Last updated: 2020-09-04
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Knit directory: BH3profiling/analysis/
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Load
Use baseline level from DBP profiling
Prepare sample background annotations
[1] 56
If multiple concentrations are identified as significant, only show the most significant concentration. x-axis is the cell viability after drug treatment, so higher values mean higher drug resistance
[1] 64
[1] 9 64
If multiple concentrations are identified as significant, only show the most significant concentration. x-axis is the cell viability after drug treatment, so higher values mean higher drug resistance
[1] 9 31
If multiple concentrations are identified as significant, only show the most significant concentration.
If multiple concentrations are identified as significant, only show the most significant concentration.
If multiple concentrations are identified as significant, only show the most significant concentration.
If multiple concentrations are identified as significant, only show the most significant concentration.
If multiple concentrations are identified as significant, only show the most significant concentration. Higher CI value means more synergy in drug combinations
If multiple concentrations are identified as significant, only show the most significant concentration.
Test whether the BH3 profile can explain additional variance in drug response compared to genetic alone
[1] 56
Prepare genomics
Genes that will be included in the multivariate model
[1] "IGHV.status" "del11q" "del13q" "del14q" "del17p"
[6] "trisomy12" "ATM" "NOTCH1" "SF3B1" "TP53"
# A tibble: 8 x 2
feature drugNumber
<chr> <int>
1 FS1 13
2 A133 12
3 MS1 10
4 BIM 5
5 ABT199 4
6 BAD 4
7 HRKy 3
8 PUMA 3
[1] 64
Prepare genomics
Genes that will be included in the multivariate model
[1] "IGHV.status" "del11q" "del13q" "del17p" "trisomy12"
[6] "ATM" "NOTCH1" "SF3B1" "TP53"
# A tibble: 6 x 2
feature drugNumber
<chr> <int>
1 BIM 10
2 FS1 9
3 MS1 4
4 PUMA 4
5 A133 3
6 BAD 1
BH3 profiling
Drug responses
RNAseq
For genomic data
Function to Generate the explanatory dataset for each drug response
Clean and integrate multi-omics data
Function for multi-variate regression
Perform lasso regression
Function for plotting variance explained for each measurement
# A tibble: 11 x 5
drug `genetic(13)` `BH3(9)` `expression(20)` `all(42)`
<chr> <dbl> <dbl> <dbl> <dbl>
1 Duvelisib 0.0969 0.401 0.157 0.522
2 Foretinib 0.0873 0.323 0.178 0.439
3 SB-203580 0.123 0.246 0.00730 0.458
4 Idelalisib 0.153 0.245 0.156 0.245
5 Selisistat 0.127 0.209 0.0321 0.135
6 Quizartinib 0.00369 0.164 0 0
7 Cisplatin 0 0.157 0.00333 0.147
8 BML-277 0.00264 0.152 0.0176 0.244
9 D4476 0 0.147 0.108 0.147
10 MI-503 0 0.132 0 0.0725
11 TW-37 0.0187 0.106 0.0109 0.0627
Prepare clean data for feature selection
Perform lasso regression
Function for the heatmap plot
Plot all heatmaps
BH3 profiling
Drug responses
RNAseq
For genomic data
Clean and integrate multi-omics data
Perform lasso regression
# A tibble: 7 x 5
drug `genetic(12)` `BH3(9)` `expression(20)` `all(41)`
<chr> <dbl> <dbl> <dbl> <dbl>
1 venetoclax 0.0752 0.444 0.308 0.618
2 selumetinib 0.184 0.337 0.146 0.444
3 SNS-032 0.189 0.292 0.154 0.538
4 rigosertib 0.131 0.252 0 0.0626
5 silmitasertib 0.172 0.209 0.0230 0.185
6 KX2-391 0.122 0.196 0.128 0.466
7 vorinostat 0.00329 0.112 0.0930 0.168
Prepare clean data for feature selection
Perform lasso regression
Plot all heatmaps
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] gtable_0.3.0 glmnet_4.0-2
[3] Matrix_1.2-18 DESeq2_1.26.0
[5] latex2exp_0.4.0 forcats_0.5.0
[7] stringr_1.4.0 dplyr_1.0.0
[9] purrr_0.3.4 readr_1.3.1
[11] tidyr_1.1.0 tibble_3.0.3
[13] ggplot2_3.3.2 tidyverse_1.3.0
[15] SummarizedExperiment_1.16.1 DelayedArray_0.12.3
[17] BiocParallel_1.20.1 matrixStats_0.56.0
[19] Biobase_2.46.0 GenomicRanges_1.38.0
[21] GenomeInfoDb_1.22.1 IRanges_2.20.2
[23] S4Vectors_0.24.4 BiocGenerics_0.32.0
[25] IHW_1.14.0 limma_3.42.2
[27] cowplot_1.0.0 qgraph_1.6.5
[29] jyluMisc_0.1.5
loaded via a namespace (and not attached):
[1] utf8_1.1.4 shinydashboard_0.7.1 tidyselect_1.1.0
[4] RSQLite_2.2.0 AnnotationDbi_1.48.0 htmlwidgets_1.5.1
[7] grid_3.6.0 maxstat_0.7-25 munsell_0.5.0
[10] codetools_0.2-16 DT_0.14 withr_2.2.0
[13] colorspace_1.4-1 knitr_1.29 rstudioapi_0.11
[16] ggsignif_0.6.0 labeling_0.3 huge_1.3.4.1
[19] git2r_0.27.1 slam_0.1-47 GenomeInfoDbData_1.2.2
[22] lpsymphony_1.14.0 mnormt_1.5-5 KMsurv_0.1-5
[25] bit64_0.9-7 farver_2.0.3 rprojroot_1.3-2
[28] vctrs_0.3.1 generics_0.0.2 TH.data_1.0-10
[31] xfun_0.15 sets_1.0-18 R6_2.4.1
[34] locfit_1.5-9.4 bitops_1.0-6 fgsea_1.12.0
[37] assertthat_0.2.1 promises_1.1.1 scales_1.1.1
[40] multcomp_1.4-13 nnet_7.3-14 sandwich_2.5-1
[43] workflowr_1.6.2 rlang_0.4.7 genefilter_1.68.0
[46] splines_3.6.0 rstatix_0.6.0 acepack_1.4.1
[49] broom_0.7.0 checkmate_2.0.0 yaml_2.2.1
[52] reshape2_1.4.4 abind_1.4-5 modelr_0.1.8
[55] crosstalk_1.1.0.1 d3Network_0.5.2.1 backports_1.1.8
[58] httpuv_1.5.4 Hmisc_4.4-0 tools_3.6.0
[61] relations_0.6-9 psych_1.9.12.31 lavaan_0.6-6
[64] ellipsis_0.3.1 gplots_3.0.4 RColorBrewer_1.1-2
[67] Rcpp_1.0.5 plyr_1.8.6 base64enc_0.1-3
[70] visNetwork_2.0.9 zlibbioc_1.32.0 RCurl_1.98-1.2
[73] ggpubr_0.4.0 rpart_4.1-15 pbapply_1.4-2
[76] zoo_1.8-8 haven_2.3.1 cluster_2.1.0
[79] exactRankTests_0.8-31 fs_1.4.2 magrittr_1.5
[82] data.table_1.12.8 openxlsx_4.1.5 reprex_0.3.0
[85] survminer_0.4.7 mvtnorm_1.1-1 whisker_0.4
[88] hms_0.5.3 shinyjs_1.1 mime_0.9
[91] evaluate_0.14 xtable_1.8-4 XML_3.98-1.20
[94] rio_0.5.16 jpeg_0.1-8.1 readxl_1.3.1
[97] shape_1.4.4 gridExtra_2.3 compiler_3.6.0
[100] mice_3.11.0 KernSmooth_2.23-17 crayon_1.3.4
[103] htmltools_0.5.0 mgcv_1.8-31 corpcor_1.6.9
[106] later_1.1.0.1 Formula_1.2-3 geneplotter_1.64.0
[109] lubridate_1.7.9 DBI_1.1.0 dbplyr_1.4.4
[112] MASS_7.3-51.6 car_3.0-8 cli_2.0.2
[115] marray_1.64.0 gdata_2.18.0 igraph_1.2.5
[118] BDgraph_2.62 pkgconfig_2.0.3 km.ci_0.5-2
[121] foreign_0.8-71 piano_2.2.0 foreach_1.5.0
[124] xml2_1.3.2 annotate_1.64.0 pbivnorm_0.6.0
[127] XVector_0.26.0 drc_3.0-1 rvest_0.3.5
[130] digest_0.6.25 rmarkdown_2.3 cellranger_1.1.0
[133] fastmatch_1.1-0 survMisc_0.5.5 htmlTable_2.0.1
[136] curl_4.3 shiny_1.5.0 gtools_3.8.2
[139] rjson_0.2.20 lifecycle_0.2.0 nlme_3.1-148
[142] glasso_1.11 jsonlite_1.7.0 carData_3.0-4
[145] fansi_0.4.1 pillar_1.4.6 lattice_0.20-41
[148] fastmap_1.0.1 httr_1.4.1 plotrix_3.7-8
[151] survival_3.2-3 glue_1.4.1 zip_2.0.4
[154] fdrtool_1.2.15 iterators_1.0.12 png_0.1-7
[157] bit_1.1-15.2 stringi_1.4.6 blob_1.2.1
[160] memoise_1.1.0 latticeExtra_0.6-29 caTools_1.18.0