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dan-latest-app.R
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library(bioDist)
library(gplots)
library(gtools)
library(limma)
library(DT)
library(png)
library(plotrix)
library(plyr)
heatmap.22 <- function (x, Rowv = TRUE, Colv = if (symm) "Rowv" else TRUE,
distfun.row = dist, distfun.col = dist, hclustfun = hclust, dendrogram = c("both",
"row", "column", "none"), reorderfun = function(d, w) reorder(d,
w), symm = FALSE, scale = c("none", "row", "column"),
na.rm = TRUE, revC = identical(Colv, "Rowv"), add.expr, breaks,
symbreaks = any(x < 0, na.rm = TRUE) || scale != "none",
col = "heat.colors", colsep, rowsep, sepcolor = "white",
sepwidth = c(0.05, 0.05), cellnote, notecex = 1, notecol = "cyan",
na.color = par("bg"), trace = c("column", "row", "both",
"none"), tracecol = "cyan", hline = median(breaks), vline = median(breaks),
linecol = tracecol, margins = c(5, 5), ColSideColors, RowSideColors,
cexRow = 0.2 + 1/log10(nr), cexCol = 0.2 + 1/log10(nc), labRow = NULL,
labCol = NULL, srtRow = NULL, srtCol = NULL, adjRow = c(0,
NA), adjCol = c(NA, 0), offsetRow = 0.5, offsetCol = 0.5,
colRow = NULL, colCol = NULL, key = TRUE, keysize = 1.5,
density.info = c("histogram", "density", "none"), denscol = tracecol,
symkey = any(x < 0, na.rm = TRUE) || symbreaks, densadj = 0.25,
key.title = NULL, key.xlab = NULL, key.ylab = NULL, key.xtickfun = NULL,
key.ytickfun = NULL, key.par = list(), main = NULL, xlab = NULL,
ylab = NULL, lmat = NULL, lhei = NULL, lwid = NULL, extrafun = NULL,
...)
{
scale01 <- function(x, low = min(x), high = max(x)) {
x <- (x - low)/(high - low)
x
}
retval <- list()
scale <- if (symm && missing(scale))
"none"
else match.arg(scale)
dendrogram <- match.arg(dendrogram)
trace <- match.arg(trace)
density.info <- match.arg(density.info)
if (length(col) == 1 && is.character(col))
col <- get(col, mode = "function")
if (!missing(breaks) && any(duplicated(breaks)))
stop("breaks may not contian duplicate values")
if (!missing(breaks) && (scale != "none"))
warning("Using scale=\"row\" or scale=\"column\" when breaks are",
"specified can produce unpredictable results.", "Please consider using only one or the other.")
if (is.null(Rowv) || is.na(Rowv))
Rowv <- FALSE
if (is.null(Colv) || is.na(Colv))
Colv <- FALSE
else if (all(Colv == "Rowv"))
Colv <- Rowv
if (length(di <- dim(x)) != 2 || !is.numeric(x))
stop("`x' must be a numeric matrix")
nr <- di[1]
nc <- di[2]
if (nr <= 1 || nc <= 1)
stop("`x' must have at least 2 rows and 2 columns")
if (!is.numeric(margins) || length(margins) != 2)
stop("`margins' must be a numeric vector of length 2")
if (missing(cellnote))
cellnote <- matrix("", ncol = ncol(x), nrow = nrow(x))
if (!inherits(Rowv, "dendrogram")) {
if (((is.logical(Rowv) && !isTRUE(Rowv)) || (is.null(Rowv))) &&
(dendrogram %in% c("both", "row"))) {
warning("Discrepancy: Rowv is FALSE, while dendrogram is `",
dendrogram, "'. Omitting row dendogram.")
if (dendrogram == "both")
dendrogram <- "column"
else dendrogram <- "none"
}
}
if (!inherits(Colv, "dendrogram")) {
if (((is.logical(Colv) && !isTRUE(Colv)) || (is.null(Colv))) &&
(dendrogram %in% c("both", "column"))) {
warning("Discrepancy: Colv is FALSE, while dendrogram is `",
dendrogram, "'. Omitting column dendogram.")
if (dendrogram == "both")
dendrogram <- "row"
else dendrogram <- "none"
}
}
if (inherits(Rowv, "dendrogram")) {
ddr <- Rowv
rowInd <- order.dendrogram(ddr)
if (length(rowInd) > nr || any(rowInd < 1 | rowInd >
nr))
stop("Rowv dendrogram doesn't match size of x")
if (length(rowInd) < nr)
nr <- length(rowInd)
}
else if (is.integer(Rowv)) {
distr <- distfun.row(x)
hcr <- hclustfun(distr)
ddr <- as.dendrogram(hcr)
ddr <- reorderfun(ddr, Rowv)
rowInd <- order.dendrogram(ddr)
if (nr != length(rowInd))
stop("row dendrogram ordering gave index of wrong length")
}
else if (isTRUE(Rowv)) {
Rowv <- rowMeans(x, na.rm = na.rm)
distr <- distfun.row(x)
hcr <- hclustfun(distr)
ddr <- as.dendrogram(hcr)
ddr <- reorderfun(ddr, Rowv)
rowInd <- order.dendrogram(ddr)
if (nr != length(rowInd))
stop("row dendrogram ordering gave index of wrong length")
}
else if (!isTRUE(Rowv)) {
rowInd <- nr:1
ddr <- as.dendrogram(hclust(dist(diag(nr))))
}
else {
rowInd <- nr:1
ddr <- as.dendrogram(Rowv)
}
if (inherits(Colv, "dendrogram")) {
ddc <- Colv
colInd <- order.dendrogram(ddc)
if (length(colInd) > nc || any(colInd < 1 | colInd >
nc))
stop("Colv dendrogram doesn't match size of x")
if (length(colInd) < nc)
nc <- length(colInd)
}
else if (identical(Colv, "Rowv")) {
if (nr != nc)
stop("Colv = \"Rowv\" but nrow(x) != ncol(x)")
if (exists("ddr")) {
ddc <- ddr
colInd <- order.dendrogram(ddc)
}
else colInd <- rowInd
}
else if (is.integer(Colv)) {
distc <- distfun.col(if (symm)
x
else t(x))
hcc <- hclustfun(distc)
ddc <- as.dendrogram(hcc)
ddc <- reorderfun(ddc, Colv)
colInd <- order.dendrogram(ddc)
if (nc != length(colInd))
stop("column dendrogram ordering gave index of wrong length")
}
else if (isTRUE(Colv)) {
Colv <- colMeans(x, na.rm = na.rm)
distc <- distfun.col(if (symm)
x
else t(x))
hcc <- hclustfun(distc)
ddc <- as.dendrogram(hcc)
ddc <- reorderfun(ddc, Colv)
colInd <- order.dendrogram(ddc)
if (nc != length(colInd))
stop("column dendrogram ordering gave index of wrong length")
}
else if (!isTRUE(Colv)) {
colInd <- 1:nc
ddc <- as.dendrogram(hclust(dist(diag(nc))))
}
else {
colInd <- 1:nc
ddc <- as.dendrogram(Colv)
}
retval$rowInd <- rowInd
retval$colInd <- colInd
retval$call <- match.call()
x <- x[rowInd, colInd]
x.unscaled <- x
cellnote <- cellnote[rowInd, colInd]
if (is.null(labRow))
labRow <- if (is.null(rownames(x)))
(1:nr)[rowInd]
else rownames(x)
else labRow <- labRow[rowInd]
if (is.null(labCol))
labCol <- if (is.null(colnames(x)))
(1:nc)[colInd]
else colnames(x)
else labCol <- labCol[colInd]
if (!is.null(colRow))
colRow <- colRow[rowInd]
if (!is.null(colCol))
colCol <- colCol[colInd]
if (scale == "row") {
retval$rowMeans <- rm <- rowMeans(x, na.rm = na.rm)
x <- sweep(x, 1, rm)
retval$rowSDs <- sx <- apply(x, 1, sd, na.rm = na.rm)
x <- sweep(x, 1, sx, "/")
}
else if (scale == "column") {
retval$colMeans <- rm <- colMeans(x, na.rm = na.rm)
x <- sweep(x, 2, rm)
retval$colSDs <- sx <- apply(x, 2, sd, na.rm = na.rm)
x <- sweep(x, 2, sx, "/")
}
if (missing(breaks) || is.null(breaks) || length(breaks) <
1) {
if (missing(col) || is.function(col))
breaks <- 16
else breaks <- length(col) + 1
}
if (length(breaks) == 1) {
if (!symbreaks)
breaks <- seq(min(x, na.rm = na.rm), max(x, na.rm = na.rm),
length = breaks)
else {
extreme <- max(abs(x), na.rm = TRUE)
breaks <- seq(-extreme, extreme, length = breaks)
}
}
nbr <- length(breaks)
ncol <- length(breaks) - 1
if (class(col) == "function")
col <- col(ncol)
min.breaks <- min(breaks)
max.breaks <- max(breaks)
x[x < min.breaks] <- min.breaks
x[x > max.breaks] <- max.breaks
if (missing(lhei) || is.null(lhei))
lhei <- c(keysize, 4)
if (missing(lwid) || is.null(lwid))
lwid <- c(keysize, 4)
if (missing(lmat) || is.null(lmat)) {
lmat <- rbind(4:3, 2:1)
if (!missing(ColSideColors)) {
if (!is.character(ColSideColors) || length(ColSideColors) !=
nc)
stop("'ColSideColors' must be a character vector of length ncol(x)")
lmat <- rbind(lmat[1, ] + 1, c(NA, 1), lmat[2, ] +
1)
lhei <- c(lhei[1], 0.2, lhei[2])
}
if (!missing(RowSideColors)) {
if (!is.character(RowSideColors) || length(RowSideColors) !=
nr)
stop("'RowSideColors' must be a character vector of length nrow(x)")
lmat <- cbind(lmat[, 1] + 1, c(rep(NA, nrow(lmat) -
1), 1), lmat[, 2] + 1)
lwid <- c(lwid[1], 0.2, lwid[2])
}
lmat[is.na(lmat)] <- 0
}
if (length(lhei) != nrow(lmat))
stop("lhei must have length = nrow(lmat) = ", nrow(lmat))
if (length(lwid) != ncol(lmat))
stop("lwid must have length = ncol(lmat) =", ncol(lmat))
op <- par(no.readonly = TRUE)
on.exit(par(op))
layout(lmat, widths = lwid, heights = lhei, respect = FALSE)
plot.index <- 1
if (!missing(RowSideColors)) {
par(mar = c(margins[1], 0, 0, 0.5))
image(rbind(1:nr), col = RowSideColors[rowInd], axes = FALSE)
plot.index <- plot.index + 1
}
if (!missing(ColSideColors)) {
par(mar = c(0.5, 0, 0, margins[2]))
image(cbind(1:nc), col = ColSideColors[colInd], axes = FALSE)
plot.index <- plot.index + 1
}
par(mar = c(margins[1], 0, 0, margins[2]))
x <- t(x)
cellnote <- t(cellnote)
if (revC) {
iy <- nr:1
if (exists("ddr"))
ddr <- rev(ddr)
x <- x[, iy]
cellnote <- cellnote[, iy]
}
else iy <- 1:nr
image(1:nc, 1:nr, x, xlim = 0.5 + c(0, nc), ylim = 0.5 +
c(0, nr), axes = FALSE, xlab = "", ylab = "", col = col,
breaks = breaks, ...)
retval$carpet <- x
if (exists("ddr"))
retval$rowDendrogram <- ddr
if (exists("ddc"))
retval$colDendrogram <- ddc
retval$breaks <- breaks
retval$col <- col
if (!invalid(na.color) & any(is.na(x))) {
mmat <- ifelse(is.na(x), 1, NA)
image(1:nc, 1:nr, mmat, axes = FALSE, xlab = "", ylab = "",
col = na.color, add = TRUE)
}
if (is.null(srtCol) && is.null(colCol))
axis(1, 1:nc, labels = labCol, las = 2, line = -0.5 +
offsetCol, tick = 0, cex.axis = cexCol, hadj = adjCol[1],
padj = adjCol[2])
else {
if (is.null(srtCol) || is.numeric(srtCol)) {
if (missing(adjCol) || is.null(adjCol))
adjCol = c(1, NA)
if (is.null(srtCol))
srtCol <- 90
xpd.orig <- par("xpd")
par(xpd = NA)
xpos <- axis(1, 1:nc, labels = rep("", nc), las = 2,
tick = 0)
text(x = xpos, y = par("usr")[3] - (1 + offsetCol) *
strheight("M"), labels = labCol, adj = adjCol,
cex = cexCol, srt = srtCol, col = colCol)
par(xpd = xpd.orig)
}
else warning("Invalid value for srtCol ignored.")
}
if (is.null(srtRow) && is.null(colRow)) {
axis(4, iy, labels = labRow, las = 2, line = -0.5 + offsetRow,
tick = 0, cex.axis = cexRow, hadj = adjRow[1], padj = adjRow[2])
}
else {
if (is.null(srtRow) || is.numeric(srtRow)) {
xpd.orig <- par("xpd")
par(xpd = NA)
ypos <- axis(4, iy, labels = rep("", nr), las = 2,
line = -0.5, tick = 0)
text(x = par("usr")[2] + (1 + offsetRow) * strwidth("M"),
y = ypos, labels = labRow, adj = adjRow, cex = cexRow,
srt = srtRow, col = colRow)
par(xpd = xpd.orig)
}
else warning("Invalid value for srtRow ignored.")
}
if (!is.null(xlab))
mtext(xlab, side = 1, line = margins[1] - 1.25)
if (!is.null(ylab))
mtext(ylab, side = 4, line = margins[2] - 1.25)
if (!missing(add.expr))
eval(substitute(add.expr))
if (!missing(colsep))
for (csep in colsep) rect(xleft = csep + 0.5, ybottom = 0,
xright = csep + 0.5 + sepwidth[1], ytop = ncol(x) +
1, lty = 1, lwd = 1, col = sepcolor, border = sepcolor)
if (!missing(rowsep))
for (rsep in rowsep) rect(xleft = 0, ybottom = (ncol(x) +
1 - rsep) - 0.5, xright = nrow(x) + 1, ytop = (ncol(x) +
1 - rsep) - 0.5 - sepwidth[2], lty = 1, lwd = 1,
col = sepcolor, border = sepcolor)
min.scale <- min(breaks)
max.scale <- max(breaks)
x.scaled <- scale01(t(x), min.scale, max.scale)
if (trace %in% c("both", "column")) {
retval$vline <- vline
vline.vals <- scale01(vline, min.scale, max.scale)
for (i in 1:length(colInd)) {
if (!is.null(vline)) {
abline(v = i - 0.5 + vline.vals, col = linecol,
lty = 2)
}
xv <- rep(i, nrow(x.scaled)) + x.scaled[, i] - 0.5
xv <- c(xv[1], xv)
yv <- 1:length(xv) - 0.5
lines(x = xv, y = yv, lwd = 1, col = tracecol, type = "s")
}
}
if (trace %in% c("both", "row")) {
retval$hline <- hline
hline.vals <- scale01(hline, min.scale, max.scale)
for (i in 1:length(rowInd)) {
if (!is.null(hline)) {
abline(h = i - 0.5 + hline.vals, col = linecol,
lty = 2)
}
yv <- rep(i, ncol(x.scaled)) + x.scaled[i, ] - 0.5
yv <- rev(c(yv[1], yv))
xv <- length(yv):1 - 0.5
lines(x = xv, y = yv, lwd = 1, col = tracecol, type = "s")
}
}
if (!missing(cellnote))
text(x = c(row(cellnote)), y = c(col(cellnote)), labels = c(cellnote),
col = notecol, cex = notecex)
plot.index <- plot.index + 1
par(mar = c(margins[1], 0, 0, 0))
if (dendrogram %in% c("both", "row")) {
flag <- try(stats:::plot.dendrogram(ddr, horiz = TRUE, axes = FALSE,
yaxs = "i", leaflab = "none"))
if ("try-error" %in% class(flag)) {
cond <- attr(flag, "condition")
if (!is.null(cond) && conditionMessage(cond) == "evaluation nested too deeply: infinite recursion / options(expressions=)?")
stop("Row dendrogram too deeply nested, recursion limit exceeded. Try increasing option(\"expressions\"=...).")
}
}
else plot.new()
par(mar = c(0, 0, if (!is.null(main)) 5 else 0, margins[2]))
if (dendrogram %in% c("both", "column")) {
flag <- try(stats:::plot.dendrogram(ddc, axes = FALSE, xaxs = "i",
leaflab = "none"))
if ("try-error" %in% class(flag)) {
cond <- attr(flag, "condition")
if (!is.null(cond) && conditionMessage(cond) == "evaluation nested too deeply: infinite recursion / options(expressions=)?")
stop("Column dendrogram too deeply nested, recursion limit exceeded. Try increasing option(\"expressions\"=...).")
}
}
else plot.new()
if (!is.null(main))
title(main, cex.main = 1.5 * op[["cex.main"]])
if (key) {
mar <- c(5, 4, 2, 1)
if (!is.null(key.xlab) && is.na(key.xlab))
mar[1] <- 2
if (!is.null(key.ylab) && is.na(key.ylab))
mar[2] <- 2
if (!is.null(key.title) && is.na(key.title))
mar[3] <- 1
par(mar = mar, cex = 0.75, mgp = c(2, 1, 0))
if (length(key.par) > 0)
do.call(par, key.par)
tmpbreaks <- breaks
if (symkey) {
max.raw <- max(abs(c(x, breaks)), na.rm = TRUE)
min.raw <- -max.raw
tmpbreaks[1] <- -max(abs(x), na.rm = TRUE)
tmpbreaks[length(tmpbreaks)] <- max(abs(x), na.rm = TRUE)
}
else {
min.raw <- min.breaks
max.raw <- max.breaks
}
z <- seq(min.raw, max.raw, by = min(diff(breaks)/100))
image(z = matrix(z, ncol = 1), col = col, breaks = tmpbreaks,
xaxt = "n", yaxt = "n")
par(usr = c(0, 1, 0, 1))
if (is.null(key.xtickfun)) {
lv <- pretty(breaks)
xv <- scale01(as.numeric(lv), min.raw, max.raw)
xargs <- list(at = xv, labels = lv)
}
else {
xargs <- key.xtickfun()
}
xargs$side <- 1
do.call(axis, xargs)
if (is.null(key.xlab)) {
if (scale == "row")
key.xlab <- "Row Z-Score"
else if (scale == "column")
key.xlab <- "Column Z-Score"
else key.xlab <- "Value"
}
if (!is.na(key.xlab)) {
mtext(side = 1, key.xlab, line = par("mgp")[1], padj = 0.5,
cex = par("cex") * par("cex.lab"))
}
if (density.info == "density") {
dens <- density(x, adjust = densadj, na.rm = TRUE,
from = min.scale, to = max.scale)
omit <- dens$x < min(breaks) | dens$x > max(breaks)
dens$x <- dens$x[!omit]
dens$y <- dens$y[!omit]
dens$x <- scale01(dens$x, min.raw, max.raw)
lines(dens$x, dens$y/max(dens$y) * 0.95, col = denscol,
lwd = 1)
if (is.null(key.ytickfun)) {
yargs <- list(at = pretty(dens$y)/max(dens$y) *
0.95, labels = pretty(dens$y))
}
else {
yargs <- key.ytickfun()
}
yargs$side <- 2
do.call(axis, yargs)
if (is.null(key.title))
key.title <- "Color Key\nand Density Plot"
if (!is.na(key.title))
title(key.title)
par(cex = 0.5)
if (is.null(key.ylab))
key.ylab <- "Density"
if (!is.na(key.ylab))
mtext(side = 2, key.ylab, line = par("mgp")[1],
padj = 0.5, cex = par("cex") * par("cex.lab"))
}
else if (density.info == "histogram") {
h <- hist(x, plot = FALSE, breaks = breaks)
hx <- scale01(breaks, min.raw, max.raw)
hy <- c(h$counts, h$counts[length(h$counts)])
lines(hx, hy/max(hy) * 0.95, lwd = 1, type = "s",
col = denscol)
if (is.null(key.ytickfun)) {
yargs <- list(at = pretty(hy)/max(hy) * 0.95,
labels = pretty(hy))
}
else {
yargs <- key.ytickfun()
}
yargs$side <- 2
do.call(axis, yargs)
if (is.null(key.title))
key.title <- "Color Key\nand Histogram"
if (!is.na(key.title))
title(key.title)
par(cex = 0.5)
if (is.null(key.ylab))
key.ylab <- "Count"
if (!is.na(key.ylab))
mtext(side = 2, key.ylab, line = par("mgp")[1],
padj = 0.5, cex = par("cex") * par("cex.lab"))
}
else if (is.null(key.title))
title("Color Key")
if (trace %in% c("both", "column")) {
vline.vals <- scale01(vline, min.raw, max.raw)
if (!is.null(vline)) {
abline(v = vline.vals, col = linecol, lty = 2)
}
}
if (trace %in% c("both", "row")) {
hline.vals <- scale01(hline, min.raw, max.raw)
if (!is.null(hline)) {
abline(v = hline.vals, col = linecol, lty = 2)
}
}
}
else {
par(mar = c(0, 0, 0, 0))
plot.new()
}
retval$colorTable <- data.frame(low = retval$breaks[-length(retval$breaks)],
high = retval$breaks[-1], color = retval$col)
retval$layout <- list(lmat = lmat, lhei = lhei, lwid = lwid)
if (!is.null(extrafun))
extrafun()
invisible(retval)
}
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))))
quarter.circle <- function (x, y, radius, quadrant = 1, nv = 100, border = NULL, col = NA, lty = 1, lwd = 1){
xylim <- par("usr")
plotdim <- par("pin")
ymult <- getYmult()
angle.inc <- 2 * pi/nv
angles <- seq(0, 2 * pi - angle.inc, by = angle.inc)
if (length(col) < length(radius))
col <- rep(col, length.out = length(radius))
for (circle in 1:length(radius)) {
xv <- cos(angles) * radius[circle] + x
yv <- sin(angles) * radius[circle] * ymult + y
if (quadrant == 1)
polygon(c(0, radius[circle] + x, xv[xv >= x & yv >= y], 0),
c(0, 0, yv[xv >= x & yv >= y], radius[circle] * ymult + y),
border = border, col = col[circle], lty = lty, lwd = lwd)
if (quadrant == 2)
polygon(c(0, 0, xv[xv < x & yv >= y], -radius[circle] + x),
c(0, radius[circle] * ymult + y, yv[xv < x & yv >= y], 0),
border = border, col = col[circle], lty = lty, lwd = lwd)
if (quadrant == 3)
polygon(c(0, -radius[circle] + x, xv[xv < x & yv < y], 0),
c(0, 0, yv[xv < x & yv < y], -radius[circle] * ymult + y),
border = border, col = col[circle], lty = lty, lwd = lwd)
if (quadrant == 4)
polygon(c(0, 0, xv[xv >= x & yv < y], radius[circle] + x),
c(0, -radius[circle] * ymult + y, yv[xv >= x & yv < y], 0),
border = border, col = col[circle], lty = lty, lwd = lwd)}}
ui <- fluidPage(
titlePanel("Gene Expression Comparison App"),
sidebarLayout(
div(class = "col-sm-5",
tags$form(class = "well",
selectInput("dataset", "Dataset", "GSE67985", "GSE67985", width = "30%"),
div(style="padding-bottom:3px", "Chai, Yang; Parada, Carolina; Grimaldi, Alexandre; Ho, Thach-Vu; Harunaga, Jill; Samuels, Bridget; Johnson, Daniel."),
div(style="padding-bottom:3px", "Distal/proximal mandible/maxilla at embryonic day 10.5/11.5/12.5/13.5/14.5, 3 replicates/condition."),
div(style="padding-bottom:3px", "Affymetrix Mouse Genome 430 2.0 Array."),
div(style="padding-bottom:7px", "Mouse drawing by Darrin Lunde and Nguyen Truong Son."),
conditionalPanel("!output.datloaded",
helpText("Data is loading (may take a minute) ...")),
conditionalPanel("output.datloaded",
helpText("Data is loaded."))),
tags$form(class = "well",
div(tags$label("Select genes/probesets and colors (optional for global analysis)")),
bootstrapPage(
div(style="display:inline-block; width:15%", textInput("gene1", NULL, "name")),
div(style="display:inline-block; width:15%", textInput("gene2", NULL, "name")),
div(style="display:inline-block; width:15%", textInput("gene3", NULL, "name")),
div(style="display:inline-block; width:15%", textInput("gene4", NULL, "name")),
div(style="display:inline-block; width:15%", textInput("gene5", NULL, "name")),
div(style="display:inline-block; width:15%", textInput("gene6", NULL, "name"))),
bootstrapPage(
div(style="display:inline-block; width:15%", textInput("col1", NULL, "blue")),
div(style="display:inline-block; width:15%", textInput("col2", NULL, "red")),
div(style="display:inline-block; width:15%", textInput("col3", NULL, "forestgreen")),
div(style="display:inline-block; width:15%", textInput("col4", NULL, "magenta")),
div(style="display:inline-block; width:15%", textInput("col5", NULL, "darkorange")),
div(style="display:inline-block; width:15%", textInput("col6", NULL, "gold")))),
tags$form(class = "well",
checkboxGroupInput("sel", "Select samples (each is three replicates)",
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$form(class = "well",
splitLayout(cellWidths = c("35%", "65%"),
radioButtons("comp", "Compare condition",
choices = c(
"distal vs. proximal" = "place",
"maxilla vs. mandible" = "bone",
"age (earliest vs. latest selected)" = "age"),
selected = "place"),
list(conditionalPanel("input.comp == 'place'",
radioButtons("invert_place", br(), c("proximal down, distal up" = "normal",
"distal down, proximal up" = "inverted"), "normal", inline = T)),
conditionalPanel("input.comp == 'bone'",
radioButtons("invert_bone", list(br(), br()), c("mandible down, maxilla up" = "normal",
"maxilla down, mandible up" = "inverted"), "normal", inline = T)),
conditionalPanel("input.comp == 'age'",
radioButtons("invert_age", list(br(), br(), br(), br()),
c("earliest down, latest up" = "normal",
"latest down, earliest up" = "inverted"), "normal", inline = T)))),
conditionalPanel("output.numsel > 6",
checkboxInput("heatadjust", "For heatmap, factor out comparisons other than the one selected",
value = T))),
tags$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("average probeset" = "AVE", "most highly expressed probeset" = "A",
"most differentially expressed probeset" = "M", "all probesets" = "Z"),
selected = "AVE", inline = T),
radioButtons("heatcol", "For heatmap, high values are",
choices = c("green" = "rg", "red" = "gr"), selected = "rg", inline = T),
radioButtons("distfun", "For heatmap, cluster genes using",
choices = c("absolute correlation" = "AC", "correlation" = "C", "Euclidean distance" = "E"),
selected = "AC", inline = T),
radioButtons("heatscale", "For heatmap, scale genes by",
choices = c("mean-centering" = "MC", "Z-score" = "Z", "none" = "N"), selected = "MC", inline = T),
radioButtons("basecol", "For individual plots, zero point is",
choices = c("E10.5", "mean of ages"), selected = "E10.5", inline = T))),
mainPanel(width = 7,
tabsetPanel(
tabPanel("Global",
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%")),
tabPanel("Individual",
downloadButton("download.bullseyes", "Download individual plot(s)"),
plotOutput("bullseyes")
)))
))
server <- function(input, output){
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)
load("data/jaws3.R")
load("data/exprs.R")
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)
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 (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 (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, "maxilla", "mandible")
two <- ifelse(invert, "mandible", "maxilla")}
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))}
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
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)})
output$numsel <- reactive(length(c(ones, twos)))
outputOptions(output, "numsel", suspendWhenHidden = FALSE)
dat.sel <- dat[, c(ones, twos, "symbol")]
if (input$summary == "AVE"){
dat.sel <- ddply(dat.sel, .(symbol), numcolwise(mean))
dat.sel <- dat.sel[dat.sel$symbol != "NA", ]
rownames(dat.sel) <- dat$probeset[match(dat.sel$symbol, dat$symbol)]}
if (input$summary == "A"){
dat.sel$A <- rowMeans(dat.sel)
dat.sel$P <- rownames(dat.sel)
dat.single <- dat.sel[dat.sel$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[, colnames(dat.sel)[!colnames(dat.sel) %in% c("A", "P")]]}
if (input$summary == "M"){
dat.sel$MM <- abs(rowMeans(dat.sel[, twos]) - rowMeans(dat.sel[, ones]))
dat.sel$P <- rownames(dat.sel)
dat.single <- dat.sel[dat.sel$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[, colnames(dat.sel)[!colnames(dat.sel) %in% c("MM", "P")]]}
fit <- lmFit(dat.sel[, colnames(dat.sel)[colnames(dat.sel) != "symbol"]], design)
efit <- eBayes(fit)
dat.sel$A <- rowMeans(dat.sel[, colnames(dat.sel)[colnames(dat.sel) != "symbol"]])
dat.sel$M <- rowMeans(dat.sel[, twos]) - rowMeans(dat.sel[, ones])
if (invert) dat.sel$M <- -dat.sel$M
if (!is.null(dat.sel$symbol))
dat.sel$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)
dat.sel$color[dat.sel$symbol == mousecase(input$gene6) | rownames(dat.sel) == mousecase(input$gene6)] <-
tolower(input$col6)
lfc <- ifelse(input$log, abs(as.numeric(input$fc)), log2(abs(as.numeric(input$fc))))
top <- topTable(efit, coef = which(colnames(design) == target.col),
lfc = lfc, p.value = as.numeric(input$fdr), num = Inf)
top <- merge(top, dat[, c("probeset", "symbol", "desc")], by.x = 0, by.y = "probeset")
top.colored <- top[rownames(top) %in% dat.sel$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, ]
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$log) axis(2, ats, log.labs)
else axis(2, ats, raw.labs, cex.axis = ifelse(lim > 6, 0.75, 1))
abline(h = lfc * c(-1, 1), lty = 2)}
plotPNG(ma.plot, "ma.plot.png", width = 1500, height = 750, res = 300, pointsize = 4)
ma.plot()
})
output$download.ma.plot <- downloadHandler("Facebase_Microarray_MA_Plot.png",
function(file) file.copy("ma.plot.png", file))
output$heatmap <- renderPlot({
heatmap <- function(){
withProgress({
if (nrow(dat.top) < 2) return(plot.null())
dat.heat <- as.matrix(dat.top[, sels])
dat.heat <- dat.heat - mean(dat.heat)
if (input$heatadjust){
for (col in colnames(control)[colnames(control) != "(Intercept)"]){
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}}}
if (input$heatscale %in% c("MC", "Z")) dat.heat <- sweep(dat.heat, 1, rowMeans(dat.heat))
if (input$heatscale == "Z") dat.heat <- sweep(dat.heat, 1, apply(dat.heat, 1, sd), "/")
weights <- (ncol(dat.heat):1) + 10000
weights[colnames(dat.heat) %in% twos] <-
weights[colnames(dat.heat) %in% twos] + ifelse(xor(invert, input$comp == "bone"), -100, 100)
extreme <- ceiling(10 * max(dat.heat)) / 10
heatmap.22(t(dat.heat),
Rowv = as.integer(weights),
col = ifelse(input$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.row = dist,
distfun.col = function(...) {
if (input$distfun == "E") return(dist(...))
if (input$distfun == "AC") return(cor.dist(..., abs = T))
if (input$distfun == "C") return(cor.dist(..., abs = F))},
labCol = dat.top$symbol, colCol = dat.top$color)
incProgress(1)}, value = NULL, message = "Calculations in progress...")}
plotPNG(heatmap, "heatmap.png", width = 1500, height = 750, res = 300, pointsize = 6)
heatmap()
})
output$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 <- dat.top$color[match(top$Probeset, rownames(dat.top))]
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)))
})
output$bullseyes <- renderPlot({
bullseyes <- function(){
genes <- c(input$gene1, input$gene2, input$gene3, input$gene4, input$gene5, input$gene6)
good.genes <- which(genes %in% dat$symbol | genes %in% dat$probeset)
genes <- genes[good.genes]
if (length(genes) == 0) return(plot.null)
colors <- c(input$col1, input$col2, input$col3, input$col4, input$col5, input$col6)
colors <- colors[good.genes]
exprs <- dat[dat$symbol %in% genes, ]
exprs <- data.frame(
symbol = exprs$symbol,
probeset = exprs$probeset,
E14.5MaxD = rowMeans(exprs[, grep("14.5MaxD", colnames(exprs))]),
E13.5MaxD = rowMeans(exprs[, grep("13.5MaxD", colnames(exprs))]),
E12.5MaxD = rowMeans(exprs[, grep("12.5MaxD", colnames(exprs))]),
E11.5MaxD = rowMeans(exprs[, grep("11.5MaxD", colnames(exprs))]),
E10.5MaxD = rowMeans(exprs[, grep("10.5MaxD", colnames(exprs))]),
E14.5MaxP = rowMeans(exprs[, grep("14.5MaxP", colnames(exprs))]),
E13.5MaxP = rowMeans(exprs[, grep("13.5MaxP", colnames(exprs))]),
E12.5MaxP = rowMeans(exprs[, grep("12.5MaxP", colnames(exprs))]),
E11.5MaxP = rowMeans(exprs[, grep("11.5MaxP", colnames(exprs))]),
E10.5MaxP = rowMeans(exprs[, grep("10.5MaxP", colnames(exprs))]),
E14.5MndD = rowMeans(exprs[, grep("14.5MndD", colnames(exprs))]),
E13.5MndD = rowMeans(exprs[, grep("13.5MndD", colnames(exprs))]),
E12.5MndD = rowMeans(exprs[, grep("12.5MndD", colnames(exprs))]),
E11.5MndD = rowMeans(exprs[, grep("11.5MndD", colnames(exprs))]),
E10.5MndD = rowMeans(exprs[, grep("10.5MndD", colnames(exprs))]),
E14.5MndP = rowMeans(exprs[, grep("14.5MndP", colnames(exprs))]),
E13.5MndP = rowMeans(exprs[, grep("13.5MndP", colnames(exprs))]),
E12.5MndP = rowMeans(exprs[, grep("12.5MndP", colnames(exprs))]),
E11.5MndP = rowMeans(exprs[, grep("11.5MndP", colnames(exprs))]),
E10.5MndP = rowMeans(exprs[, grep("10.5MndP", colnames(exprs))]),
stringsAsFactors = F)
if (input$summary %in% c("AVE")){
exprs.full <- exprs
exprs <- ddply(exprs, .(symbol), numcolwise(mean))
for (i in 1:nrow(exprs)) exprs$probeset[i] <-
paste(exprs.full$probeset[exprs.full$symbol == exprs$symbol[i]], collapse = "\n")
exprs <- exprs[, c(1, 22, 2:21)]}
if (input$summary == "A"){
exprs$rms <- rowMeans(exprs[, 3:21])
maxs <- ddply(exprs, .(symbol), summarise, mx = which.max(rms))
expr <- exprs[0, ]
for (sym in maxs$symbol)
expr <- rbind(expr, exprs[exprs$symbol == sym, ][maxs$mx[maxs$symbol == sym], ])
exprs <- expr}
if (input$summary == "M"){
exprs$range <- apply(exprs[, -(1:2)], 1, function(x) diff(range(x)))
maxs <- ddply(exprs, .(symbol), summarise, mx = which.max(range))
expr <- exprs[0, ]
for (sym in maxs$symbol)
expr <- rbind(expr, exprs[exprs$symbol == sym, ][maxs$mx[maxs$symbol == sym], ])
exprs <- expr}
exprs <- exprs[order(match(exprs$symbol, genes), exprs$probeset), ]
if (input$basecol == "mean of ages")
exprs[, -(1:2)] <- sweep(exprs[, -(1:2)], 1, rowMeans(exprs[, -(1:2)]))
if (input$basecol == "E10.5") exprs[, -(1:2)] <-
sweep(exprs[, -(1:2)], 1, rowMeans(exprs[, grep("E10.5", colnames(exprs))]))
if (nrow(exprs) == 1) par(mfrow = c(1, 1)) else par(mfrow = c(ceiling(nrow(exprs) / 2), 2))
par(mar = c(1, 1, 1, 1))
mn <- min(unlist(exprs[, -(1:2)]))
mx <- max(unlist(exprs[, -(1:2)]))
two <- ifelse(nrow(exprs) == 2, T, F)
for (i in 1:nrow(exprs)){
expr <- exprs[i, ]
pal <- colorRampPalette(c("white", colors[which(genes == expr$symbol)]))(1001)
cols <- function(x) pal[round((x - mn) / (mx - mn) * 1000) + 1]
plot(c(-1.25, 1.25), c(-1.875, 1.875), type = "n", xaxt = "n", yaxt = "n", frame.plot = F)
rasterImage(bg, par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4])
dmax <- unlist(expr[, grep("MaxD", colnames(expr))])
pmax <- unlist(expr[, grep("MaxP", colnames(expr))])
dmnd <- unlist(expr[, grep("MndD", colnames(expr))])
pmnd <- unlist(expr[, grep("MndP", colnames(expr))])
all <- c(dmax, pmax, dmnd, pmnd)
if (length(dmax) == 0) return(plot.null())
quarter.circle(0, 0, c(1.025, 0.825, 0.625, 0.425, 0.225),
quadrant = 1, border = NA, col = cols(dmax))
quarter.circle(0, 0, c(1.025, 0.825, 0.625, 0.425, 0.225),
quadrant = 2, border = NA, col = cols(pmax))
quarter.circle(0, 0, c(1.025, 0.825, 0.625, 0.425, 0.225),
quadrant = 3, border = NA, col = cols(pmnd))
quarter.circle(0, 0, c(1.025, 0.825, 0.625, 0.425, 0.225),
quadrant = 4, border = NA, col = cols(dmnd))
draw.circle(0, 0, c(1.025, 0.825, 0.625, 0.425, 0.225), border = "black", lwd = 0.5)
segments(-1.025, c(-0.028, 0.028), 1.025, c(-0.028, 0.028), lwd = 0.5)
segments(c(-0.03, 0.03), -1.025 * getYmult(), c(-0.03, 0.03), 1.025 * getYmult(), lwd = 0.5)
rect(-2, -0.025, 2, 0.025, border = NA, col = "white")
rect(-0.025, -1.1, 0.025, 1.1, border = NA, col = "white")
text(c(0.125, 0.325, 0.525, 0.725, 0.925), 0, c("E10.5", "E11.5", "E12.5", "E13.5", "E14.5"),
cex = ifelse(two, 0.75, 1))
arctext("distal maxilla", c(0, 0), 1.1, middle = pi / 4, cex = ifelse(two, 1, 1.5))
arctext("proximal maxilla", c(0, 0), 1.1, middle = 3 * pi / 4, cex = ifelse(two, 1, 1.5))
arctext("proximal mandible", c(0, 0), 1.1, middle = 5 * pi / 4, clockwise = F, cex = ifelse(two, 1, 1.5))
arctext("distal mandible", c(0, 0), 1.1, middle = 7 * pi / 4, clockwise = F, cex = ifelse(two, 1, 1.5))
text(1.2, 1.95, expr$symbol, adj = 1, cex = ifelse(two, 2, 3))
text(1.2, 1.85, expr$probeset, adj = c(1, 1), cex = ifelse(two, 1, 1.5))
rect(seq(0.575, 1.245, 0.005), -1.9, seq(0.58, 1.25, 0.005), -1.75, border = NA,
col = colorRampPalette(cols(c(min(all), max(all))))(135))
segments(0.575 - min(all) / diff(range(all)) * (1.245 - 0.575), -1.9,
0.575 - min(all) / diff(range(all)) * (1.245 - 0.575), -1.75, col = "white", lwd = ifelse(two, 3, 4))
text(c(0.63, 1.19), -1.95,