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.
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 S4td1## 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- characterThe 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 26 -none- listt_df <- load_templates(template_path(td1))
summary(t_df)## WellRow WellColumn Gnumber Concentration
## Length:768 Length:768 Length:768 Length:768
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Gnumber_2 Concentration_2 Template
## Length:768 Length:768 Length:768
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :characterr_df <- suppressMessages(load_results(result_path(td1)))
summary(r_df)## Barcode WellRow WellColumn ReadoutValue
## Length:4587 Length:4587 Min. : 1.00 Min. : 12627
## Class :character Class :character 1st Qu.: 6.50 1st Qu.: 67905
## Mode :character Mode :character Median :12.00 Median : 140865
## Mean :12.49 Mean : 263996
## 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. :704.0l_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 listThe function installAllDeps assists in installing package dependencies.
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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_2.30.0 MultiAssayExperiment_1.28.0
## [3] gDRimport_1.0.0 PharmacoGx_3.6.0
## [5] CoreGx_2.6.0 SummarizedExperiment_1.32.0
## [7] Biobase_2.62.0 GenomicRanges_1.54.0
## [9] GenomeInfoDb_1.38.0 IRanges_2.36.0
## [11] S4Vectors_0.40.0 MatrixGenerics_1.14.0
## [13] matrixStats_1.0.0 BiocGenerics_0.48.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_1.8.7 magrittr_2.0.3
## [4] rmarkdown_2.25 zlibbioc_1.48.0 vctrs_0.6.4
## [7] magicaxis_2.2.14 RCurl_1.98-1.12 htmltools_0.5.6.1
## [10] S4Arrays_1.2.0 lambda.r_1.2.4 plotrix_3.8-2
## [13] cellranger_1.1.0 SparseArray_1.2.0 sass_0.4.7
## [16] pracma_2.4.2 KernSmooth_2.23-22 bslib_0.5.1
## [19] htmlwidgets_1.6.2 desc_1.4.2 plyr_1.8.9
## [22] testthat_3.2.0 futile.options_1.0.1 cachem_1.0.8
## [25] igraph_1.5.1 mime_0.12 lifecycle_1.0.3
## [28] piano_2.18.0 pkgconfig_2.0.3 Matrix_1.6-1.1
## [31] R6_2.5.1 fastmap_1.1.1 GenomeInfoDbData_1.2.11
## [34] shiny_1.7.5.1 digest_0.6.33 colorspace_2.1-0
## [37] rprojroot_2.0.3 pkgload_1.3.3 SnowballC_0.7.1
## [40] fansi_1.0.5 abind_1.4-5 coop_0.6-3
## [43] compiler_4.3.1 downloader_0.4 marray_1.80.0
## [46] backports_1.4.1 BiocParallel_1.36.0 gDRutils_1.0.0
## [49] bench_1.1.3 qs_0.25.5 gplots_3.1.3
## [52] maps_3.4.1 MASS_7.3-60 DelayedArray_0.28.0
## [55] gtools_3.9.4 caTools_1.18.2 tools_4.3.1
## [58] NISTunits_1.0.1 httpuv_1.6.12 relations_0.6-13
## [61] glue_1.6.2 promises_1.2.1 grid_4.3.1
## [64] checkmate_2.2.0 cluster_2.1.4 reshape2_1.4.4
## [67] fgsea_1.28.0 generics_0.1.3 gtable_0.3.4
## [70] sm_2.2-5.7.1 data.table_1.14.8 RApiSerialize_0.1.2
## [73] stringfish_0.15.8 utf8_1.2.4 XVector_0.42.0
## [76] RANN_2.6.1 pillar_1.9.0 stringr_1.5.0
## [79] limma_3.58.0 BumpyMatrix_1.10.0 later_1.3.1
## [82] dplyr_1.1.3 lattice_0.22-5 tidyselect_1.2.0
## [85] knitr_1.44 bookdown_0.36 futile.logger_1.4.3
## [88] xfun_0.40 shinydashboard_0.7.2 statmod_1.5.0
## [91] brio_1.1.3 DT_0.30 visNetwork_2.1.2
## [94] stringi_1.7.12 rematch_2.0.0 yaml_2.3.7
## [97] boot_1.3-28.1 evaluate_0.22 codetools_0.2-19
## [100] lsa_0.73.3 tibble_3.2.1 BiocManager_1.30.22
## [103] cli_3.6.1 RcppParallel_5.1.7 xtable_1.8-4
## [106] munsell_0.5.0 jquerylib_0.1.4 readxl_1.4.3
## [109] Rcpp_1.0.11 mapproj_1.2.11 parallel_4.3.1
## [112] sets_1.0-24 ellipsis_0.3.2 ggplot2_3.4.4
## [115] assertthat_0.2.1 bitops_1.0-7 slam_0.1-50
## [118] celestial_1.4.6 scales_1.2.1 crayon_1.5.2
## [121] rlang_1.1.1 formatR_1.14 cowplot_1.1.1
## [124] fastmatch_1.1-4 shinyjs_2.1.0