dan-orig-app.R

# Loads R packages:
library(bioDist)
library(gplots)
library(limma)
library(DT)


# Some little helper functions:

leftstr <- function(x, n = 1) substr(x, 1, min(n, nchar(x)))

rightstr <- function(x, n = 1) substr(x, nchar(x) - min(n - 1, nchar(x) - 1), nchar(x))

plot.null <- function()

plot(0, 0, col = "transparent", xlab = "", ylab = "", xaxt = "n", yaxt = "n", frame.plot = F)

mousecase <- function(x) paste0(toupper(substr(x, 1, 1)), tolower(substr(x, 2, nchar(x))))


# This is the UI side. Nothing in here should be important for you.


ui <- fluidPage(

  titlePanel("Gene Expression Comparison App"),

  sidebarLayout(

  div(class = "col-sm-5",

  tags$form(class = "well",

  selectInput("dataset", "Dataset", "GSE67985", "GSE67985", width = "30%"),

  div(style="padding-bottom:3px", "Chai, Yang; Parada, Carolina; Grimaldi, Alexandre; Ho, Thach-Vu; Harunaga, Jill; Samuels, Bridget."),

   div(style="padding-bottom:3px", "Distal/proximal mandible/maxilla at embryonic day 10.5/11.5/12.5/13.5/14.5, 3 replicates/condition."),

   div(style="padding-bottom:7px", "Affymetrix Mouse Genome 430 2.0 Array."),

 
conditionalPanel("!output.datloaded",

    helpText("Data is loading (may take a minute) ...")),

    conditionalPanel("output.datloaded", 

    helpText("Data is loaded."))),

  tags$form(class = "well",

  checkboxGroupInput("sel", "Select",

      choices = c(

        "E10.5_Mnd_D", "E11.5_Mnd_D", "E12.5_Mnd_D", "E13.5_Mnd_D", "E14.5_Mnd_D",

        "E10.5_Mnd_P", "E11.5_Mnd_P", "E12.5_Mnd_P", "E13.5_Mnd_P", "E14.5_Mnd_P",

        "E10.5_Max_D", "E11.5_Max_D", "E12.5_Max_D", "E13.5_Max_D", "E14.5_Max_D",

        "E10.5_Max_P", "E11.5_Max_P", "E12.5_Max_P", "E13.5_Max_P", "E14.5_Max_P"), inline = T)),

  tags$form(class = "well",

  splitLayout(cellWidths = c("35%", "65%"),

    radioButtons("comp", "Compare",

    choices = c(

    "proximal vs. distal" = "place",

    "maxilla vs. mandible" = "bone",

    "age (earliest vs. latest selected)" = "age"),

    selected = "place"),

    list(conditionalPanel("input.comp == 'place'", 

    radioButtons("invert_place", br(), c("proximal down, distal up" = "normal", 

    "distal down, proximal up" = "inverted"), "normal", inline = T)),

conditionalPanel("input.comp == 'bone'",

    radioButtons("invert_bone", list(br(), br()), c("maxilla down, mandible up" = "normal", 

    "mandible down, maxilla up" = "inverted"), "normal", inline = T)),

conditionalPanel("input.comp == 'age'",

    radioButtons("invert_age", list(br(), br(), br(), br()),

    c("earliest down, latest up" = "normal", 

    "latest down, earliest up" = "inverted"), "normal", inline = T))))),

  tags$form(class= "well",

      bootstrapPage(

    div(style="display:inline-block; width:30%", textInput("fc", "Fold change cut-off", 2)),

        div(style="display:inline-block", checkboxInput("log", "log2", T))),    

    textInput("fdr", "False discovery rate", 0.01, width = "30%"),

    textInput("max", "Max. DE genes/probesets", Inf, width = "30%"),

    radioButtons("summary", "For each gene, show",

    choices = c("all probesets" = "Z", "most highly expressed probeset" = "A",

    "most differentially expressed probeset" = "M"), selected = "Z", inline = T),

    radioButtons("heatcol", "For heatmap, high values are",

    choices = c("green" = "rg", "red" = "gr"), selected = "rg", inline = T)),

  tags$form(class = "well",

    div(tags$label("Highlight any gene/probeset in any color")),

    bootstrapPage(

  div(style="display:inline-block; width:18%", textInput("gene1", NULL, "name")),

  div(style="display:inline-block; width:18%", textInput("gene2", NULL, "name")),

  div(style="display:inline-block; width:18%", textInput("gene3", NULL, "name")),

   div(style="display:inline-block; width:18%", textInput("gene4", NULL, "name")),

  div(style="display:inline-block; width:18%", textInput("gene5", NULL, "name"))),

      bootstrapPage(

       div(style="display:inline-block; width:18%", textInput("col1", NULL, "blue")),

  div(style="display:inline-block; width:18%", textInput("col2", NULL, "red")),

  div(style="display:inline-block; width:18%", textInput("col3", NULL, "forestgreen")),

  div(style="display:inline-block; width:18%", textInput("col4", NULL, "magenta")),

  div(style="display:inline-block; width:18%", textInput("col5", NULL, "darkorange"))))),

  mainPanel(width = 7, 

downloadButton("download.heatmap", "Download heatmap"),

plotOutput("heatmap", height = "450px"), br(),

downloadButton("download.ma.plot", "Download MA plot"),

plotOutput("ma.plot", height = "500px"), br(),

    downloadButton("download.table", "Download table"),
br(), br(),

    dataTableOutput("table", width = "90%"))))


# This is the server side of shiny.


server <- function(input, output){


# This sets up the contrasts in the data. Five ages, two bones, two regions per bone = 20 conditions.

# Three replicates per condition = 60 samples.


age <- factor(rep(c(10.5, 11.5, 12.5, 13.5, 14.5), each = 12))

bone <- factor(rep(rep(c("Max", "Mnd"), each = 6), 5))

place <- relevel(factor(rep(rep(c("D", "P"), each = 3), 10)), "P")

full.design <- model.matrix(~age + bone + place)

rownames(full.design) <- paste0(age, bone, place, 1:3)


# This design matrix is for later when we do regressions on each row of the data table, using only certain columns.


# This just opens the file from the web.


repeat{

con <- url("http://www.danielezrajohnson.com/exprs.R")

t <- try(load(con))

close(con)

if (!inherits(t, "try-error")) break}


# This just opens the file from the web.


rownames(dat) <- dat[, "probeset"]

genes.tab <- xtabs(~unlist(dat$symbol))

single.genes <- names(genes.tab)[genes.tab == 1]

output$datloaded <- reactive(is.numeric(nrow(dat)))

outputOptions(output, "datloaded", suspendWhenHidden = FALSE)


# This identifies which samples the user has selected.

# The samples called “ones” will be contrasted with those called “twos”.

# The rest of the code here tries to make sure that you’re comparing like with like,

# e.g. comparing two against two, not two against three, etc.


output$table <- renderDataTable({

sel <- input$sel

sel <- substr(sel, 2, nchar(sel))

sel <- gsub("_", "", sel)

sels <- character()

for (s in sel) sels <- c(sels, paste0(s, 1:3))

## if the comparison is “place” (distal vs. proximal):

if (input$comp == "place"){ 

target.col <- "placeD"

ones <- grep("P", sels, value = T)

twos <- grep("D", sels, value = T)

ones.reduced <- gsub("P", "", ones)

twos.reduced <- gsub("D", "", twos)

ones <- ones[ones.reduced %in% twos.reduced]

twos <- twos[twos.reduced %in% ones.reduced]

invert <- ifelse(input$invert_place == "inverted", T, F)

one <- ifelse(invert, "distal", "proximal")

two <- ifelse(invert, "proximal", "distal”)}

## if the comparison is “bone” (maxilla vs. mandible):

if (input$comp == "bone"){

target.col <- "boneMnd"

ones <- grep("Max", sels, value = T)

twos <- grep("Mnd", sels, value = T)

ones.reduced <- gsub("Max", "", ones)

twos.reduced <- gsub("Mnd", "", twos)

ones <- ones[ones.reduced %in% twos.reduced]

twos <- twos[twos.reduced %in% ones.reduced]

invert <- ifelse(input$invert_bone == "inverted", T, F)

one <- ifelse(invert, "mandible", "maxilla")

two <- ifelse(invert, "maxilla", "mandible”)}

## if the comparison is “age” (e.g. E10.5 vs. E11.5, or E12.5 vs. E14.5):

ages <- unique(substr(sels, 1, 4))

if (length(ages)) age.1 <- min(ages) else age.1 <- 10.5

young.col <- paste0("age", age.1)

if (input$comp == "age"){

age.2 <- max(ages)

target.col <- paste0("age", age.2)

ones <- grep(age.1, sels, value = T)

twos <- grep(age.2, sels, value = T)

ones.reduced <- gsub(age.1, "", ones)

twos.reduced <- gsub(age.2, "", twos)

ones <- ones[ones.reduced %in% twos.reduced]

twos <- twos[twos.reduced %in% ones.reduced]

invert <- ifelse(input$invert_age == "inverted", T, F)

one <- ifelse(invert, paste0("E", age.2), paste0("E", age.1))

two <- ifelse(invert, paste0("E", age.1), paste0("E", age.2))


# All of the above section is just getting “ones” and “twos” to represent the right samples.


# If I’m going to compare E10.5 distal mandible with E10.5 proximal mandible (3 samples vs. 3 samples), this is simple.

# But if I wanted to compare distal mandible vs. proximal mandible using all age stages, I want to control for age.

# The function lmFit() is going to do the regression models, but you have to give it all the necessary information.

# So “constant” are the contrasts that don’t vary within the whole comparison (e.g. Age, if all samples are E10.5).

# “Control” are the columns that do vary, but are not the primary one/two contrast.


full.des <- full.design[c(ones, twos), ]

constant <- apply(full.des, 2, function(x) length(unique(x)) == 1)

constant[1] <- F

constant[names(constant) == young.col] <- T

design.cols <- names(constant[constant == F])

design <- data.frame(lapply(design.cols, function(x){col <- list(full.des[, x])}))

names(design) <- design.cols

control.cols <- design.cols[design.cols != target.col]

control <- data.frame(lapply(control.cols, function(x){col <- list(full.des[, x])}))

names(control) <- control.cols


# This just means don’t show the MA plot or heat map if something has gone wrong:

if (!nrow(design) | !target.col %in% colnames(design) | !all(sels %in% c(ones, twos))) return({

output$ma.plot <- renderPlot(plot.null())

output$heatmap <- renderPlot(plot.null())

data.frame(NULL)})


# “dat.sel” is just the columns we will be comparing:

dat.sel <- dat[, c(ones, twos)]

cn <- colnames(dat.sel)


# There is a user option for how to summarize probesets. Many genes have more than one probeset,

# so we have more than one measurement of a gene expression level for some genes.

# The default is to show all the probesets separately.


# If summary = “A” then we choose, for each gene, the probeset with the highest expression:


if (input$summary == "A"){

dat.sel$A <- rowMeans(dat.sel)

dat.sel$P <- rownames(dat.sel)

dat.sel$symbol <- unlist(dat$symbol)

dat.single <- dat.sel[dat.sel$symbol %in% single.genes, ]

dat.multiple <- dat.sel[!dat.sel$symbol %in% single.genes, ]

dat.agg <- aggregate(A ~ symbol, dat.multiple, max)

dat.multiple <- merge(dat.agg, dat.multiple, sort = F)

rownames(dat.multiple) <- dat.multiple$P

dat.sel <- rbind(dat.single, dat.multiple)

dat.sel <- dat.sel[, cn]}


# If summary = “M” then we choose, for each gene, the probeset with the most DIFFERENTIAL expression

# that is, the biggest difference between the ones and the twos. (People don’t usually use this.)


if (input$summary == "M"){

dat.sel$MM <- abs(rowMeans(dat.sel[, twos]) - rowMeans(dat.sel[, ones]))

dat.sel$P <- rownames(dat.sel)

dat.sel$symbol <- unlist(dat$symbol)

dat.single <- dat.sel[dat.sel$symbol %in% single.genes, ]

dat.multiple <- dat.sel[!dat.sel$symbol %in% single.genes, ]

dat.agg <- aggregate(MM ~ symbol, dat.multiple, max)

dat.multiple <- merge(dat.agg, dat.multiple, sort = F)

rownames(dat.multiple) <- dat.multiple$P

dat.sel <- rbind(dat.single, dat.multiple)

dat.sel <- dat.sel[, cn]}


# Now we fit the regression models with lmFit(). The variable A refers to the average between all samples

# in the comparison (ones and twos) and M refers to the difference between the average of ones and the average of twos.


fit <- lmFit(dat.sel, design)

efit <- eBayes(fit)

dat.sel$A <- rowMeans(dat.sel)

dat.sel$M <- rowMeans(dat.sel[, twos]) - rowMeans(dat.sel[, ones])


# This part has to do with coloring and details of the output.

if (invert) dat.sel$M <- -dat.sel$M

dat.sel$symbol <- unlist(sapply(rownames(dat.sel), function(x) dat$symbol[dat$probeset == x]))

dat.sel$symbol[is.na(dat.sel$symbol)] <-  rownames(dat.sel)[is.na(dat.sel$symbol)]

dat.sel$color <- "black"

dat.sel$color[dat.sel$symbol == mousecase(input$gene1) | rownames(dat.sel) == mousecase(input$gene1)] <- 

tolower(input$col1)

dat.sel$color[dat.sel$symbol == mousecase(input$gene2) | rownames(dat.sel) == mousecase(input$gene2)] <- 

tolower(input$col2)

dat.sel$color[dat.sel$symbol == mousecase(input$gene3) | rownames(dat.sel) == mousecase(input$gene3)] <- 

tolower(input$col3)

dat.sel$color[dat.sel$symbol == mousecase(input$gene4) | rownames(dat.sel) == mousecase(input$gene4)] <- 

tolower(input$col4)

dat.sel$color[dat.sel$symbol == mousecase(input$gene5) | rownames(dat.sel) == mousecase(input$gene5)] <- 

tolower(input$col5)


# Based on user input, we have “lfc” which is a fold-change cut-off for differentially expressed genes.

# Also based on user input, we have “fdr” which is a false discovery rate from the Benjamini-Hochberg correction.

# Notice that the FDR p-values are the only thing that we get out of the regression models.


lfc <- ifelse(input$log, abs(as.numeric(input$fc)), log2(abs(as.numeric(input$fc))))


# “top” refers to the table in the bottom right of the output. This is the primary output.

# The MA plot is the second most important output.

# The heat map is the third most important output.


# This part is just formatting the “top” table, which is generated by topTable() and the fit or efit object from lmFit():


top <- topTable(efit, coef = which(colnames(design) == target.col), 

lfc = lfc, p.value = as.numeric(input$fdr), num = Inf)

top.colored <- top[rownames(top) %in% dat.sel$probeset[dat.sel$color != "black"], ]

if (nrow(top.colored)){

top.black <- top[1:min(nrow(top), as.numeric(input$max)), ]

top <- merge(top.colored, top.black, all = T)}

if (nrow(top)){

if (invert) top$logFC <- -top$logFC

top <- top[order(top$logFC, decreasing = T), ]

top$FC <- 2 ^ top$logFC

top$FC[top$logFC < 0] <- -1 / top$FC[top$logFC < 0]

top$AveExpr <- round(top$AveExpr, 3)

top$logFC <- round(top$logFC, 2)

top$FC <- round(top$FC, 2)

top$adj.P.Val <- format(top$adj.P.Val, scientific = T, digits = 2)

top$desc <- format(top$desc, justify = "left")

top$symbol <- format(top$symbol, justify = "left")

top <- top[, c("Row.names", "symbol", "AveExpr", "logFC", "FC", "adj.P.Val", "desc")]

colnames(top) <- c("Probeset", "Gene", "Ave.Expr", "Log2FC", "Fold.Change", "Adj.P.Val", "Description")}

else top <- data.frame(NULL)

dat.top <- dat.sel[rownames(dat.sel) %in% top$Probeset, ]


# This draws the MA plot, which is a scatterplot with A on the x-axis and M on the y-axis.

# I make it so that you can color selected points. (The same colors go on the table and the heat map row labels.)

# Most of this is just formatting the plot axes and making sure the colors are right:

output$ma.plot <- renderPlot({

ma.plot <- function(){

par(mar = c(5, 5, 5, 8))

lim <- max(abs(range(dat.sel$M)))

plot(NULL, cex = 0.9,

xaxt = "n", yaxt = "n",

xlab = "Average Expression",

xlim = range(dat.sel$A),

ylim = c(-lim * 1.05, lim * 1.05), 

ylab = paste0(one,

paste(rep(" ", ifelse(input$log, 15, 20)), collapse = ""),

ifelse(input$log, "Log2 Fold Change", "Fold Change"),

paste(rep(" ", ifelse(input$log, 15, 20)), collapse = ""), two),

cex.lab = 1.2, font.main = 1, cex.main = 1.2,

main = paste(unique(gsub("1$|2$|3$", "", c(ones, twos))), collapse = " "))

points(dat.sel$A[dat.sel$color == "black"], dat.sel$M[dat.sel$color == "black"], pch = 21, cex = 0.9)

points(dat.sel$A[dat.sel$color != "black"], dat.sel$M[dat.sel$color != "black"], pch = 21, cex = 0.9,

col = dat.sel$color[dat.sel$color != "black"])

if (nrow(dat.top)){

points(dat.top$A, dat.top$M, pch = 21, cex = 0.9, col = dat.top$color, bg = dat.top$color)

text(dat.top$A, dat.top$M, dat.top$symbol, col = dat.top$color, 

pos = ifelse(dat.top$M > 0, 3, 1), offset = 0.4, cex = 0.9)}

ats.0 <- seq(1, 9, 1)

ats <- c(-1 * rev(ats.0), 0, ats.0)

log.labs <- ats

log.labs[log.labs > 0] <- paste0("+", log.labs[log.labs > 0])

raw.labs <- 2 ^ ats

raw.labs[raw.labs < 1] <- -1 / raw.labs[raw.labs < 1]

raw.labs[raw.labs > 1] <- paste0("+", raw.labs[raw.labs > 1])

axis(1, seq(0, 20, 1))

if (input$log) axis(2, ats, log.labs)

else axis(2, ats, raw.labs, cex.axis = ifelse(lim > 6, 0.75, 1))

abline(h = lfc * c(-1, 1), lty = 2)}

plotPNG(ma.plot, "ma.plot.png", width = 2000, height = 1000, res = 300)

ma.plot()

})

output$download.ma.plot <- downloadHandler("Facebase_Microarray_MA_Plot.png", 

function(file) file.copy("ma.plot.png", file))


# The next part is the heat map. Part of this involves normalizing the data.

# Sometimes people standardize each row (gene) so that the ups and downs (M)

# can be visualized apart from the differences in A (which don’t mean much).

# I did this in a different way. I think the user should have the option

# of whether to scale the data for the heat map. Another issue is how to

# cluster the rows and columns, there are several options for the distance

# matrix, like Euclidean, Pearson correlation, and absolute Pearson correlation.

# Again I think this could be a user choice. (I used Pearson correlation, which

# makes sense for samples (columns). For rows, I’d prefer absolute Pearson

# correlation, but heatmap.2() doesn’t allow a different distance metric for rows and columns…


# The function heatmap.2() is from the gplots package. It draws the heat map,

# does the clustering, and prints the dendrograms, etc.


output$heatmap <- renderPlot({

heatmap <- function(){

withProgress({

if (nrow(dat.top) < 2) return(plot.null())

dat.heat <- as.matrix(dat.top[, sels])

for (col in colnames(control)){

for (v in unique(control[, col])){

mm <- mean(dat.heat[, rownames(control)[control[, col] == v]])

dat.heat[, rownames(control)[control[, col] == v]] <- 

dat.heat[, rownames(control)[control[, col] == v]] - mm}}

dat.heat <- sweep(dat.heat, 1, rowMeans(dat.heat))

weights <- (ncol(dat.heat):1) + 10000

weights[colnames(dat.heat) %in% twos] <- 

weights[colnames(dat.heat) %in% twos] + ifelse(invert, -100, 100)

if (input$log) extreme <- max(abs(dat.top$M)) / 2

else extreme <- 2 ^ max(abs(dat.top$M)) / 2

extreme <- ceiling(10 * extreme) / 10


heatmap.2(t(dat.heat),

Rowv = as.integer(weights),

col = ifelse(input$heatcol == "rg", redgreen, greenred),

cexCol = min(1.5, 0.2 + 1/log10(nrow(dat.heat))),

scale = "none", key = T, key.title = NA, lwid = c(1, 4), lhei = c(1.5, 4),

density.info = "density", densadj = 0.5, denscol = "white",

key.ylab = NA, key.xlab = ifelse(input$log, "Log2 Fold Change", "Fold Change"),

key.par = list(cex.lab = 1.25, cex.axis = 1, tcl = -0.35, mgp = c(2, 1, 0)),

key.ytickfun = function() list(labels = F, tick = F),

key.xtickfun = function(){

breaks <- parent.frame()$breaks

list(at = c(0, 0.2, 0.4, 0.6, 0.8, 1),

labels = round(seq(-extreme, extreme, length = 6), 1), 

padj = -0.25)},

trace = "none", margins = c(5, 10),

distfun = function(...) cor.dist(..., abs = F),

labCol = dat.top$symbol, colCol = dat.top$color)

incProgress(1)}, value = NULL, message = "Calculations in progress...")}

plotPNG(heatmap, "heatmap.png", width = 2000, height = 1000, res = 300)

heatmap()

})

output$download.heatmap <- downloadHandler("Facebase_Microarray_Heatmap.png",

function(file) file.copy("heatmap.png", file))

rownames(top) <- NULL

output$download.table <- downloadHandler("Facebase_Microarray_Table.csv",

function(file) write.csv(as.data.frame(top), file, row.names = F))

top$Color <- unlist(sapply(top$Probeset, function(x) dat.top$color[rownames(dat.top) == x]))

top <- formatStyle(datatable(top), "Color", target = "row", 

color = styleEqual(

c(0, input$col1, input$col2, input$col3, input$col4, input$col5),

c("white", input$col1, input$col2, input$col3, input$col4, input$col5)))

})


}

shinyApp(ui = ui, server = server)