Analysis of the dynamic BH3 profiling data (AUC)
Junyan Lu
2020 Sept. 3
Last updated: 2020-09-04
Checks: 6 1
Knit directory: BH3profiling/analysis/
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Load and preprocess BH3 profiling data
Load
Prepare sample background annotations
Compare baseline and dynmiac BH3 profile
Explore data structure
Hierarchical clustering
PCA
Without idelalisib 
Whether combination is explained by peptide or drug treatment
Pairs that show syngergistic effect
Associate syngergistic effect with genomics
Calculate combination index (CI) for each sample and combination
Table of associations
P value histogram
All 
Box plots for the significant associations (P < 0.01)
If multiple concentrations are identified as significant, only show the most significant concentration. 
Associations with methylation cluster
Plot associations with p value < 0.1
Associate synergistic effect (CI) wit drug responses
CI in Annexin data
Preprocessing
Scatter plots showing significant correlations (5% FDR)
If multiple concentrations are identified as significant, only show the most significant concentration. 
Overall drug effect in Annexin data
Preprocessing
Scatter plots showing significant correlations (5% FDR)
If multiple concentrations are identified as significant, only show the most significant concentration. 
Associate overall cytC release wit drug responses
Overall drug effect in Annexin data
Preprocessing
Scatter plots showing significant correlations (P <0.01)
If multiple concentrations are identified as significant, only show the most significant concentration. 
Association with ex-vivo drug responses (IC50 screen)
Preprocessing
[1] 56P-value histogram
Table of significant correlations
Table of significant correlations (same drug treatment)
Scatter plots showing significant correlations (same drug)
If multiple concentrations are identified as significant, only show the most significant concentration. 
Association with ex-vivo drug responses (1000CPS screen)
Preprocessing
[1] 64[1] 64P-value histogram
Table of significant correlations
Table of significant correlations (same drug treatment)
If multiple concentrations are identified as significant, only show the most significant concentration. 
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] latex2exp_0.4.0 forcats_0.5.0
[3] stringr_1.4.0 dplyr_1.0.0
[5] purrr_0.3.4 readr_1.3.1
[7] tidyr_1.1.0 tibble_3.0.3
[9] ggplot2_3.3.2 tidyverse_1.3.0
[11] SummarizedExperiment_1.16.1 DelayedArray_0.12.3
[13] BiocParallel_1.20.1 matrixStats_0.56.0
[15] Biobase_2.46.0 GenomicRanges_1.38.0
[17] GenomeInfoDb_1.22.1 IRanges_2.20.2
[19] S4Vectors_0.24.4 BiocGenerics_0.32.0
[21] IHW_1.14.0 limma_3.42.2
[23] pheatmap_1.0.12 cowplot_1.0.0
[25] corrplot_0.84 jyluMisc_0.1.5
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.8 fastmatch_1.1-0
[4] drc_3.0-1 workflowr_1.6.2 igraph_1.2.5
[7] shinydashboard_0.7.1 splines_3.6.0 crosstalk_1.1.0.1
[10] TH.data_1.0-10 lpsymphony_1.14.0 digest_0.6.25
[13] htmltools_0.5.0 fansi_0.4.1 gdata_2.18.0
[16] magrittr_1.5 cluster_2.1.0 openxlsx_4.1.5
[19] modelr_0.1.8 sandwich_2.5-1 piano_2.2.0
[22] colorspace_1.4-1 rvest_0.3.5 blob_1.2.1
[25] haven_2.3.1 xfun_0.15 crayon_1.3.4
[28] RCurl_1.98-1.2 jsonlite_1.7.0 survival_3.2-3
[31] zoo_1.8-8 glue_1.4.1 survminer_0.4.7
[34] gtable_0.3.0 zlibbioc_1.32.0 XVector_0.26.0
[37] car_3.0-8 abind_1.4-5 scales_1.1.1
[40] mvtnorm_1.1-1 DBI_1.1.0 relations_0.6-9
[43] rstatix_0.6.0 Rcpp_1.0.5 plotrix_3.7-8
[46] xtable_1.8-4 foreign_0.8-71 km.ci_0.5-2
[49] DT_0.14 htmlwidgets_1.5.1 httr_1.4.1
[52] fgsea_1.12.0 gplots_3.0.4 RColorBrewer_1.1-2
[55] ellipsis_0.3.1 mice_3.11.0 farver_2.0.3
[58] pkgconfig_2.0.3 dbplyr_1.4.4 labeling_0.3
[61] tidyselect_1.1.0 rlang_0.4.7 later_1.1.0.1
[64] munsell_0.5.0 cellranger_1.1.0 tools_3.6.0
[67] visNetwork_2.0.9 cli_2.0.2 generics_0.0.2
[70] broom_0.7.0 fdrtool_1.2.15 evaluate_0.14
[73] fastmap_1.0.1 yaml_2.2.1 knitr_1.29
[76] fs_1.4.2 zip_2.0.4 survMisc_0.5.5
[79] caTools_1.18.0 nlme_3.1-148 mime_0.9
[82] slam_0.1-47 xml2_1.3.2 compiler_3.6.0
[85] rstudioapi_0.11 curl_4.3 ggsignif_0.6.0
[88] marray_1.64.0 reprex_0.3.0 stringi_1.4.6
[91] lattice_0.20-41 Matrix_1.2-18 shinyjs_1.1
[94] KMsurv_0.1-5 vctrs_0.3.1 pillar_1.4.6
[97] lifecycle_0.2.0 data.table_1.12.8 bitops_1.0-6
[100] httpuv_1.5.4 R6_2.4.1 promises_1.1.1
[103] KernSmooth_2.23-17 gridExtra_2.3 rio_0.5.16
[106] codetools_0.2-16 MASS_7.3-51.6 gtools_3.8.2
[109] exactRankTests_0.8-31 assertthat_0.2.1 rprojroot_1.3-2
[112] withr_2.2.0 multcomp_1.4-13 GenomeInfoDbData_1.2.2
[115] mgcv_1.8-31 hms_0.5.3 grid_3.6.0
[118] rmarkdown_2.3 carData_3.0-4 git2r_0.27.1
[121] maxstat_0.7-25 ggpubr_0.4.0 sets_1.0-18
[124] shiny_1.5.0 lubridate_1.7.9