Interface with the 'ClinicalOmicsDB' API, Allowing for Easy Data Downloading and Importing.

clinicalomicsdbR

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/.

Installation

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.

Parameters

• 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.

Functions

• new() - Create new clinicalomicsdbR object. Needed before any other function
• filter(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 dataframe
• download_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.

Examples

Filter and Download

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.

Filter and Get Data Frame

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