Explore the BH3 landscape of CLL estiamted by DBP (Individual concentrations)
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
Use baseline level from DBP profiling
Prepare sample background annotations
Explore data structure
Hierarchical clustering and heatmaps
Pricipal component analysis
Associations between PCs and patient background
Associations with P-value < 0.05
# A tibble: 3 x 3
# Groups: PC, feature [3]
PC feature p.value
<chr> <chr> <dbl>
1 PC3 Methylation_Cluster 0.0000338
2 PC2 IGHV.status 0.00941
3 PC1 trisomy12 0.0187 Plot associations 
Plot feature loadings on the first three PCs 
Association test with patient genomic background
Table of associations
P value histogram
All 
Per-gene 
Association network (10% FDR)
Summarise plot for all concentrations
1 is the highest concentration and 5 is the lowest concentration. Only the P-values < 0.05 are colored.
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 < 0.05
Association with transcriptomics
Preprocessing
RNAseq
BH3 profiling
Association test for each feature
Number of significant associations per feature (10% FDR)
Table of significant associations
Plot top 9 significant associations
Pathway enrichment analysis for features that associate with gene expression
[1] "No sets passed the criteria"
[1] "No sets passed the criteria"$ABT199_0.5uM
$FS1_1uM
NULL
$ABT199_0.1uM
$BAD_1uM
$A133_20uM
$A133_1uM
$BAD_0.05uM
$FS1_5uM
$HRKy_10uM
$BIM_0.005uM
NULL
$BAD_0.1uM
$BIM_0.01uM
$BIM_0.1uM
$ABT199_0.01uM
$BIM_0.05uM
$MS1_1uM
$A133_10uM
Association with proteomics
Preprocessing
Proteomics
[1] 4316 21BH3 profiling
Association test for each feature
Number of significant associations per feature (10% FDR)
Table of significant associations
Plot top 9 significant associations
Pathway enrichment analysis for features that associate with gene expression
[1] "No sets passed the criteria"
[1] "No sets passed the criteria"
[1] "No sets passed the criteria"
[1] "No sets passed the criteria"$BAD_0.05uM
$PUMA_0.1uM
$ABT199_0.1uM
NULL
$ABT199_0.5uM
NULL
$BAD_0.1uM
$FS1_1uM
$MS1_1uM
$A133_1uM
NULL
$ABT199_0.01uM
$BAD_1uM
NULL
$PUMA_0.5uM
Association with energy metabolomics
Preprocessing
Proteomics
[1] 11 53BH3 profiling
Association test for each feature
Number of significant associations per feature (10% FDR)
Table of significant associations
Plot top 9 significant associations
Association with spontaneous apoptosis measured by image data
Scatter plots showing significant correlations (5% FDR)
If multiple concentrations are identified as significant, only show the most significant concentration. 
Association between baseline ATP levels of cells and BH3 profile
The baseline ATP levels represent the cell viability without any drug treatment in our screen. There are three features, ATP level at 0 hour, ATP level at 48 hour and the difference of those two values. These measurements can in some degree represent the intrinsic vulnerability/metabolic activity of the cells.
Association test between baseline ATP and BH3 profile
Scatter plots showing significant correlations (5% FDR)
If multiple concentrations are identified as significant, only show the most significant concentration. 
"ATP_diff" is the measurement of ATP loss during 48h culturing
Association between CLL-PD and BH3 profile
CLL-PD is the newly identified biomarker for CLL that potentially correlates with CLL cell proliferation
Scatter plots showing significant correlations (10% FDR)
If multiple concentrations are identified as significant, only show the most significant concentration. 
Multivariate feature selection for explaining BH3 profling in CLL
Data pre-processing
BH3 profiling
RNAseq
Methylation
For genomic data
For demographic and clinical data
Function to Generate the explanatory dataset for each BH3 profile
Calulate bioenergetic variance explained by multi-omics data set
Training models
Clean and integrate multi-omics data
Function for multi-variate regression
Perform lasso regression
Ploting results
Function for plotting variance explained for each measurement 
Using LASSO model to select important features
Training models
Prepare clean data for feature selection
Perform lasso regression
Ploting results
Function for the heatmap plot
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] pheatmap_1.0.12 cowplot_1.0.0
[29] corrplot_0.84 qgraph_1.6.5
[31] 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] KernSmooth_2.23-17 crayon_1.3.4 htmltools_0.5.0
[103] mgcv_1.8-31 corpcor_1.6.9 later_1.1.0.1
[106] Formula_1.2-3 geneplotter_1.64.0 lubridate_1.7.9
[109] DBI_1.1.0 dbplyr_1.4.4 MASS_7.3-51.6
[112] car_3.0-8 cli_2.0.2 marray_1.64.0
[115] gdata_2.18.0 igraph_1.2.5 BDgraph_2.62
[118] pkgconfig_2.0.3 km.ci_0.5-2 foreign_0.8-71
[121] piano_2.2.0 foreach_1.5.0 xml2_1.3.2
[124] annotate_1.64.0 pbivnorm_0.6.0 XVector_0.26.0
[127] drc_3.0-1 rvest_0.3.5 digest_0.6.25
[130] rmarkdown_2.3 cellranger_1.1.0 fastmatch_1.1-0
[133] survMisc_0.5.5 htmlTable_2.0.1 curl_4.3
[136] shiny_1.5.0 gtools_3.8.2 rjson_0.2.20
[139] lifecycle_0.2.0 nlme_3.1-148 glasso_1.11
[142] jsonlite_1.7.0 carData_3.0-4 fansi_0.4.1
[145] pillar_1.4.6 lattice_0.20-41 fastmap_1.0.1
[148] httr_1.4.1 plotrix_3.7-8 survival_3.2-3
[151] glue_1.4.1 zip_2.0.4 fdrtool_1.2.15
[154] iterators_1.0.12 png_0.1-7 bit_1.1-15.2
[157] stringi_1.4.6 blob_1.2.1 memoise_1.1.0
[160] latticeExtra_0.6-29 caTools_1.18.0