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+ Over decades, many departments across gRED and Roche have generated +large amounts of drug response screening data using Genentech’s rich +drug compounds inventory. While extensive labor and time has been +invested to generate these data, they are not analyzed in a standardized +manner for meaningful comparison. On one hand, large screens are +performed across many cell lines and drugs in a semi-automated manner. +On the other hand, small-scale studies, which focused on factors that +contribute to sensitivity and resistance to certain therapies, are +generally performed by each individual scientist with limited +automation. These are complementary approaches but were rarely handled +the same way. Commercial softwares are available for analyzing large +datasets, whereas researchers for small-scale datasets often process +data ad hoc through software like PRISM.
+Here, we propose a suite of computational tools that enable the +processing, archiving, and visualization of drug response data from any +experiment, regardless of size or experimental design, thus ensuring +reproducibility and implementation of the Findable, Accessible, +Interoperable, and Reusable (F.A.I.R.) principles, with the goal of +making this accessible to the public community.
+For now we share a subset of the gDR suite components for +pre-processing and processing the data.
+gDR suite consists of a few packages that power our app and make it a +comprehensive tool. All the packages under the gDR umbrella are stored +in the gDR platform +GitHub organization.
+We are happy to share with you our packages for importing, processing +and managing gDR data: - gDRimport - gDRcore - gDRutils - +gDRtestData
+The gDR data model is based on the SummarizedExperiment and +BumpyMatrix. If readers are unfamiliar with these data models, we +recommend first reading SummarizedExperiment +vignettes, followed by the BumpyMatrix +vignettes. The SummarizedExperiment data structure enables ease of +subsetting within the SummarizedExperiment object, but also provides +ease when trying to correlate drug response data with genomic data, as +these data may jointly stored in a MultiAssayExperiment. The BumpyMatrix +allows for storage of multi-dimensional data while retaining a matrix +abstraction.
+This data structure is the core data structure that all downstream +processing functions as well as visualization tools operate off of.
+The gDR suite was designed in a modular manner, such that a user can +jump into the “standard” end-to-end gDR processing pipeline at several +entry points as is suitable for his or her needs. The full pipeline +involves:
+ manifest, template(s), raw data
+ |
+ | 1. Aggregating all raw data and metadata
+ | into a single long table.
+ |
+ V
+ single, long table
+ |
+ | 2. Transforming the long table into
+ | a SummarizedExperiment object with BumpyMatrix assays
+ | by specifying what columns belong on rows,
+ | columns, and nested.
+ |
+ V
+ SummarizedExperiment object
+ with raw and treated assays
+ |
+ | 3. Normalizing, averaging, and fitting data.
+ |
+ V
+ SummarizedExperiment object
+ with raw, treated, normalized,
+ averaged, and metric assays,
+ ready for use by downstream visualization
+ A user should be able to enter any part of this pipeline as long as +they are able to create the intermediate object (i.e., the individual +manifest, template, and raw data files, or a single, long table, or a +SummarizedExperiment object with Bumpy assays).
+The gDR suite ultimately requires a single, long merged table +containing both raw data and metadata.
+To support a common use case, we provide a convenience function that +takes three objects: manifest, template(s), and raw data to create this +single, long merged table for the user. The manifest contains metadata +on the experimental design, template files specify the drugs and cell +lines used, and the raw data output files obtained from a plate reader +or a scanner.
+Exemplary data can be found here:
+
+library(gDR)
+#> Loading required package: gDRcore
+#> Loading required package: gDRimport
+#> Loading required package: gDRutils
+# get test data from gDRimport package
+# i.e. paths to manifest, templates and results files
+td <- get_test_data()
+manifest_path(td)[1] +“/usr/local/lib/R/site-library/gDRimport/extdata/data1/manifest.xlsx”
+
+template_path(td)[1] +“/usr/local/lib/R/site-library/gDRimport/extdata/data1/Template_7daytreated.xlsx” +[2] +“/usr/local/lib/R/site-library/gDRimport/extdata/data1/Template_Untreated.xlsx”
+
+result_path(td)[1] +“/usr/local/lib/R/site-library/gDRimport/extdata/data1/RawData_day0.xlsx” +[2] +“/usr/local/lib/R/site-library/gDRimport/extdata/data1/RawData_day7.xlsx”
+Using the convenience function import_data, the long
+table is easily created:
+# Import data
+imported_data <-
+ import_data(manifest_path(td), template_path(td), result_path(td))
+head(imported_data)This function will expect certain “identifiers” that tell the
+processing functions which columns in the long table map to certain
+expected fields, so that each column is interpreted correctly. For more
+details regarding these identifiers, see the “Details” section of
+?identifiers. Use set_env_identifier or
+set_SE_identifiers to set up the correct mappings between
+the expected fields and your long table column names.
Next, we can transform the long table into our initial +SummarizedExperiment object. To do so, we need to tell the software: - +What should go on rows and columns versus be nested in the assay. - +Which rows in our table to consider as “control” versus “treated” for +normalization. - Which data type should be converted into SE.
+We can do so by setting the untreated_tag identifier
+like
+set_env_identifier("untreated_tag" = c("MY_CONTROL_TERMINOLOGY_HERE")).
+specifying the nested_keys argument within
+create_and_normalize_SE and specifiying
+data_type.
+inl <- prepare_input(imported_data)
+#> Warning in .set_nested_confounders(nested_confounders = nested_confounders, : 'Plate' nested confounder(s) is/are not present in the data.
+#> Switching into 'Barcode' nested confounder(s).
+detected_data_types <- names(inl$exps)
+detected_data_types
+#> [1] "combination" "single-agent"
+se <- create_and_normalize_SE(
+ inl$df_list[["single-agent"]],
+ data_type = "single-agent",
+ nested_confounders = inl$nested_confounders)
+#> INFO [2024-02-14 13:42:29]
+#> INFO [2024-02-14 13:42:29]
+se
+#> class: SummarizedExperiment
+#> dim: 3 6
+#> metadata(3): identifiers experiment_metadata Keys
+#> assays(3): RawTreated Controls Normalized
+#> rownames(3): G00002_drug_002_moa_A_168 G00004_drug_004_moa_A_168
+#> G00011_drug_011_moa_B_168
+#> rowData names(4): Gnumber DrugName drug_moa Duration
+#> colnames(6): CL00011_cellline_BA_breast_cellline_BA_unknown_26
+#> CL00012_cellline_CA_breast_cellline_CA_unknown_30 ...
+#> CL00015_cellline_FA_breast_cellline_FA_unknown_42
+#> CL00018_cellline_IB_breast_cellline_IB_unknown_54
+#> colData names(6): clid CellLineName ... subtype ReferenceDivisionTimeNote that this has created a SummarizedExperiment object with
+rowData, colData, metadata and 3
+assays.
Next, we can average and fit the data of interest.
+
+se <- average_SE(se, data_type = "single-agent")
+se <- fit_SE(se, data_type = "single-agent")
+se
+#> class: SummarizedExperiment
+#> dim: 3 6
+#> metadata(5): identifiers experiment_metadata Keys fit_parameters
+#> .internal
+#> assays(5): RawTreated Controls Normalized Averaged Metrics
+#> rownames(3): G00002_drug_002_moa_A_168 G00004_drug_004_moa_A_168
+#> G00011_drug_011_moa_B_168
+#> rowData names(4): Gnumber DrugName drug_moa Duration
+#> colnames(6): CL00011_cellline_BA_breast_cellline_BA_unknown_26
+#> CL00012_cellline_CA_breast_cellline_CA_unknown_30 ...
+#> CL00015_cellline_FA_breast_cellline_FA_unknown_42
+#> CL00018_cellline_IB_breast_cellline_IB_unknown_54
+#> colData names(6): clid CellLineName ... subtype ReferenceDivisionTimeSteps (2) and (3) can be combined into a single step through a
+convenience function: runDrugResponseProcessingPipeline.
+Moreover, the output is MultiAssayExperiment object with
+one experiment per each detected data type. Currently four data types
+are supported: ‘single-agent’, ‘cotreatment’, ‘codilution’ and ‘matrix’.
+The first three data types are processed via the ‘single-agent’ model
+while the ‘marix’ data is processed via the ‘combintation’ model.
+# Run gDR pipeline
+mae <- runDrugResponseProcessingPipeline(imported_data)
+mae
+#> A MultiAssayExperiment object of 2 listed
+#> experiments with user-defined names and respective classes.
+#> Containing an ExperimentList class object of length 2:
+#> [1] combination: SummarizedExperiment with 2 rows and 6 columns
+#> [2] single-agent: SummarizedExperiment with 3 rows and 6 columns
+#> Functionality:
+#> experiments() - obtain the ExperimentList instance
+#> colData() - the primary/phenotype DataFrame
+#> sampleMap() - the sample coordination DataFrame
+#> `$`, `[`, `[[` - extract colData columns, subset, or experiment
+#> *Format() - convert into a long or wide DataFrame
+#> assays() - convert ExperimentList to a SimpleList of matrices
+#> exportClass() - save data to flat filesNote that our final MultiAssayExperiment object can be made up of +multiple experiments with multiple assays:
+
+names(mae)
+#> [1] "combination" "single-agent"
+SummarizedExperiment::assayNames(mae[[1]])
+#> [1] "RawTreated" "Controls" "Normalized" "Averaged"
+#> [5] "excess" "all_iso_points" "isobolograms" "scores"
+#> [9] "Metrics"Each assay from each experiment can be easily transformed to
+data.table format using convert_se_assay_to_dt
+function:
+library(kableExtra)
+se <- mae[["single-agent"]]
+head(convert_se_assay_to_dt(se, "Metrics"))
+#> rId cId
+#> <char> <char>
+#> 1: G00002_drug_002_moa_A_168 CL00011_cellline_BA_breast_cellline_BA_unknown_26
+#> 2: G00002_drug_002_moa_A_168 CL00011_cellline_BA_breast_cellline_BA_unknown_26
+#> 3: G00004_drug_004_moa_A_168 CL00011_cellline_BA_breast_cellline_BA_unknown_26
+#> 4: G00004_drug_004_moa_A_168 CL00011_cellline_BA_breast_cellline_BA_unknown_26
+#> 5: G00011_drug_011_moa_B_168 CL00011_cellline_BA_breast_cellline_BA_unknown_26
+#> 6: G00011_drug_011_moa_B_168 CL00011_cellline_BA_breast_cellline_BA_unknown_26
+#> x_mean x_AOC x_AOC_range xc50 x_max ec50
+#> <num> <num> <num> <num> <num> <num>
+#> 1: 0.9157913 0.08420865 0.05733832 7.4683485 0.4225500 5.7741811
+#> 2: 0.7420679 0.25793215 0.25820178 4.1712724 0.3065500 100.0000000
+#> 3: 0.6675856 0.33241443 0.32656451 0.4942034 0.0260500 0.4793539
+#> 4: 0.5092443 0.49075566 0.48159819 0.3199532 -0.4177500 0.5757493
+#> 5: 0.3225188 0.67748125 NA -Inf 0.3225188 NA
+#> 6: 0.2600750 0.73992500 NA -Inf 0.2600750 NA
+#> x_inf x_0 h r2 x_sd_avg fit_type
+#> <num> <num> <num> <num> <num> <char>
+#> 1: 0.14499197 1 1.3311221 0.9199573 0.8794050 DRC3pHillFitModelFixS0
+#> 2: -0.76364407 1 0.2918356 0.7732475 0.7276889 DRC3pHillFitModelFixS0
+#> 3: 0.04254419 1 2.9149107 0.9751992 0.7335509 DRC3pHillFitModelFixS0
+#> 4: -0.48824612 1 1.1597048 0.9858751 0.5983619 DRC3pHillFitModelFixS0
+#> 5: NA NA NA NA 0.2540203 DRCTooFewPointsToFit
+#> 6: NA NA NA NA 0.3936048 DRCTooFewPointsToFit
+#> maxlog10Concentration N_conc normalization_type fit_source Gnumber DrugName
+#> <num> <int> <char> <char> <char> <char>
+#> 1: 1.000000 9 RV gDR G00002 drug_002
+#> 2: 1.000000 9 GR gDR G00002 drug_002
+#> 3: 1.000000 9 RV gDR G00004 drug_004
+#> 4: 1.000000 9 GR gDR G00004 drug_004
+#> 5: -0.823909 1 RV gDR G00011 drug_011
+#> 6: -0.823909 1 GR gDR G00011 drug_011
+#> drug_moa Duration clid CellLineName Tissue parental_identifier subtype
+#> <char> <num> <char> <char> <char> <char> <char>
+#> 1: moa_A 168 CL00011 cellline_BA breast cellline_BA unknown
+#> 2: moa_A 168 CL00011 cellline_BA breast cellline_BA unknown
+#> 3: moa_A 168 CL00011 cellline_BA breast cellline_BA unknown
+#> 4: moa_A 168 CL00011 cellline_BA breast cellline_BA unknown
+#> 5: moa_B 168 CL00011 cellline_BA breast cellline_BA unknown
+#> 6: moa_B 168 CL00011 cellline_BA breast cellline_BA unknown
+#> ReferenceDivisionTime
+#> <num>
+#> 1: 26
+#> 2: 26
+#> 3: 26
+#> 4: 26
+#> 5: 26
+#> 6: 26Once the data is stored in the database, there are multiple ways to +visualize the data depending on the scientific needs. The primary method +to do is through our RShiny visualization tool ‘gDRviz’. Here, users can +search and select experiments present in the database, and use +downstream visualization modules to look at dose response curves, +heatmaps, etc.
+
+sessionInfo()
+#> R version 4.3.0 (2023-04-21)
+#> Platform: x86_64-pc-linux-gnu (64-bit)
+#> Running under: Ubuntu 22.04.3 LTS
+#>
+#> Matrix products: default
+#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
+#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
+#>
+#> 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
+#>
+#> time zone: Etc/UTC
+#> tzcode source: system (glibc)
+#>
+#> attached base packages:
+#> [1] stats graphics grDevices utils datasets methods base
+#>
+#> other attached packages:
+#> [1] kableExtra_1.3.4 gDR_1.1.4 gDRutils_1.1.5 gDRimport_1.1.4
+#> [5] gDRcore_1.1.11 BiocStyle_2.30.0
+#>
+#> loaded via a namespace (and not attached):
+#> [1] viridisLite_0.4.2 bitops_1.0-7
+#> [3] fastmap_1.1.1 RCurl_1.98-1.14
+#> [5] BumpyMatrix_1.10.0 TH.data_1.1-2
+#> [7] digest_0.6.34 lifecycle_1.0.4
+#> [9] survival_3.5-5 magrittr_2.0.3
+#> [11] compiler_4.3.0 rlang_1.1.3
+#> [13] sass_0.4.8 drc_3.0-1
+#> [15] tools_4.3.0 plotrix_3.8-4
+#> [17] utf8_1.2.4 yaml_2.3.8
+#> [19] data.table_1.15.0 knitr_1.45
+#> [21] lambda.r_1.2.4 S4Arrays_1.2.0
+#> [23] DelayedArray_0.28.0 xml2_1.3.6
+#> [25] abind_1.4-5 multcomp_1.4-25
+#> [27] BiocParallel_1.36.0 purrr_1.0.2
+#> [29] BiocGenerics_0.48.1 desc_1.4.3
+#> [31] grid_4.3.0 stats4_4.3.0
+#> [33] fansi_1.0.6 colorspace_2.1-0
+#> [35] scales_1.3.0 MASS_7.3-58.4
+#> [37] gtools_3.9.5 MultiAssayExperiment_1.28.0
+#> [39] SummarizedExperiment_1.32.0 cli_3.6.2
+#> [41] mvtnorm_1.2-4 rmarkdown_2.25
+#> [43] crayon_1.5.2 ragg_1.2.7
+#> [45] rstudioapi_0.15.0 httr_1.4.7
+#> [47] readxl_1.4.3 cachem_1.0.8
+#> [49] stringr_1.5.1 zlibbioc_1.48.0
+#> [51] splines_4.3.0 rvest_1.0.3
+#> [53] assertthat_0.2.1 parallel_4.3.0
+#> [55] BiocManager_1.30.22 formatR_1.14
+#> [57] cellranger_1.1.0 XVector_0.42.0
+#> [59] matrixStats_1.2.0 vctrs_0.6.5
+#> [61] webshot_0.5.5 Matrix_1.6-5
+#> [63] sandwich_3.1-0 jsonlite_1.8.8
+#> [65] carData_3.0-5 bookdown_0.37
+#> [67] car_3.1-2 IRanges_2.36.0
+#> [69] S4Vectors_0.40.2 systemfonts_1.0.5
+#> [71] testthat_3.2.1 jquerylib_0.1.4
+#> [73] rematch_2.0.0 glue_1.7.0
+#> [75] pkgdown_2.0.7 codetools_0.2-19
+#> [77] stringi_1.8.3 futile.logger_1.4.3
+#> [79] GenomeInfoDb_1.38.6 GenomicRanges_1.54.1
+#> [81] munsell_0.5.0 tibble_3.2.1
+#> [83] pillar_1.9.0 htmltools_0.5.7
+#> [85] brio_1.1.4 GenomeInfoDbData_1.2.11
+#> [87] R6_2.5.1 textshaping_0.3.7
+#> [89] evaluate_0.23 lattice_0.21-8
+#> [91] Biobase_2.62.0 futile.options_1.0.1
+#> [93] backports_1.4.1 memoise_2.0.1
+#> [95] bslib_0.6.1 svglite_2.1.3
+#> [97] SparseArray_1.2.4 checkmate_2.3.1
+#> [99] xfun_0.42 fs_1.6.3
+#> [101] MatrixGenerics_1.14.0 zoo_1.8-12
+#> [103] pkgconfig_2.0.3Allison Vuong. Author. +
+Bartosz Czech. Author. +
+Arkadiusz Gladki. Maintainer, author. +
+Marc Hafner. Author. +
+Dariusz Scigocki. Author. +
+Janina Smola. Author. +
+Sergiu Mocanu. Author. +
+Source: DESCRIPTION
Vuong A, Czech B, Gladki A, Hafner M, Scigocki D, Smola J, Mocanu S (2024). +gDR: Umbrella package for R packages in the gDR suite. +https://github.com/gdrplatform/gDR, https://gdrplatform.github.io/gDR/. +
+@Manual{,
+ title = {gDR: Umbrella package for R packages in the gDR suite},
+ author = {Allison Vuong and Bartosz Czech and Arkadiusz Gladki and Marc Hafner and Dariusz Scigocki and Janina Smola and Sergiu Mocanu},
+ year = {2024},
+ note = {https://github.com/gdrplatform/gDR, https://gdrplatform.github.io/gDR/},
+}
+