Introduction

ImmPort study data is available for download in two formats currently: MySQL and TSV (Tab) formats. The RImmPort workflow is as follows: 1) MySQL formatted study data: User downloads one or more studies in MySQL zip files. Unzips the files. Loads local database instance. Connects to the database. Sets the ImmPort data source to the connection handle. Invokes RImmPort functions. 2) Tab: User downloads one or more studies in Tab format. Passes the folder where the zip files are located to an RImmPort function that builds SQLite database. Connects to the database. Sets the ImmPort data source to the connection handle. Invokes RImmPort functions.

User downloads study data of interest from the ImmPort website ( http://www.immport.org ) **. Depending on the file format MySQL or Tab the data is loaded into a local MySQL and SQLite database respectively. The user installs the RImmPort package, loads the RImmPort library, connects to the ImmPort database, and calls RImmPort methods to load study data from the database into R. Please refer to RImmPort_Article.pdf for a detailed discussion on RImmPort.

** User need to regsiter to the ImmPort website for downloading the datasets.

Initial Steps

Download MySQL or Tab formatted data of studies of interest from the ImmPort website
If working with MySQL-format, load the data in to a local MySQL database
Install and load RImmPort package, and other required packages.

Load the RImmPort library

library(RImmPort)
library(DBI)
library(sqldf)
library(plyr)

Setup ImmPort data source that all RImmPort functions will use

Option 1: ImmPort MySQL database

Download zip files of ImmPort study data in MySQL format. e.g.’SDY139’ and ‘SDY208’

Load the data into a local MySQL database

Connect to the ImmPort MySQL database.

# provide appropriate connection parameters
mysql_conn <- dbConnect(MySQL(), user="username", password="password", 
                   dbname="database",host="host")

Set the data source as the ImmPort MySQL database.

setImmPortDataSource(mysql_conn)

Option 2: ImmPort SQLite database

Download zip files of ImmPort data, in Tab format. e.g.’SDY139’ and ‘SDY208’

# get the directory where ImmPort sample data is stored in the directory structure of RImmPort package
studies_dir <- system.file("extdata", "ImmPortStudies", package = "RImmPort")

# set tab_dir to the folder where the zip files are located
tab_dir <- file.path(studies_dir, "Tab")
list.files(tab_dir)

Build a local SQLite ImmPort database instance.

# set db_dir to the folder where the database file 'ImmPort.sqlite' should be stored
db_dir <- file.path(studies_dir, "Db")

# build a new ImmPort SQLite database with the data in the downloaded zip files
buildNewSqliteDb(tab_dir, db_dir)

list.files(db_dir)

Connect to the ImmPort SQLite database

# get the directory of a sample SQLite database that has been bundled into the RImmPort package
db_dir <- system.file("extdata", "ImmPortStudies", "Db", package = "RImmPort")

# connect to the private instance of the ImmPort database
sqlite_conn <- dbConnect(SQLite(), dbname=file.path(db_dir, "ImmPort.sqlite"))

Set the data source to the ImmPort SQLite DB

setImmPortDataSource(sqlite_conn)

## [1] 1

NOTE: In rest of the script, all RImmPort functions will use the SQLite ImmPort database as the data source.

Get all study ids

getListOfStudies()

## [1] "SDY139" "SDY208"

Get all data of a specific study

The getStudyFromDatabase queries the ImmPort database for the entire dataset of a specific study, and instantiates the Study reference class with that data.

?Study

# load all the data of study: `SDY139`
study_id <- 'SDY139'
sdy139 <- getStudy(study_id)

## loading Study ID =  SDY139 
## loading Demographics data....done 
## loading Concomitant Medications data....done 
## loading Exposure data....done 
## loading Substance Use data....done 
## loading Adverse Events data....done 
## loading Protocol Deviations data....done 
## loading Medical History data....done 
## loading Associated Persons Medical History data....done 
## loading Laboratory Test Results data....done 
## loading Physical Examination data....done 
## loading Vital Signs data....done 
## loading Questionnaires data....done 
## loading Findings About data....done 
## loading Skin Response data....done 
## loading Genetics Findings data....loading HLA Typing Results data....done 
## loading Array Results data....done 
## done 
## loading Protein Quantification data....loading ELISA Results data....done 
## loading MBAA Results data....done 
## done 
## loading Cellular Quantification data....loading FCS Results data....done 
## loading ELISPOT Results data....done 
## done 
## loading Nucleic Acid Quantification data....loading PCR Results data....done 
## done 
## loading Titer Assay Results data....loading HAI Assay Results data....done 
## loading Neut. Ab Titer Results data....done 
## done 
## loading TrialArms data....done 
## loading Trial Visits data....done 
## loading  TrialInclusionExclusionCriteria data....done 
## loading TrialSummary data.... SDY139  done 
## done loading Study ID =  SDY139

# access Demographics data of SDY139
dm_df <- sdy139$special_purpose$dm_l$dm_df
head(dm_df)

##   STUDYID DOMAIN   USUBJID AGE   AGEU     SEX RACE ETHNIC      SPECIES STRAIN
## 1  SDY139     DM SUB118053   2 Months Unknown <NA>   <NA> Mus musculus BALB/c
## 2  SDY139     DM SUB118054   2 Months Unknown <NA>   <NA> Mus musculus BALB/c
## 3  SDY139     DM SUB118055   2 Months Unknown <NA>   <NA> Mus musculus BALB/c
## 4  SDY139     DM SUB118056   2 Months Unknown <NA>   <NA> Mus musculus BALB/c
## 5  SDY139     DM SUB118057   2 Months Unknown <NA>   <NA> Mus musculus BALB/c
## 6  SDY139     DM SUB118058   2 Months Unknown <NA>   <NA> Mus musculus BALB/c
##   SBSTRAIN  ARMCD    ARM
## 1     <NA> ARM678 BALB/c
## 2     <NA> ARM678 BALB/c
## 3     <NA> ARM678 BALB/c
## 4     <NA> ARM678 BALB/c
## 5     <NA> ARM678 BALB/c
## 6     <NA> ARM678 BALB/c

# access Concomitant Medications data of SDY139
cm_df <- sdy139$interventions$cm_l$cm_df
head(cm_df)

## NULL

# get Trial Title from Trial Summary
ts_df <- sdy139$trial_design$ts_l$ts_df
title <- ts_df$TSVAL[ts_df$TSPARMCD== "TITLE"]
title

## [1] "The peptide specificity of the endogenous T follicular helper cell repertoire generated after protein immunization"

Get the list of Domain names.

Note that some RImmPort functions take a domain name as input.

# get the list of names of all supported Domains
getListOfDomains()

##                           Domain Name Domain Code
## 1                      Adverse Events          AE
## 2             Concomitant Medications          CM
## 3                        Demographics          DM
## 4                            Exposure          EX
## 5                     Medical History          MH
## 6  Associated Persons Medical History        APMH
## 7             Laboratory Test Results          LB
## 8                Physical Examination          PE
## 9                 Protocol Deviations          DV
## 10                         Trial Arms          TA
## 11                       Trial Visits          TV
## 12 Trial Inclusion Exclusion Criteria          TI
## 13                      Trial Summary          TS
## 14                      Substance Use          SU
## 15                        Vital Signs          VS
## 16                     Questionnaires          QS
## 17                     Findings About          FA
## 18                      Skin Response          SR
## 19                  Genetics Findings          PF
## 20             Protein Quantification          ZA
## 21            Cellular Quantification          ZB
## 22        Nucleic Acid Quantification          ZC
## 23                Titer Assay Results          ZD

?"Demographics Domain"

Get list of studies with specifc domain data

The Domain name should be exact to what is found in the list of Domain names.

# get list of studies with Cellular Quantification data
domain_name <- "Cellular Quantification"
study_ids_l <- getStudiesWithSpecificDomainData(domain_name)
study_ids_l

## [1] "SDY139" "SDY208"

Get specifc domain data of one or more studies