R package to interface with the ClinicalOmicsDB API. Can be used to download data for your own analysis, or directly load study information into a dataframe for exploration.
Designed with the structure from https://r-pkgs.org/.
To install the latest stable release, run
install.packages("clinicalomicsdbR")You can install the development version of clinicalomicsdbR from GitHub with:
# install.packages("devtools")
devtools::install_github("bzhanglab/clinicalomicsdbR")See Examples below to see how to use.
hostname - base URL of the website containing the
ClinicalOmicsDB API. Only change if you are running a custom
service.study_list - list containing all the studies that were
filtered by the filter() function.new() - Create new clinicalomicsdbR object. Needed
before any other functionfilter(drugs, cancers) - filters studies matching
provided arguments. drugs is a list and can be individual
drugs or combinations. See the ClinicalOmicsDB website for all options.
cancers can contain multiple cancers.download(output_dir) - downloads all studies from
filter() into output_dir.dataframe() - loads all the studies from
filter() into a list, with column study_list
that contains the names of the studies and df that contains
a list of the study data information.dataframe_from_id(study_id) - loads a study with id
from study_id into a dataframedownload_from_id(study_id, output_dir) - downsloads a
study with id from study_id into a folder
output_dir. See the examples below for more information on
how to use.Filters studies for those which used rituximab or ipilimumab then
downloads them to the studies folder.
library(clinicalomicsdbR)
clinicalomicsdbR$new()$filter(drugs = c("ipilimumab", "rituximab"))$download(output_dir = tempdir()) # downloads all files
#> Filtered to 4 studies.
#> Downloading study Gide_Cell_2019_pembro_ipi.csv from https://bcm.box.com/shared/static/swf5fywqcqmf75600g7v8irt2a9agnqo.csv
#> Downloading study VanAllen_antiCTLA4_2015.csv from https://bcm.box.com/shared/static/v0sphd7ht487qk96xbwjokgkbkjpexom.csv
#> Downloading study Gide_Cell_2019_nivo_ipi.csv from https://bcm.box.com/shared/static/jwv108f6cy4kvyeqer95jdugla53m1zt.csv
#> Downloading study GSE35935.csv from https://bcm.box.com/shared/static/8icr4i6gbbp6lgd01iscbss4v7lnj6c5.csv
#> Downloaded 4 studies.Filters studies for those which used rituximab or ipilimumab then gets data frame.
Notes: output_dir is optional. Defaults to
clindb.
library(clinicalomicsdbR)
res <- clinicalomicsdbR$new()$filter(drugs = c("ipilimumab", "rituximab"))$dataframe(); # downloads all files
#> Filtered to 4 studies.
#> Getting dataframe of study Gide_Cell_2019_pembro_ipi.csv from https://bcm.box.com/shared/static/swf5fywqcqmf75600g7v8irt2a9agnqo.csv
#> Getting dataframe of study VanAllen_antiCTLA4_2015.csv from https://bcm.box.com/shared/static/v0sphd7ht487qk96xbwjokgkbkjpexom.csv
#> Getting dataframe of study Gide_Cell_2019_nivo_ipi.csv from https://bcm.box.com/shared/static/jwv108f6cy4kvyeqer95jdugla53m1zt.csv
#> Getting dataframe of study GSE35935.csv from https://bcm.box.com/shared/static/8icr4i6gbbp6lgd01iscbss4v7lnj6c5.csv
for (study in res[["study_list"]]) {
print(ncol(res[["df"]][[study]]))
}
#> [1] 15194
#> [1] 15059
#> [1] 17145
#> [1] 20321