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dyna_wid_surv1.R
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dyna_wid_surv1.R
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library(shiny)
library(gridExtra)
library(ggnet)
library(ggplot2)
#library(sna)
#library(intergraph)
#library(network)
#load("/home/shu/VDLab_scripts/BioGrid/run_APcluster/Cluster_stage3/exmp_name.RData")
ui <- fluidPage(
# App title ----
titlePanel("Module consensus"),
# selectInput("select", label = "Select Exemplar",
# choices = c("select exemplar", c("TCGA_new_PRAD_3371", "TCGA_new_OV_2762", "TCGA_new_SARC_2120",
# "TCGA_new_BRCA_985", "TCGA_new_OV_2951", "TCGA_new_PRAD_3255",
# "TCGA_new_PRAD_3139", "TCGA_new_PAAD_3694", "TCGA_new_BRCA_543",
# "TCGA_new_SKCM_1967", "TCGA_new_OV_2699", "TCGA_new_SARC_2128",
# "TCGA_new_GBM_2492", "TCGA_new_LUAD_1047", "TCGA_new_LUAD_1417",
# "TCGA_new_PRAD_3416", "TCGA_new_SARC_2214", "TCGA_new_SKCM_2002",
# "TCGA_new_BRCA_691", "TCGA_new_GBM_2568", "TCGA_new_LUAD_1380",
# "TCGA_new_PRAD_3320", "TCGA_new_LUAD_1109", "TCGA_new_SARC_2162",
# "TCGA_new_BRCA_442", "TCGA_new_SARC_2255", "TCGA_new_BRCA_718",
# "TCGA_new_BRCA_804", "TCGA_new_GBM_2476", "TCGA_new_SARC_2062",
# "TCGA_new_PRAD_3167", "TCGA_new_PRAD_3312", "TCGA_new_PRAD_3487",
# "TCGA_new_GBM_2555", "TCGA_new_PRAD_3575", "TCGA_new_SKCM_1647",
# "TCGA_new_LUAD_1291", "TCGA_new_SARC_2192", "TCGA_new_BRCA_487",
# "TCGA_new_GBM_2445", "TCGA_new_GBM_2388", "TCGA_new_GBM_2439",
# "TCGA_new_SKCM_1736", "TCGA_new_SKCM_1757", "TCGA_new_SKCM_1874",
# "TCGA_new_SKCM_1963", "TCGA_new_SKCM_1987", "TCGA_new_OV_2786",
# "TCGA_new_PAAD_3754", "TCGA_new_SARC_2152", "TCGA_new_BRCA_416",
# "TCGA_new_LUAD_1454", "TCGA_new_BRCA_278", "TCGA_new_LUAD_1423",
# "TCGA_new_BRCA_293", "TCGA_new_BRCA_405", "TCGA_new_BRCA_932",
# "TCGA_new_PRAD_3117", "TCGA_new_BRCA_650", "TCGA_new_BRCA_864",
# "TCGA_new_LUAD_1132", "TCGA_new_PAAD_3601", "TCGA_new_PAAD_3731",
# "TCGA_new_PRAD_3125", "TCGA_new_PRAD_3298", "TCGA_new_BRCA_172",
# "TCGA_new_BRCA_558", "TCGA_new_BRCA_870", "TCGA_new_PRAD_3098",
# "TCGA_new_PRAD_3392", "TCGA_new_PRAD_3446", "TCGA_new_SKCM_1808",
# "TCGA_new_BRCA_164", "TCGA_new_BRCA_209", "TCGA_new_BRCA_813",
# "TCGA_new_BRCA_688", "TCGA_new_LUAD_1021", "TCGA_new_LUAD_1240",
# "TCGA_new_PAAD_3727", "TCGA_new_SARC_2163", "TCGA_new_GBM_2267",
# "TCGA_new_GBM_2599", "TCGA_new_LUAD_1062", "TCGA_new_LUAD_1191",
# "TCGA_new_OV_2796", "TCGA_new_PAAD_3750", "TCGA_new_BRCA_261",
# "TCGA_new_BRCA_491", "TCGA_new_BRCA_716", "TCGA_new_PAAD_3746",
# "TCGA_new_PRAD_3426", "TCGA_new_SKCM_1970", "TCGA_new_BRCA_871",
# "TCGA_new_GBM_2426", "TCGA_new_OV_2916", "TCGA_new_PRAD_3369",
# "TCGA_new_BRCA_977", "TCGA_new_LUAD_1178", "TCGA_new_PRAD_3191",
# "TCGA_new_PRAD_3204", "TCGA_new_PRAD_3222", "TCGA_new_SARC_2051",
# "TCGA_new_SKCM_1899", "TCGA_new_BRCA_704", "TCGA_new_BRCA_953",
# "TCGA_new_OV_3046", "TCGA_new_PRAD_3367", "TCGA_new_SARC_2099",
# "TCGA_new_SKCM_1928", "TCGA_new_BRCA_561", "TCGA_new_BRCA_608",
# "TCGA_new_BRCA_896", "TCGA_new_PRAD_3085", "TCGA_new_PRAD_3228",
# "TCGA_new_PRAD_3305", "TCGA_new_SKCM_1635", "TCGA_new_SKCM_1798",
# "TCGA_new_BRCA_119", "TCGA_new_BRCA_304", "TCGA_new_BRCA_636",
# "TCGA_new_BRCA_859", "TCGA_new_LUAD_1440", "TCGA_new_PRAD_3199",
# "TCGA_new_PRAD_3473", "TCGA_new_BRCA_889", "TCGA_new_SARC_2158",
# "TCGA_new_SKCM_1601", "TCGA_new_BRCA_344", "TCGA_new_BRCA_668",
# "TCGA_new_PAAD_3724", "TCGA_new_PRAD_3364", "TCGA_new_BRCA_142",
# "TCGA_new_BRCA_415", "TCGA_new_BRCA_837", "TCGA_new_PRAD_3114",
# "TCGA_new_PRAD_3170", "TCGA_new_PRAD_3456", "TCGA_new_SARC_2022",
# "TCGA_new_SKCM_1690", "TCGA_new_SKCM_1732", "TCGA_new_SARC_2030"
# )),
# selected = NULL, multiple = FALSE),
selectInput("select", label = "Select Exemplar",
choices = c("select exemplar", c("TCGA_new_BRCA_119", "TCGA_new_BRCA_142", "TCGA_new_BRCA_164",
"TCGA_new_BRCA_172", "TCGA_new_BRCA_209", "TCGA_new_BRCA_261",
"TCGA_new_BRCA_278", "TCGA_new_BRCA_293", "TCGA_new_BRCA_304",
"TCGA_new_BRCA_344", "TCGA_new_BRCA_405", "TCGA_new_BRCA_415",
"TCGA_new_BRCA_416", "TCGA_new_BRCA_442", "TCGA_new_BRCA_487",
"TCGA_new_BRCA_491", "TCGA_new_BRCA_543", "TCGA_new_BRCA_558",
"TCGA_new_BRCA_561", "TCGA_new_BRCA_608", "TCGA_new_BRCA_636",
"TCGA_new_BRCA_650", "TCGA_new_BRCA_668", "TCGA_new_BRCA_688",
"TCGA_new_BRCA_691", "TCGA_new_BRCA_704", "TCGA_new_BRCA_716",
"TCGA_new_BRCA_718", "TCGA_new_BRCA_804", "TCGA_new_BRCA_813",
"TCGA_new_BRCA_837", "TCGA_new_BRCA_859", "TCGA_new_BRCA_864",
"TCGA_new_BRCA_870", "TCGA_new_BRCA_871", "TCGA_new_BRCA_889",
"TCGA_new_BRCA_896", "TCGA_new_BRCA_932", "TCGA_new_BRCA_953",
"TCGA_new_BRCA_977", "TCGA_new_BRCA_985", "TCGA_new_GBM_2267",
"TCGA_new_GBM_2388", "TCGA_new_GBM_2426", "TCGA_new_GBM_2439",
"TCGA_new_GBM_2445", "TCGA_new_GBM_2476", "TCGA_new_GBM_2492",
"TCGA_new_GBM_2555", "TCGA_new_GBM_2568", "TCGA_new_GBM_2599",
"TCGA_new_LUAD_1021", "TCGA_new_LUAD_1047", "TCGA_new_LUAD_1062",
"TCGA_new_LUAD_1109", "TCGA_new_LUAD_1132", "TCGA_new_LUAD_1178",
"TCGA_new_LUAD_1191", "TCGA_new_LUAD_1240", "TCGA_new_LUAD_1291",
"TCGA_new_LUAD_1380", "TCGA_new_LUAD_1417", "TCGA_new_LUAD_1423",
"TCGA_new_LUAD_1440", "TCGA_new_LUAD_1454", "TCGA_new_OV_2699",
"TCGA_new_OV_2762", "TCGA_new_OV_2786", "TCGA_new_OV_2796", "TCGA_new_OV_2916",
"TCGA_new_OV_2951", "TCGA_new_OV_3046", "TCGA_new_PAAD_3601",
"TCGA_new_PAAD_3694", "TCGA_new_PAAD_3724", "TCGA_new_PAAD_3727",
"TCGA_new_PAAD_3731", "TCGA_new_PAAD_3746", "TCGA_new_PAAD_3750",
"TCGA_new_PAAD_3754", "TCGA_new_PRAD_3085", "TCGA_new_PRAD_3098",
"TCGA_new_PRAD_3114", "TCGA_new_PRAD_3117", "TCGA_new_PRAD_3125",
"TCGA_new_PRAD_3139", "TCGA_new_PRAD_3167", "TCGA_new_PRAD_3170",
"TCGA_new_PRAD_3191", "TCGA_new_PRAD_3199", "TCGA_new_PRAD_3204",
"TCGA_new_PRAD_3222", "TCGA_new_PRAD_3228", "TCGA_new_PRAD_3255",
"TCGA_new_PRAD_3298", "TCGA_new_PRAD_3305", "TCGA_new_PRAD_3312",
"TCGA_new_PRAD_3320", "TCGA_new_PRAD_3364", "TCGA_new_PRAD_3367",
"TCGA_new_PRAD_3369", "TCGA_new_PRAD_3371", "TCGA_new_PRAD_3392",
"TCGA_new_PRAD_3416", "TCGA_new_PRAD_3426", "TCGA_new_PRAD_3446",
"TCGA_new_PRAD_3456", "TCGA_new_PRAD_3473", "TCGA_new_PRAD_3487",
"TCGA_new_PRAD_3575", "TCGA_new_SARC_2022", "TCGA_new_SARC_2030",
"TCGA_new_SARC_2051", "TCGA_new_SARC_2062", "TCGA_new_SARC_2099",
"TCGA_new_SARC_2120", "TCGA_new_SARC_2128", "TCGA_new_SARC_2152",
"TCGA_new_SARC_2158", "TCGA_new_SARC_2162", "TCGA_new_SARC_2163",
"TCGA_new_SARC_2192", "TCGA_new_SARC_2214", "TCGA_new_SARC_2255",
"TCGA_new_SKCM_1601", "TCGA_new_SKCM_1635", "TCGA_new_SKCM_1647",
"TCGA_new_SKCM_1690", "TCGA_new_SKCM_1732", "TCGA_new_SKCM_1736",
"TCGA_new_SKCM_1757", "TCGA_new_SKCM_1798", "TCGA_new_SKCM_1808",
"TCGA_new_SKCM_1874", "TCGA_new_SKCM_1899", "TCGA_new_SKCM_1928",
"TCGA_new_SKCM_1963", "TCGA_new_SKCM_1967", "TCGA_new_SKCM_1970",
"TCGA_new_SKCM_1987", "TCGA_new_SKCM_2002")),
selected = NULL, multiple = FALSE),
selectInput("Anno", label = "Annotation", choices = c("select Annotation", "MsigDB", "GO_MF", "GO_BP", "GO_CC"),
selected = NULL, multiple = FALSE),
selectInput("Can", label = "Cancer", choices = c("Cancer type", c("BRCA", "GBM", "LUAD", "OV", "PAAD", "PRAD", "SARC", "SKCM", "All")),
selected = NULL, multiple = FALSE),
# helpText("Degree based filtering, range: 100-50"),
# numericInput(inputId = "num", h3("Filtered modules"), n),
sliderInput("num", "Consensus Module number",
min = 1, max = 7, value = 1, step= 1),
sliderInput("in1", "Mutation frequency per patient", min = 1, max = 50,
value = 2, step= 1),
#mainPanel(plotOutput(outputId = "distPlot", height = 1000, width = 1000))
mainPanel(
tabsetPanel(
tabPanel("Plot",
fluidRow(
plotOutput("distPlot"),
tableOutput("view")))),
tabPanel("Summary", tableOutput("view1"))
)
)
# )
##modules
#load("/home/shu/VDLab_scripts/BioGrid/run_APcluster/Cluster_stage3/trim_merge_modules_all.RData")
load("trim_merge_modules_all.RData")
#test_int <- lapply(trim_merge_modules_all, function(x)x[["TCGA_new_BRCA_261"]])[["TCGA_new_SARC_2120"]]
##plots
#load("/home/shu/VDLab_scripts/BioGrid/run_APcluster/Cluster_stage3/merge_vis_plots_all.RData")
load("merge_vis_plots_all.RData")
##Pathways
#load("/home/shu/VDLab_scripts/BioGrid/run_APcluster/Cluster_stage3/trim_merge_pathways_all.RData")
load("trim_merge_pathways_all.RData")
#d_tab <- trim_merge_pathways_max[[5]][["TCGA_new_SARC_2120"]]
#d_tab[,c(2,3)] <- sapply(d_tab[,c(2,3)], as.integer)
#samp_mut_mat
#load("~/VDLab_scripts/BioGrid/sample_mutation_matrix_union_biog.RData")
load("sample_mutation_matrix_union_biog_pub.RData")
#samp_mut_mat <- s3_mut_mat
#rm(s3_mut_mat)
##change sample name to current alias
##GO annotations
load("trim_merge_GO_MF_all.RData")
load("trim_merge_GO_BP_all.RData")
#trim_merge_GO_BP
load("trim_merge_GO_CC_all.RData")
##For cluster property plot
load("clust_pat_freq.RData")
##For survival plots
#load("survplots_merge_modules_all_alt.RData")
load("survplots_merge_modules_all_alt_new.RData")
surv_dat <- nt1
rm(nt1)
#t1 <- read.delim("~/VDLab_scripts/BioGrid/run_APcluster/cancer_id_pat_anno_alias_bg.tsv", header = T, sep= "\t")
#r1 <- as.character(t1[match(rownames(samp_mut_mat), t1$pat_id),3])
#rownames(samp_mut_mat) <- r1
#save(samp_mut_mat, file = "sample_mutation_matrix_union_biog_pub.RData")
# Define server logic ----
server <- function(input, output, session) {
# select <- reactive({
# validate(
# need(length(trim_merge_modules_all[[input$num]][[input$select]]) != 0, "Please select a different module")
# )
# get(trim_merge_modules_all[[input$num]][[input$select]])
# })
##update number of proteins based on module number and cluster
observeEvent(input$num, {
updateSliderInput(session = session, inputId = "in1",
max = length(trim_merge_modules_all[[input$num]][[input$select]]))
})
output$distPlot <- renderPlot({
validate(
need(input$select != "select exemplar", "Please select an exemplar!!"),
need(input$Anno != "select Annotation", "Please select annotation database!!"),
need(input$Can != "Cancer type", "Please select Cancer type!!"),
need(length(trim_merge_modules_all[[input$num]][[input$select]]) != 0,
"Please select a different module")
)
##cancer frequency
merge_modules_pat1 <- lapply(trim_merge_modules_all, function(x)x[[input$select]])
# merge_modules_pat1 <- dataInput()
merge_modules_pat2 <- lapply(merge_modules_pat1, function(x)rowSums(samp_mut_mat[,colnames(samp_mut_mat) %in% x]))
can_freq <- lapply(merge_modules_pat2, function(x) x[x >= input$in1])
can_freq1 <- lapply(can_freq,function(x)as.data.frame(table(unlist(lapply(strsplit(names(x), split = "_"),
function(y)y[3])))))
# y <- select()
# mut_can <- rowSums(samp_mut_mat[,colnames(samp_mut_mat) %in% y])
# mut_can <- mut_can[mut_can >= input$in1 ]
# x <- as.data.frame(table(unlist(lapply(strsplit(names(mut_can), split = "_"), function(y)y[3]))))
x <- can_freq1[[input$num]]
#validate(need(!is.null(dim(x)), "Please select a lower frequency"))
#need(!is.null(dim(x)), "Please select a lower frequency")
if(nrow(x) == 0){
plot(1,1,col="white", axes=FALSE)
text(1,1,"Please choose a lower frequency", cex = 2)
}else{
##p_clust
df_clust <- clust_pat_freq[[input$select]]
colnames(df_clust) <- c("Cancer", "Patients")
p_clu <- ggplot(data=df_clust, aes(x=Cancer, y=Patients)) + geom_bar(stat="identity", fill="gray") +
ggtitle(paste("Patients in Cluster", sum(df_clust$Patients), sep = ":")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
axis.text=element_text(size=8,face="bold"), plot.title = element_text(size = 10, face = "bold"))
# annotate("text", x=dim(df_clust)[1]*0.9, y=max(df_clust$Patients)*0.9,
# label= paste("Total Patients:", sum(df_clust$Patients), sep = "\n"), size = 3)
p_clu <- p_clu + coord_flip()
##p1 condition starts here
colnames(x) <- c("Cancer", "Patients")
# plot(x, xlab = "Cancer", main = "Cancer prevalence")
p <- ggplot(data=x, aes(x=Cancer, y=Patients)) + geom_bar(stat="identity", fill="gray") +
ggtitle(paste(input$in1, "Mutation(s) in", sum(x$Patients), "patients", sep = " ")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
axis.text=element_text(size=8,face="bold"), plot.title = element_text(size = 10, face = "bold"))
# annotate("text", x=dim(x)[1]*0.9, y=max(x$Patients)*0.9,
# label= paste("Total Patients:", sum(x$Patients), sep = "\n"), size = 3)
p1 <- p + coord_flip()
##mp1_prot
#mat1 <- samp_mut_mat[,colnames(samp_mut_mat) %in% y]
# mp1_prot <- colSums(mat1[rowSums(mat1) >= input$in1,,drop=FALSE])
# d_mp1_prot <- as.data.frame(mp1_prot)
trim_merge_modules_mat <- lapply(merge_modules_pat1, function(x)samp_mut_mat[,colnames(samp_mut_mat) %in% x])
mp1_prot <- lapply(trim_merge_modules_mat, function(x) colSums(x[rowSums(x) >= input$in1,,drop=FALSE]))
d_mp1_prot <- as.data.frame(mp1_prot[[input$num]])
colnames(d_mp1_prot) <- "Frequency"
d_mp1_prot$prot <- rownames(d_mp1_prot)
p2 <- ggplot(data=d_mp1_prot, aes(x=prot, y=Frequency)) + geom_bar(stat="identity", fill="gray") +
ggtitle(paste("Patients:", sum(x$Patients), ";mut/pat:",input$in1, sep = "")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
axis.text=element_text(size=8,face="bold"), plot.title = element_text(size = 10, face = "bold"))
# annotate("text", x=dim(d_mp1_prot)[1]*0.9, y=max(d_mp1_prot$Patients)*0.9,
# label= paste("Total Patients:", sum(d_mp1_prot$Patients), sep = "\n"), size = 2.5)
p2 <- p2 + coord_flip()
##total
#mat2_sum <- colSums(samp_mut_mat[,colnames(samp_mut_mat) %in% y])
#d2 <- as.data.frame(mat2_sum)
trim_merge_modules_freq <- lapply(merge_modules_pat1, function(x)colSums(samp_mut_mat[,colnames(samp_mut_mat) %in% x]))
d2 <- as.data.frame(trim_merge_modules_freq[[input$num]])
colnames(d2) <- "Frequency"
d2$prot <- rownames(d2)
pat_freq <- lapply(merge_modules_pat2, function(x) x[x >= 1])
pat_freq1 <- lapply(pat_freq,function(x)as.data.frame(table(unlist(lapply(strsplit(names(x), split = "_"),
function(y)y[3])))))
pat_freq2 <- pat_freq1[[input$num]]
p3 <- ggplot(data=d2, aes(x=prot, y=Frequency)) + geom_bar(stat="identity", fill="gray") +
ggtitle(paste("Total patients", sum(pat_freq2$Freq), sep = ":")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
axis.text=element_text(size=8,face="bold"), plot.title = element_text(size = 10, face = "bold"))
# annotate("text", x=dim(d2)[1]*0.9, y=max(d2$Patients)*0.9,
# label= paste("Total Patients:", sum(d2$Patients), sep = "\n"), size = 2.5)
p3 <- p3 + coord_flip()
##graphs
p4 <- merge_vis_plots_all[[input$num]][[input$select]]
##survival plots
#Null plots
if(is.null(surv_dat[[input$num]][[input$select]])){
df <- data.frame()
p5 <- ggplot(df) + geom_point() + xlim(0, 1) + ylim(0, 1) + ggtitle("No survival plot available for this module!!")
}
else if(is.null(surv_dat[[input$num]][[input$select]][[input$Can]])){
df <- data.frame()
p5 <- ggplot(df) + geom_point() + xlim(0, 1) + ylim(0, 1) + ggtitle("No survival plot available for this cancer type!!")
}
else if(!is.null(surv_dat[[input$num]][[input$select]][[input$Can]])){
p5 <- surv_dat[[input$num]][[input$select]][[input$Can]]
}
grid.arrange(p_clu, p1, p2, p3, p4, p5, ncol=6, widths = c(5,5,5,5,8,8))
}
})
##table
d_tab <- reactive({
if(input$Anno == "MsigDB"){
df_tab <- trim_merge_pathways_max[[input$num]][[input$select]]
df_tab[,c(2,3)] <- sapply(df_tab[,c(2,3)], as.integer)
df_tab
}
else if(input$Anno == "GO_MF"){
df_tab <- trim_merge_GO_MF[[input$num]][[input$select]]
df_tab
}
else if(input$Anno == "GO_BP"){
df_tab <- trim_merge_GO_BP[[input$num]][[input$select]]
df_tab
}
else if(input$Anno == "GO_CC"){
df_tab <- trim_merge_GO_CC[[input$num]][[input$select]]
df_tab
}
})
output$view <- renderTable({
d_tab()
}, digits = 5)
}
# Run the app ----
shinyApp(ui = ui, server = server)