Author: gDR team
Date: 2026-06-17

# gDRimport

gDR team

#### 17 June 2026

# Contents

# 1 Overview

The `gDRimport` package is a part of the gDR suite. It helps to prepare raw drug response data for downstream processing. It mainly contains helper functions for importing/loading/validating dose response data provided in different file formats.

# 2 Use Cases

## 2.1 Test Data

There are currently four test datasets that can be used to see what’s the expected input data for the gDRimport.

    # primary test data
    td1 <- get_test_data()
    summary(td1)

    ##        Length         Class          Mode
    ##             1 gdr_test_data            S4

    td1

    ## class: gdr_test_data
    ## slots: manifest_path result_path template_path ref_m_df ref_r1_r2 ref_r1 ref_t1_t2 ref_t1

    # test data in Tecan format
    td2 <- get_test_Tecan_data()
    summary(td2)

    ##          Length Class  Mode
    ## m_file   1      -none- character
    ## r_files  1      -none- character
    ## t_files  1      -none- character
    ## ref_m_df 1      -none- character
    ## ref_r_df 1      -none- character
    ## ref_t_df 1      -none- character

    # test data in D300 format
    td3 <- get_test_D300_data()
    summary(td3)

    ##        Length Class  Mode
    ## f_96w  6      -none- list
    ## f_384w 6      -none- list

    # test data obtained from EnVision
    td4 <- get_test_EnVision_data()
    summary(td4)

    ##            Length Class  Mode
    ## m_file      1     -none- character
    ## r_files    28     -none- character
    ## t_files     2     -none- character
    ## ref_l_path  1     -none- character

## 2.2 Load data

The `load_data` is the key function. It wraps `load_manifest`, `load_templates` and `load_results` functions and supports different file formats.

    ml <- load_manifest(manifest_path(td1))
    summary(ml)

    ##         Length Class      Mode
    ## data     4     data.table list
    ## headers 27     -none-     list

    t_df <- load_templates(template_path(td1))
    summary(t_df)

    ##       WellRow        WellColumn       Gnumber      Concentration
    ##  Length   :768   Length   :768   Length   :768   Length   :768
    ##  N.unique : 16   N.unique : 24   N.unique :  3   N.unique : 10
    ##  N.blank  :  0   N.blank  :  0   N.blank  :  0   N.blank  :  0
    ##  Min.nchar:  1   Min.nchar:  1   Min.nchar:  6   Min.nchar:  1
    ##  Max.nchar:  1   Max.nchar:  2   Max.nchar:  7   Max.nchar: 19
    ##                                  NAs      :128   NAs      :448
    ##      Gnumber_2    Concentration_2      Template
    ##  Length   :768   Length   :768    Length   :768
    ##  N.unique :  2   N.unique :  2    N.unique :  2
    ##  N.blank  :  0   N.blank  :  0    N.blank  :  0
    ##  Min.nchar:  6   Min.nchar:  1    Min.nchar: 23
    ##  Max.nchar:  7   Max.nchar:  4    Max.nchar: 25
    ##  NAs      :448   NAs      :448

    r_df <- suppressMessages(load_results(result_path(td1)))
    summary(r_df)

    ##       Barcode          WellRow       WellColumn     ReadoutValue
    ##  Length   :4587   Length   :4587   Min.   : 1.00   Min.   :  12627
    ##  N.unique :   6   N.unique :  16   1st Qu.: 6.50   1st Qu.:  67905
    ##  N.blank  :   0   N.blank  :   0   Median :12.00   Median : 140865
    ##  Min.nchar:   1   Min.nchar:   1   Mean   :12.49   Mean   : 263996
    ##  Max.nchar:   1   Max.nchar:   1   3rd Qu.:18.00   3rd Qu.: 324707
    ##                                    Max.   :24.00   Max.   :2423054
    ##  BackgroundValue
    ##  Min.   :332.0
    ##  1st Qu.:351.0
    ##  Median :374.0
    ##  Mean   :453.2
    ##  3rd Qu.:570.0
    ##  Max.   :704.0

    l_tbl <-
      suppressMessages(
        load_data(manifest_path(td1), template_path(td1), result_path(td1)))
    summary(l_tbl)

    ##            Length Class      Mode
    ## manifest   4      data.table list
    ## treatments 7      data.table list
    ## data       5      data.table list

# 3 PRISM

PRISM, the Multiplexed cancer cell line screening platform, facilitates rapid screening of a broad spectrum of drugs across more than 900 human cancer cell line models, employing a high-throughput, multiplexed approach. Publicly available PRISM data can be downloaded from the DepMap website (DepMap).

The `gDRimport` package provides support for processing PRISM data at two levels: LEVEL5 and LEVEL6.

- LEVEL5 Data: This format encapsulates all information about drugs, cell lines, and viability within a single file. To process LEVEL5 PRISM data, you can use the `convert_LEVEL5_prism_to_gDR_input()` function. This function not only transforms and cleans the data but also executes the gDR pipeline for further analysis.

- LEVEL6 Data: In LEVEL6, PRISM data is distributed across three separate files:

prism_data: containing collapsed log fold change data for viability assays. cell_line_data: providing information about cell lines. treatment_data: containing treatment data.

Processing LEVEL6 PRISM data can be accomplished using the `convert_LEVEL6_prism_to_gDR_input()` function, which requires paths to these three files as input arguments.

### 3.0.1 Processing LEVEL5 PRISM Data

To process LEVEL5 PRISM data, you can use the following function:

    convert_LEVEL5_prism_to_gDR_input("path_to_file")

Replace “path_to_file” with the actual path to your LEVEL5 PRISM data file. This function will handle the transformation, cleaning, and execution of the gDR pipeline automatically.

### 3.0.2 Processing LEVEL6 PRISM Data

To process LEVEL6 PRISM data, you can use the following function:

    convert_LEVEL6_prism_to_gDR_input("prism_data_path", "cell_line_data_path", "treatment_data_path")

Replace “prism_data_path”, “cell_line_data_path”, and “treatment_data_path” with the respective paths to your LEVEL6 PRISM data files.

## 3.1 Package installation

The function `installAllDeps` assists in installing package dependencies.

# SessionInfo

    sessionInfo()

    ## R version 4.6.0 RC (2026-04-17 r89917)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_GB              LC_COLLATE=C
    ##  [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
    ##
    ## time zone: America/New_York
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] BiocStyle_2.41.0            MultiAssayExperiment_1.39.0
    ##  [3] gDRimport_1.11.4            PharmacoGx_3.17.0
    ##  [5] CoreGx_2.17.0               SummarizedExperiment_1.43.0
    ##  [7] Biobase_2.73.1              GenomicRanges_1.65.0
    ##  [9] Seqinfo_1.3.0               IRanges_2.47.2
    ## [11] S4Vectors_0.51.3            MatrixGenerics_1.25.0
    ## [13] matrixStats_1.5.0           BiocGenerics_0.59.7
    ## [15] generics_0.1.4
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] bitops_1.0-9          formatR_1.14          readxl_1.5.0
    ##   [4] testthat_3.3.2        rlang_1.2.0           magrittr_2.0.5
    ##   [7] shinydashboard_0.7.3  otel_0.2.0            compiler_4.6.0
    ##  [10] vctrs_0.7.3           reshape2_1.4.5        relations_0.6-17
    ##  [13] stringr_1.6.0         pkgconfig_2.0.3       crayon_1.5.3
    ##  [16] fastmap_1.2.0         backports_1.5.1       XVector_0.53.0
    ##  [19] caTools_1.18.3        promises_1.5.0        rmarkdown_2.31
    ##  [22] coop_0.6-3            xfun_0.58             cachem_1.1.0
    ##  [25] jsonlite_2.0.0        SnowballC_0.7.1       later_1.4.8
    ##  [28] DelayedArray_0.39.3   BiocParallel_1.47.0   parallel_4.6.0
    ##  [31] sets_1.0-25           cluster_2.1.8.2       R6_2.6.1
    ##  [34] bslib_0.11.0          stringi_1.8.7         RColorBrewer_1.1-3
    ##  [37] limma_3.69.2          boot_1.3-32           cellranger_1.1.0
    ##  [40] brio_1.1.5            jquerylib_0.1.4       bookdown_0.47
    ##  [43] assertthat_0.2.1      Rcpp_1.1.1-1.1        knitr_1.51
    ##  [46] downloader_0.4.1      httpuv_1.6.17         Matrix_1.7-5
    ##  [49] igraph_2.3.2          tidyselect_1.2.1      dichromat_2.0-0.1
    ##  [52] abind_1.4-8           yaml_2.3.12           stringfish_0.19.0
    ##  [55] gplots_3.3.0          codetools_0.2-20      lattice_0.22-9
    ##  [58] tibble_3.3.1          plyr_1.8.9            withr_3.0.2
    ##  [61] shiny_1.13.0          BumpyMatrix_1.21.0    S7_0.2.2
    ##  [64] evaluate_1.0.5        lambda.r_1.2.4        futile.logger_1.4.9
    ##  [67] RcppParallel_5.1.11-2 bench_1.1.4           BiocManager_1.30.27
    ##  [70] pillar_1.11.1         lsa_0.73.4            KernSmooth_2.23-26
    ##  [73] checkmate_2.3.4       DT_0.34.0             shinyjs_2.1.1
    ##  [76] piano_2.29.0          ggplot2_4.0.3         scales_1.4.0
    ##  [79] gtools_3.9.5          xtable_1.8-8          marray_1.91.0
    ##  [82] qs2_0.2.2             glue_1.8.1            slam_0.1-55
    ##  [85] tools_4.6.0           data.table_1.18.4     gDRutils_1.11.3
    ##  [88] fgsea_1.39.0          visNetwork_2.1.4      fastmatch_1.1-8
    ##  [91] cowplot_1.2.0         grid_4.6.0            cli_3.6.6
    ##  [94] futile.options_1.0.1  S4Arrays_1.13.0       rematch_2.0.0
    ##  [97] dplyr_1.2.1           gtable_0.3.6          sass_0.4.10
    ## [100] digest_0.6.39         SparseArray_1.13.2    htmlwidgets_1.6.4
    ## [103] farver_2.1.2          htmltools_0.5.9       lifecycle_1.0.5
    ## [106] statmod_1.5.2         mime_0.13