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Introduction to DataSpaceR

Ju Yeong Kim

2022-06-15

This package provides a thin wrapper around Rlabkey and connects to the the CAVD DataSpace database, making it easier to fetch datasets from specific studies.

Configuration

First, go to DataSpace now and set yourself up with an account.

In order to connect to the CAVD DataSpace via DataSpaceR, you will need a netrc file in your home directory that will contain a machine name (hostname of DataSpace), and login and password. There are two ways to create a netrc file.

Creating a netrc file with writeNetrc

On your R console, create a netrc file using a function from DataSpaceR:

This will create a netrc file in your home directory. Make sure you have a valid login and password.

Manually creating a netrc file

Alternatively, you can manually create a netrc file.

The following three lines must be included in the .netrc or _netrc file either separated by white space (spaces, tabs, or newlines) or commas. Multiple such blocks can exist in one file.

machine dataspace.cavd.org
login myuser@domain.com
password supersecretpassword

See here for more information about netrc.

Initiate a connection

We’ll be looking at study cvd256. If you want to use a different study, change that string. You can instantiate multiple connections to different studies simultaneously.

library(DataSpaceR)
con <- connectDS()
con
#> <DataSpaceConnection>
#>   URL: https://dataspace.cavd.org
#>   User: jmtaylor@scharp.org
#>   Available studies: 273
#>     - 77 studies with data
#>     - 5049 subjects
#>     - 423195 data points
#>   Available groups: 3
#>   Available publications: 1530
#>     - 12 publications with data

The call to connectDS instantiates the connection. Printing the object shows where it’s connected and the available studies.

knitr::kable(head(con$availableStudies))
study_name short_name title type status stage species start_date strategy network data_availability ni_data_availability
cor01 NA The correlate of risk targeted intervention study (CORTIS): A randomized, partially-blinded, clinical trial of isoniazid and rifapentine (3HP) therapy to prevent pulmonary tuberculosis in high-risk individuals identified by a transcriptomic correlate of risk Phase III Inactive Assays Completed Human NA NA GH-VAP NA NA
cvd232 Parks_RV_232 ​Limiting Dose Vaginal SIVmac239 Challenge of RhCMV-SIV vaccinated Indian rhesus macaques. Pre-Clinical NHP Inactive Assays Completed Rhesus macaque 2009-11-24 Vector vaccines (viral or bacterial) CAVD NA NA
cvd234 Zolla-Pazner_Mab_test1 Study Zolla-Pazner_Mab_Test1 Antibody Screening Inactive Assays Completed Non-Organism Study 2009-02-03 Prophylactic neutralizing Ab CAVD NA NA
cvd235 mAbs potency Weiss mAbs potency Antibody Screening Inactive Assays Completed Non-Organism Study 2008-08-21 Prophylactic neutralizing Ab CAVD NA NA
cvd236 neutralization assays neutralization assays Antibody Screening Active In Progress Non-Organism Study 2009-02-03 Prophylactic neutralizing Ab CAVD NA NA
cvd238 Gallo_PA_238 HIV-1 neutralization responses in chronically infected individuals Antibody Screening Inactive Assays Completed Non-Organism Study 2009-01-08 Prophylactic neutralizing Ab CAVD NA NA

con$availableStudies shows the available studies in the CAVD DataSpace. Check out the reference page of DataSpaceConnection for all available fields and methods.

cvd256 <- con$getStudy("cvd256")
cvd256
#> <DataSpaceStudy>
#>   Study: cvd256
#>   URL: https://dataspace.cavd.org/CAVD/cvd256
#>   Available datasets:
#>     - Binding Ab multiplex assay
#>     - Demographics
#>     - Neutralizing antibody
#>   Available non-integrated datasets:

con$getStudy creates a connection to the study cvd256. Printing the object shows where it’s connected, to what study, and the available datasets.

knitr::kable(cvd256$availableDatasets)
name label n integrated
BAMA Binding Ab multiplex assay 6740 TRUE
Demographics Demographics 121 TRUE
NAb Neutralizing antibody 1419 TRUE
knitr::kable(cvd256$treatmentArm)
arm_id arm_part arm_group arm_name randomization coded_label last_day description
cvd256-NA-A-A NA A A Vaccine Group A Vaccine 168 DNA-C 4 mg administered IM at weeks 0, 4, and 8 AND NYVAC-C 10^7pfu/mL administered IM at week 24
cvd256-NA-B-B NA B B Vaccine Group B Vaccine 168 DNA-C 4 mg administered IM at weeks 0 and 4 AND NYVAC-C 10^7pfu/mL administered IM at weeks 20 and 24

Available datasets and treatment arm information for the connection can be accessed by availableDatasets and treatmentArm.

Fetching datasets

We can grab any of the datasets listed in the connection (availableDatasets).

NAb <- cvd256$getDataset("NAb")
dim(NAb)
#> [1] 1419   33
colnames(NAb)
#>  [1] "participant_id"      "participant_visit"   "visit_day"          
#>  [4] "assay_identifier"    "summary_level"       "specimen_type"      
#>  [7] "antigen"             "antigen_type"        "virus"              
#> [10] "virus_type"          "virus_insert_name"   "clade"              
#> [13] "neutralization_tier" "tier_clade_virus"    "target_cell"        
#> [16] "initial_dilution"    "titer_ic50"          "titer_ic80"         
#> [19] "response_call"       "nab_lab_source_key"  "lab_code"           
#> [22] "exp_assayid"         "titer_id50"          "titer_id80"         
#> [25] "nab_response_id50"   "nab_response_id80"   "slope"              
#> [28] "vaccine_matched"     "study_prot"          "virus_full_name"    
#> [31] "virus_species"       "virus_host_cell"     "virus_backbone"

The cvd256 object is an R6 class, so it behaves like a true object. Functions (like getDataset) are members of the object, thus the $ semantics to access member functions.

We can get detailed variable information using getDatasetDescription. getDataset and getDatasetDescription accept either the name or label field listed in availableDatasets.

knitr::kable(cvd256$getDatasetDescription("NAb"))
fieldName caption type description
ParticipantId Participant ID Text (String) Subject identifier
antigen Antigen name Text (String) The name of the antigen (virus) being tested.
antigen_type Antigen type Text (String) The standardized term for the type of virus used in the construction of the nAb antigen.
assay_identifier Assay identifier Text (String) Name identifying assay
clade Virus clade Text (String) The clade (gene subtype) of the virus (antigen) being tested.
exp_assayid Experimental Assay Design Code Integer Unique ID assigned to the experiment design of the assay for tracking purposes.
initial_dilution Initial dilution Number (Double) Indicates the initial specimen dilution.
lab_code Lab ID Text (String) A code indicating the lab performing the assay.
nab_lab_source_key Data provenance Integer Details regarding the provenance of the assay results.
nab_response_ID50 Response call ID50 True/False (Boolean) Indicates if neutralization is detected based on ID50 titer.
nab_response_ID80 Response call ID80 True/False (Boolean) Indicates if neutralization is detected based on ID80 titer.
neutralization_tier Neutralization tier Text (String) A classification specific to HIV NAb assay design, in which an antigen is assessed for its ease of neutralization (1=most easily neutralized, 3=least easily neutralized)
response_call Response call True/False (Boolean) Indicates if neutralization is detected.
slope Slope Number (Double) The slope calculated using the difference between 50% and 80% neutralization.
specimen_type Specimen type Text (String) The type of specimen used in the assay. For nAb assays, this is generally serum or plasma.
study_prot Study Protocol Text (String) Study protocol
summary_level Data summary level Text (String) Defines the level at which the magnitude or response has been summarized (e.g. summarized at the isolate level).
target_cell Target cell Text (String) The cell line used in the assay to determine infection (lack of neutralization). Generally TZM-bl or A3R5, but can also be other cell lines or non-engineered cells.
tier_clade_virus Neutralization tier + Antigen clade + Virus Text (String) A combination of neutralization tier, antigen clade, and virus used for filtering.
titer_ID50 Titer ID50 Number (Double) The adjusted value of 50% maximal inhibitory dilution (ID50).
titer_ID80 Titer ID80 Number (Double) The adjusted value of 80% maximal inhibitory dilution (ID80).
titer_ic50 Titer IC50 Number (Double) The half maximal inhibitory concentration (IC50).
titer_ic80 Titer IC80 Number (Double) The 80% maximal inhibitory concentration (IC80).
vaccine_matched Antigen vaccine match indicator True/False (Boolean) Indicates if the interactive part of the antigen was designed to match the immunogen in the vaccine.
virus Virus name Text (String) The term for the virus (antigen) being tested.
virus_backbone Virus backbone Text (String) Indicates the backbone used to generate the virus if from a different plasmid than the envelope.
virus_full_name Virus full name Text (String) The full name of the virus used in the construction of the nAb antigen.
virus_host_cell Virus host cell Text (String) The host cell used to incubate the virus stock.
virus_insert_name Virus insert name Text (String) The amino acid sequence inserted in the virus construct.
virus_species Virus species Text (String) A classification for virus species using informal taxonomy.
virus_type Virus type Text (String) The type of virus used in the construction of the nAb antigen.
visit_day Visit Day Integer Target study day defined for a study visit. Study days are relative to Day 0, where Day 0 is typically defined as enrollment and/or first injection.

To get only a subset of the data and speed up the download, filters can be passed to getDataset. The filters are created using the makeFilter function of the Rlabkey package.

cvd256Filter <- makeFilter(c("visit_day", "EQUAL", "0"))
NAb_day0 <- cvd256$getDataset("NAb", colFilter = cvd256Filter)
dim(NAb_day0)
#> [1] 709  33

See ?makeFilter for more information on the syntax.

Creating a connection to all studies

To fetch data from multiple studies, create a connection at the project level.

cavd <- con$getStudy("")

This will instantiate a connection at the CAVD level. Most functions work cross study connections just like they do on single studies.

You can get a list of datasets available across all studies.

cavd
#> <DataSpaceStudy>
#>   Study: CAVD
#>   URL: https://dataspace.cavd.org/CAVD
#>   Available datasets:
#>     - Binding Ab multiplex assay
#>     - Demographics
#>     - Enzyme-Linked ImmunoSpot
#>     - Intracellular Cytokine Staining
#>     - Neutralizing antibody
#>     - PK MAb
#>   Available non-integrated datasets:
knitr::kable(cavd$availableDatasets)
name label n integrated
BAMA Binding Ab multiplex assay 170320 TRUE
Demographics Demographics 5049 TRUE
ELISPOT Enzyme-Linked ImmunoSpot 5610 TRUE
ICS Intracellular Cytokine Staining 195883 TRUE
NAb Neutralizing antibody 51382 TRUE
PKMAb PK MAb 3217 TRUE

In all-study connection, getDataset will combine the requested datasets. Note that in most cases, the datasets will have too many subjects for quick data transfer, making filtering of the data a necessity. The colFilter argument can be used here, as described in the getDataset section.

conFilter <- makeFilter(c("species", "EQUAL", "Human"))
human <- cavd$getDataset("Demographics", colFilter = conFilter)
dim(human)
#> [1] 3142   36
colnames(human)
#>  [1] "subject_id"                      "subject_visit"                  
#>  [3] "species"                         "subspecies"                     
#>  [5] "sexatbirth"                      "race"                           
#>  [7] "ethnicity"                       "country_enrollment"             
#>  [9] "circumcised_enrollment"          "bmi_enrollment"                 
#> [11] "agegroup_range"                  "agegroup_enrollment"            
#> [13] "age_enrollment"                  "study_label"                    
#> [15] "study_start_date"                "study_first_enr_date"           
#> [17] "study_fu_complete_date"          "study_public_date"              
#> [19] "study_network"                   "study_last_vaccination_day"     
#> [21] "study_type"                      "study_part"                     
#> [23] "study_group"                     "study_arm"                      
#> [25] "study_arm_summary"               "study_arm_coded_label"          
#> [27] "study_randomization"             "study_product_class_combination"
#> [29] "study_product_combination"       "study_short_name"               
#> [31] "study_grant_pi_name"             "study_strategy"                 
#> [33] "study_prot"                      "genderidentity"                 
#> [35] "studycohort"                     "bmi_category"

Check out the reference page of DataSpaceStudy for all available fields and methods.

Connect to a saved group

A group is a curated collection of participants from filtering of treatments, products, studies, or species, and it is created in the DataSpace App.

Let’s say you are using the App to filter and visualize data and want to save them for later or explore in R with DataSpaceR. You can save a group by clicking the Save button on the Active Filter Panel.

We can browse available the saved groups or the curated groups by DataSpace Team via availableGroups.

knitr::kable(con$availableGroups)
group_id label original_label description created_by shared n studies
220 NYVAC durability comparison NYVAC_durability Compare durability in 4 NHP studies using NYVAC-C (vP2010) and NYVAC-KC-gp140 (ZM96) products. ehenrich TRUE 78 cvd281, cvd434, cvd259, cvd277
228 HVTN 505 case control subjects HVTN 505 case control subjects Participants from HVTN 505 included in the case-control analysis drienna TRUE 189 vtn505
230 HVTN 505 polyfunctionality vs BAMA HVTN 505 polyfunctionality vs BAMA Compares ICS polyfunctionality (CD8+, Any Env) to BAMA mfi-delta (single Env antigen) in the HVTN 505 case control cohort drienna TRUE 170 vtn505

To fetch data from a saved group, create a connection at the project level with a group ID. For example, we can connect to the “NYVAC durability comparison” group which has group ID 220 by getGroup.

nyvac <- con$getGroup(220)
nyvac
#> <DataSpaceStudy>
#>   Group: NYVAC durability comparison
#>   URL: https://dataspace.cavd.org/CAVD
#>   Available datasets:
#>     - Binding Ab multiplex assay
#>     - Demographics
#>     - Enzyme-Linked ImmunoSpot
#>     - Intracellular Cytokine Staining
#>     - Neutralizing antibody
#>   Available non-integrated datasets:

Retrieving a dataset is the same as before.

NAb_nyvac <- nyvac$getDataset("NAb")
dim(NAb_nyvac)
#> [1] 4281   33

Access Virus Metadata

DataSpace maintains metadata about all viruses used in Neutralizing Antibody (NAb) assays. This data can be accessed through the app on the NAb antigen page and NAb MAb antigen page.

We can access this metadata in DataSpaceR with con$virusMetadata:

knitr::kable(head(con$virusMetadata))
assay_identifier cds_virus_id virus virus_type neutralization_tier clade antigen_control virus_full_name virus_name_other virus_species virus_host_cell virus_backbone panel_names
NAB MAB cds_1 0013095-2.11 Env Pseudotype 2 NA 0 0013095-2.11 [SG3Δenv] 293T/17 NA HIV 293T/17 SG3Δenv Tiered diverse panel
NAB MAB cds_2 001428-2.42 Env Pseudotype 2 C 0 001428-2.42 [SG3Δenv] 293T/17 NA HIV 293T/17 SG3Δenv Tiered diverse panel
NAB MAB cds_3 0041.v3.c18 Env Pseudotype 2 C 0 0041.v3.c18 [SG3Δenv] 293T/17 0041.V3.C18 HIV 293T/17 SG3Δenv NA
NAB MAB cds_4 0077.v1.c16 Env Pseudotype 2 C 0 0077.v1.c16 [SG3Δenv] 293T/17 0077.v1.c16 HIV 293T/17 SG3Δenv NA
NAB cds_252 00836-2.5 Env Pseudotype 1B C 0 00836-2.5 [SG3Δenv] 293T/17 NA HIV 293T/17 SG3Δenv Tiered diverse panel
NAB MAB cds_5 0260.v5.c1 Env Pseudotype 2 A 0 0260.v5.c1 [SG3Δenv] 293T/17 0260.V5.C1 HIV 293T/17 SG3Δenv Tiered diverse panel

Access monoclonal antibody data

See other vignette for a tutorial on accessing monoclonal antibody data with DataSpaceR:

vignette("Monoconal_Antibody_Data")

Browse and Download Publication Data

DataSpace maintains a curated collection of relevant publications, which can be accessed through the Publications page through the app. Metadata about these publications can be accessed through DataSpaceR with con$availablePublications.

See Publication Data vignette for a tutorial on accessing publication data through DataSpaceR.

vignette("Publication_Data")
#> Warning: vignette 'Publication_Data' not found

Reference Tables

The followings are the tables of all fields and methods that work on DataSpaceConnection and DataSpaceStudy objects and could be used as a quick reference.

DataSpaceConnection

Name Description
availableStudies The table of available studies.
availableGroups The table of available groups.
availablePublications The table of available publications.
mabGrid The filtered mAb grid.
mabGridSummary The summarized mAb grid with updated n_ columns and geometric_mean_curve_ic50.
virusMetadata Metadata about all viruses in the DataSpace.
filterMabGrid Filter rows in the mAb grid by specifying the values to keep in the columns found in the mabGrid field.
resetMabGrid Reset the mAb grid to the unfiltered state.
getMab Create a DataSpaceMab object by filtered mabGrid.
getStudy Create a DataSpaceStudy object by study.
getGroup Create a DataSpaceStudy object by group.
downloadPublicationData Download data from a chosen publication.

DataSpaceStudy

Name Description
study The study name.
group The group name.
availableDatasets The table of datasets available in the study object.
treatmentArm The table of treatment arm information for the connected study. Not available for all study connection.
dataDir The default target directory for downloading non-integrated datasets.
studyInfo Stores the information about the study.
getDataset Get a dataset from the connection.
getDatasetDescription Get variable information.
setDataDir Set default target directory for downloading non-integrated datasets.

DataSpaceMab

Name Description
studyAndMabs The table of available mAbs by study.
mabs The table of available mAbs and their attributes.
nabMab The table of mAbs and their neutralizing measurements against viruses.
studies The table of available studies.
assays The table of assay status by study.
variableDefinitions The table of variable definitions.

Session information

sessionInfo()
#> R version 4.1.2 (2021-11-01)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.5 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.utf8       LC_NUMERIC=C             
#>  [3] LC_TIME=en_US.utf8        LC_COLLATE=en_US.utf8    
#>  [5] LC_MONETARY=en_US.utf8    LC_MESSAGES=en_US.utf8   
#>  [7] LC_PAPER=en_US.utf8       LC_NAME=C                
#>  [9] LC_ADDRESS=C              LC_TELEPHONE=C           
#> [11] LC_MEASUREMENT=en_US.utf8 LC_IDENTIFICATION=C      
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] data.table_1.14.2 DataSpaceR_0.7.5  knitr_1.37       
#> 
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.8       digest_0.6.29    assertthat_0.2.1 R6_2.5.1        
#>  [5] jsonlite_1.8.0   magrittr_2.0.2   evaluate_0.15    highr_0.9       
#>  [9] httr_1.4.2       stringi_1.7.6    curl_4.3.2       tools_4.1.2     
#> [13] stringr_1.4.0    Rlabkey_2.8.3    xfun_0.29        compiler_4.1.2

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.