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 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-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_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: 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_2.32.0 MultiAssayExperiment_1.30.0
## [3] gDRimport_1.2.0 PharmacoGx_3.8.0
## [5] CoreGx_2.8.0 SummarizedExperiment_1.34.0
## [7] Biobase_2.64.0 GenomicRanges_1.56.0
## [9] GenomeInfoDb_1.40.0 IRanges_2.38.0
## [11] S4Vectors_0.42.0 MatrixGenerics_1.16.0
## [13] matrixStats_1.3.0 BiocGenerics_0.50.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 formatR_1.14 readxl_1.4.3
## [4] testthat_3.2.1.1 rlang_1.1.3 magrittr_2.0.3
## [7] shinydashboard_0.7.2 compiler_4.4.0 vctrs_0.6.5
## [10] reshape2_1.4.4 relations_0.6-13 stringr_1.5.1
## [13] pkgconfig_2.0.3 crayon_1.5.2 fastmap_1.1.1
## [16] backports_1.4.1 XVector_0.44.0 caTools_1.18.2
## [19] utf8_1.2.4 promises_1.3.0 rmarkdown_2.26
## [22] UCSC.utils_1.0.0 coop_0.6-3 xfun_0.43
## [25] zlibbioc_1.50.0 cachem_1.0.8 jsonlite_1.8.8
## [28] SnowballC_0.7.1 later_1.3.2 DelayedArray_0.30.0
## [31] BiocParallel_1.38.0 parallel_4.4.0 sets_1.0-25
## [34] cluster_2.1.6 R6_2.5.1 stringi_1.8.3
## [37] bslib_0.7.0 RColorBrewer_1.1-3 qs_0.26.1
## [40] limma_3.60.0 pkgload_1.3.4 boot_1.3-30
## [43] cellranger_1.1.0 brio_1.1.5 jquerylib_0.1.4
## [46] bookdown_0.39 assertthat_0.2.1 Rcpp_1.0.12
## [49] knitr_1.46 downloader_0.4 httpuv_1.6.15
## [52] Matrix_1.7-0 igraph_2.0.3 tidyselect_1.2.1
## [55] abind_1.4-5 yaml_2.3.8 stringfish_0.16.0
## [58] gplots_3.1.3.1 codetools_0.2-20 lattice_0.22-6
## [61] tibble_3.2.1 plyr_1.8.9 shiny_1.8.1.1
## [64] BumpyMatrix_1.12.0 evaluate_0.23 lambda.r_1.2.4
## [67] desc_1.4.3 futile.logger_1.4.3 RcppParallel_5.1.7
## [70] bench_1.1.3 BiocManager_1.30.22 pillar_1.9.0
## [73] lsa_0.73.3 KernSmooth_2.23-22 checkmate_2.3.1
## [76] DT_0.33 shinyjs_2.1.0 piano_2.20.0
## [79] generics_0.1.3 rprojroot_2.0.4 ggplot2_3.5.1
## [82] munsell_0.5.1 scales_1.3.0 RApiSerialize_0.1.2
## [85] gtools_3.9.5 xtable_1.8-4 marray_1.82.0
## [88] glue_1.7.0 slam_0.1-50 tools_4.4.0
## [91] data.table_1.15.4 gDRutils_1.2.0 fgsea_1.30.0
## [94] visNetwork_2.1.2 fastmatch_1.1-4 cowplot_1.1.3
## [97] grid_4.4.0 colorspace_2.1-0 GenomeInfoDbData_1.2.12
## [100] cli_3.6.2 futile.options_1.0.1 fansi_1.0.6
## [103] S4Arrays_1.4.0 rematch_2.0.0 dplyr_1.1.4
## [106] gtable_0.3.5 sass_0.4.9 digest_0.6.35
## [109] SparseArray_1.4.0 htmlwidgets_1.6.4 htmltools_0.5.8.1
## [112] lifecycle_1.0.4 httr_1.4.7 statmod_1.5.0
## [115] mime_0.12