#' SCONE Report Browser: Browse Evaluation of Normalization Performance
#'
#' This function opens a shiny application session for visualizing performance
#' of a variety of normalization schemes.
#'
#' @param x a \code{SconeExperiment} object
#' @param methods character specifying the normalizations to report.
#' @param qc matrix. QC metrics to be used for QC evaluation report. Required.
#' @param bio factor. A biological condition (variation to be preserved).
#' Default NULL.
#' @param batch factor. A known batch variable (variation to be removed).
#' Default NULL.
#' @param negcon character. Genes to be used as negative controls for
#' evaluation. These genes should be expected not to change according to the
#' biological phenomenon of interest. Default empty character.
#' @param poscon character. Genes to be used as positive controls for
#' evaluation. These genes should be expected to change according to the
#' biological phenomenon of interest. Default empty character.
#' @param eval_proj function. Projection function for evaluation (see
#' \code{\link{score_matrix}} for details). If NULL, PCA is used for
#' projection.
#' @param eval_proj_args list. List of args passed to projection function as
#' eval_proj_args.
#'
#' @importFrom RColorBrewer brewer.pal
#' @importFrom rARPACK svds
#' @importFrom graphics boxplot
#' @importFrom utils write.csv
#' @importFrom stats setNames var
#' @export
#'
#' @return An object that represents the SCONE report app.
#'
#' @examples
#' set.seed(101)
#' mat <- matrix(rpois(1000, lambda = 5), ncol=10)
#' colnames(mat) <- paste("X", 1:ncol(mat), sep="")
#' obj <- SconeExperiment(mat)
#' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
#' evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2,
#' bpparam = BiocParallel::SerialParam())
#' qc = as.matrix(cbind(colSums(mat),colSums(mat > 0)))
#' rownames(qc) = colnames(mat)
#' colnames(qc) = c("NCOUNTS","NGENES")
#' \dontrun{
#' sconeReport(res,rownames(get_params(res)), qc = qc)
#' }
#'
sconeReport = function(x, methods,
qc,
bio = NULL, batch = NULL,
poscon = character(), negcon = character(),
eval_proj = NULL,
eval_proj_args = NULL){
if (!requireNamespace("shiny", quietly = TRUE)) {
stop("shiny package needed for sconeReport()")
}
if (!requireNamespace("DT", quietly = TRUE)) {
stop("DT package needed for sconeReport()")
}
if (!requireNamespace("NMF", quietly = TRUE)) {
stop("NMF package needed for sconeReport()")
}
if (!requireNamespace("reshape2", quietly = TRUE)) {
stop("reshape2 package needed for sconeReport()")
}
if (!requireNamespace("plotly", quietly = TRUE)) {
stop("plotly package needed for sconeReport()")
}
if (!requireNamespace("visNetwork", quietly = TRUE)) {
stop("visNetwork package needed for sconeReport()")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
stop("ggplot2 package needed for sconeReport()")
}
scone_res = list()
# List of normalized matrices
scone_res$normalized_data = lapply(as.list(methods),
FUN = function(z){
get_normalized(x,method = z,log=TRUE)
})
names(scone_res$normalized_data) = methods
# Parameter matrix
scone_res$params = get_params(x)[methods,]
# Merged score matrix
scone_res$scores = cbind(get_scores(x),get_score_ranks(x))[methods,]
colnames(scone_res$scores)[ncol(scone_res$scores)] = "mean_score_rank"
## ----- If NULL classifications, Replace with NA ------
if(is.null(bio)){
bio = factor(rep("NA",ncol(scone_res$normalized_data[[1]])))
}
if(is.null(batch)){
batch = factor(rep("NA",ncol(scone_res$normalized_data[[1]])))
}
## ----- Network Data for Visualization -----
# Matrix nodes in scone_res
leaves = rownames(scone_res$params)
# Parents
lay1 = unique(gsub(",[^,]*$","",leaves))
lay2 = unique(gsub(",[^,]*$","",lay1))
lay3 = unique(gsub(",[^,]*$","",lay2))
lay4 = unique(gsub(",[^,]*$","",lay3))
all_nodes = c(leaves,lay1,lay2,lay3,lay4)
rm(list = c("lay1","lay2","lay3","lay4"))
# Collapse no-op names for all node names
all_nodes = unique(gsub("^,*|,*$","",
gsub("\\,+",",",
gsub("none|no_uv|no_bio|no_batch","",
all_nodes))))
# Order by decreasing number of operations (root at the end)
all_nodes = all_nodes[order(paste0(gsub(".+",",",all_nodes),
gsub("[^,]*","",all_nodes)),
decreasing = TRUE)]
# Collapse no-op names for all leaves
leaves = gsub("^,*|,*$","",
gsub("\\,+",",",
gsub("none|no_uv|no_bio|no_batch","",
leaves)))
# Re-title nodes in scone_res and root (instead of collapsed names)
titles = all_nodes
names(titles) = paste0("_",all_nodes)
titles[paste0("_",leaves)] = rownames(scone_res$params)
titles[titles == ""] = "none"
# Create Nodes
nodes <- data.frame(id = 1:length(all_nodes),
title = titles,
group = c("NoData",
"Loaded")[1 +
(all_nodes %in% leaves)])
# Create Edges
edges = data.frame()
for(i in 1:length(all_nodes)){
parents = as.vector(sapply(all_nodes,
function(x){
grepl(paste0("^",x),all_nodes[i])
}))
parents = parents & (all_nodes != all_nodes[i])
if(any(parents)){
edges = rbind(edges,cbind(min(which(parents )),i))
}
}
colnames(edges)= c("from","to")
# Re-map indices according to performance
new_title_list = rownames(scone_res$params) # Ordered by perfomance
new_title_list = c(new_title_list,
as.character(
nodes$title[!nodes$title %in%
new_title_list])
)
old_ids = nodes[paste0("_",
gsub("^,*|,*$","",
gsub("\\,+",",",
gsub("none|no_uv|no_uv|no_bio|no_batch",
"",new_title_list)))),]$id
old_nodes = nodes
old_edges = edges
for(i in 1:length(old_ids)){
nodes$id[old_nodes$id == old_ids[i]] = i
edges[old_edges == old_ids[i]] = i
}
nodes = nodes[order(nodes$id),]
## ----- Color Variable -----
cc <- c(brewer.pal(9, "Set1"), brewer.pal(8, "Set2"),brewer.pal(12,"Set3"),
brewer.pal(8, "Accent"))
## ----- UI Definition -----
ui <- shiny::fluidPage(
shiny::titlePanel("SCONE Report Browser"),
shiny::sidebarLayout(
shiny::sidebarPanel(
shiny::selectInput("norm_code", label = "Normalization",
choices = structure(as.list(rownames(scone_res$params)),
names = rownames(scone_res$params)),
selected = rownames(scone_res$params)[1]),
shiny::selectInput("color_code", label = "Stratify Plots by",
choices = list("Batch"="batch",
"Biological Condition"="bio",
"PAM Cluster"="clust"),
selected = "batch"),
shiny::sliderInput("dim", label = "Reduced Dimension (PCA)",
min=2, max=min(length(batch)-1,10),
value = 3),
shiny::sliderInput("k", label = "Number of Clusters (PAM)",
min=2, max=10,
value = 2),
shiny::helpText(paste0("Please note that with many cells (>500),",
" some plots may take several seconds ",
"(sometimes minutes) to appear.")),
shiny::downloadLink('downloadData', 'Download')
),
shiny::mainPanel(shiny::tabsetPanel(type = "tabs",
shiny::tabPanel("Overview",
shiny::br(),
visNetworkOutput("norm_net",
width = "650px"),
shiny::br(),
DT::dataTableOutput('tbl_score')
),
shiny::tabPanel("PCA",
shiny::br(),
shiny::p(paste0("This panel shows principal ",
"component analysis ",
"(PCA) results",
" on different subsets ",
"of genes.")),
shiny::br(),
shiny::h6("Variance Explained (All Genes)"),
shiny::p(paste0("Use this plot",
" to decide on the ",
"dimensionality of the reduced",
" spaced used for evaluation.")),
shiny::plotOutput("plot_scree",width = "650px",
height = "400px"),
shiny::br(),
shiny::h6("2D (All Genes)"),
shiny::plotOutput("plot_base",
width = "650px",
height = "450px"),
shiny::br(),
shiny::h6("3D Interactive (All Genes)"),
plotlyOutput("plot3d_base",
width = "650px",
height = "650px"),
shiny::br(),
shiny::selectInput("gene_set",
label = "Gene selection",
choices =
list("Negative Controls"=
"neg",
"Positive Controls"=
"pos"),
selected = "neg"),
shiny::h6("2D (Select Genes)"),
shiny::p(paste0("Visualize cells by PCs",
" of control gene sets.")),
shiny::plotOutput("plot_select",
width = "650px",
height = "450px")
),
shiny::tabPanel("QC",
shiny::br(),
shiny::p(paste0("This panel shows the absolute",
" correlations between",
" Principal",
" Components (PCs) of",
" expression",
" matrix vs. PCs of the QC",
" (Quality Control) measure ",
"matrix. It also shows the PCA ",
"of the QC measures. Note that",
" the PCA score signs are not",
" meaningful (e.g.,",
" 'good' cells",
" could have a low value of ",
"PC1 and 'bad' cells a",
" high value).")),
shiny::br(),
plotlyOutput('qccorPlot',
width = "650px",
height = "450px"),
shiny::h5("PCA of QC metrics"),
shiny::h6("2D"),
shiny::plotOutput("plot_qc",
width = "650px",
height = "450px"),
shiny::h6("3D Interactive"),
plotlyOutput("plot3d_qc",
width = "650px",
height = "650px")
),
shiny::tabPanel("Silhouette",
shiny::br(),
shiny::p(paste0("Silhouette Width per",
" Sample and Contingency",
" Tables")),
shiny::br(),
shiny::plotOutput("plotsil",
width = "650px",
height = "450px"),
shiny::selectInput("cat1",
label =
"Row Class",
choices =
list("Batch"=
"batch",
"Biological Condition"=
"bio", "PAM Cluster"=
"clust"),
selected = "batch"),
shiny::selectInput("cat2",
label = "Column Class",
choices =
list("Batch"=
"batch",
"Biological Condition"=
"bio",
"PAM Cluster"=
"clust"),
selected = "bio"),
shiny::tableOutput("cat_tab")
),
shiny::tabPanel("Control Genes",
shiny::br(),
shiny::p(paste0("Heatmap of control genes, ",
"colored by all",
" three categories.")),
shiny::br(),
shiny::p("Positive controls:"),
shiny::plotOutput("hmposcon",
width = "650px",
height = "450px"),
shiny::p("Negative controls:"),
shiny::plotOutput("hmnegcon",
width = "650px",
height = "450px")
),
shiny::tabPanel("Stratified PCA",
shiny::br(),
shiny::p(paste0("Violin plots showing",
" conditional ",
"distributions of",
" selected Principal",
" Component.")),
shiny::br(),
shiny::sliderInput("pcsel",
label =
paste0("Selected",
" Principal ",
"Component"),
min=1, max=
min(length(batch)-1,10),
value = 1),
shiny::h6("All Genes:"),
shiny::plotOutput("violin_base",
width = "650px",
height = "450px"),
shiny::selectInput("gene_set2",
label = "Gene selection",
choices =
list("Negative Controls"=
"neg",
"Positive Controls"=
"pos"),
selected = "neg"),
shiny::h6("Select Genes:"),
shiny::plotOutput("violin_select",
width = "650px",
height = "450px")
),
shiny::tabPanel("Relative Log-Expression",
shiny::br(),
shiny::p(paste0("Relative ",
"Log-Expression Plot ",
"for top 100 Most ",
"Variable Genes.")),
shiny::plotOutput("rle",
width = "850px",
height = "450px")
)
))))
server <- function(input, output, session) {
## ------ Overview Tab ------
# Render Network
output$norm_net <- renderVisNetwork({
# Awk: Check if any non-loaded methods are included
if(any(!all_nodes %in% leaves)){
visNetwork(nodes, edges,width = "100%",main = "Tree of Methods") %>%
visHierarchicalLayout(sortMethod = "directed" ) %>%
visGroups(groupname = "NoData", shape = "dot",
size = 10, color = list(background = "lightgrey",
border = "darkblue",
highlight =
list(background =
"black",
border = "red")),
shadow = list(enabled = TRUE)) %>%
visGroups(groupname = "Loaded", shape = "dot",
size = 20, color = list(background = "lightblue",
border = "darkblue",
highlight =
list(background = "cyan",
border = "red")),
shadow = list(enabled = TRUE)) %>%
visEdges(shadow = TRUE, arrows = list(to = list(enabled = TRUE)),
color = list(color = "darkblue", highlight = "red")) %>%
visOptions(nodesIdSelection =
list(enabled = TRUE,
values = nodes$id[nodes$group == "Loaded"],
selected =
nodes$id[nodes$title ==
rownames(scone_res$params)[1]])) %>%
visLegend(width = 0.1, position = "right", main = "Status")
}else{
visNetwork(nodes, edges,width = "100%",main = "Tree of Methods") %>%
visHierarchicalLayout(sortMethod = "directed" ) %>%
visEdges(shadow = TRUE, arrows = list(to = list(enabled = TRUE)),
color = list(color = "darkblue", highlight = "red")) %>%
visOptions(nodesIdSelection =
list(enabled = TRUE,
values = nodes$id[nodes$group == "Loaded"],
selected =
nodes$id[nodes$title ==
rownames(scone_res$params)[1]]))
}
})
# Render Table
output$tbl_score <- DT::renderDataTable({
DT::datatable(scone_res$scores,
extensions = 'Buttons',
selection = list( mode = 'single',
target = 'row',
selected = c(1)),
options=
list(columnDefs =
list(list(visible=FALSE,
targets=1:(ncol(scone_res$scores)-1))),
dom = 'Bfrtip',
buttons = list(
list(extend = 'colvis',
columns = c(1:(ncol(scone_res$scores)-1))))
)) %>%
DT::formatSignif(columns=c(1:(ncol(scone_res$scores))), digits=3)
})
# Update Menu Upon Network Selection of (Loaded) Normalization
shiny::observeEvent(input$norm_net_selected,{
if(is.character(input$norm_net_selected)){ # probably unnecessary
if(!input$norm_net_selected == ""){ # probably unnecessary
if(as.integer(input$norm_net_selected) %in%
nodes$id[nodes$group == "Loaded"]){
shiny::updateSelectInput(
session,
"norm_code",
selected = as.character(
nodes$title[nodes$id ==
as.integer(input$norm_net_selected)]
))
}
}
}
})
# Update Menu Upon Table Selection
shiny::observeEvent(input$tbl_score_rows_selected,{
if(length(input$tbl_score_rows_selected) > 0 ){ # probably unnecessary
shiny::updateSelectInput(session,
"norm_code",
selected = as.character(
nodes$title[nodes$id ==
as.integer(
input$tbl_score_rows_selected
)]
)
)
}
})
# Upon Menu Selection of Normalization
shiny::observeEvent(input$norm_code,{
# Update Network
if(is.character(input$norm_net_selected)){
# If selection is empty, then no valid node is selected.
if(input$norm_net_selected == ""){
visNetworkProxy("norm_net") %>%
visSelectNodes(id =
array(nodes$id[nodes$title == input$norm_code]))
}else{
# If selection is different from menu, then network must be updated.
if(as.character(nodes$title[nodes$id ==
as.integer(
input$norm_net_selected
)]) != input$norm_code){
visNetworkProxy("norm_net") %>%
visSelectNodes(id =
array(nodes$id[nodes$title == input$norm_code]))
}
}
}
# Update Table
# If selection is empty, then no row is selected.
if(length(input$tbl_score_rows_selected) == 0){
DT::dataTableProxy("tbl_score") %>%
DT::selectRows(nodes$id[nodes$title == input$norm_code])
}else{
# If selection is different from menu, then row must be updated.
if(input$tbl_score_rows_selected !=
nodes$id[nodes$title == input$norm_code]){
DT::dataTableProxy("tbl_score") %>%
DT::selectRows(nodes$id[nodes$title == input$norm_code])
}
}
})
## ------ PCA Tab ------
normle <- shiny::reactive({
as.matrix(scone_res$normalized_data[[input$norm_code]])
})
strat_col <- shiny::reactive({
switch(input$color_code,
bio = bio,
batch = batch,
clust = pam_obj()$clust)
})
pc_col <- shiny::reactive({
cc[strat_col()]
})
pc_gene <- shiny::reactive({
switch(input$gene_set,
pos = intersect(poscon,rownames(normle())),
neg = intersect(negcon,rownames(normle())))
})
pc_obj_base <- shiny::reactive({
# If user has not provided evaluation projection
if(is.null(eval_proj)){
prcomp(t(normle()),
center = TRUE, scale = TRUE)
} else {
proj = eval_proj(normle(),
eval_proj_args = eval_proj_args)
colnames(proj) = paste0("PC",1:ncol(proj))
pc_out = list(x = proj, sdev = apply(proj,2,sd))
pc_out
}
})
pc_obj_select <- shiny::reactive({
if(length(pc_gene()) > 0){
# If user has not provided evaluation projection
if(is.null(eval_proj)){
prcomp(t(normle()[pc_gene(),]),
center = TRUE, scale = TRUE)
} else {
proj = eval_proj(normle()[pc_gene(),],
eval_proj_args = eval_proj_args)
colnames(proj) = paste0("PC",1:ncol(proj))
pc_out = list(x = proj, sdev = apply(proj,2,sd))
pc_out
}
}else{
list()
}
})
pam_obj <- shiny::reactive({
cluster::pam(x = pc_obj_base()$x[,1:input$dim],k = input$k)
})
output$plot_scree <- shiny::renderPlot({
plot(pc_obj_base()$sdev[1:min(input$dim*2,
length(batch)-1)]^2/
sum(pc_obj_base()$sdev^2),
typ = "l",
ylab = "Proportion of Variance",
xlab = "PC Index",
log = "y")
abline(v = input$dim, col = "red",lty = 2)
})
output$plot_base <- shiny::renderPlot({
par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
plot(pc_obj_base()$x[,1:2],col = pc_col(), pch = 16)
legend("topright", inset=c(-0.3,0),
legend=levels(factor(strat_col())),
fill = cc[sort(unique(factor(strat_col())))])
})
output$plot3d_base <- plotly::renderPlotly({
PC1 <- PC2 <- PC3 <- NULL
df <- setNames(data.frame(pc_obj_base()$x[,1:3]),
c("PC1", "PC2", "PC3"))
plot_ly(df, x = ~PC1, y = ~PC2, z = ~PC3,
type = "scatter3d", mode = "markers",
marker = list(color=pc_col() ))
})
output$plot_select <- shiny::renderPlot({
par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
if(length(pc_gene()) > 0){
plot(pc_obj_select()$x[,1:2],
col = pc_col(), pch = 16)
legend("topright", inset=c(-0.3,0),
legend=levels(factor(strat_col())),
fill = cc[sort(unique(factor(strat_col())))])
}else{
plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
text(0,labels = "Control gene set is empty.")
}
})
pam_obj <- shiny::reactive({
cluster::pam(x = pc_obj_base()$x[,1:input$dim],k = input$k)
})
## ------ QC Tab ------
cor_qc <- shiny::reactive({
abs(cor(qc,pc_obj_base()$x))
})
output$qccorPlot <- plotly::renderPlotly({
metric <- value <- PC <- NULL
df = reshape2::melt(cor_qc()[,1:input$dim])
colnames(df) = c("metric","PC","value")
df$metric = factor(df$metric,levels = colnames(qc))
df$PC = factor(df$PC,
levels = paste0("PC",1:input$dim))
p <- ggplot(data = df, aes(x = PC,
fill = metric,
weight=value)) +
geom_bar(position = "dodge") +
ylim(0, 1) +
labs(x="PC of Expression",
y="Absolute Correlation") +
theme(legend.title=element_blank())
ggplotly(p)
})
pc_obj_qc <- shiny::reactive({
prcomp(as.matrix(qc),center = TRUE, scale = TRUE)
})
output$plot_qc <- shiny::renderPlot({
par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
plot(pc_obj_qc()$x[,1:2],col = pc_col(), pch = 16)
legend("topright", inset=c(-0.3,0),
legend=levels(factor(strat_col())),
fill = cc[sort(unique(factor(strat_col())))])
})
output$plot3d_qc <- plotly::renderPlotly({
if(ncol(pc_obj_qc()$x) >= 3){
PC1 <- PC2 <- PC3 <- NULL
df <- setNames(data.frame(pc_obj_qc()$x[,1:3]),
c("PC1", "PC2", "PC3"))
plot_ly(df, x = ~PC1, y = ~PC2, z = ~PC3,
type = "scatter3d",
mode = "markers",
marker = list(color=pc_col() ))
}else{
plot_ly(data.frame(), type = "scatter3d", mode = "markers")
}
})
## ------ Silhouette Tab ------
sil_obj <- shiny::reactive({
cluster::silhouette(x = as.numeric(strat_col()),
dist = dist(pc_obj_base()$x[,1:input$dim]))
})
output$plotsil <- shiny::renderPlot({
par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
if(length(unique(strat_col())) > 1){
sil = sil_obj()
o1 = order(sil[,3])
o = o1[order(-sil[,1][o1])]
barplot(sil[,3][o], main = paste0("ASW = ",
signif(mean(sil[,3]),3)),
xlab = "silhouette width",horiz=TRUE,
xlim = 1.5*range(c(sil[,3],-sil[,3])),
col = pc_col()[o],
border = pc_col()[o])
legend("topright", inset=c(-0.3,0),
legend=levels(factor(strat_col())),
fill = cc[sort(unique(factor(strat_col())))])
}else{
plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
text(0,labels = "Stratify plots by a multi-level classification.")
}
})
cat1 <- shiny::reactive({
switch(input$cat1,
bio = bio,
batch = batch,
clust = pam_obj()$clust)
})
cat2 <- shiny::reactive({
switch(input$cat2,
bio = bio,
batch = batch,
clust = pam_obj()$clust)
})
output$cat_tab <- shiny::renderTable({
table(cat1(), cat2())
})
## ------ Control Genes Tab ------
silo <- shiny::reactive({
sil = sil_obj()
o1 = order(sil[,3])
o1[order(-sil[,1][o1])]
})
output$hmnegcon <- shiny::renderPlot({
if(length(negcon) > 0){
if(length(unique(strat_col())) > 1){
NMF::aheatmap(normle()[negcon,], Colv = silo(),
annCol = list(batch = batch, bio = bio,
pam = as.factor(pam_obj()$clust)),
annColors = list(batch = cc, bio = cc, pam = cc))
}else{
plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
text(0,labels = "Stratify plots by a multi-level classification.")
}
}else{
plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
text(0,labels = "Control gene set is empty.")
}
})
output$hmposcon <- shiny::renderPlot({
if(length(poscon) > 0){
if(length(unique(strat_col())) > 1){
NMF::aheatmap(normle()[poscon,], Colv = silo(),
annCol = list(batch = batch,
bio = bio,
pam = as.factor(pam_obj()$clust)),
annColors = list(batch = cc, bio = cc, pam = cc))
}else{
plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
text(0,labels = "Stratify plots by a multi-level classification.")
}
}else{
plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
text(0,labels = "Control gene set is empty.")
}
})
## ------ Stratified PCA Tab ------
output$violin_base <- shiny::renderPlot({
Class = factor(strat_col())
Val = pc_obj_base()$x[,input$pcsel]
ggplot(data.frame(Class,Val ),aes(x = Class,y = Val)) +
geom_violin(scale = "width", trim = TRUE, aes(fill = Class))+
labs(x = "Plot Stratification", y = "PC") +
coord_cartesian(ylim = max(abs(range(Val)))*c(-1.5,1.5)) +
scale_fill_manual(values=cc[sort(unique(factor(strat_col())))]) +
geom_point(colour = "black") + guides(fill=FALSE)
})
pc_gene2 <- shiny::reactive({
switch(input$gene_set2,
pos = intersect(poscon,rownames(normle())),
neg = intersect(negcon,rownames(normle())))
})
pc_obj_select2 <- shiny::reactive({
# If selected genes exist
if(length(pc_gene2()) > 0){
# If user has not provided evaluation projection
if(is.null(eval_proj)){
prcomp(t(normle()[pc_gene2(),]),
center = TRUE, scale = TRUE)
} else {
proj = eval_proj(normle()[pc_gene2(),],
eval_proj_args = eval_proj_args)
colnames(proj) = paste0("PC",1:ncol(proj))
pc_out = list(x = proj, sdev = apply(proj,2,sd))
pc_out
}
}else{
list()
}
})
output$violin_select <- shiny::renderPlot({
if(length(pc_gene2()) > 0){
Class = factor(strat_col())
Val = pc_obj_select2()$x[,input$pcsel]
ggplot(data.frame(Class,Val ),aes(x = Class,y = Val)) +
geom_violin(scale = "width", trim = TRUE, aes(fill = Class))+
labs(x = "Plot Stratification", y = "PC") +
coord_cartesian(ylim = max(abs(range(Val)))*c(-1.5,1.5)) +
scale_fill_manual(values=cc[sort(unique(factor(strat_col())))]) +
geom_point(colour = "black") + guides(fill=FALSE)
}else{
plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
text(0,labels = "Control gene set is empty.")
}
})
## ------ Relative Log-Expression ------
output$rle <- shiny::renderPlot({
par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
if(length(unique(strat_col())) > 1){
vars = apply(exp(normle()),1,var)
is_var = rank(-vars) <= 100
median <- apply(normle()[is_var,], 1, median)
rle <- apply(normle()[is_var,], 2, function(x) x - median)
boxplot(rle[,rev(silo())],col = pc_col()[rev(silo())],
outline = FALSE, names = rep("",ncol(rle)))
abline(h=0, lty=2)
legend("topright", inset=c(-0.2,0),
legend=levels(factor(strat_col())),
fill = cc[sort(unique(factor(strat_col())))])
}else{
plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
text(0,labels = "Stratify plots by a multi-level classification.")
}
})
## ----- Download Button -----
output$downloadData <- shiny::downloadHandler(
filename = function() {
paste('scone_out-', Sys.Date(), '.csv', sep='')
},
content = function(con) {
datt = cbind(as.character(bio),
as.character(batch),
as.character(pam_obj()$clust))
colnames(datt) = c("Class-Bio","Class-Batch","Class-Pam")
rownames(datt) = colnames(normle())
datt = rbind(t(datt),normle())
write.csv(datt, con)
}
)
}
# Shiny App
shiny::shinyApp(ui = ui, server = server)
}
