151 rows and 492 columns, from GISTIC discretized copy number alteration calls
matrix with 19658 rows and 461 columns, of gene expression data
RaggedExperiment with 30897 rows and 495 columns, of mutation data
matrix with 22 rows and 550 columns, of cibersort based deconvolution data
matrix with 39 rows and 461 columns, of xcell based deconvolution data
matrix with 8 rows and 461 columns, of epic based deconvolution data
matrix with 11 rows and 461 columns, of quantiseq based deconvolution data
matrix with 11 rows and 461 columns, of mcp-counter based deconvolution data
data.frame with 4 rows and 461 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 416 columns, of risk scores and AR scores
TCGA data was obtained from the latest GDC's XenaBrowser release.
A MultiAssayExperiment corresponding to the study and its available omics.
https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Prostate%20Cancer%20(PRAD)
(PubMed)
mae_tcga <- getPCa('tcga')mae_tcga <- getPCa('tcga')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates from True et al.
An MAE object spanning 32 men with prostate cancer
matrix with 3615 rows and 32 columns, of gene expression data
matrix with 22 rows and 32 columns, of cibersort based deconvolution data
matrix with 8 rows and 32 columns, of epic based deconvolution data
matrix with 11 rows and 32 columns, of quantiseq based deconvolution data
matrix with 7 rows and 32 columns, of mcp-counter based deconvolution data
matrix with 2 rows and 32 columns, of risk scores and AR scores
data.frame with 4 rows and 32 columns, of cell types based on ESTIMATE method
The expression data for this dataset was produced by an older two colour chip that provides the relative expression levels for between tumor and normal. Due to the chip type, there was a lack of gene overlap with some of the downstream methods, thus some downstream methods are missing.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5132
mae_true <- getPCa('true')mae_true <- getPCa('true')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates from Wallace et al.
An MAE object spanning 83 men with prostate cancer
matrix with 12783 rows and 89 columns, of gene expression data
matrix with 22 rows and 83 columns, of cibersort based deconvolution data
matrix with 39 rows and 89 columns, of xcell based deconvolution data
matrix with 8 rows and 89 columns, of epic based deconvolution data
matrix with 11 rows and 89 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 89 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 89 columns, of risk scores and AR scores
matrix with 11 rows and 89 columns, of mcp-counter based deconvolution data
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6956
(PubMed)
mae_wallace <- getPCa('wallace')mae_wallace <- getPCa('wallace')
MultiAssayExperiment object containing gene expression (gex) and immune cell estimates from Wang et al.
An MAE object spanning 148 men with prostate cancer
matrix with 12798 rows and 148 columns, of gene expression data
matrix with 22 rows and 148 columns, of cibersort based deconvolution data
matrix with 39 rows and 148 columns, of xcell based deconvolution data
matrix with 8 rows and 148 columns, of epic based deconvolution data
matrix with 11 rows and 148 columns, of quantiseq based deconvolution data
data.frame with 4 rows and 148 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 148 columns, of risk scores and AR scores
matrix with 11 rows and 148 columns, of mcp-counter based deconvolution data
148 prostate samples, with various amounts of tumor, stroma, BPH and atrophic gland, were used for this study.
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8218
(PubMed)
mae_wang <- getPCa('wang')mae_wang <- getPCa('wang')
A MultiAssayExperiment object containing gene expression (gex) and immune cell estimates for Weiner et al.
An MAE spanning 838 prostate cancer samples of two cohorts
matrix of 17410 rows and 838 columns of gene expression data
matrix with 22 rows and 838 columns, of cibersort based deconvolution data
matrix with 39 rows and 838 columns, the xcell deconvolution of the expression data
matrix with 8 rows and 838 columns, the epic deconvolution of the expression data
matrix with 11 rows and 838 columns, the quantiseq deconvolution of the expression data
data.frame with 4 rows and 838 columns, of cell types based on ESTIMATE method
matrix with 4 rows and 838 columns, of risk scores and AR scores
matrix with 11 rows and 838 columns, the mcp-counter deconvolution of the expression data
A MultiAssayExperiment corresponding to the study and its available omics.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157548
(PubMed)
mae_weiner <- getPCa('weiner')mae_weiner <- getPCa('weiner')
curatedPCaData provides MultiAssayExperiment container objects that are constructed from ExperimentHub. User provides PCa dataset name (see list on the overview-vignette or help files).
getPCa(dataset, assays, timestamp, sampletypes, verbose = FALSE, ...)getPCa(dataset, assays, timestamp, sampletypes, verbose = FALSE, ...)
dataset |
character() of PCa cancer cohort names (e.g., 'abida') |
assays |
character() A vector of PCa assays. If not included, returns all available for the selected dataset; see below for more details. |
timestamp |
character(1) "20230215" indicating the data version to obtain from 'ExperimentHub'. See 'timestamp' section details. |
sampletypes |
A character vector of sample types to include in the MAE. If the parameter is missing, then all samples are returned. Allowed values are: 'primary', 'metastatic', 'normal', 'BPH', 'atrophic' |
verbose |
logical(1) Whether to show the dataset currenlty being (down)loaded (default TRUE) |
... |
Additional arguments passed on to the
|
This function will check against available resources in ExperimentHub. For a list of datasets, see the overview-vignette or below dataset listing.
a MultiAssayExperiment of the specified assays and cancer cohort
Following shortnames can be used with getPCa to query for their omics from ExperimentHub. For more detailed description of the studies, see their corresponding R documentation file. Further details are reported in the overview-vignette.
Shortname Rd documentation file Longer name(s) ---------------------------------------------------------------------------- abida curatedPCaDatasets_abida Abida et al. baca curatedPCaDatasets_baca Baca et al. barbieri curatedPCaDatasets_barbieri Barbieri et al. barwick curatedPCaDatasets_barwick Barwick et al. chandran curatedPCaDatasets_chandran Chandran et al., Yu et al. friedrich curatedPCaDatasets_friedrich Friedrich et al. hieronymus curatedPCaDatasets_hieronymus Hieronymus et al. icgcca curatedPCaDatasets_icgcca ICGC Canadian subset igc curatedPCaDatasets_igc International Genomics Consortium (PCa subset) kim curatedPCaDatasets_kim Kim et al. kunderfranco curatedPCaDatasets_kunderfranco Kunderfranco et al. ren curatedPCaDatasets_ren Ren et al. sun curatedPCaDatasets_sun Sun et al. taylor curatedPCaDatasets_taylor Taylor et al. tcga curatedPCaDatasets_tcga The Cancer Genome Atlas (PCa subset, PRAD-prefix) true curatedPCaDatasets_true True et al. wallace curatedPCaDatasets_wallace Wallace et al. wang curatedPCaDatasets_wang Wang et al. weiner curatedPCaDatasets_weiner Weiner et al.
The list of ExperimentList assay names and their descriptions.
These assays can be entered as part of the assays argument in the
main function.
ExperimentList data types Description ---------------------------------------------------------------------------- gex.rma Gene expression values (RMA normalized) gex.logq (Log-quantile) gex.relz (Relative z-scores) gex.logr (Log-ratios) gex.rsem.log (Log-RSEM) cna.gistic Copy number alteration (GISTIC calls) cna.logr (Log-ratios) mut Somatic mutation calls cibersort Immune deconvolution as estimated by CIBERSORTx xcell xCell epic EPIC quantiseq quanTIseq mcp MCP-counter estimate ESTIMATE scores Various risk and AR scores; see vignette references.
"20230215" The timestamp is updated in case the data is updated. In this case, this section describes the changes made for new timestamps. At this time, the only data deposit is from 2023, Feb 15th, indicated by "20230215".
curatedPCaData-package
mae_taylor <- getPCa( dataset = "taylor", timestamp = "20230215" )mae_taylor <- getPCa( dataset = "taylor", timestamp = "20230215" )
This function accesses the metadata available together with the curatedPCaData package and creates a vector of unique study identifier names available in the ExperimentHub resources. This vector is useful, for example, when applying the 'getPCa' to create MAE from the studies.
getPCaStudies(uniqs = TRUE)getPCaStudies(uniqs = TRUE)
uniqs |
Should only unique instances be returned; by default TRUE |
Uses the internal 'metadata.csv' file, which is also used to provide the metadata available in ExperimentHub.
A character vector with short study identifiers, e.g. 'abida', 'baca', ...
studyids <- getPCaStudies()studyids <- getPCaStudies()
Create two types of summary matrices; first one takes all unique assay names, and summarizes sample counts available for all provided studies. The second matrix takes gene expression, copy number alteration, and somatic mutation data, and displays sample counts for each omic individually, as well as all combinations of intersections for samples available for multi-omics.
getPCaSummarySamples(maes)getPCaSummarySamples(maes)
maes |
List of MultiAssayExperiment objects to summarize |
A list with two elements: first is a matrix depicting sample counts in each unique assay name for the studies; second one is a matrix with sample count overlap between the three main omics (GEX = gene expression; CNA = copy number alteration; MUT = somatic mutations).
mae_taylor <- getPCa('taylor') mae_tcga <- getPCa('tcga') getPCaSummarySamples(maes = list(Taylor = mae_taylor, TCGA = mae_tcga))mae_taylor <- getPCa('taylor') mae_tcga <- getPCa('tcga') getPCaSummarySamples(maes = list(Taylor = mae_taylor, TCGA = mae_tcga))
Create a summary table of key characteristics for each study
getPCaSummaryStudies(maes)getPCaSummaryStudies(maes)
maes |
A list of MAE objects or a character vector with study short ids |
A table with study names, references, sample counts, data source, and other notes.
studies <- getPCaStudies(uniqs = TRUE) tab <- getPCaSummaryStudies(studies)studies <- getPCaStudies(uniqs = TRUE) tab <- getPCaSummaryStudies(studies)
Create summary for survival-like columns (0/1 event with follow-up time) for colData metadata in curatedPCaData
getPCaSummarySurv(maes, event.name, time.name)getPCaSummarySurv(maes, event.name, time.name)
maes |
List of MultiAssayExperiment objects to summarize |
event.name |
Name for the colData column for the end-point event; typically one of 'days_to_disease_specific_recurrence' or 'days_to_overall_survival' |
time.name |
Name for the colData column for the time for the end-point until event or censoring; typically one of 'disease_specific_recurrence_status' or 'overall_survival_status' |
A matrix describing the event and follow-up information as character strings
mae_taylor <- getPCa('taylor') mae_tcga <- getPCa('tcga') getPCaSummarySurv(maes = list(Taylor = mae_taylor, TCGA = mae_tcga), event.name = 'disease_specific_recurrence_status', time.name = 'days_to_disease_specific_recurrence')mae_taylor <- getPCa('taylor') mae_tcga <- getPCa('tcga') getPCaSummarySurv(maes = list(Taylor = mae_taylor, TCGA = mae_tcga), event.name = 'disease_specific_recurrence_status', time.name = 'days_to_disease_specific_recurrence')
Create a variable value availability tables; NA-value in addition to provided 'vals' and Other-values for debugging or anomalies
getPCaSummaryTable(maes, var.name, vals, nas = TRUE)getPCaSummaryTable(maes, var.name, vals, nas = TRUE)
maes |
List of MultiAssayExperiment objects to summarize |
var.name |
Name of the metadata variable to look for in colData |
vals |
Possible values for the metadata variable to tabulate |
nas |
Whether NA or values other than those contained in 'vals' should be appended as extra columns |
A matrix describing the tabulates 'vals' and their proportions along with missingness
mae_taylor <- getPCa('taylor') mae_tcga <- getPCa('tcga') getPCaSummaryTable(maes = list(Taylor = mae_taylor, TCGA = mae_tcga), var.name = 'gleason_grade', vals = 5:10)mae_taylor <- getPCa('taylor') mae_tcga <- getPCa('tcga') getPCaSummaryTable(maes = list(Taylor = mae_taylor, TCGA = mae_tcga), var.name = 'gleason_grade', vals = 5:10)
This data frame contains the clinical fields which were the aim for extraction when gathering metadata for each dataset. Variable types, ranges, and description is reported.
template_pradtemplate_prad
An object of class data.frame with 68 rows and 6 columns.
A data.frame of the PCa template
data(template_prad) head(template_prad)data(template_prad) head(template_prad)