/
plotmzrt.R
1120 lines (1072 loc) · 40 KB
/
plotmzrt.R
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#' plot intensity of peaks across samples or samples across peaks
#' @param data matrix
#' @param lv factor vector for the column
#' @param indexx index for matrix row
#' @param indexy index for matrix column
#' @param ... parameters for `title` function
#' @return parallel coordinates plot
#' @examples
#' data(list)
#' # selected peaks across samples
#' plotpeak(t(list$data), lv = as.factor(c(rep(1,5),rep(2,nrow(list$data)-5))),1:10,1:10)
#' # selected samples across peaks
#' plotpeak(list$data, lv = as.factor(list$group$sample_group),1:10,1:10)
#' @export
plotpeak <- function(data,
lv = NULL,
indexx = NULL,
indexy = NULL,
...) {
# from http://karolis.koncevicius.lt/posts/r_base_plotting_without_wrappers/
data <- as.matrix(data)
grDevices::palette(c("cornflowerblue", "red3", "orange"))
if (!is.null(indexx)) {
data <- data[indexx,]
}
if (!is.null(indexy)) {
data <- data[, indexy]
lv <- lv[indexy]
}
graphics::plot.new()
graphics::plot.window(xlim = c(1, nrow(data)), ylim = range(data))
graphics::grid(nx = NA, ny = NULL)
graphics::abline(
v = seq_len(nrow(data)),
col = "grey",
lwd = 5,
lty = "dotted"
)
if (!is.null(lv)) {
graphics::matlines(data, col = lv, lty = 1)
graphics::legend(
'top',
inset = c(0, -.05),
legend = unique(lv),
col = unique(lv),
lwd = 3,
bty = 'n',
horiz = TRUE
)
} else{
graphics::matlines(
data,
col = grDevices::rgb(
0,
0,
0,
maxColorValue = 255,
alpha = 100
),
lty = 1
)
}
graphics::axis(2, lwd = 0, las = 2)
graphics::mtext(
stats::variable.names(t(data)),
3,
at = seq_len(nrow(data)),
line = 1,
col = "darkgrey"
)
}
#' plot ridgeline density plot
#' @param data matrix
#' @param lv factor vector for the column
#' @param indexx index for matrix row
#' @param indexy index for matrix column
#' @param ... parameters for `title` function
#' @return ridgeline density plot
#' @examples
#' data(list)
#' plotridge(t(list$data),indexy=c(1:10),xlab = 'Intensity',ylab = 'peaks')
#' plotridge(log(list$data),as.factor(list$group$sample_group),xlab = 'Intensity',ylab = 'peaks')
#' @export
plotridge <- function(data,
lv = NULL,
indexx = NULL,
indexy = NULL,
...) {
# from http://karolis.koncevicius.lt/posts/r_base_plotting_without_wrappers/
data <- as.matrix(data)
if (!is.null(indexx)) {
data <- data[indexx,]
}
if (!is.null(indexy)) {
data <- data[, indexy]
lv <- lv[indexy]
}
dens <- apply(data, 2, stats::density)
xs <- Map(getElement, dens, "x")
ys <- Map(getElement, dens, "y")
ys <- Map(function(x)
(x - min(x)) / max(x - min(x)) * 1.5, ys)
ys <- Map(`+`, ys, length(ys):1)
graphics::plot.new()
graphics::plot.window(xlim = range(xs), ylim = c(1, length(ys) +
1.5))
graphics::abline(h = length(ys):1, col = "grey")
if (!is.null(lv) & nlevels(lv) != 1) {
col <- grDevices::hcl.colors(nlevels(lv), "Zissou", alpha = 0.8)
Map(graphics::polygon,
xs,
ys,
col = col[match(lv, unique(lv))])
graphics::legend(
'topright',
inset = c(0, -.05),
legend = unique(lv),
col = col[unique(lv)],
lwd = 10,
bty = 'n',
horiz = TRUE
)
} else{
Map(
graphics::polygon,
xs,
ys,
col = grDevices::rgb(
0,
0,
0,
maxColorValue = 255,
alpha = 100
)
)
}
graphics::axis(1, tck = -0.01)
graphics::mtext(
names(dens),
2,
at = length(ys):1,
las = 2,
padj = 0
)
graphics::title(...)
}
#' plot 1-d density for multiple samples
#' @param data matrix
#' @param lv factor vector for the column
#' @param indexx index for matrix row
#' @param indexy index for matrix column
#' @param ... parameters for `title` function
#' @return NULL
#' @examples
#' data(list)
#' plotrug(list$data)
#' plotrug(log(list$data), lv = as.factor(list$group$sample_group))
#' @export
plotrug <- function(data,
lv = NULL,
indexx = NULL,
indexy = NULL,
...) {
data <- as.matrix(data)
if (!is.null(indexx)) {
data <- data[indexx,]
}
if (!is.null(indexy)) {
data <- data[, indexy]
lv <- lv[indexy]
}
rugs <- apply(data, 2, function(x)
x[order(x)])
rugs <- as.list(as.data.frame(rugs))
rugs <- lapply(rugs, function(x)
x[is.finite(x)])
graphics::plot.new()
graphics::plot.window(xlim = range(rugs), ylim = c(1, length(rugs) +
1))
len <- Map(length, rugs)
idx <- seq_along(rugs)
yl <- Map(function(x, y)
rep(y - 0.3, x), len, idx)
yh <- Map(function(x, y)
rep(y + 0.3, x), len, idx)
if (!is.null(lv)) {
col <- grDevices::hcl.colors(nlevels(lv), "Zissou", alpha = 0.8)
Map(graphics::segments,
rugs,
yl,
rugs,
yh,
col = col[match(lv, unique(lv))])
graphics::legend(
'top',
inset = c(0, -.05),
legend = unique(lv),
col = unique(lv),
lwd = 3,
bty = 'n',
horiz = TRUE
)
} else{
Map(
graphics::segments,
rugs,
yl,
rugs,
yh,
col = grDevices::rgb(
0,
0,
0,
maxColorValue = 255,
alpha = 100
)
)
}
graphics::axis(1, tck = -0.01)
graphics::mtext(
names(rugs),
2,
at = length(yl):1,
las = 2,
padj = 0
)
graphics::title(...)
}
#' plot the scatter plot for peaks list with threshold
#' @param list list with data as peaks list, mz, rt and group information
#' @param rt vector range of the retention time
#' @param ms vector vector range of the m/z
#' @param inscf Log intensity cutoff for peaks across samples. If any peaks show a intensity higher than the cutoff in any samples, this peaks would not be filtered. default 5
#' @param rsdcf the rsd cutoff of all peaks in all group, default 30
#' @param imputation parameters for `getimputation` function method
#' @param ... parameters for `plot` function
#' @return data fit the cutoff
#' @examples
#' data(list)
#' plotmr(list)
#' @export
plotmr <- function(list,
rt = NULL,
ms = NULL,
inscf = 5,
rsdcf = 30,
imputation = "l",
...) {
graphics::par(mar = c(5, 4.2, 6.1, 2.1), xpd = TRUE)
list <- getdoe(list,
rsdcf = rsdcf,
inscf = inscf,
imputation = imputation)
lif <-
getfilter(list, rowindex = list$rsdindex &
list$insindex)
data <- lif$groupmean
dataname <- colnames(data)
mz <- lif$mz
RT <- lif$rt
suppressWarnings(if (sum(nrow(data) > 0) > 0) {
n <- dim(data)[2]
col <- grDevices::rainbow(n, alpha = 0.318)
graphics::plot(
mz ~ RT,
xlab = "Retention Time(s)",
ylab = "m/z",
type = "n",
pch = 19,
ylim = ms,
xlim = rt,
...
)
for (i in 1:n) {
cex <- as.numeric(cut((log10(data[, i] + 1) -
inscf),
breaks = c(0, 1, 2, 3, 4, Inf) / 2
)) / 2
cexlab <- c(
paste0(inscf, "-", inscf + 0.5),
paste0(inscf + 0.5, "-", inscf + 1),
paste0(inscf +
1, "-", inscf + 1.5),
paste0(inscf +
1.5, "-", inscf + 2),
paste0(">", inscf +
2)
)
if (!is.null(ms) & !is.null(rt)) {
graphics::points(
x = RT[RT > rt[1] & RT <
rt[2] &
mz > ms[1] &
mz < ms[2]],
y = mz[RT >
rt[1] &
RT < rt[2] &
mz > ms[1] &
mz <
ms[2]],
cex = cex,
col = col[i],
pch = 19
)
} else {
graphics::points(
x = RT,
y = mz,
cex = cex,
col = col[i],
pch = 19
)
}
}
graphics::legend(
"topright",
legend = dataname,
col = col,
pch = 19,
horiz = TRUE,
bty = "n",
inset = c(0,-0.25)
)
graphics::legend(
"topleft",
legend = cexlab,
title = "Intensity in Log scale",
pt.cex = c(1, 2, 3, 4, 5) / 4,
pch = 19,
bty = "n",
horiz = TRUE,
cex = 0.7,
col = grDevices::rgb(0,
0, 0, 0.318),
inset = c(0,-0.25)
)
} else if (NROW(data) > 0) {
graphics::plot(
mz ~ RT,
xlab = "Retention Time(s)",
ylab = "m/z",
type = "n",
pch = 19,
ylim = ms,
xlim = rt,
...
)
cex <- as.numeric(cut((log10(
data + 1
) -
inscf), breaks = c(0, 1, 2, 3, 4, Inf) /
2)) / 2
cexlab <- c(
paste0(inscf, "-", inscf + 0.5),
paste0(inscf + 0.5, "-", inscf + 1),
paste0(inscf +
1, "-", inscf + 1.5),
paste0(inscf +
1.5, "-", inscf + 2),
paste0(">", inscf +
2)
)
col <- grDevices::rgb(0, 0, 0, alpha = 0.318)
if (!is.null(ms) & !is.null(rt)) {
graphics::points(
x = RT[RT > rt[1] & RT <
rt[2] &
mz > ms[1] &
mz < ms[2]],
y = mz[RT >
rt[1] &
RT < rt[2] &
mz > ms[1] & mz <
ms[2]],
,
col = col,
cex = cex,
pch = 19
)
} else {
graphics::points(
x = RT,
y = mz,
cex = cex,
pch = 19,
col = col
)
}
graphics::legend(
"topright",
legend = unique(list$group$sample_group),
col = col,
pch = 19,
horiz = TRUE,
bty = "n",
inset = c(0,-0.25)
)
graphics::legend(
"topleft",
legend = cexlab,
title = "Intensity in Log scale",
pt.cex = c(1, 2, 3, 4, 5) / 4,
pch = 19,
bty = "n",
horiz = TRUE,
cex = 0.7,
col = grDevices::rgb(0,
0, 0, 0.318),
inset = c(0,-0.25)
)
} else
{
graphics::plot(
1,
xlab = "Retention Time(s)",
ylab = "m/z",
main = "No peaks found",
ylim = ms,
xlim = rt,
type = "n",
pch = 19,
...
)
})
}
#' plot the diff scatter plot for peaks list with threshold between two groups
#' @param list list with data as peaks list, mz, rt and group information
#' @param ms the mass range to plot the data
#' @param inscf Log intensity cutoff for peaks across samples. If any peaks show a intensity higher than the cutoff in any samples, this peaks would not be filtered. default 5
#' @param rsdcf the rsd cutoff of all peaks in all group
#' @param imputation parameters for `getimputation` function method
#' @param ... parameters for `plot` function
#' @return NULL
#' @examples
#' data(list)
#' plotmrc(list)
#' @export
plotmrc <- function(list,
ms = c(100, 800),
inscf = 5,
rsdcf = 30,
imputation = "l",
...) {
list <- getdoe(
list,
rsdcf = rsdcf,
inscf = inscf,
imputation = imputation,
)
lif <-
getfilter(list, rowindex = list$rsdindex &
list$insindex)
data <- lif$groupmean
dataname <- colnames(data)
mz <- lif$mz
rt <- lif$rt
graphics::par(mar = c(5, 4.2, 6.1, 2.1), xpd = TRUE)
suppressWarnings(if (!is.na(data[1, 1])) {
diff1 <- data[, 1] - data[, 2]
diff2 <- data[, 2] - data[, 1]
diff1[diff1 < 0] <- 0
diff2[diff2 < 0] <- 0
name1 <- paste0(dataname[1], "-", dataname[2])
name2 <- paste0(dataname[2], "-", dataname[1])
cex1 <- as.numeric(cut((log10(
diff1 + 1
) - inscf),
breaks <- c(0, 1, 2, 3, 4, Inf) / 2)) / 2
cex2 <- as.numeric(cut((log10(
diff2 + 1
) - inscf),
breaks <- c(0, 1, 2, 3, 4, Inf) / 2)) / 2
cexlab <- c(
paste0(inscf, "-", inscf + 0.5),
paste0(inscf +
0.5, "-", inscf + 1),
paste0(inscf + 1, "-",
inscf + 1.5),
paste0(inscf + 1.5, "-", inscf +
2),
paste0(">", inscf + 2)
)
graphics::plot(
mz ~ rt,
xlab = "Retention Time(s)",
ylab = "m/z",
ylim = ms,
cex = cex1,
col = grDevices::rgb(0,
0, 1, 0.618),
pch = 19,
...
)
graphics::points(
mz ~ rt,
cex = cex2,
col = grDevices::rgb(1,
0, 0, 0.618),
pch = 19
)
graphics::legend(
'topleft',
legend = cexlab,
title = "Intensity in Log scale",
pt.cex = c(1, 2, 3, 4, 5) / 2,
pch = 19,
col = grDevices::rgb(0,
0, 0, 0.618),
bty = "n",
horiz = TRUE,
inset = c(0,-0.25)
)
graphics::legend(
'topright',
legend = c(name1,
name2),
pch = 19,
col = c(
grDevices::rgb(0,
0, 1, 0.618),
grDevices::rgb(1, 0, 0, 0.618)
),
bty = "n",
horiz = TRUE,
inset = c(0,-0.25)
)
} else {
graphics::plot(
1,
xlab = "Retention Time(s)",
ylab = "m/z",
main = "No peaks found",
ylim = ms,
type = "n",
pch = 19,
...
)
})
}
#' plot the rsd influences of data in different groups
#' @param list list with data as peaks list, mz, rt and group information
#' @param ms the mass range to plot the data
#' @param inscf Log intensity cutoff for peaks across samples. If any peaks show a intensity higher than the cutoff in any samples, this peaks would not be filtered. default 5
#' @param rsdcf the rsd cutoff of all peaks in all group
#' @param imputation parameters for `getimputation` function method
#' @param ... other parameters for `plot` function
#' @return NULL
#' @examples
#' data(list)
#' plotrsd(list)
#' @export
plotrsd <- function(list,
ms = c(100, 800),
inscf = 5,
rsdcf = 100,
imputation = "l",
...) {
graphics::par(mar = c(5, 4.2, 6.1, 2.1), xpd = TRUE)
cexlab <- c("<20%", "20-40%", "40-60%", "60-80%", ">80%")
list <- getdoe(list,
rsdcf = rsdcf,
inscf = inscf,
imputation = imputation)
lif <-
getfilter(list, rowindex = list$rsdindex &
list$insindex)
data <- lif$groupmean
dataname <- colnames(data)
mz <- lif$mz
rt <- lif$rt
rsd <- lif$grouprsd
if (is.null(dim(rsd))) {
n <- 1
col <- grDevices::rainbow(1)
cex <- as.numeric(cut(rsd, breaks = c(0, 20,
40, 60, 80, Inf))) / 2
dataname <- unique(lif$group$sample_group)
graphics::plot(
mz ~ rt,
xlab = "Retention Time(s)",
ylab = "m/z",
main = "RSD(%) distribution",
type = "n",
pch = 19,
...
)
graphics::points(
x = rt,
y = mz,
ylim = ms,
cex = cex,
col = col,
pch = 19
)
graphics::legend(
"topright",
legend = dataname,
col = col,
horiz = TRUE,
pch = 19,
bty = "n",
inset = c(0,-0.25)
)
graphics::legend(
"topleft",
legend = cexlab,
pt.cex = c(1,
2, 3, 4, 5) / 2,
pch = 19,
bty = "n",
horiz = TRUE,
cex = 0.8,
col = grDevices::rgb(0, 0, 0, 0.318),
inset = c(0,-0.25)
)
} else{
n <- dim(rsd)[2]
col <- grDevices::rainbow(n, alpha = 0.318)
graphics::plot(
mz ~ rt,
xlab = "Retention Time(s)",
ylab = "m/z",
main = "RSD(%) distribution",
type = "n",
pch = 19,
...
)
for (i in 1:n) {
cex <- as.numeric(cut(rsd[, i], breaks = c(
0, 20,
40, 60, 80, Inf
))) / 2
graphics::points(
x = rt,
y = mz,
ylim = ms,
cex = cex,
col = col[i],
pch = 19
)
}
graphics::legend(
"topright",
legend = dataname,
col = col,
horiz = TRUE,
pch = 19,
bty = "n",
inset = c(0,-0.25)
)
graphics::legend(
"topleft",
legend = cexlab,
pt.cex = c(1,
2, 3, 4, 5) / 2,
pch = 19,
bty = "n",
horiz = TRUE,
cex = 0.8,
col = grDevices::rgb(0, 0, 0, 0.318),
inset = c(0,-0.25)
)
}
}
#' plot the PCA for multiple samples
#' @param data data row as peaks and column as samples
#' @param lv group information
#' @param index index for selected peaks
#' @param center parameters for PCA
#' @param scale parameters for scale
#' @param xrange x axis range for return samples, default NULL
#' @param yrange y axis range for return samples, default NULL
#' @param pch default pch would be the first character of group information or samples name
#' @param ... other parameters for `plot` function
#' @return if xrange and yrange are not NULL, return file name of all selected samples on 2D score plot
#' @examples
#' data(list)
#' plotpca(list$data, lv = as.character(list$group$sample_group))
#' @export
plotpca <- function(data,
lv = NULL,
index = NULL,
center = TRUE,
scale = TRUE,
xrange = NULL,
yrange = NULL,
pch = NULL,
...) {
data <- as.matrix(data)
if (!is.null(index)) {
data <- data[index,]
}
if (is.null(lv)) {
pch0 <- colnames(data)
} else {
pch0 <- lv
}
pcao <-
stats::prcomp(t(data), center = center, scale = scale)
pcaoVars <-
signif(((pcao$sdev) ^ 2) / (sum((pcao$sdev) ^ 2)),
3) * 100
if (!is.null(pch)) {
graphics::plot(
pcao$x[, 1],
pcao$x[, 2],
xlab = paste("PC1:",
pcaoVars[1], "% of Variance Explained"),
ylab = paste("PC2:",
pcaoVars[2], "% of Variance Explained"),
cex = 2,
pch = pch,
...
)
} else{
graphics::plot(
pcao$x[, 1],
pcao$x[, 2],
xlab = paste("PC1:",
pcaoVars[1], "% of Variance Explained"),
ylab = paste("PC2:",
pcaoVars[2], "% of Variance Explained"),
cex = 2,
pch = pch0,
...
)
}
# pch = ifelse(hasArg(pch),pch,pch0)
if (!(is.null(xrange) & is.null(yrange))) {
return(colnames(data)[pcao$x[, 1] > xrange[1] &
pcao$x[, 1] < xrange[2] &
pcao$x[, 2] > yrange[1] &
pcao$x[, 2] < yrange[2]])
}
}
#' Plot the heatmap of mzrt profiles
#' @param data data row as peaks and column as samples
#' @param lv group information
#' @param index index for selected peaks
#' @return NULL
#' @examples
#' data(list)
#' plothm(list$data, lv = as.factor(list$group$sample_group))
#' @export
plothm <- function(data, lv, index = NULL) {
data <- as.matrix(data)
icolors <-
(grDevices::colorRampPalette(rev(
RColorBrewer::brewer.pal(11,
"RdYlBu")
)))(100)
zlim <- range(data)
if (!is.null(index)) {
data <- data[index, order(lv)]
} else {
data <- data[, order(lv)]
}
plotchange <- function(zlim) {
breaks <- seq(zlim[1], zlim[2], round((zlim[2] -
zlim[1]) / 10))
poly <- vector(mode = "list", length(icolors))
graphics::plot(
1,
1,
t = "n",
xlim = c(0, 1),
ylim = zlim,
xaxt = "n",
yaxt = "n",
xaxs = "i",
yaxs = "i",
ylab = "",
xlab = "",
frame.plot = FALSE
)
graphics::axis(
4,
at = breaks,
labels = round(breaks),
las = 1,
pos = 0.4,
cex.axis = 0.8
)
p <- graphics::par("usr")
graphics::text(
p[2] + 2,
mean(p[3:4]),
labels = "intensity",
xpd = NA,
srt = -90
)
bks <-
seq(zlim[1], zlim[2], length.out = (length(icolors) +
1))
for (i in seq(poly)) {
graphics::polygon(
c(0.1, 0.1, 0.3, 0.3),
c(bks[i],
bks[i + 1], bks[i + 1], bks[i]),
col = icolors[i],
border = NA
)
}
}
pos <- cumsum(as.numeric(table(lv) / sum(table(lv)))) -
as.numeric(table(lv) / sum(table(lv))) / 2
posv <-
cumsum(as.numeric(table(lv) / sum(table(lv))))[1:(nlevels(lv) -
1)]
graphics::layout(matrix(rep(c(1, 1, 1, 2), 10), 10,
4, byrow = TRUE))
graphics::par(mar = c(3, 6, 2, 1))
graphics::image(
t(data),
col = icolors,
xlab = "samples",
main = "peaks intensities on log scale",
xaxt = "n",
yaxt = "n",
zlim = zlim
)
graphics::axis(1,
at = pos,
labels = levels(lv),
cex.axis = 0.8)
graphics::axis(
2,
at = seq(0, 1, 1 / (nrow(data) - 1)),
labels = rownames(data),
cex.axis = 1,
las = 2
)
graphics::abline(v = posv)
graphics::par(mar = c(3, 1, 2, 6))
plotchange(zlim)
}
#' plot the density for multiple samples
#' @param data data row as peaks and column as samples
#' @param lv group information
#' @param index index for selected peaks
#' @param name name on the figure for samples
#' @param lwd the line width for density plot, default 1
#' @param ... parameters for `plot` function
#' @return NULL
#' @examples
#' data(list)
#' plotden(list$data, lv = as.character(list$group$sample_group),ylim = c(0,1))
#' @export
plotden <- function(data,
lv,
index = NULL,
name = NULL,
lwd = 1,
...) {
data <- as.matrix(data)
if (!is.null(index)) {
data <- data[index,]
}
xlim <- range(log10(data + 1), na.rm = TRUE)
if (is.null(lv)) {
col <- as.numeric(as.factor(colnames(data)))
coli <- unique(colnames(data))
} else {
col <- as.numeric(as.factor(lv))
coli <- unique(lv)
}
graphics::plot(
1,
1,
typ = 'n',
main = paste0('Metabolites profiles changes in ',
name,
' samples'),
xlab = 'Intensity(log based 10)',
ylab = 'Density',
xlim = c(xlim[1], xlim[2] + 1),
...
)
for (i in 1:(ncol(data))) {
graphics::lines(stats::density(log10(data[, i] + 1)),
col = col[i],
lwd = lwd)
}
graphics::legend(
"topright",
legend = coli,
col = unique(col),
pch = 19,
bty = "n"
)
}
#' Relative Log Abundance (RLA) plots
#' @param data data row as peaks and column as samples
#' @param lv factor vector for the group information
#' @param type 'g' means group median based, other means all samples median based.
#' @param ... parameters for boxplot
#' @return Relative Log Abundance (RLA) plots
#' @examples
#' data(list)
#' plotrla(list$data, as.factor(list$group$sample_group))
#' @export
plotrla <- function(data, lv, type = "g", ...) {
data <- as.matrix(data)
data <- log(data)
data[is.nan(data) | is.infinite(data)] <- 0
outmat <- NULL
if (type == "g") {
for (lvi in levels(lv)) {
submat <- data[, lv == lvi]
median <- apply(submat, 1, median)
tempmat <- sweep(submat, 1, median, "-")
outmat <- cbind(outmat, tempmat)
}
} else {
median <- apply(data, 1, median)
outmat <- sweep(data, 1, median, "-")
}
outmat <- outmat[, order(lv)]
graphics::boxplot(outmat, ...)
graphics::abline(h = 0)
}
#' Relative Log Abundance Ridge (RLAR) plots for samples or peaks
#' @param data data row as peaks and column as samples
#' @param lv factor vector for the group information of samples
#' @param type 'g' means group median based, other means all samples median based.
#' @return Relative Log Abundance Ridge(RLA) plots
#' @examples
#' data(list)
#' plotridges(list$data, as.factor(list$group$sample_group))
#' @export
plotridges <- function(data, lv, type = "g") {
data <- as.matrix(data)
data <- log(data)
data[is.nan(data) | is.infinite(data)] <- 0
outmat <- NULL
if (type == "g") {
for (lvi in levels(lv)) {
submat <- data[, lv == lvi]
if (is.null(dim(submat))) {
tempmat <- submat
} else{
median <- apply(submat, 1, median)
tempmat <-
sweep(submat, 1, median, "-")
}
outmat <- cbind(outmat, tempmat)
}
} else {
median <- apply(data, 1, median)
outmat <- sweep(data, 1, median, "-")
}
plotridge(outmat, lv, xlab = "Relative Log Abundance", ylab = 'Samples')
}
#' plot density weighted intensity for multiple samples
#' @param list list with data as peaks list, mz, rt and group information