Accessing Monoclonal Antibody Data

Ju Yeong Kim

Jason Taylor


Workflow overview

Using the DataSpace app, the workflow of using the mAb grid is the following:

  1. Navigate to the mAb Grid and browse the available mAb mixtures
  2. Select the mAb mixtures that you’d like to investigate
  3. Or filter rows by using columns:
    • mAb/Mixture
    • donor species
    • isotype
    • HXB2 location
    • tiers
    • clades
    • viruses
  4. Click “Neutralization Curves” or “IC50 Titer Heatmap” to visualize the mAb data
  5. Click “Export CSV” or “Export Excel” to download the mAb data

DataSpaceR offers a similar interface:

  1. Browse the mAb Grid by con$mabGridSummary
  2. Select the mAb mixtures by filtering the mAb grid using any columns found in con$mabGrid using con$filterMabGrid()
  3. Use con$getMab() to retrieve the mAb data

Browse the mAb Grid

You can browse the mAb Grid by calling the mabGridSummary field in the connection object:

con <- connectDS()

DT::datatable(con$mabGridSummary, options = list(autoWidth = TRUE, scrollX = TRUE))

This table is designed to mimic the mAb grid found in the app.

One can also access the unsummarized data from the mAb grid by calling con$mabGrid.

Filter the mAb grid

You can filter rows in the grid by specifying the values to keep in the columns found in the field con$mabGrid: mab_mixture, donor_species, isotype, hxb2_location, tiers, clades, viruses, and studies. filterMabGrid takes the column and the values and filters the underlying tables (private fields), and when you call the mabGridSummary or (which is actually an active binding), it returns the filtered grid with updated n_ columns and geometric_mean_curve_ic50.

# filter the grid by viruses
con$filterMabGrid(using = "virus", value = c("242-14", "Q23.17", "6535.3", "BaL.26", "DJ263.8"))

# filter the grid by donor species (llama)
con$filterMabGrid(using = "donor_species", value = "llama")

# check the updated grid
DT::datatable(con$mabGridSummary, options = list(autoWidth = TRUE, scrollX = TRUE))

Or we can use method chaining to call multiple filter methods and browse the grid. Method chaining is unique to R6 objects and related to the pipe. See Hadley Wickham’s Advanced R for more info

  filterMabGrid(using = "hxb2_location", value = c("Env", "gp160"))$
  filterMabGrid(using = "donor_species", value = "llama")$

Retrieve column values from the mAb grid

You can retrieve values from the grid by mab_mixture, donor_species, isotype, hxb2_location, tier, clade, virus, and studies, or any variables found in the mabGrid field in the connection object via data.table operations.

# retrieve available viruses in the filtered grid
con$mabGrid[, unique(virus)]
#> [1] "6535.3"  "Q23.17"  "242-14"  "BaL.26"  "DJ263.8"

# retrieve available clades for 1H9 mAb mixture in the filtered grid
con$mabGrid[mab_mixture == "1H9", unique(clade)]
#> [1] "CRF02_AG" "02A1"     "B"

Create a DataSpaceMab object

After filtering the grid, you can create a DataSpaceMab object that contains the filtered mAb data.

mab <- con$getMab()
#> <DataSpaceMab>
#>   URL:
#>   User:
#>   Summary:
#>     - 3 studies
#>     - 14 mAb mixtures
#>     - 1 neutralization tiers
#>     - 4 clades
#>   Filters:
#>     - virus: 242-14, Q23.17, 6535.3, BaL.26, DJ263.8
#>     - mab_donor_species: llama

There are 6 public fields available in the DataSpaceMab object: studyAndMabs, mabs, nabMab, studies, assays, and variableDefinitions, and they are equivalent to the sheets in the excel file or the csv files you would download from the app via “Export Excel”/“Export CSV”.

DT::datatable(mab$nabMab, options = list(autoWidth = TRUE, scrollX = TRUE))

View metadata concerning the mAb object

There are several metadata fields that can be exported in the mAb object.

#>  [1] ".__enclos_env__"     "variableDefinitions" "assays"             
#>  [4] "studies"             "nabMab"              "mabs"               
#>  [7] "studyAndMabs"        "config"              "clone"              
#> [10] "refresh"             "print"               "initialize"

Session information

#> R version 3.6.2 (2019-12-12)
#> Platform: x86_64-apple-darwin15.6.0 (64-bit)
#> Running under: macOS Catalina 10.15.2
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> other attached packages:
#> [1] DataSpaceR_0.7.3
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.3        Rlabkey_2.3.3     knitr_1.26        magrittr_1.5     
#>  [5] xtable_1.8-4      R6_2.4.1          rlang_0.4.2       fastmap_1.0.1    
#>  [9] stringr_1.4.0     httr_1.4.1        highr_0.8         tools_3.6.2      
#> [13] DT_0.10           data.table_1.12.8 xfun_0.11         htmltools_0.4.0  
#> [17] crosstalk_1.0.0   yaml_2.2.0        digest_0.6.23     assertthat_0.2.1 
#> [21] shiny_1.4.0       later_1.0.0       promises_1.1.0    htmlwidgets_1.5.1
#> [25] curl_4.3          mime_0.7          evaluate_0.14     rmarkdown_2.0    
#> [29] stringi_1.4.3     compiler_3.6.2    jsonlite_1.6      httpuv_1.5.2