Association between clinical outcomes and BH3 profile (Individual concentrations)
Junyan Lu
2020-09-04
Last updated: 2020-09-04
Checks: 6 1
Knit directory: BH3profiling/analysis/
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Preprocessing
Load data
Use baseline level from DBP profiling
Prepare sample background annotations
Association between PCs and clinical outcomes
PCA
PCA
Plot p value and hazard ratio 
Kaplan-Meiler plots
KM plot for time to treatment (TTT) 
Correlation between PCs and CLL-PD
# A tibble: 6 x 2
# Groups: PC [6]
PC p.value
<chr> <dbl>
1 PC2 0.0000126
2 PC3 0.144
3 PC9 0.178
4 PC10 0.196
5 PC7 0.234
6 PC8 0.535 PC2 is associated with CLL-PD
Anova Table (Type II tests)
Response: value
Sum Sq Df F value Pr(>F)
IGHV.status 3202 1 2.6452 0.110028
LF4 11487 1 9.4895 0.003326 **
Residuals 61734 51
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Select BH3 markers for outcome prediction
Prepare data
Univariate test
Table for TTT
Table for OS
Selection using multi-vairate model
Prepare data
Select based on C-index
TTT
Plot Harrel's C comparison 
Forest plot of selected markers
BH3 profile does not seem to provide additional information for predicting prognosis
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] tidyverse_1.3.0 SummarizedExperiment_1.16.1
[11] DelayedArray_0.12.3 BiocParallel_1.20.1
[13] matrixStats_0.56.0 Biobase_2.46.0
[15] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
[17] IRanges_2.20.2 S4Vectors_0.24.4
[19] BiocGenerics_0.32.0 maxstat_0.7-25
[21] survminer_0.4.7 ggpubr_0.4.0
[23] ggplot2_3.3.2 survival_3.2-3
[25] jyluMisc_0.1.5 cowplot_1.0.0
[27] limma_3.42.2
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 digest_0.6.25 htmltools_0.5.0
[13] fansi_0.4.1 gdata_2.18.0 magrittr_1.5
[16] cluster_2.1.0 openxlsx_4.1.5 modelr_0.1.8
[19] sandwich_2.5-1 piano_2.2.0 colorspace_1.4-1
[22] blob_1.2.1 rvest_0.3.5 haven_2.3.1
[25] xfun_0.15 crayon_1.3.4 RCurl_1.98-1.2
[28] jsonlite_1.7.0 zoo_1.8-8 glue_1.4.1
[31] gtable_0.3.0 zlibbioc_1.32.0 XVector_0.26.0
[34] car_3.0-8 abind_1.4-5 scales_1.1.1
[37] mvtnorm_1.1-1 DBI_1.1.0 relations_0.6-9
[40] rstatix_0.6.0 Rcpp_1.0.5 plotrix_3.7-8
[43] xtable_1.8-4 foreign_0.8-71 km.ci_0.5-2
[46] DT_0.14 htmlwidgets_1.5.1 httr_1.4.1
[49] fgsea_1.12.0 gplots_3.0.4 ellipsis_0.3.1
[52] mice_3.11.0 farver_2.0.3 pkgconfig_2.0.3
[55] dbplyr_1.4.4 utf8_1.1.4 labeling_0.3
[58] tidyselect_1.1.0 rlang_0.4.7 later_1.1.0.1
[61] munsell_0.5.0 cellranger_1.1.0 tools_3.6.0
[64] visNetwork_2.0.9 cli_2.0.2 generics_0.0.2
[67] broom_0.7.0 evaluate_0.14 fastmap_1.0.1
[70] yaml_2.2.1 knitr_1.29 fs_1.4.2
[73] zip_2.0.4 survMisc_0.5.5 caTools_1.18.0
[76] nlme_3.1-148 mime_0.9 slam_0.1-47
[79] xml2_1.3.2 compiler_3.6.0 rstudioapi_0.11
[82] curl_4.3 ggsignif_0.6.0 marray_1.64.0
[85] reprex_0.3.0 stringi_1.4.6 lattice_0.20-41
[88] Matrix_1.2-18 shinyjs_1.1 KMsurv_0.1-5
[91] vctrs_0.3.1 pillar_1.4.6 lifecycle_0.2.0
[94] data.table_1.12.8 bitops_1.0-6 httpuv_1.5.4
[97] R6_2.4.1 promises_1.1.1 KernSmooth_2.23-17
[100] gridExtra_2.3 rio_0.5.16 codetools_0.2-16
[103] MASS_7.3-51.6 gtools_3.8.2 exactRankTests_0.8-31
[106] assertthat_0.2.1 rprojroot_1.3-2 withr_2.2.0
[109] multcomp_1.4-13 GenomeInfoDbData_1.2.2 mgcv_1.8-31
[112] hms_0.5.3 grid_3.6.0 rmarkdown_2.3
[115] carData_3.0-4 git2r_0.27.1 sets_1.0-18
[118] shiny_1.5.0 lubridate_1.7.9