Last updated: 2022-11-03
Checks: 6 1
Knit directory: EMBL2016/analysis/
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Load datasets
[1] "Loading data ../../var/newEMBL_processed_20220506.RData"
[1] "Loading data ../../var/patmeta_210324.RData"
Remove counter screen and CV plates
Remove low-quality samples and problematic drugs based on previous QA analysis (not used)
2(new). Remove low-quality samples, all drugs are kept
Use incubation effect adjusted viabilities
Remove the two lowest concentrations
Use AUC under IC50 curve instead of mean viability to summarise drug effect
Show drugs that were not tested for all samples
nTotal <- length(unique(screenData$patID))
#drug that are not screened in all samples
drugTab <- distinct(screenData, patID, name) %>% mutate_all(as.character) %>%
group_by(name) %>% summarise(n = length(patID)) %>% filter(n<nTotal) %>%
arrange(n)
drugTab %>% DT::datatable()
Those drug will be kept for hypothesis testing, but may not be for clustering and PCA.
Remove drug with Pro- and MCEMBL-
screenData <- screenData %>% filter(!str_detect(name, "PRO|CHEMBL|MCEMBL"))
How many patient samples and drugs left?
#patient samples
length(unique(screenData$patID))
[1] 186
#drugs
length(unique(screenData$name))
[1] 408
Annoate target class
tarAnno <- read_csv2("../data/targetAnnotation_all.csv")
ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
Rows: 427 Columns: 13
── Column specification ────────────────────────────────────────────────────────
Delimiter: ";"
chr (12): drugID, nameEMBL2016, drugSynonyms, target, targetFamily, pathway,...
lgl (1): nameSMART
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
screenData <- mutate(screenData, class = tarAnno[match(emblObjID, tarAnno$drugID),]$Class)
Save a list of used and exclued samples
usedSampleList <- screenData %>% distinct(patientID, sampleID, batch, diagnosis)
exclSampleList <- emblNew %>% filter(!sampleID %in% usedSampleList$sampleID) %>%
distinct(patID, sampleID, batch, diagnosis)
write_csv2(usedSampleList,"../docs/usedSamples_all.csv")
write_csv2(exclSampleList,"../docs/excludedSamples_all.csv")
Used and excluded samples: usedSamples_all.csv
excludedSamples_all.csv
Save R object
save(screenData, drugAnno, file = "../output/screenData.RData")
sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
[4] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[7] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
[10] readxl_1.4.0 Biobase_2.56.0 BiocGenerics_0.42.0
loaded via a namespace (and not attached):
[1] backports_1.4.1 fastmatch_1.1-3
[3] drc_3.0-1 jyluMisc_0.1.5
[5] workflowr_1.7.0 igraph_1.3.4
[7] shinydashboard_0.7.2 splines_4.2.0
[9] crosstalk_1.2.0 BiocParallel_1.30.3
[11] GenomeInfoDb_1.32.2 TH.data_1.1-1
[13] digest_0.6.29 htmltools_0.5.3
[15] fansi_1.0.3 magrittr_2.0.3
[17] googlesheets4_1.0.0 cluster_2.1.3
[19] tzdb_0.3.0 limma_3.52.2
[21] modelr_0.1.8 matrixStats_0.62.0
[23] vroom_1.5.7 sandwich_3.0-2
[25] piano_2.12.0 colorspace_2.0-3
[27] rvest_1.0.2 haven_2.5.0
[29] xfun_0.31 crayon_1.5.1
[31] RCurl_1.98-1.7 jsonlite_1.8.0
[33] survival_3.4-0 zoo_1.8-10
[35] glue_1.6.2 survminer_0.4.9
[37] gtable_0.3.0 gargle_1.2.0
[39] zlibbioc_1.42.0 XVector_0.36.0
[41] DelayedArray_0.22.0 car_3.1-0
[43] abind_1.4-5 scales_1.2.0
[45] mvtnorm_1.1-3 DBI_1.1.3
[47] relations_0.6-12 rstatix_0.7.0
[49] Rcpp_1.0.9 plotrix_3.8-2
[51] xtable_1.8-4 bit_4.0.4
[53] km.ci_0.5-6 stats4_4.2.0
[55] DT_0.23 htmlwidgets_1.5.4
[57] httr_1.4.3 fgsea_1.22.0
[59] gplots_3.1.3 ellipsis_0.3.2
[61] pkgconfig_2.0.3 sass_0.4.2
[63] dbplyr_2.2.1 utf8_1.2.2
[65] tidyselect_1.1.2 rlang_1.0.4
[67] later_1.3.0 munsell_0.5.0
[69] cellranger_1.1.0 tools_4.2.0
[71] visNetwork_2.1.0 cachem_1.0.6
[73] cli_3.3.0 generics_0.1.3
[75] broom_1.0.0 evaluate_0.15
[77] fastmap_1.1.0 yaml_2.3.5
[79] bit64_4.0.5 knitr_1.39
[81] fs_1.5.2 survMisc_0.5.6
[83] caTools_1.18.2 mime_0.12
[85] slam_0.1-50 xml2_1.3.3
[87] BiocStyle_2.24.0 compiler_4.2.0
[89] rstudioapi_0.13 ggsignif_0.6.3
[91] marray_1.74.0 reprex_2.0.1
[93] bslib_0.4.0 stringi_1.7.8
[95] lattice_0.20-45 Matrix_1.4-1
[97] KMsurv_0.1-5 shinyjs_2.1.0
[99] vctrs_0.4.1 pillar_1.8.0
[101] lifecycle_1.0.1 BiocManager_1.30.18
[103] jquerylib_0.1.4 data.table_1.14.2
[105] cowplot_1.1.1 bitops_1.0-7
[107] httpuv_1.6.5 GenomicRanges_1.48.0
[109] R6_2.5.1 promises_1.2.0.1
[111] KernSmooth_2.23-20 gridExtra_2.3
[113] IRanges_2.30.0 codetools_0.2-18
[115] MASS_7.3-58 gtools_3.9.3
[117] exactRankTests_0.8-35 assertthat_0.2.1
[119] SummarizedExperiment_1.26.1 rprojroot_2.0.3
[121] withr_2.5.0 multcomp_1.4-19
[123] S4Vectors_0.34.0 GenomeInfoDbData_1.2.8
[125] parallel_4.2.0 hms_1.1.1
[127] grid_4.2.0 rmarkdown_2.14
[129] MatrixGenerics_1.8.1 carData_3.0-5
[131] googledrive_2.0.0 git2r_0.30.1
[133] maxstat_0.7-25 ggpubr_0.4.0
[135] sets_1.0-21 shiny_1.7.2
[137] lubridate_1.8.0