Kinase activity analysis
Last updated: 2023-05-26
Checks: 5 1
Knit directory:
LungCancer_SotilloLab/analysis/
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Load packages and dataset
Packages
#package
library(SummarizedExperiment)
library(PHONEMeS) #PHONEMeS-ILP
library(BioNet)
library(OmnipathR)
library(hash)
library(MultiAssayExperiment)
library(PhosR)
library(directPA)
library(tidyverse)
source("../code/utils.R")
knitr::opts_chunk$set(warning = FALSE, message = FALSE, autodep = TRUE)
data(PhosphoSitePlus)
load("../output/allResList_RUN5_timeBased.RData")Using Decoupler for estimating kinase activity (for Figure 6A)
Preprocessing
Construct a mouse version of phonemesPKN
### Construct kinase-substrate interaction network
omnipath_ptm <- get_signed_ptms(enzsub = import_omnipath_enzsub(organism = 10090),
interactions = import_omnipath_interactions(organism = 10090))
omnipath_ptm <- omnipath_ptm[omnipath_ptm$modification %in% c("dephosphorylation", "phosphorylation"), ]
# Filter out ProtMapper
omnipath_ptm_filtered <- omnipath_ptm %>%
dplyr::filter(!(stringr::str_detect(omnipath_ptm$source, "ProtMapper") & n_resources == 1))
# select target (substrate_genesymbol) and source (enzyme_genesymbol)
KSN <- omnipath_ptm_filtered[, c(4, 3)]
# add phosphorylation site to target
KSN$substrate_genesymbol <- paste(KSN$substrate_genesymbol, omnipath_ptm_filtered$residue_type, sep = "_")
KSN$substrate_genesymbol <- paste(KSN$substrate_genesymbol, omnipath_ptm_filtered$residue_offset, sep = "")
# set direction and likelihood of interaction
KSN$mor <- ifelse(omnipath_ptm_filtered$modification == "phosphorylation", 1, -1)
KSN$likelihood <- 1
# we remove ambiguous modes of regulations
KSN$id <- paste(KSN$substrate_genesymbol, KSN$enzyme_genesymbol, sep = "")
KSN <- KSN[!(duplicated(KSN$id) | duplicated(KSN$id, fromLast = TRUE)), ]
KSN <- KSN[, -5]
# rename KSN to fit decoupler format
names(KSN)[1:3] <- c("target", "source", "interaction")
KSN <- KSN[c("source", "interaction", "target")]
phonemesPKN <- KSN %>% filter(interaction ==1)
#rm(KSN, omnipath_ptm, omnipath_ptm_filtered, omnipath_sd, omniR, sif)Using Decoupler to estimate kinase activities
Function for calculating kinase scores
calcKinaseScore <- function(resTab, phonemesPKN, pCut = 0.05, ifFDR = FALSE) {
decoupler_network <- phonemesPKN %>%
dplyr::rename("mor" = interaction) %>%
tibble::add_column("likelihood" = 1)
# get differential phosphorylation sites
resTab <- resTab %>%
arrange(pval) %>% distinct(site, .keep_all = TRUE)
if (ifFDR) {
resTab <- mutate(resTab, pval = adj_pval)
}
inputTab <- filter(resTab, pval <= pCut, site %in% phonemesPKN$target) %>%
select(site, t_statistic) %>% dplyr::rename(t = t_statistic) %>%
data.frame() %>% column_to_rownames("site")
decoupler_network <- decoupleR::intersect_regulons(mat = inputTab,
network = decoupler_network,
.source = source,
.target = target,
minsize = 5)
correlated_regulons <- decoupleR::check_corr(decoupler_network) %>%
dplyr::filter(correlation >= 0.9)
decoupler_network <- decoupler_network %>%
dplyr::filter(!source %in% correlated_regulons$source.2)
kinase_activity <- decoupleR::run_wmean(mat = as.matrix(inputTab),
network = decoupler_network,
sparse = FALSE)
return(kinase_activity)
}Calculate kinase activity score for each time point and comparison
phosRes <- allResList$diffRatio
kinResTab <- lapply(names(phosRes),function(eachTime) {
lapply(unique(phosRes[[eachTime]]$compare), function(eachCompare) {
resTab <- phosRes[[eachTime]] %>% filter(compare == eachCompare)
calcKinaseScore(resTab,phonemesPKN, pCut = 1, ifFDR = FALSE) %>% mutate(time = eachTime, compare = eachCompare)
}) %>% bind_rows()
}) %>% bind_rows() %>%
filter(statistic == "wmean") %>%
select(-statistic, -condition) %>%
mutate(timeCompare = paste0(time, "_", compare))
scoreTab <- select(kinResTab, source, score, timeCompare)
pTab <- select(kinResTab, source, p_value, timeCompare)
#add zero to not estimated values
fullTab <- scoreTab %>%
pivot_wider(names_from = timeCompare, values_from = score) %>%
mutate(across(starts_with("time_"), replace_na, 0)) %>%
pivot_longer(starts_with("time_"), names_to = "timeCompare", values_to = "score" ) %>%
left_join(distinct(kinResTab, timeCompare, time, compare), by = "timeCompare") %>%
left_join(pTab, by = c("source","timeCompare")) %>%
mutate(p_value = ifelse(is.na(p_value),1,p_value)) %>%
dplyr::rename(kinase = "source")Table output of kinase activity scores
writexl::write_xlsx(select(fullTab, kinase, score, time, compare, p_value), "../docs/kinase_activity_decoupler.xlsx")Heatmap plot for kinase activity
plotTab <- mutate(fullTab, sig = ifelse(p_value <=0.05, "*", ""))%>%
mutate(time = paste0(str_remove(time,"time_"), "h"))
ggplot(plotTab, aes(x=time, y = kinase,fill = score)) +
geom_tile() +
geom_text(aes(label = sig), vjust = 0.5) +
facet_wrap(~compare) +
scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0) +
scale_x_discrete(expand = c(0,0)) +scale_y_discrete(expand = c(0,0)) +
theme_bw() +
ylab("Kinase")
ggsave("../docs/kinase_decoupler_heatmap.pdf", height = 15, width = 10)PDF file: kinase_decoupler_heatmap.pdf
KinasePA analysis for estimating difference in kinase activity scores (perhaps for a supplementary plot)
Phospho at 10min
10 mins
Differential results
resList <- allResList$diffRatio$time_0.17 %>%
filter(compare %in% c("combo_DMSO","brigatinib_DMSO", "dasatinib_DMSO"))
phosTab <- resList %>%
mutate(site = paste0(str_replace(toupper(site),"_",";"),";")) %>%
select(site, t_statistic, compare) %>%
arrange(abs(t_statistic)) %>%
distinct(site, compare,.keep_all = TRUE) %>%
pivot_wider(names_from = compare, values_from = t_statistic) %>%
data.frame() %>% column_to_rownames("site")Pair-wise comparison
Combo versus brigatinib
pdf("../docs/DPA_combo_briga_10min.pdf", height = 9, width = 9)
z1 <- perturbPlot2d(Tc=phosTab[,c(1,2)], annotation=PhosphoSite.mouse, cex=0.5, xlim=c(-8, 8), ylim=c(-8, 8), main="Combo versus brigatinib (10 min)")
dev.off()quartz_off_screen
2 PDF file: DPA_combo_briga_10min.pdf
Combo versus dasatinib
pdf("../docs/DPA_combo_dasa_10min.pdf", height = 9, width = 9)
z1 <- perturbPlot2d(Tc=phosTab[,c(1,3)], annotation=PhosphoSite.mouse, cex=0.5, xlim=c(-8, 8), ylim=c(-8, 8), main="Combo versus dasatinib (10 min)")
dev.off()quartz_off_screen
2 PDF file: DPA_combo_dasa_10min.pdf
16 h
Differential results
resList <- allResList$diffRatio$time_16 %>%
filter(compare %in% c("combo_DMSO","brigatinib_DMSO","dasatinib_DMSO"))
phosTab <- resList %>%
mutate(site = paste0(str_replace(toupper(site),"_",";"),";")) %>%
select(site, t_statistic, compare) %>%
arrange(abs(t_statistic)) %>%
distinct(site, compare,.keep_all = TRUE) %>%
pivot_wider(names_from = compare, values_from = t_statistic) %>%
data.frame() %>% column_to_rownames("site")
phosTab <- phosTab[,c("combo_DMSO","brigatinib_DMSO","dasatinib_DMSO")]Combo versus brigatinib
pdf("../docs/DPA_combo_briga_16h.pdf", height = 9, width = 9)
z1 <- perturbPlot2d(Tc=phosTab[,c(1,2)], annotation=PhosphoSite.mouse, cex=0.5, xlim=c(-8, 8), ylim=c(-8, 8), main="Combo versus brigatinib (16h)")
dev.off()quartz_off_screen
2 PDF file: DPA_combo_briga_16h.pdf
Combo versus dasatinib
pdf("../docs/DPA_combo_dasa_16h.pdf", height = 9, width = 9)
z1 <- perturbPlot2d(Tc=phosTab[,c(1,3)], annotation=PhosphoSite.mouse, cex=0.5, xlim=c(-8, 8), ylim=c(-8, 8), main="Combo versus dasatinib (16h)")
dev.off()quartz_off_screen
2 PDF file: DPA_combo_dasa_16h.pdf
sessionInfo()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] forcats_0.5.1 stringr_1.4.1
[3] dplyr_1.0.9 purrr_0.3.4
[5] readr_2.1.2 tidyr_1.2.0
[7] tibble_3.1.8 ggplot2_3.4.1
[9] tidyverse_1.3.2 directPA_1.5
[11] PhosR_1.6.0 MultiAssayExperiment_1.22.0
[13] hash_2.2.6.2 OmnipathR_3.4.7
[15] BioNet_1.56.0 RBGL_1.72.0
[17] graph_1.74.0 PHONEMeS_2.0.1
[19] SummarizedExperiment_1.26.1 Biobase_2.56.0
[21] GenomicRanges_1.48.0 GenomeInfoDb_1.32.2
[23] IRanges_2.30.0 S4Vectors_0.34.0
[25] BiocGenerics_0.42.0 MatrixGenerics_1.8.1
[27] matrixStats_0.62.0
loaded via a namespace (and not attached):
[1] readxl_1.4.0 backports_1.4.1 circlize_0.4.15
[4] workflowr_1.7.0 systemfonts_1.0.4 plyr_1.8.7
[7] igraph_1.3.4 digest_0.6.30 htmltools_0.5.4
[10] viridis_0.6.2 fansi_1.0.3 magrittr_2.0.3
[13] checkmate_2.1.0 memoise_2.0.1 googlesheets4_1.0.0
[16] tzdb_0.3.0 limma_3.52.2 Biostrings_2.64.0
[19] modelr_0.1.8 vroom_1.5.7 prettyunits_1.1.1
[22] colorspace_2.0-3 rvest_1.0.2 blob_1.2.3
[25] rappdirs_0.3.3 textshaping_0.3.6 haven_2.5.0
[28] xfun_0.31 crayon_1.5.2 RCurl_1.98-1.7
[31] jsonlite_1.8.3 glue_1.6.2 ruv_0.9.7.1
[34] gtable_0.3.0 gargle_1.2.0 zlibbioc_1.42.0
[37] XVector_0.36.0 DelayedArray_0.22.0 car_3.1-0
[40] shape_1.4.6 decoupleR_2.2.2 abind_1.4-5
[43] scales_1.2.0 pheatmap_1.0.12 DBI_1.1.3
[46] GGally_2.1.2 rstatix_0.7.0 Rcpp_1.0.9
[49] viridisLite_0.4.0 progress_1.2.2 bit_4.0.4
[52] proxy_0.4-27 preprocessCore_1.58.0 httr_1.4.3
[55] RColorBrewer_1.1-3 calibrate_1.7.7 ellipsis_0.3.2
[58] farver_2.1.1 pkgconfig_2.0.3 reshape_0.8.9
[61] sass_0.4.2 dbplyr_2.2.1 utf8_1.2.2
[64] labeling_0.4.2 tidyselect_1.1.2 rlang_1.0.6
[67] reshape2_1.4.4 later_1.3.0 AnnotationDbi_1.58.0
[70] munsell_0.5.0 cellranger_1.1.0 tools_4.2.0
[73] cachem_1.0.6 cli_3.4.1 generics_0.1.3
[76] RSQLite_2.2.15 statnet.common_4.6.0 broom_1.0.0
[79] evaluate_0.15 fastmap_1.1.0 ggdendro_0.1.23
[82] ragg_1.2.2 yaml_2.3.5 knitr_1.39
[85] bit64_4.0.5 fs_1.5.2 KEGGREST_1.36.3
[88] dendextend_1.16.0 xml2_1.3.3 compiler_4.2.0
[91] rstudioapi_0.13 curl_4.3.2 png_0.1-7
[94] e1071_1.7-11 ggsignif_0.6.3 reprex_2.0.1
[97] bslib_0.4.1 stringi_1.7.8 highr_0.9
[100] logger_0.2.2 lattice_0.20-45 Matrix_1.5-4
[103] vctrs_0.5.2 pillar_1.8.0 lifecycle_1.0.3
[106] jquerylib_0.1.4 GlobalOptions_0.1.2 bitops_1.0-7
[109] httpuv_1.6.6 R6_2.5.1 pcaMethods_1.88.0
[112] promises_1.2.0.1 network_1.17.2 gridExtra_2.3
[115] writexl_1.4.0 MASS_7.3-58 assertthat_0.2.1
[118] rprojroot_2.0.3 withr_2.5.0 GenomeInfoDbData_1.2.8
[121] hms_1.1.1 grid_4.2.0 coda_0.19-4
[124] class_7.3-20 rmarkdown_2.14 carData_3.0-5
[127] googledrive_2.0.0 git2r_0.30.1 ggpubr_0.4.0
[130] lubridate_1.8.0