Sample-specific pathway score analysis
Junyan Lu
11 May 2022
Last updated: 2022-05-11
Checks: 6 1
Knit directory: EMBL2016/analysis/
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In this analysis, sample-specific pathway scores were firstly assigned to each sample by using ssGSEA method. The 1/viabilities after drug treatment were considered as gene expressions and pathway memberships of the targets were considered as gene sets. The sample-specific pathways scores can be considered as some kind of dependencies on the pathways, i.e, the samples that have higher scores are the samples that are in general more sensitive to the drugs from that pathways. Then the samples-specific pathway scores can be associated to disease types or genomic as in other analyses.
Load libraries and dataset
Load datasets
Prepare data for ssGSEA
Drug response matrix
viabMat <- screenData %>% select(patientID, viab.auc, Drug) %>%
group_by(patientID, Drug) %>% summarise(viab = 1/mean(viab.auc)) %>%
pivot_wider(names_from = patientID, values_from = viab) %>%
data.frame() %>% column_to_rownames("Drug") %>%
as.matrix()Prepare pathway sets
pathwaySet <- lapply(unique(targetAnno$pathway), function(x) {
filter(targetAnno, pathway == x)$nameEMBL2016
})
names(pathwaySet) <- unique(targetAnno$pathway)Perform ssGSEA
res <- gsva(viabMat, pathwaySet, method ="ssgsea", verbose = FALSE, min.sz=2)Overview of the sample-specific pathway scores
Heatmap and hierarchical clustering
All entities
Only CLL
TSNE
UMAP (alternative for T-SNE)
#Calculate UMAP layout, which can better retain global structure
plotTab <- smallvis(t(res), method = "umap", perplexity = 30,
eta = 0.01, epoch_callback = FALSE, verbose = FALSE)
colnames(plotTab) <- c("x","y")
plotTab <- plotTab %>% as.tibble() %>% mutate(Patient.ID = colnames(viabMat)) %>%
left_join(select(patMeta, Patient.ID, diagnosis, IGHV.status, project), by = "Patient.ID") %>%
mutate(diagnosis = as.character(diagnosis), project = as.character(project)) %>%
mutate(diagnosis = ifelse(diagnosis == "CLL" & IGHV.status == "U", "U-CLL",diagnosis)) %>%
filter(!is.na(diagnosis))UMAP plot (better retain global structure) 
Disease-specific pathway dependence
[1] "AML"
[1] "MCL"
[1] "T-PLL"
[1] "B-NHL"
[1] "B-PLL"p value heatmap 
Associations with genomic within CLL
Without blocking for IGHV
Only mutations occurred at least 5 times will be included in the test
Number of significant associations per gene (10% FDR)
A table of significant associations
filter(pTab, p.adj <=0.1) %>% mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Volcano plots
#filter genes with significant assocaitions
useGene <- unique(filter(pTab, p.adj <=0.05)$gene)
plotList <- lapply(useGene, function(n) {
eachTab <- filter(pTab, gene %in% n, !is.na(p)) %>%
mutate(direction = ifelse(p.adj > 0.05, "n.s.",
ifelse(logFC>0, "resistant","sensitive"))) %>%
mutate(drugLabel = ifelse(direction == "n.s.","",drug))
#pCut <- -log10(max(filter(eachTab, p.adj <=0.1)$p))
pCut <- -log10(0.05)
ggplot(eachTab, aes(x=logFC, y = -log10(p.adj))) +
geom_point(aes(col = direction)) +
ggrepel::geom_text_repel(aes(label = drugLabel), max.overlaps = Inf) +
scale_color_manual(values = c("n.s."="grey60","resistant" = "red","sensitive" = "blue")) +
geom_hline(yintercept = pCut, linetype = "dashed", color = "orange") +
ylab("-log10(adjusted P value)") + xlab("log2(fold change)") +
ggtitle(sprintf("%s (mutated vs unmutated)", n)) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, size=15, face ="bold"),
legend.position = "bottom",
axis.text = element_text(size=14),
axis.title = element_text(size=14))
})
plot_grid(plotlist = plotList, ncol=2)
Blocking for IGHV
Number of significant associations per gene (10% FDR)
Associations pass 10% FDR are colored by genes.
Volcano plots
#filter genes with significant assocaitions
useGene <- unique(filter(pTab.block, p.adj <=0.05)$gene)
plotList <- lapply(useGene, function(n) {
eachTab <- filter(pTab.block, gene %in% n, !is.na(p)) %>%
mutate(direction = ifelse(p.adj > 0.05, "n.s.",
ifelse(logFC>0, "resistant","sensitive"))) %>%
mutate(drugLabel = ifelse(direction == "n.s.","",drug))
#pCut <- -log10(max(filter(eachTab, p.adj <=0.1)$p))
pCut <- -log10(0.05)
ggplot(eachTab, aes(x=logFC, y = -log10(p.adj))) +
geom_point(aes(col = direction)) +
ggrepel::geom_text_repel(aes(label = drugLabel), max.overlaps = Inf) +
scale_color_manual(values = c("n.s."="grey60","resistant" = "red","sensitive" = "blue")) +
geom_hline(yintercept = pCut, linetype = "dashed", color = "orange") +
ylab("-log10(adjusted P value)") + xlab("log2(fold change)") +
ggtitle(sprintf("%s (mutated vs unmutated)", n)) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, size=15, face ="bold"),
legend.position = "bottom",
axis.text = element_text(size=14),
axis.title = element_text(size=14))
})
plot_grid(plotlist = plotList, ncol=2)
PDF version: volcano_withBlocking.pdf
A table of significant associations
filter(pTab.block, p.adj <=0.1) %>% mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[4] purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[7] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[10] cowplot_1.1.1 smallvis_0.0.0.9000 limma_3.50.0
[13] Rtsne_0.15 RColorBrewer_1.1-2 pheatmap_1.0.12
[16] GSVA_1.42.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-2 ellipsis_0.3.2
[3] rprojroot_2.0.2 XVector_0.34.0
[5] GenomicRanges_1.46.1 fs_1.5.2
[7] rstudioapi_0.13 farver_2.1.0
[9] ggrepel_0.9.1 DT_0.20
[11] bit64_4.0.5 AnnotationDbi_1.56.2
[13] fansi_1.0.2 lubridate_1.8.0
[15] xml2_1.3.3 sparseMatrixStats_1.6.0
[17] cachem_1.0.6 knitr_1.37
[19] jsonlite_1.7.3 workflowr_1.7.0
[21] broom_0.7.11 annotate_1.72.0
[23] dbplyr_2.1.1 png_0.1-7
[25] graph_1.72.0 HDF5Array_1.22.1
[27] BiocManager_1.30.16 compiler_4.1.2
[29] httr_1.4.2 backports_1.4.1
[31] assertthat_0.2.1 Matrix_1.4-0
[33] fastmap_1.1.0 cli_3.1.1
[35] later_1.3.0 BiocSingular_1.10.0
[37] htmltools_0.5.2 tools_4.1.2
[39] rsvd_1.0.5 gtable_0.3.0
[41] glue_1.6.1 GenomeInfoDbData_1.2.7
[43] Rcpp_1.0.8 Biobase_2.54.0
[45] cellranger_1.1.0 jquerylib_0.1.4
[47] vctrs_0.3.8 Biostrings_2.62.0
[49] rhdf5filters_1.6.0 crosstalk_1.2.0
[51] DelayedMatrixStats_1.16.0 xfun_0.29
[53] rvest_1.0.2 beachmat_2.10.0
[55] lifecycle_1.0.1 irlba_2.3.5
[57] XML_3.99-0.8 zlibbioc_1.40.0
[59] scales_1.1.1 vroom_1.5.7
[61] BiocStyle_2.22.0 hms_1.1.1
[63] promises_1.2.0.1 MatrixGenerics_1.6.0
[65] parallel_4.1.2 SummarizedExperiment_1.24.0
[67] rhdf5_2.38.0 SingleCellExperiment_1.16.0
[69] yaml_2.2.1 memoise_2.0.1
[71] sass_0.4.0 stringi_1.7.6
[73] RSQLite_2.2.9 highr_0.9
[75] S4Vectors_0.32.3 ScaledMatrix_1.2.0
[77] BiocGenerics_0.40.0 BiocParallel_1.28.3
[79] GenomeInfoDb_1.30.0 rlang_0.4.12
[81] pkgconfig_2.0.3 matrixStats_0.61.0
[83] bitops_1.0-7 evaluate_0.14
[85] lattice_0.20-45 Rhdf5lib_1.16.0
[87] htmlwidgets_1.5.4 labeling_0.4.2
[89] bit_4.0.4 tidyselect_1.1.1
[91] GSEABase_1.56.0 magrittr_2.0.1
[93] R6_2.5.1 IRanges_2.28.0
[95] generics_0.1.1 DelayedArray_0.20.0
[97] DBI_1.1.2 withr_2.4.3
[99] pillar_1.6.5 haven_2.4.3
[101] KEGGREST_1.34.0 RCurl_1.98-1.5
[103] modelr_0.1.8 crayon_1.4.2
[105] utf8_1.2.2 tzdb_0.2.0
[107] rmarkdown_2.11 readxl_1.3.1
[109] grid_4.1.2 FNN_1.1.3
[111] blob_1.2.2 git2r_0.29.0
[113] reprex_2.0.1 digest_0.6.29
[115] xtable_1.8-4 httpuv_1.6.5
[117] stats4_4.1.2 munsell_0.5.0
[119] bslib_0.3.1