gversmee/dbgap2x repository overview

⁠Using dbgap2x, R package to explore, download and decrypt phenotypic and genomic data from dbGaP

You can test this software:

• Using the dockerized version on your local device by running

docker run -p 80:8888 -v /var/run/docker.sock:/var/run/docker.sock  gversmee/dbgap2x

and then open your web browser at http://localhost⁠, and use the password dbgap2x

• Using your local R by installing the package with

install.packages("devtools")
devtools::install_github("gversmee/dbgap2x")

For using the package with a fresh R installation, make sure your system has the following libraries: libcurl4-openssl-dev libssl-dev libxml2-dev. Example for a debian system:

sudo apt-get update
sudo apt-get install libcurl4-openssl-dev libssl-dev libxml2-dev -y

⁠Introduction

⁠Load the package

#devtools::install_github("gversmee/dbgap2x", force = TRUE)
library(dbgap2x)

⁠Get the list of the function for this new package

lsf.str("package:dbgap2x")

browse.dbgap : function (phs, no.browser = FALSE)  
browse.study : function (phs, no.browser = FALSE)  
consent.groups : function (phs)  
datatables.dict : function (phs)  
dbgap.data_dict : function (xml, dest)  
dbgap.decrypt : function (files, key = FALSE)  
dbgap.download : function (krt, key = FALSE)  
is.parent : function (phs)  
n.pop : function (phs, consentgroups = TRUE, gender = TRUE)  
n.tables : function (phs)  
n.variables : function (...)  
parent.study : function (phs)  
phs.version : function (phs)  
search.dbgap : function (term, no.browser = FALSE)  
study.name : function (phs)  
sub.study : function (phs)  
variables.dict : function (phs)  

⁠Search for dbGaP studies

⁠Let's try to explore the "Jackson Heart Study" cohort that exists on dbGaP.

⁠We created the function "browse.dbgap", which helps you to find the studies related to the term that you search for.

search.dbgap("Jackson")

https://www.ncbi.nlm.nih.gov/gap/?term=Jackson%5BStudy+Name%5D 
                          

⁠dbGaP returns the list of the studies related to your term. As you see, there are 6 studies associated with the "Jackson Heart Study" (JHS). One of these study is the main one a.k.a the "parent study", whereas the other ones are substudies. In this case, phs000286.v5.p1 is the parent study. Firslty, we can use the phs.version() function in order to be sure that this is the latest version of the study. We can abbreviate the phs name by giving just the digit, or we can use the full dbGaP id.

phs.version("286")

'phs000286.v6.p2'

⁠The is.parent() function is usefull to test if a study is a parent study or a substudy

is.parent("000286") # JHS main cohort
is.parent("phs499") # substudy "CARe" for JHS

TRUE

FALSE

⁠If you don't know the parent study of a substudy, try parent.study()

parent.study("phs000499")

1. 'phs000286.v6.p2'
2. 'Jackson Heart Study (JHS) Cohort'

⁠On the other side, use sub.study() to get the name and IDs of the substudies from a parent one

sub.study("286")

⁠If you want to get the name of a study from its dbGaP id, use study.name()

study.name("286")

'Jackson Heart Study (JHS) Cohort'

⁠Finally, you can watch your study on dbGaP with browse.dbgap().

⁠If a website exists for this study, you can browse it using browse.study()

browse.dbgap("286", no.browser = TRUE)
browse.study("286", no.browser = TRUE)

'https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000286.v6.p2⁠'

'https://www.jacksonheartstudy.org⁠'

⁠Explore the characteristics of your study

⁠For each dbGaP study, there can be multiple consent groups that will have there specificities. Use consent.groups to know the number and the name of the consent groups in the study that you are exploring. Let's keep focusing on JHS.

JHS <- "phs000286"
consent.groups(JHS)

⁠Use n.pop() to know the number of patient included in each groups

n.pop(JHS)
n.pop(JHS, consentgroups = FALSE)

9774

⁠Use n.tables() and n.variables() to get the number of datatables in your study and the total number of variables

n.tables(JHS)
n.variables(JHS)

112

4856

⁠datatables.dict() will return a data frame with the datatables IDs (phtxxxxxx) and description of your study

tablesdict <- datatables.dict(JHS)
head(tablesdict)

⁠variables.dict() will return a data frame with the variables IDs (phvxxxxxx), their name in the study, the datatable where they come from and their description

vardict <- variables.dict(JHS)
head(vardict)