Overview of BH3 profile
Junyan Lu
2020 Sept. 3
Last updated: 2022-07-06
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
Save a table of patient information
Data structure
Data dimension
Number of samples
[1] 73Overview of CytC release data
For each concentration
For AUC
Dose response curves
Hierarchical clustering and heatmaps
Pricipal component analysis
PCA biplot
Associations between PCs and patient background
Associations with P-value < 0.05
# A tibble: 4 × 3
# Groups: PC, feature [4]
PC feature p.value
<chr> <chr> <dbl>
1 PC2 NOTCH1 0.00129
2 PC2 IGHV.status 0.00286
3 PC1 trisomy12 0.0102
4 PC2 Methylation_Cluster 0.0356 Plot associations
Plot feature loadings on the first three PCs
Feature correlations
AUC
Individual concentrations
Association test with patient genomic background
Prepare patient genomic background
[1] "IGHV.status" "del11q" "del13q" "del17p" "trisomy12"
[6] "NOTCH1" "SF3B1" "TP53" Plot to summarise genomic background
Association test
Test for Genomics
Methylation cluster
Table of associations
P value scatter plot
Box plots for the significant associations (10% FDR)
If multiple concentrations are identified as significant, only show
the most significant concentration.
Genomic correlation with individual concentrations
Association test
Summary heatmap plot for all concentrations
Associations with pretreatment status
Test for Genomics
# A tibble: 7 × 4
feature p.value estimate p.adj
<chr> <dbl> <dbl> <dbl>
1 ABT199 0.0174 11.7 0.122
2 BAD 0.0460 9.51 0.161
3 BIM 0.414 2.99 0.580
4 FS1 0.190 4.23 0.333
5 HRKy 0.946 -0.0745 0.946
6 MS1 0.787 1.49 0.918
7 PUMA 0.116 6.85 0.270
Pearson's Chi-squared test with Yates' continuity correction
data: patAnno$IGHV.status and patAnno$pretreat
X-squared = 4.9003, df = 1, p-value = 0.02685Estimate the confounding effect of pretreatment status
How many treated and untreated samples?
no yes
55 18 Adjust for pretreatment status
Test for Genomics
Methylation cluster
Combine
Test only in untreated
Test for Genomics
Methylation cluster
Table for comparing results
Association with transcriptomics
Preprocessing
RNAseq
BH3 profiling
Association test for each feature
Number of significant associations per feature (10% FDR)
# A tibble: 7 × 2
feature n
<chr> <int>
1 ABT199 110
2 BAD 64
3 BIM 0
4 FS1 0
5 HRKy 0
6 MS1 0
7 PUMA 0Table of significant associations
Heamap for significant correlations
Plot top 9 significant associations
Plot top 9 significant associations for ABT199
Plot selected associations
Pathway enrichment analysis for features that associate with gene expression
Cancer Hallmark
KEGG
Record siginificant RNAs for later feature selection
Gene set heatmap
Association with proteomics
Preprocessing
Proteomics
[1] 3314 30BH3 profiling
Association test for each feature
Number of significant associations per feature (10% FDR)
None passed 10% FDR
Table of significant associations (P<0.01)
Plot top 9 associations
Association with the spontaneous apoptosis of CLL cells
Measured by image data
PCs
Individual peptide
If multiple concentrations are identified as significant, only show
the most significant concentration.
Association with basline Annexin data
P.Value coef feature adj.P.Val peptide conc concIndex
1 0.5419874 -0.11384658 BIM 0.9110941 BIM 5e-03 1
2 0.5769504 -0.10420050 BAD 0.9110941 BAD 5e-02 1
3 0.6122063 -0.09473453 MS1 0.9110941 MS1 1e+01 1
4 0.6383630 0.08785945 HRKy 0.9110941 HRKy 1e+01 1
5 0.7044989 -0.07094563 PUMA 0.9110941 PUMA 1e-01 1
6 0.8722659 0.03010788 ABT199 0.9110941 ABT199 1e-02 1
7 0.9110941 0.02091168 FS1 0.9110941 FS1 1e+01 1No significant correlations?
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.
“ATP_diff” is the measurement of ATP loss during 48h culturing
Multivariate feature selection for explaining BH3 profling in CLL
Data pre-processing
BH3 profiling
RNAseq
For genomic data
For demographic and clinical data
Function to Generate the explanatory dataset for each BH3 profile
Clean and integrate multi-omics data
Perform multi-variate regression
LASSO model
Training models
Function for multi-variate regression
Perform lasso regression
Ploting R2
Function for plotting variance explained for each measurement
Ploting heatmap of selected features
Function for the heatmap plot
Plot all heatmaps
Multivariate linear regression without LASSO penalty
Training model
Function for multi-variate regression without lasso penalty
Perform linear regression without penalization
Ploting R2
Heatmap of selected features, without lasso penalty (0.1 Pvalue)
Plot all heatmaps
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gtable_0.3.0 glmnet_4.1-4
[3] Matrix_1.4-1 DESeq2_1.36.0
[5] latex2exp_0.9.4 forcats_0.5.1
[7] stringr_1.4.0 dplyr_1.0.9
[9] purrr_0.3.4 readr_2.1.2
[11] tidyr_1.2.0 tibble_3.1.7
[13] ggplot2_3.3.6 tidyverse_1.3.1
[15] SummarizedExperiment_1.26.1 Biobase_2.56.0
[17] GenomicRanges_1.48.0 GenomeInfoDb_1.32.2
[19] IRanges_2.30.0 S4Vectors_0.34.0
[21] BiocGenerics_0.42.0 MatrixGenerics_1.8.0
[23] matrixStats_0.62.0 IHW_1.24.0
[25] limma_3.52.2 pheatmap_1.0.12
[27] cowplot_1.1.1 corrplot_0.92
[29] qgraph_1.9.2 jyluMisc_0.1.5
loaded via a namespace (and not attached):
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[13] withr_2.5.0 colorspace_2.0-3 highr_0.9
[16] knitr_1.39 rstudioapi_0.13 ggsignif_0.6.3
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