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app.R
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app.R
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####----Install and load packages----####
packages <- c("shiny","shinythemes","shinyjqui","pheatmap","RColorBrewer",
"ggdendro","factoextra","dplyr","DT","viridis","readr",
"shinycssloaders","stringr","tools","plotly","reshape2")
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
install.packages(packages[!installed_packages])
}
invisible(lapply(packages, library, character.only = TRUE))
#bioconductor packages
bioCpacks <- c("clusterProfiler")
installed_packages_BIOC <- bioCpacks %in% rownames(installed.packages())
if (any(installed_packages_BIOC == FALSE)) {
BiocManager::install(bioCpacks[!installed_packages_BIOC], ask = F)
}
invisible(lapply(bioCpacks, library, character.only = TRUE))
####---- Read in Files----####
GeneSet_File <- "GeneSet_Data/Comprehensive_GeneSet.RData"
# R Data list load function for naming
loadRData <- function(fileName){
#loads an RData file, and returns it
load(fileName)
get(ls()[ls() != "fileName"])
}
gs <- loadRData(GeneSet_File)
#learn Jaccard index function
jaccard <- function(a, b) {
a = unique(a)
b = unique(b)
intersection = length(intersect(a, b))
union = length(a) + length(b) - intersection
return (1-(intersection/union))
}
#increase file upload size
options(shiny.maxRequestSize=50*1024^2)
shinytheme("sandstone")
ui <-
navbarPage("{ Jaccard Pathway Connectivity Index }",
####----Intra-Pathway Connectivity----####
tabPanel("Pathway Connectivity",
fluidPage(
title = "Pathway Connectivity",
sidebarLayout(
sidebarPanel(
tabsetPanel(
####----Pathway Input----####
tabPanel("Pathway Parameters",
p(),
h4("Upload Pathways of Interest"),
fluidRow(
column(9,
fileInput("UserPathwayFile","Upload File (.gmt/.tsv/.txt/.RData)",
accept = c(".gmt",".tsv",".txt"))
),
column(3,
checkboxInput("HeaderCheckIntra","Header",value = T))
),
h4("Clustering Parameters"),
fluidRow(
column(4,
selectInput("ClustMethodIntra","Clustering Method:",
choices = c("ward.D", "complete", "ward.D2", "single", "average", "mcquitty", "median", "centroid"))
),
column(8,
numericInput("NumClusters", step = 1, label = "Number of Clusters (Cut Tree with ~k)", value = 10)
)
),
checkboxInput("ViewClustTabIntra","View Cluster Results Table"),
uiOutput("rendClustTabIntra"),
downloadButton("dnldClustTabIntra","Download Cluster Result"),
h4("SIF Download"),
numericInput("JaccDistCutoff","Jaccard Distance Cutoff",
min = 0,max = 1, step = 0.1, value = 0.9, width = "200px"),
checkboxInput("PrevSIF","Preview SIF File",value = F),
uiOutput("rendSIFPreview"),
downloadButton("dnldSIFTabIntra","Download SIF File")
),
####----Figure Parameters----####
tabPanel("Figure Parameters",
h4("Heatmap Parameters"),
selectInput("ColorPaletteIntra", "Select Color Palette:",
choices = c("Red/Blue" = "original",
"OmniBlueRed" = "OmniBlueRed",
"LightBlue/BlackRed" = "LightBlueBlackRed",
"Green/Black/Red" = "GreenBlackRed",
"Yellow/Green/Blue" = "YlGnBu","Inferno" = "Inferno",
"Viridis" = "Viridis","Plasma" = "Plasma",
"Reds" = "OrRd","Blues" = "PuBu","Greens" = "Greens")),
fluidRow(
column(6,
checkboxInput("HeatColNamesIntra","Show Heatmap Column Names", value = F),
numericInput("HeatColFontIntra", "Heatmap Column Font Size:",
min = 5, max = 75,
value = 12, step = 1),
numericInput("HeatColDendHeight","Column Dendrogram Height:",
value = 50, step = 1)
),
column(6,
checkboxInput("HeatRowNamesIntra","Show Heatmap Row Names", value = F),
numericInput("HeatRowFontIntra", "Heatmap Row Font Size:",
min = 5, max = 75,
value = 9, step = 1),
numericInput("HeatRowDendHeight","Row Dendrogram Height:",
value = 50, step = 1)
)
),
h4("Connectivity Visualization Parameters"),
selectInput("ConnView","View Connectivity as:",
choices = c("Phylogeny" = "phylogenic","Dendrogram" = "rectangle","Circular" = "circular")),
uiOutput("rendPhyloLayout"),
fluidRow(
column(6,
checkboxInput("ShowConnLabels","Show Labels:",value = T)
),
column(6,
numericInput("ConnFontSize","Font Size:",
value = 0.4, step = 0.1)
)
)
)
)
),
mainPanel(
tabsetPanel(
####----Jaccard Table----####
tabPanel("Jaccard Pathway Connectivity Table",
p(),
div(DT::dataTableOutput("JaccTableIntra"), style = "font-size:12px"),
uiOutput("renddnldJaccTabIntra")
),
####----Jaccard Heatmap----####
tabPanel("Heatmap",
withSpinner(jqui_resizable(plotOutput('JaccHeatmapIntra', width = "100%", height = "800px")), type = 6)
),
####----Jaccard Clustering----####
tabPanel("Clustering",
uiOutput("rendJaccDendo")
),
####----Jaccard Cluster Annotation----####
tabPanel("Gene Clusters and Annoation",
fileInput("UserAnnotationFile","Upload Annotation File",
accept = c(".tsv",".txt",".csv")),
div(DT::dataTableOutput("ClusterTabAnno"), style = "font-size:12px"),
uiOutput("renddnldClusterTabAnno")
)
)
)
)
)
)
)
server <- function(input, output, session) {
####----Render UI----####
output$rendClustTabIntra <- renderUI({
if (input$ViewClustTabIntra == TRUE) {
div(DT::dataTableOutput("ClustTabIntra"), style = "font-size:10px; height:400px; overflow-X: scroll")
}
})
output$rendSIFPreview <- renderUI({
if (input$PrevSIF == TRUE) {
div(DT::dataTableOutput("SIFPreview"), style = "font-size:10px; height:400px; overflow-X: scroll")
}
})
output$rendSIFPreviewINTER <- renderUI({
if (input$PrevSIFINTER == TRUE) {
div(DT::dataTableOutput("SIFPreviewINTER"), style = "font-size:10px; height:400px; overflow-X: scroll")
}
})
output$rendClustTabInter <- renderUI({
if (input$ViewClustTabInter == TRUE) {
div(DT::dataTableOutput("ClustTabInter"), style = "font-size:10px; height:400px; overflow-X: scroll")
}
})
output$renddnldJaccTabIntra <- renderUI({
req(input$UserPathwayFile)
downloadButton("dnldJaccTabIntra","Download Jaccard Connectivity Table")
})
output$renddnldJaccTabInter <- renderUI({
req(input$UserPathwayFile)
downloadButton("dnldJaccTabInter","Download Jaccard Connectivity Table")
})
output$rendJaccDendo <- renderUI({
VisType <- input$ConnView
if (VisType == "rectangle" | VisType == "phylogenic") {
withSpinner(jqui_resizable(plotlyOutput('JaccDendoIntra', width = "100%", height = "900px")), type = 6)
}
else if (VisType == "circular") {
withSpinner(jqui_resizable(plotOutput('JaccDendoIntraCirc', width = "100%", height = "900px")), type = 6)
}
})
output$rendPhyloLayout <- renderUI({
if (input$ConnView == "phylogenic") {
selectInput("PhyloLayout","Choose Phylogenic Tree Layout:",
choices = c("Auto Layout" = "layout.auto",
"Force Directed (DrL) Layout" = "layout_with_drl",
"Tree Layout" = "layout_as_tree",
"GEM Layout" = "layout.gem",
"Mulitdimensional Scaling Layout" = "layout.mds",
"Large Graph Layout" = "layout_with_lgl"))
}
})
####----Reactives----####
## User Upload gene set list reactive
user_gs <- reactive({
header_check <- input$HeaderCheckIntra
gs.u <- input$UserPathwayFile
ext <- tools::file_ext(gs.u$datapath)
req(gs.u)
#validate(need(ext == c("gmt","tsv","txt", "RData"), "Please upload .gmt, .tsv, .txt, or .RData file"))
validate(need(ext == c("tsv","txt"), "Please upload .tsv or .txt file"))
gmt <- as.data.frame(read_delim(gs.u$datapath, delim = '\t', col_names = header_check))
paths <- as.vector(gmt[,1])
paths <- gsub("[[:punct:]]",".",paths)
gs_u <- gs[unique(paths)]
gs_u <- Filter(Negate(is.null), gs_u)
## If user provides GMT file
#if (ext == "gmt") {
# gmt <- read.gmt(gs.u$datapath)
# colnames(gmt) <- c("term","gene")
# gs_u <- list()
# for (i in unique(gmt[,1])){
# gs_u[[i]] <- gmt[gmt[,1] == i,]$gene
# }
#}
#
## If user provides RData list file
#else if (ext == "RData") {
# gs_u <- loadRData(gs.u$datapath)
#}
#
## If user provides tab-delim two-col file
#else if (ext == "txt" | ext == "tsv") {
#
# gmt <- as.data.frame(read_delim(gs.u$datapath, delim = '\t', col_names = header_check))
#
# if (ncol(gmt) == 1) {
#
# paths <- as.vector(gmt[,1])
# paths <- gsub("[[:punct:]]",".",paths)
# gs_u <- gs[unique(paths)]
#
# }
# else if (ncol(gmt) == 2) {
#
# colnames(gmt) <- c("term","gene")
# gs_u <- list()
# for (i in unique(gmt[,1])){
# gs_u[[i]] <- gmt[gmt[,1] == i,]$gene
# }
#
# }
# else if (ncol(gmt) > 2) {
#
# paths <- as.vector(gmt[,1])
# paths <- gsub("[[:punct:]]",".",paths)
# gs_u <- gs[unique(paths)]
#
# }
#
#}
gs_u
})
user_anno <- reactive({
gs.u <- input$UserAnnotationFile
ext <- tools::file_ext(gs.u$datapath)
req(gs.u)
validate(need(ext == c("tsv","txt","csv"), "Please upload .tsv, .txt, or .csv file"))
if (ext == "txt" | ext == "tsv") {
df <- as.data.frame(read_delim(gs.u$datapath, delim = '\t', col_names = T, comment = "##"))
}
else if (ext == "csv") {
df <- as.data.frame(read_delim(gs.u$datapath, delim = ',', col_names = T))
}
df
})
## New Subset data frame based on gene set terms searched
GeneSetTableSearched <- reactive({
## Gather Search Terms
gs_search <- input$GeneSetSerachTerm
gs_search <- unlist(strsplit(gs_search," "))
gs_search <- unlist(strsplit(gs_search,"[[:punct:]]"))
## Subset Gene Set Table for searched terms
GeneSetTable_searched <- GeneSetTable[apply(GeneSetTable, 1, function(x) any(grepl(paste(gs_search, collapse = "|"), x, ignore.case = T))), ]
GeneSetTable_searched
})
## Gene Set List Selected
our_gs <- reactive({
if (input$GeneSetSelection == 1) {
## Get gene set category name
gs_category <- GeneSetCatTable[input$GeneSetCategoryTable_rows_selected,2]
## Get all gene set names from that category
gs_names <- GeneSetTable[which(GeneSetTable$GeneSet_Sub_Category == gs_category),"GeneSet_Name"]
## Subset gene set list for specified gene sets
gs_o <- gs[gs_names]
gs_o
}
else if (input$GeneSetSelection == 2) {
if (input$RunSearchedForGeneSets == TRUE) {
## Reactive gene set table based on search terms
GeneSetTable_sub <- GeneSetTableSearched()
## Get gene set names from subset table
gs_names <- GeneSetTable_sub$GeneSet_Name
## Subset gene set list for specified gene sets
gs_o <- gs[gs_names]
gs_o
}
}
## Compare with all gene sets
else if (input$GeneSetSelection == 3) {
if (input$RunAllGeneSets == TRUE) {
gs_o <- gs
gs_o
}
}
})
## Jaccard Matrix Generation
jacc_react_Intra <- reactive({
if (length(user_gs()) > 0) {
gs1 <- user_gs() #User Gene Sets Input
gs2 <- user_gs() #User Gene Sets Input
jac_list <- list()
for (i in 1:length(gs1)) {
jac_vect <- c()
for (j in 1:length(gs2)) {
gs1_genes <- gs1[[i]]
gs2_genes <- gs2[[j]]
jac_index <- jaccard(gs1_genes,gs2_genes)
jac_vect <- c(jac_vect,jac_index)
}
jac_list[[paste(names(gs1)[i],sep = "")]] <- jac_vect
}
jac_df_Intra <- do.call(rbind,jac_list)
colnames(jac_df_Intra) <- names(gs2)
jac_df_Intra
}
})
## Jaccard Matrix Generation
jacc_react_Inter <- reactive({
if (length(our_gs()) > 0 & length(user_gs()) > 0) {
gs1 <- our_gs() #Refernce Gene Sets Selected
gs2 <- user_gs() #User Gene Sets Input
jac_list <- list()
for (i in 1:length(gs1)) {
jac_vect <- c()
for (j in 1:length(gs2)) {
gs1_genes <- gs1[[i]]
gs2_genes <- gs2[[j]]
jac_index <- jaccard(gs1_genes,gs2_genes)
jac_vect <- c(jac_vect,jac_index)
}
jac_list[[paste(names(gs1)[i],sep = "")]] <- jac_vect
}
jac_df_Inter <- do.call(rbind,jac_list)
colnames(jac_df_Inter) <- names(gs2)
jac_df_Inter
}
})
####----Data Tables----####
## Gene Set Category table for tab 1
output$GeneSetCategoryTable <- DT::renderDataTable({
DT::datatable(GeneSetCatTable,
selection = list(mode = 'single', selected = 1),
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")),
rownames = F)
})
## Gene Set Search Table for tab 2
output$GeneSetSearchedTable <- DT::renderDataTable({
GeneSetTable_searched <- GeneSetTableSearched()
DT::datatable(GeneSetTable_searched,
options = list(dom = 'lrtp',
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")),
rownames = F)
})
## Jaccard Matrix Table - INTRA pathway
output$JaccTableIntra <- DT::renderDataTable({
## Jaccard Table
jacc_df <- as.data.frame(jacc_react_Intra())
colnames(jacc_df) <- gsub("[[:punct:]]", " ",colnames(jacc_df))
DT::datatable(jacc_df,
extensions = "FixedColumns",
options = list(lengthMenu = c(10,20,50,100,1000,5000,10000),
pageLength = 20,
scrollX = TRUE,
autoWidth = TRUE,
fixedColumns = list(leftColumns = 1)),
selection=list(mode = "multiple")) %>%
formatRound(columns = c(1:ncol(jacc_df)), digits = 4)
})
## Jaccard Matrix Table - Inter Pathway
output$JaccTableInter <- DT::renderDataTable({
## Jaccard Table
jacc_df <- as.data.frame(jacc_react_Inter())
colnames(jacc_df) <- gsub("[[:punct:]]", " ",colnames(jacc_df))
DT::datatable(jacc_df,
extensions = "FixedColumns",
options = list(lengthMenu = c(10,20,50,100,1000,5000,10000),
pageLength = 20,
scrollX = TRUE,
autoWidth = TRUE,
fixedColumns = list(leftColumns = 1)),
selection=list(mode = "multiple")) %>%
formatRound(columns = c(1:ncol(jacc_df)), digits = 4)
})
## Cluster Table
output$ClustTabIntra <- DT::renderDataTable({
## Data matrix
jacc_df <- jacc_react_Intra()
jacc_mat <- as.matrix(jacc_df)
clust_me <- input$ClustMethodIntra
hm_res <- pheatmap::pheatmap(jacc_mat,
clustering_method = clust_me,
silent = T)
jacc_df <- jacc_react_Intra()
cut_k <- input$NumClusters
clustTab <- data.frame(Pathways = rownames(jacc_df),
Cluster = cutree(hm_res$tree_row,k = cut_k))
DT::datatable(clustTab,
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")),
rownames = F)
})
output$ClusterTabAnno <- DT::renderDataTable({
if (is.null(input$UserAnnotationFile) == F) {
## user annotation table
anno_df <- user_anno()
}
## user gs list
gs_u <- user_gs()
## cluster table
## Data matrix
jacc_df <- jacc_react_Intra()
jacc_mat <- as.matrix(jacc_df)
clust_me <- input$ClustMethodIntra
hm_res <- pheatmap::pheatmap(jacc_mat,
clustering_method = clust_me,
silent = T)
jacc_df <- jacc_react_Intra()
cut_k <- input$NumClusters
clustTab <- data.frame(Pathways = rownames(jacc_df),
Cluster = cutree(hm_res$tree_row,k = cut_k))
## table of gene set with genes
nameVector <- unlist(mapply(function(x,y){ rep(y, length(x)) }, gs_u, names(gs_u)))
resultDF <- cbind.data.frame(Pathways = nameVector,Genes = unlist(gs_u))
rownames(resultDF) <- 1:nrow(resultDF)
## double check punct matches
clustTab$Pathways <- gsub("[[:punct:]]",".",clustTab$Pathways)
resultDF$Pathways <- gsub("[[:punct:]]",".",resultDF$Pathways)
## Merge geneset and cluster table
df2 <- merge(resultDF,clustTab, by = "Pathways")
if (is.null(input$UserAnnotationFile) == F) {
colnames(anno_df)[1] <- "Genes"
df3 <- merge(df2,anno_df,by = "Genes", all.x = T)
}
else if (is.null(input$UserAnnotationFile) == T) {
df3 <- df2
}
df4 <- df3 %>%
relocate(Pathways,Cluster,Genes)
df5 <- df4[order(df4$Pathways),]
DT::datatable(df5,
options = list(lengthMenu = c(10,20,50,100,1000,5000,10000),
pageLength = 20,
scrollX = TRUE),
rownames = F,
selection=list(mode = "multiple"))
})
## Cluster Table
output$ClustTabInter <- DT::renderDataTable({
hm_res <- JaccHeatInter_react()
jacc_df <- jacc_react_Inter()
cut_k <- input$NumClustersInter
clustTab <- data.frame(Pathways = rownames(jacc_df),
Cluster = cutree(hm_res$tree_row,k = cut_k))
DT::datatable(clustTab,
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")),
rownames = F)
})
output$SIFPreview <- DT::renderDataTable({
jacc_df <- jacc_react_Intra()
dist_cutoff <- input$JaccDistCutoff
jacc_df <- as.data.frame(jacc_df)
jacc_df_melt <- melt(jacc_df)
jacc_cols <- colnames(jacc_df)
jacc_df_melt$Pathway_B <- rep_len(jacc_cols,length.out = nrow(jacc_df_melt))
colnames(jacc_df_melt)[c(1,2)] <- c("Pathway_A","Jaccard_Distance")
jacc_df_melt <- jacc_df_melt[which(round(jacc_df_melt$Jaccard_Distance,4) <= dist_cutoff),]
jacc_df_melt <- jacc_df_melt[which(jacc_df_melt$Pathway_A != jacc_df_melt$Pathway_B),]
jacc_df_melt <- jacc_df_melt[!duplicated(t(apply(jacc_df_melt,1,sort))),]
DT::datatable(jacc_df_melt,
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")),
rownames = F)
})
output$SIFPreviewINTER <- DT::renderDataTable({
jacc_df <- jacc_react_Inter()
dist_cutoff <- input$JaccDistCutoffINTER
jacc_df <- as.data.frame(jacc_df)
jacc_df_melt <- melt(jacc_df)
jacc_cols <- colnames(jacc_df)
jacc_df_melt$Pathway_B <- rep_len(jacc_cols,length.out = nrow(jacc_df_melt))
colnames(jacc_df_melt)[c(1,2)] <- c("Pathway_A","Jaccard_Distance")
jacc_df_melt <- jacc_df_melt[which(round(jacc_df_melt$Jaccard_Distance,4) <= dist_cutoff),]
jacc_df_melt <- jacc_df_melt[which(jacc_df_melt$Pathway_A != jacc_df_melt$Pathway_B),]
jacc_df_melt <- jacc_df_melt[!duplicated(t(apply(jacc_df_melt,1,sort))),]
DT::datatable(jacc_df_melt,
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")),
rownames = F)
})
####----Plots----####
## Jaccard Heatmap - INTRA
output$JaccHeatmapIntra <- renderPlot({
## User inputs
row_font <- input$HeatRowFontIntra
col_font <- input$HeatColFontIntra
row_name <- input$HeatRowNamesIntra
col_name <- input$HeatColNamesIntra
col_pall <- input$ColorPaletteIntra
clust_me <- input$ClustMethodIntra
row_dend <- input$HeatRowDendHeight
col_dend <- input$HeatColDendHeight
## Data matrix
jacc_df <- jacc_react_Intra()
jacc_mat <- as.matrix(jacc_df)
## Color choice
minimum = min(jacc_mat)
maximum = max(jacc_mat)
#if (abs(min(jacc_mat)) > abs(max(jacc_mat))) {
# jacc_mat[jacc_mat < -abs(max(jacc_mat))] = -abs(max(jacc_mat))
#} else {
# jacc_mat[jacc_mat > abs(min(jacc_mat))] = abs(min(jacc_mat))
#}
bk = c(seq(minimum,minimum/2, length=100), seq(minimum/2,maximum/2,length=100),seq(maximum/2,maximum,length=100))
#Heatmap color
col_sets <- c("OrRd","PuBu","Greens","YlGnBu")
if (col_pall == "original") {
HeatMap_Colors <- c("dark blue","blue","white","red", "dark red")
hmcols <- colorRampPalette(HeatMap_Colors)(length(bk)-1)
}
else if (col_pall %in% col_sets) {
HeatMap_Colors <- brewer.pal(n = 5, col_pall)
hmcols <- colorRampPalette(HeatMap_Colors)(length(bk)-1)
}
else if (col_pall == "Inferno") {
hmcols <- inferno(500)
}
else if (col_pall == "Viridis") {
hmcols <- viridis(500)
}
else if (col_pall == "Plasma") {
hmcols <- plasma(500)
}
else if (col_pall == "OmniBlueRed") {
hmcols<- colorRampPalette(c("#1984C5", "#22A7F0", "#63BFF0", "#A7D5ED", "#E2E2E2", "#E1A692", "#DE6E56", "#E14B31", "#C23728"))(length(bk)-1)
}
else if (col_pall == "LightBlueBlackRed") {
hmcols<- colorRampPalette(c("#34C5FD","black","red"))(length(bk)-1)
}
else if (col_pall == "GreenBlackRed") {
hmcols<- colorRampPalette(c("green","black","red"))(length(bk)-1)
}
pheatmap::pheatmap(jacc_mat,
fontsize_row = row_font,
fontsize_col = col_font,
show_rownames = row_name,
show_colnames = col_name,
treeheight_col = col_dend,
treeheight_row = row_dend,
angle_col = 90,
border_color = NA,
clustering_method = clust_me,
color = hmcols)
})
output$JaccHeatmapInter <- renderPlot({
## User inputs
row_font <- input$HeatRowFontInter
col_font <- input$HeatColFontInter
row_name <- input$HeatRowNamesInter
col_name <- input$HeatColNamesInter
col_pall <- input$ColorPaletteInter
#clust_me <- input$ClustMethodInter
row_dend <- input$HeatRowDendHeightInter
col_dend <- input$HeatColDendHeightInter
## Data matrix
jacc_df <- jacc_react_Inter()
jacc_mat <- as.matrix(jacc_df)
## Color choice
minimum = min(jacc_mat)
maximum = max(jacc_mat)
#if (abs(min(jacc_mat)) > abs(max(jacc_mat))) {
# jacc_mat[jacc_mat < -abs(max(jacc_mat))] = -abs(max(jacc_mat))
#} else {
# jacc_mat[jacc_mat > abs(min(jacc_mat))] = abs(min(jacc_mat))
#}
bk = c(seq(minimum,minimum/2, length=100), seq(minimum/2,maximum/2,length=100),seq(maximum/2,maximum,length=100))
#Heatmap color
col_sets <- c("OrRd","PuBu","Greens","YlGnBu")
if (col_pall == "original") {
HeatMap_Colors <- c("dark blue","blue","white","red", "dark red")
hmcols <- colorRampPalette(HeatMap_Colors)(length(bk)-1)
}
else if (col_pall %in% col_sets) {
HeatMap_Colors <- brewer.pal(n = 5, col_pall)
hmcols <- colorRampPalette(HeatMap_Colors)(length(bk)-1)
}
else if (col_pall == "Inferno") {
hmcols <- inferno(500)
}
else if (col_pall == "Viridis") {
hmcols <- viridis(500)
}
else if (col_pall == "Plasma") {
hmcols <- plasma(500)
}
else if (col_pall == "OmniBlueRed") {
hmcols<- colorRampPalette(c("#1984C5", "#22A7F0", "#63BFF0", "#A7D5ED", "#E2E2E2", "#E1A692", "#DE6E56", "#E14B31", "#C23728"))(length(bk)-1)
}
else if (col_pall == "LightBlueBlackRed") {
hmcols<- colorRampPalette(c("#34C5FD","black","red"))(length(bk)-1)
}
else if (col_pall == "GreenBlackRed") {
hmcols<- colorRampPalette(c("green","black","red"))(length(bk)-1)
}
pheatmap::pheatmap(jacc_mat,
fontsize_row = row_font,
fontsize_col = col_font,
show_rownames = row_name,
show_colnames = col_name,
treeheight_col = col_dend,
treeheight_row = row_dend,
angle_col = 90,
border_color = NA,
clustering_method = clust_me,
color = hmcols)
})
output$JaccDendoIntra <- renderPlotly({
VisType <- input$ConnView
if (VisType == "rectangle" | VisType == "phylogenic") {
jacc_mat <- jacc_react_Intra()
clust_me <- input$ClustMethodIntra
nclust <- input$NumClusters
LableChoice <- input$ShowConnLabels
FontSize <- input$ConnFontSize
hc <- hclust(dist(jacc_mat), method = clust_me)
hc_dend <- as.dendrogram(hc)
dend_data <- dendro_data(hc_dend)
lab_order <- label(dend_data)[3]
lab_vect <- lab_order$label
clust_df <- data.frame(Pathways = rownames(jacc_mat),cluster = cutree(hc,k = nclust))
clust_df2 <- clust_df[match(lab_vect,clust_df$Pathways),]
colnames(clust_df2)[1] <- "label"
clust_df2$cluster <- as.factor(clust_df2$cluster)
dend_data[["labels"]] <- merge(dend_data[["labels"]],clust_df2, by="label")
if (VisType == "rectangle") {
FontSize <- FontSize * 6
g <- ggdendrogram(dend_data,
rotate = TRUE,
theme_dendro = TRUE,
leaf_labels = F,
segments = T,
labels = F) +
labs(title = "") +
scale_y_continuous(expand = c(0.4, 0))
if (LableChoice == TRUE) {
g <- g + geom_text(data=label(dend_data), aes(x, y, label=label, hjust=1, color=cluster),size=FontSize)
}
gp <- ggplotly(g)
}
else if (VisType == "phylogenic") {
if (is.null(input$PhyloLayout) == TRUE) {
PhyloLayout <- "layout.auto"
}
else if (is.null(input$PhyloLayout) == FALSE) {
PhyloLayout <- input$PhyloLayout
}
if (LableChoice == TRUE) {
k = nclust
gp <- ggplotly(
fviz_dend(
hc_dend,
cex = FontSize,
k = nclust,
show_labels = TRUE,
type = "phylogenic",
phylo_layout = PhyloLayout)
)
for(i in seq(k, 1)){
gp$x$data[[i+1]]$text <- gp$x$data[[i+1+k]]$text
}
}
else if (LableChoice == FALSE) {
k = nclust
gp <- ggplotly(
fviz_dend(
hc,
cex = FontSize,
k = k,
show_labels = TRUE,
type = "phylogenic",
phylo_layout = PhyloLayout)
)
gp$x$data[[1]]$text <- NA
for(i in seq(k, 1)){
gp$x$data[[i+1]]$text <- gp$x$data[[i+1+k]]$text
gp$x$data[[i + 1 + k]] <- NULL
}
}
}
gp
}
})
output$JaccDendoIntraCirc <- renderPlot({
VisType <- input$ConnView
if (VisType == "circular") {
jacc_mat <- jacc_react_Intra()
clust_me <- input$ClustMethodIntra
nclust <- input$NumClusters
LableChoice <- input$ShowConnLabels
FontSize <- input$ConnFontSize
hc <- hclust(dist(jacc_mat), method = clust_me)
hc_dend <- as.dendrogram(hc)
f <- fviz_dend(hc_dend, k = nclust, cex = FontSize, color_labels_by_k = T, type = "circular", show_labels = LableChoice) +
theme(legend.position="none") +
labs(title = "") +
theme(axis.ticks.x = element_blank(),axis.text.x = element_blank(),axis.title.x = element_blank(),
axis.ticks.y = element_blank(),axis.text.y = element_blank(),axis.title.y = element_blank())
f
}
})
####----Download Handlers----####
## Download Cluster Table - INTRA
output$dnldClustTabIntra <- downloadHandler(
filename = function() {
gs.u <- input$UserPathwayFile
file_name <- gs.u$name
file_name <- tools::file_path_sans_ext(gs.u)
paste(file_name,"_Cluster_Result.txt", sep = '')
},
content = function(file) {
## Data matrix
jacc_df <- jacc_react_Intra()
jacc_mat <- as.matrix(jacc_df)
clust_me <- input$ClustMethodIntra
hm_res <- pheatmap::pheatmap(jacc_mat,
clustering_method = clust_me,
silent = T)