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R Interface to the CAVD DataSpace

Home Page: https://docs.ropensci.org/DataSpaceR

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dataspacer's Introduction

DataSpaceR

DataSpaceR is an R interface to the CAVD DataSpace, a data sharing and discovery tool that facilitates exploration of HIV immunological data from pre-clinical and clinical HIV vaccine studies.

This package is intended for use by immunologists, bioinformaticians, and statisticians in HIV vaccine research, or anyone interested in the analysis of HIV immunological data across assays, studies, and time.

This package simplifies access to the database by taking advantage of the standardization of the database to hide all the Rlabkey specific code away from the user, and it allows the users to access the study-specific datasets via an object-oriented paradigm.

Examples & Documentation

For more detailed examples and detailed documentation, see the introductory vignette and the pkgdown site.

For a quick guide of how to use the API, see our cheat sheet .

Installation

Install from CRAN:

install.packages("DataSpaceR")

You can install the latest development version from GitHub with devtools:

# install.packages("devtools")
devtools::install_github("ropensci/DataSpaceR")

Register and set DataSpace credential

The database is accessed with the user’s credentials. A netrc file storing login and password information is required.

Create an account and read the terms of use
On your R console, create a netrc file using a function from DataSpaceR:

library(DataSpaceR)
writeNetrc(
  login = "[email protected]", 
  password = "yourSecretPassword",
  netrcFile = "/your/home/directory/.netrc" # use getNetrcPath() to get the default path 
)

This will create a netrc file in your home directory.

Alternatively, you can manually create a netrc file in the computer running R.

On Windows, this file should be named _netrc
On UNIX, it should be named .netrc
The file should be located in the user’s home directory, and the permissions on the file should be unreadable for everybody except the owner
To determine home directory, run Sys.getenv("HOME") in R

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 [email protected]
password supersecretpassword

See here for more information about netrc.

Usage

The general idea is that the user:

creates an instance of DataSpaceConnection class via connectDS
browses available studies and groups in the instance via availableStudies and availableGroups
creates a connection to a specific study via getStudy or a group via getGroup
retrieves datasets by name via getDataset

for example:

library(DataSpaceR)
#> By exporting data from the CAVD DataSpace, you agree to be bound by the Terms of Use available on the CAVD DataSpace sign-in page at https://dataspace.cavd.org

con <- connectDS()
con
#> <DataSpaceConnection>
#>   URL: https://dataspace.cavd.org
#>   User: [email protected]
#>   Available studies: 273
#>     - 77 studies with data
#>     - 5049 subjects
#>     - 423195 data points
#>   Available groups: 6
#>   Available publications: 1530
#>     - 12 publications with data

connectDS() will create a connection to DataSpace.

available studies can be listed by `availableStudies` field

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

available groups can be listed by `availableGroups` field

knitr::kable(con$availableGroups)

group_id	label	original_label	description	created_by	shared	n	studies
216	mice	mice	NA	readjk	FALSE	75	cvd468, cvd483, cvd316, cvd331
217	CAVD 242	CAVD 242	This is a fake group for CAVD 242	readjk	FALSE	30	cvd242
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
224	cvd338	cvd338	NA	readjk	FALSE	36	cvd338
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

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

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

create an instance of `cvd408`

cvd408 <- con$getStudy("cvd408")
cvd408
#> <DataSpaceStudy>
#>   Study: cvd408
#>   URL: https://dataspace.cavd.org/CAVD/cvd408
#>   Available datasets:
#>     - Binding Ab multiplex assay
#>     - Demographics
#>     - Intracellular Cytokine Staining
#>     - Neutralizing antibody
#>   Available non-integrated datasets:
class(cvd408)
#> [1] "DataSpaceStudy" "R6"

available datasets can be listed by `availableDatasets` field

knitr::kable(cvd408$availableDatasets)

name	label	n	integrated
BAMA	Binding Ab multiplex assay	1080	TRUE
Demographics	Demographics	20	TRUE
ICS	Intracellular Cytokine Staining	3720	TRUE
NAb	Neutralizing antibody	540	TRUE

which will print names of available datasets.

Neutralizing Antibody dataset (`NAb`) can be retrieved by:

NAb <- cvd408$getDataset("NAb")
dim(NAb)
#> [1] 540  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"

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

Note: The package uses a R6 class to represent the connection to a study and get around some of R’s copy-on-change behavior.

dataspacer's People

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seaaan helenmiller16 jmtaylor-fhcrc tzujunghuang

dataspacer's Issues

Add a field to note the package version in DS objects

> con <- DataSpaceR::connectDS()
> con$config$packageVersion
[1] ‘0.5.1’

Create contribution guide

Duplicate records found in mAb object's `mabs` value

There are duplicate records found in the mAb object's mabs value. See the code example below. Taking unique on the mabs value returns fewer records than the mabs value.

library(DataSpaceR)
con <- connectDS()
con$filterMabGrid("mab_mixture", c("PGT121", "PGDM1400"))
mab <- con$getMab()

nrow(mab$mabs)
nrow(unique(mab$mabs))

`availableGroups$studies` showing incorrectly parsed study IDs

Issue

availableGroups$studies showing incorrectly parsed study IDs.

Reproducible example

> con$availableGroups$studies
[[1]]
character(0)

[[2]]
[1] "cvd338 Pooled" "cvd317"        "cvd338"        "cvd305"        "cvd317 Pooled" "cvd324"        "cvd320"       

[[3]]
[1] "cvd483" "cvd316" "cvd331"

[[4]]
character(0)

The second group should have cvd338, cvd317, cvd305, cvd324, cvd320. "Pooled" string is not parsed for cvd338 and cvd317.

Session info

> sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS

Matrix products: default
BLAS: /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
                                                                                                                                                                                  
attached base packages:                                                                                                                                                           
[1] stats     graphics  grDevices utils     datasets  methods   base                                                                                                              
                                                                                                                                                                                  
other attached packages:                                                                                                                                                          
[1] DataSpaceR_0.5.1                                                                                                                                                              
                                                                                                                                                                                  
loaded via a namespace (and not attached):                                                                                                                                        
[1] httr_1.3.1          compiler_3.4.3      rjson_0.2.15                                                                                                                          
[4] assertthat_0.2.0    Rlabkey_2.2.1       R6_2.2.2                                                                                                                              
[7] Rcpp_0.12.16        data.table_1.10.4-3 digest_0.6.15

Make print method more informative and prettier

Utilize crayon package to make print method prettier

Use GET method instead of POST for selectRows calls

GET is slightly faster than POST

Can't retrieve datasets over 100,000 rows

Issue

getDataset() method can't retrieve all of the rows for datasets with more than 100,000 rows.

Reproducible example

> library(DataSpaceR)
> con <- connectDS()
> sdy <- con$getStudy("")
> sdy$availableDatasets
           name                           label      n
1:         BAMA      Binding Ab multiplex assay  81128
2: Demographics                    Demographics   1874
3:      ELISPOT        Enzyme-Linked ImmunoSpot   5610
4:          ICS Intracellular Cytokine Staining 137963
5:          NAb           Neutralizing antibody  46507
> ICS <- sdy$getDataset("ICS")
> dim(ICS)
[1] 100000     29

Session info

> sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS

Matrix products: default
BLAS: /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
                                                                                                                                                                                  
attached base packages:                                                                                                                                                           
[1] stats     graphics  grDevices utils     datasets  methods   base                                                                                                              
                                                                                                                                                                                  
other attached packages:                                                                                                                                                          
[1] DataSpaceR_0.5.1                                                                                                                                                              
                                                                                                                                                                                  
loaded via a namespace (and not attached):                                                                                                                                        
[1] httr_1.3.1          compiler_3.4.3      rjson_0.2.15                                                                                                                          
[4] assertthat_0.2.0    Rlabkey_2.2.1       R6_2.2.2                                                                                                                              
[7] Rcpp_0.12.16        data.table_1.10.4-3 digest_0.6.15

Add an option to `getDataset` method to return dataset merged with demographics dataset (and/or treatmentArm?)

library(DataSpaceR)
con <- connectDS()
cavd <- con$getStudy("")

# get ICS dataset with demographics and treatment arm info merged
ICS <- cavd$getDataset("ICS", mergeExtra = TRUE)

Navigate to MAb Grid
Select "MAb/Mixture"
Click "Export CSV" or "Export Excel" to download MAb data

Potential workflow with DataSpaceR:

devtools::install_github("CAVDDataSpace/DataSpaceR", ref = "fb_mAb")

# connect to DataSpace
library(DataSpaceR)
con <- connectDS()

# explore mAb Grid and decide on which MAb to pull data
con$mAbGrid

# filter mAb grid by mixture name, donor species, viruses, isotype, hxb2 location, viruses, clades, tiers, curve IC50), or studies
con$filterMabGrid(using = "isotype", value = "IgG")
con$filterMabGrid(using = "donor_species", value = "llama")

# get mAb dataset of mAb fixtures from filtered mAb grid
myMab <- con$getMab()

# explore mAb object (mimic the contents of excel file or csv files)
myMab$mabs
myMab$nabMab
myMab$studies
myMab$assays
myMab$metadata
myMab$variableDefinitions
myMab$studyAndMabs

https://github.com/LabKey/cds/blob/bdc9346651346029f8d623e99617f2cc53533450/webapp/Connector/src/utility/MabQuery.js#L493

Dataset variable formatting suggestion

Suggestion

When getting data back from DSR there are mixed casing in the returned dataset.

con <- connectDS()
vtn <- con$getStudy("vtn505")
ics <- vtn$getDataset("ICS")
names(ics)

...returns...

 [1] "ParticipantId"                      "ParticipantVisit/Visit"            
 [3] "visit_day"                          "assay_identifier"                  
 [5] "summary_level"                      "cell_type"                         
 [7] "cell_name"                          "antigen"                           
 [9] "antigen_type"                       "peptide_pool"                      
[11] "protein"                            "protein_panel"                     
[13] "protein_panel_protein"              "protein_panel_protein_peptide_pool"
[15] "specimen_type"                      "functional_marker_name"            
[17] "functional_marker_type"             "clade"                             
[19] "vaccine_matched"                    "response_call"                     
[21] "pctpos"                             "pctpos_adj"                        
[23] "pctpos_neg"                         "lab_code"                          
[25] "exp_assayid"                        "ics_lab_source_key"                
[27] "response_method"                    "control"                           
[29] "pooled_info"                        "study_prot"                        
[31] "functionality_score"                "polyfunctionality_score"

Here, ParticipantId and ParticipantVisit/Visit are formatted differently than the rest of the variables. consider formatting the names of these two fields to match the others.

Create a FAQ page on pkgdown site

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ropensci / dataspacer Goto Github PK

dataspacer's Introduction

DataSpaceR

Examples & Documentation

Installation

Register and set DataSpace credential

Usage

for example:

available studies can be listed by availableStudies field

available groups can be listed by availableGroups field

create an instance of cvd408

available datasets can be listed by availableDatasets field

Neutralizing Antibody dataset (NAb) can be retrieved by:

Meta

dataspacer's People

Contributors

Stargazers

Watchers

Forkers

dataspacer's Issues

Issue

Reproducible example

Session info

Issue

Reproducible example

Session info

Suggestion

Recommend Projects

Recommend Topics

Recommend Org

available studies can be listed by `availableStudies` field

available groups can be listed by `availableGroups` field

create an instance of `cvd408`

available datasets can be listed by `availableDatasets` field

Neutralizing Antibody dataset (`NAb`) can be retrieved by: