Network analysis of drug pertubation proteomic/phosphoproteomic data
Last updated: 2023-03-13
Checks: 5 1
Knit directory:
LungCancer_SotilloLab/analysis/
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Load packages and dataset
Packages
#package
library(SummarizedExperiment)
library(MultiAssayExperiment)
library(tidyverse)
source("../code/utils.R")
knitr::opts_chunk$set(warning = FALSE, message = FALSE, autodep = TRUE)Pre-processed data
load("../output/processedData_RUN5.RData")
#load saved result list
load("../output/allResList_RUN5_timeBased.RData")
#List of mitochondiral genes
mitoList <- readxl::read_xls("../data/Mouse.MitoCarta3.0.xls", sheet = 2)$Symbol
#geneset files
gmts <- list(Hallmark = "../data/gmts/mh.all.v2022.1.Mm.symbols.gmt",
CanonicalPathway = "../data/gmts/m2.cp.v2022.1.Mm.symbols.gmt",
TF = "../data/gmts/m3.gtrd.v2022.1.Mm.symbols.gmt",
Kinase = "../data/gmts/Kinase_substrate.gmt",
Kinase_noSite = "../data/gmts/Kinase_substrate_noSite.gmt")Kinase-target network
10 mins
Construct network
Differential results
resList <- allResList$diffRatio$time_0.17 %>%
filter(compare %in% c("combo_DMSO","brigatinib_DMSO","dasatinib_DMSO"))Kinase-target network structure from database
kinNet <- piano::loadGSC(gmts$Kinase)$gsc
kinNet <- lapply(names(kinNet), function(x) {
tibble(Kinase = x,
Target = kinNet[[x]])
}) %>% bind_rows()netTab <- left_join(resList, kinNet, by = c(site = "Target")) %>%
filter(!is.na(Kinase)) %>%
dplyr::rename(from = Kinase, to = site)Created edge and vertex tables
edgeTab <- filter(netTab, pval <= 0.05) %>%
select(from, to, pval, adj_pval, diff, compare) %>%
mutate(compare = str_remove(compare,"_DMSO"),
logP = -log10(pval),
regulate = ifelse(diff >0 ,"up","down"))
nodeTab <- select(edgeTab, from, to) %>%
pivot_longer(c(from, to), names_to = "type",values_to = "name") %>%
distinct(name, .keep_all = TRUE) %>%
select(name, type) %>%
mutate(type = ifelse(type == "from", "Kinase", "Substrate"))Visualization in network
library(tidygraph)
library(ggraph)
tNet <- tbl_graph(nodes = nodeTab, edges = edgeTab, directed = TRUE)
phosNet <- ggraph(tNet, layout = "kk") +
geom_edge_fan(aes(color = compare, linetype = regulate, width = logP)) +
geom_node_point(aes(color = type, shape = type), size=6) +
geom_node_text(aes(label = name), repel = TRUE, size=6) +
scale_edge_linetype_manual(values = c(down = "dotted", up = "solid"))+
scale_color_manual(values = c(Kinase = "cyan", Substrate = "salmon")) +
scale_edge_color_manual(values = c(combo = "darkblue", dasatinib = "green", brigatinib = "pink")) +
theme_graph(base_family = "sans") + theme(legend.position = "bottom")
phosNet
16 hours
Construct network
Differential results
resList <- allResList$diffRatio$time_16 %>%
filter(compare %in% c("combo_DMSO","brigatinib_DMSO","dasatinib_DMSO"))Kinase-target network structure from database
kinNet <- piano::loadGSC(gmts$Kinase)$gsc
kinNet <- lapply(names(kinNet), function(x) {
tibble(Kinase = x,
Target = kinNet[[x]])
}) %>% bind_rows()netTab <- left_join(resList, kinNet, by = c(site = "Target")) %>%
filter(!is.na(Kinase)) %>%
dplyr::rename(from = Kinase, to = site)Created edge and vertex tables
edgeTab <- filter(netTab, pval <= 0.05) %>%
select(from, to, pval, adj_pval, diff, compare) %>%
mutate(compare = str_remove(compare,"_DMSO"),
logP = -log10(pval),
regulate = ifelse(diff >0 ,"up","down"))
nodeTab <- select(edgeTab, from, to) %>%
pivot_longer(c(from, to), names_to = "type",values_to = "name") %>%
distinct(name, .keep_all = TRUE) %>%
select(name, type) %>%
mutate(type = ifelse(type == "from", "Kinase", "Substrate"))Visualization in network
library(tidygraph)
library(ggraph)
tNet <- tbl_graph(nodes = nodeTab, edges = edgeTab, directed = TRUE)
phosNet <- ggraph(tNet, layout = "kk") +
geom_edge_fan(aes(color = compare, linetype = regulate, width =logP)) +
geom_node_point(aes(color = type, shape = type), size=6) +
geom_node_text(aes(label = name), repel = TRUE, size=6) +
scale_edge_linetype_manual(values = c(down = "dotted", up = "solid"))+
scale_color_manual(values = c(Kinase = "cyan", Substrate = "salmon")) +
scale_edge_color_manual(values = c(combo = "darkblue", dasatinib = "green", brigatinib = "pink")) +
theme_graph(base_family = "sans") + theme(legend.position = "bottom")
phosNet
STRING network construction
10 min, comparing drugs to DMSO
Prepare data
resList <- allResList$diffProt$time_0.17 %>%
filter(compare %in% c("combo_DMSO","brigatinib_DMSO","dasatinib_DMSO")) %>%
filter(pval < 0.01)Construct STRING network
library(STRINGdb)
string_db <- STRINGdb$new(version = "11.5", species = 10090, network_type="physical", input_directory="../data/STRING/")WARNING: Score threshold is not specified. We will be using medium stringency cut-off of 400.Up-regulated
subList <- filter(resList, diff>0)
strNet <- string_db$map(data.frame(subList), "symbol", removeUnmappedRows = TRUE)Warning: we couldn't map to STRING 0% of your identifiersedgeTab <- string_db$get_interactions(strNet$STRING_id) %>%
distinct(from, to)
nodeTab <- subList %>%
mutate(name = strNet[match(toupper(symbol), strNet$symbol),]$STRING_id) %>%
filter(!is.na(name), !is.na(symbol)) %>%
distinct(name, symbol, compare) %>%
mutate(compare = str_remove(compare,"_DMSO"))
nodeGroup <- nodeTab %>% select(name, compare) %>%
mutate(fillVal =compare) %>%
mutate(fillVal = ifelse(fillVal == "dasatinib","dasa",
ifelse(fillVal == "brigatinib","brig","combo"))) %>%
distinct(name, compare, fillVal) %>%
pivot_wider(names_from = compare, values_from = fillVal) %>%
#mutate(across(everything(),replace_na,"")) %>%
mutate(groupType = paste0(dasatinib,"_",brigatinib,"_",combo)) %>%
mutate(groupType = str_remove_all(groupType,"NA_|_NA")) %>%
mutate(groupType = ifelse(groupType == "dasa_brig_combo","all",groupType))
nodeTab <- mutate(nodeTab,
nodeType = nodeGroup[match(name, nodeGroup$name),]$groupType) %>%
distinct(name, nodeType, symbol)
#remove isolated nodes
edgeTab <- filter(edgeTab, from %in% nodeTab$name, to %in% nodeTab$name)
nodeTab <- filter(nodeTab, name %in% edgeTab$from | name %in% edgeTab$to)upNet <- tbl_graph(nodes = nodeTab, edges = edgeTab, directed = FALSE)
ggraph(upNet, layout = "igraph", algorithm = "nicely") +
geom_edge_link() +
geom_node_point(aes(color = nodeType), size=6) +
geom_node_text(aes(label = symbol), size=4) +
theme_graph(base_family = "sans") + theme(legend.position = "bottom") 
Down-regulated
subList <- filter(resList, diff<0)
strNet <- string_db$map(data.frame(subList), "symbol", removeUnmappedRows = TRUE)Warning: we couldn't map to STRING 1% of your identifiersedgeTab <- string_db$get_interactions(strNet$STRING_id) %>%
distinct(from, to)
nodeTab <- subList %>%
mutate(name = strNet[match(toupper(symbol), strNet$symbol),]$STRING_id) %>%
filter(!is.na(name)) %>%
select(name, symbol, compare) %>%
mutate(compare = str_remove(compare,"_DMSO"))
nodeGroup <- nodeTab %>% select(name, compare) %>%
mutate(fillVal =compare) %>%
mutate(fillVal = ifelse(fillVal == "dasatinib","dasa",
ifelse(fillVal == "brigatinib","brig","combo"))) %>%
distinct(name, compare, fillVal) %>%
pivot_wider(names_from = compare, values_from = fillVal) %>%
#mutate(across(everything(),replace_na,"")) %>%
mutate(groupType = paste0(dasatinib,"_",brigatinib,"_",combo)) %>%
mutate(groupType = str_remove_all(groupType,"NA_|_NA")) %>%
mutate(groupType = ifelse(groupType == "dasa_brig_combo","all",groupType))
nodeTab <- mutate(nodeTab,
nodeType = nodeGroup[match(name, nodeGroup$name),]$groupType) %>%
distinct(name, nodeType, symbol)
#remove isolated nodes
#remove isolated nodes
edgeTab <- filter(edgeTab, from %in% nodeTab$name, to %in% nodeTab$name)
nodeTab <- filter(nodeTab, name %in% edgeTab$from | name %in% edgeTab$to)downNet <- tbl_graph(nodes = nodeTab, edges = edgeTab)
ggraph(downNet, layout = "igraph", algorithm = "nicely") +
geom_edge_link() +
geom_node_point(aes(color = nodeType), size=6) +
geom_node_text(aes(label = symbol), size=4) +
theme_graph(base_family = "sans") + theme(legend.position = "bottom") 
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] STRINGdb_2.8.4 ggraph_2.0.5
[3] tidygraph_1.2.1 forcats_0.5.1
[5] stringr_1.4.1 dplyr_1.0.9
[7] purrr_0.3.4 readr_2.1.2
[9] tidyr_1.2.0 tibble_3.1.8
[11] ggplot2_3.4.1 tidyverse_1.3.2
[13] MultiAssayExperiment_1.22.0 SummarizedExperiment_1.26.1
[15] Biobase_2.56.0 GenomicRanges_1.48.0
[17] GenomeInfoDb_1.32.2 IRanges_2.30.0
[19] S4Vectors_0.34.0 BiocGenerics_0.42.0
[21] MatrixGenerics_1.8.1 matrixStats_0.62.0
loaded via a namespace (and not attached):
[1] readxl_1.4.0 backports_1.4.1 fastmatch_1.1-3
[4] workflowr_1.7.0 plyr_1.8.7 igraph_1.3.4
[7] shinydashboard_0.7.2 BiocParallel_1.30.3 digest_0.6.30
[10] htmltools_0.5.4 viridis_0.6.2 fansi_1.0.3
[13] memoise_2.0.1 magrittr_2.0.3 googlesheets4_1.0.0
[16] cluster_2.1.3 tzdb_0.3.0 limma_3.52.2
[19] graphlayouts_0.8.0 modelr_0.1.8 piano_2.12.0
[22] colorspace_2.0-3 blob_1.2.3 rvest_1.0.2
[25] ggrepel_0.9.1 haven_2.5.0 xfun_0.31
[28] crayon_1.5.2 RCurl_1.98-1.7 jsonlite_1.8.3
[31] glue_1.6.2 hash_2.2.6.2 polyclip_1.10-0
[34] gtable_0.3.0 gargle_1.2.0 zlibbioc_1.42.0
[37] XVector_0.36.0 DelayedArray_0.22.0 scales_1.2.0
[40] DBI_1.1.3 relations_0.6-12 Rcpp_1.0.9
[43] plotrix_3.8-2 viridisLite_0.4.0 xtable_1.8-4
[46] bit_4.0.4 sqldf_0.4-11 DT_0.23
[49] htmlwidgets_1.5.4 httr_1.4.3 fgsea_1.22.0
[52] gplots_3.1.3 RColorBrewer_1.1-3 ellipsis_0.3.2
[55] pkgconfig_2.0.3 farver_2.1.1 sass_0.4.2
[58] dbplyr_2.2.1 utf8_1.2.2 tidyselect_1.1.2
[61] labeling_0.4.2 rlang_1.0.6 later_1.3.0
[64] munsell_0.5.0 cellranger_1.1.0 tools_4.2.0
[67] visNetwork_2.1.0 cachem_1.0.6 cli_3.4.1
[70] gsubfn_0.7 RSQLite_2.2.15 generics_0.1.3
[73] broom_1.0.0 evaluate_0.15 fastmap_1.1.0
[76] yaml_2.3.5 bit64_4.0.5 knitr_1.39
[79] fs_1.5.2 caTools_1.18.2 mime_0.12
[82] slam_0.1-50 xml2_1.3.3 compiler_4.2.0
[85] rstudioapi_0.13 png_0.1-7 marray_1.74.0
[88] reprex_2.0.1 tweenr_1.0.2 bslib_0.4.1
[91] stringi_1.7.8 highr_0.9 lattice_0.20-45
[94] Matrix_1.4-1 shinyjs_2.1.0 vctrs_0.5.2
[97] pillar_1.8.0 lifecycle_1.0.3 jquerylib_0.1.4
[100] data.table_1.14.2 bitops_1.0-7 httpuv_1.6.6
[103] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20
[106] gridExtra_2.3 codetools_0.2-18 MASS_7.3-58
[109] gtools_3.9.3 assertthat_0.2.1 chron_2.3-58
[112] proto_1.0.0 rprojroot_2.0.3 withr_2.5.0
[115] GenomeInfoDbData_1.2.8 parallel_4.2.0 hms_1.1.1
[118] grid_4.2.0 rmarkdown_2.14 googledrive_2.0.0
[121] git2r_0.30.1 sets_1.0-21 ggforce_0.3.3
[124] shiny_1.7.4 lubridate_1.8.0