Converting PharmacoSet Drug Response Data into gDR object (original) (raw)
Bartosz Czech
library(PharmacoGx)
library(gDRimport)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 from different scanner sources. In collaboration with the BHKLab, gDRimport also provides functions that can convert a PharmacoGx::PharamcoSet object into a gDR object. With this functionality, users familiar with the gDR suite of packages and methods can utilize the publically available, curated datasets from the PharmacoGx database. The main step in this process is to extract the drug dose-response data from the PharmacoSets and transform them into a data.table that can be used as input for the gDRcore::runDrugResponseProcessingPipeline.
Loading a PharmacoSet (PSet)
Whereas a user might already have a pharmacoset loaded in their R session, if they wish to obtain a different pharmacoset or use the same script in the future we provide a helper function to do so. It helps to have a user directory in which to store all pharmacosets, and by passing this directory into the function as a parameter, the function will also check to see if the PSet exists in the user-defined directory. This is to ensure that the PSet is not being re-downloaded if it already has.
pset <- getPSet("Tavor_2020")
psetConverting PharmacoSet to data.table for gDR pipeline
PharamcoSets hold data pertaining to the cell lines (@sample slot), drugs (@treatment slot), and dose response experiments (@treatmentResponse slot). The dose response data is stored in a treatmentResponseExperiment object and the function gDRimport::convert_pset_to_df extracts this information to build a data.table that can be used as input to the gDR pipeline.
# Store treatment response data in df_
dt <- convert_pset_to_df(pharmacoset = pset)
str(dt)
#> Classes 'data.table' and 'data.frame': 34516 obs. of 7 variables:
#> $ Barcode : chr "PCM-0103090_1_130695_2018-08-28" "PCM-0103090_1_130695_2018-08-28" "PCM-0103090_1_130695_2018-08-28" "PCM-0103090_1_130695_2018-08-28" ...
#> $ ReadoutValue : num 75.7 63.1 75.9 87.2 78.8 ...
#> $ Concentration : num 0.0021 0.0052 0.01 0.0299 0.0798 ...
#> $ Clid : chr "130695" "130695" "130695" "130695" ...
#> $ DrugName : chr "Ivosidenib" "Ivosidenib" "Ivosidenib" "Ivosidenib" ...
#> $ Duration : num 48 48 48 48 48 48 48 48 48 48 ...
#> $ ReferenceDivisionTime: logi NA NA NA NA NA NA ...
#> - attr(*, ".internal.selfref")=<externalptr>Running drug response pipeline with data
The subsetted data can now be used as input for the gDRcore::runDrugResponseProcessingPipeline(). The output of this function is a MultiAssayExperiment object which can be accessed with gDRutils::convert_se_assay_to_dt()
# RUN DRUG RESPONSE PROCESSING PIPELINE
se <- gDRcore::runDrugResponseProcessingPipeline(x)
se# Convert Summarized Experiments to data.table
# Available SEs : "RawTreatred", "Controls", "Normalized", "Averaged", "Metrics"
str(gDRutils::convert_se_assay_to_dt(se[[1]], "Averaged"))
str(gDRutils::convert_se_assay_to_dt(se[[1]], "Metrics"))SessionInfo
sessionInfo()
#> R version 4.5.0 (2025-04-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.22-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] MultiAssayExperiment_1.35.3 gDRimport_1.7.1
#> [3] PharmacoGx_3.13.2 CoreGx_2.13.0
#> [5] SummarizedExperiment_1.39.0 Biobase_2.69.0
#> [7] GenomicRanges_1.61.0 GenomeInfoDb_1.45.3
#> [9] IRanges_2.43.0 S4Vectors_0.47.0
#> [11] MatrixGenerics_1.21.0 matrixStats_1.5.0
#> [13] BiocGenerics_0.55.0 generics_0.1.4
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-9 testthat_3.2.3 rlang_1.1.6
#> [4] magrittr_2.0.3 shinydashboard_0.7.3 compiler_4.5.0
#> [7] vctrs_0.6.5 reshape2_1.4.4 relations_0.6-15
#> [10] stringr_1.5.1 pkgconfig_2.0.3 crayon_1.5.3
#> [13] fastmap_1.2.0 backports_1.5.0 XVector_0.49.0
#> [16] caTools_1.18.3 promises_1.3.2 rmarkdown_2.29
#> [19] UCSC.utils_1.5.0 coop_0.6-3 xfun_0.52
#> [22] cachem_1.1.0 jsonlite_2.0.0 SnowballC_0.7.1
#> [25] later_1.4.2 DelayedArray_0.35.1 BiocParallel_1.43.2
#> [28] parallel_4.5.0 sets_1.0-25 cluster_2.1.8.1
#> [31] R6_2.6.1 bslib_0.9.0 stringi_1.8.7
#> [34] RColorBrewer_1.1-3 qs_0.27.3 limma_3.65.1
#> [37] boot_1.3-31 brio_1.1.5 jquerylib_0.1.4
#> [40] assertthat_0.2.1 Rcpp_1.0.14 knitr_1.50
#> [43] downloader_0.4.1 BiocBaseUtils_1.11.0 httpuv_1.6.16
#> [46] Matrix_1.7-3 igraph_2.1.4 tidyselect_1.2.1
#> [49] dichromat_2.0-0.1 abind_1.4-8 yaml_2.3.10
#> [52] stringfish_0.16.0 gplots_3.2.0 codetools_0.2-20
#> [55] lattice_0.22-7 tibble_3.2.1 plyr_1.8.9
#> [58] shiny_1.10.0 BumpyMatrix_1.17.0 evaluate_1.0.3
#> [61] RcppParallel_5.1.10 bench_1.1.4 pillar_1.10.2
#> [64] lsa_0.73.3 KernSmooth_2.23-26 checkmate_2.3.2
#> [67] DT_0.33 shinyjs_2.1.0 piano_2.25.0
#> [70] ggplot2_3.5.2 scales_1.4.0 RApiSerialize_0.1.4
#> [73] gtools_3.9.5 xtable_1.8-4 marray_1.87.0
#> [76] glue_1.8.0 slam_0.1-55 tools_4.5.0
#> [79] data.table_1.17.2 gDRutils_1.7.2 fgsea_1.35.0
#> [82] visNetwork_2.1.2 fastmatch_1.1-6 cowplot_1.1.3
#> [85] grid_4.5.0 cli_3.6.5 S4Arrays_1.9.0
#> [88] dplyr_1.1.4 gtable_0.3.6 sass_0.4.10
#> [91] digest_0.6.37 SparseArray_1.9.0 htmlwidgets_1.6.4
#> [94] farver_2.1.2 htmltools_0.5.8.1 lifecycle_1.0.4
#> [97] httr_1.4.7 statmod_1.5.0 mime_0.13