/
heatmap.2.py
642 lines (535 loc) · 18.7 KB
/
heatmap.2.py
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"""
a direct translation of R gplots heatmap.2
"""
def heatmap(x,
## dendrogram control
Rowv=TRUE,
Colv=True, # if(symm)"Rowv" else TRUE,
distfun=dist, # find counterpart function in scipy
hclustfun=hclust, # find counterpart function in scipy
dendrogram="both", # c("both","row","column","none"),
symm=FALSE,
## data scaling
scale=None, # c("none","row", "column"),
na.rm=TRUE,
## image plot
revC=True, # identical(Colv, "Rowv"),
add.expr,
## mapping data to colors
breaks,
symbreaks=None, # min(x < 0, na.rm=TRUE) || scale!="none",
## colors
col="heat.colors",
## block sepration
colsep,
rowsep,
sepcolor="white",
sepwidth=(0.05, 0.05), # c(0.05,0.05),
## cell labeling
cellnote,
notecex=1.0,
notecol="cyan",
na.color=par("bg"), # find counterpart function in scipy/matplotlib
## level trace
trace=None, # c("column","row","both","none"),
tracecol="cyan",
hline=median(breaks), # find counterpart function in scipy/matplotlib
vline=median(breaks), # find counterpart function in scipy/matplotlib
linecol=tracecol, # need some work
## Row/Column Labeling
margins=(5,5), # c(5, 5),
ColSideColors,
RowSideColors,
cexRow = 0.2 + 1/log10(nr), # need some work
cexCol = 0.2 + 1/log10(nc), # need some work
labRow=None, # NULL
labCol=None, # NULL
## color key + density info
key=True, # TRUE,
keysize=1.5,
density.info=c("histogram","density","none"), # need some work
denscol=tracecol, # need some work
symkey=min(x < 0, na.rm=TRUE) || symbreaks, # need some work
densadj=0.25,
## plot labels
main=None, # NULL,
xlab=None, # NULL,
ylab=None, # NULL,
## plot layout
lmat=None, # NULL,
lhei=None, # NULL,
lwid=None, # NULL,
):
raise Exception("method not implemented")
#### - - - - - - - original R file - - - - - - - - - ####
## $Id: heatmap.2.R 1463 2010-12-13 16:44:17Z warnes $
heatmap.2 <- function (x,
## dendrogram control
Rowv = TRUE,
Colv=if(symm)"Rowv" else TRUE,
distfun = dist,
hclustfun = hclust,
dendrogram = c("both","row","column","none"),
symm = FALSE,
## data scaling
scale = c("none","row", "column"),
na.rm=TRUE,
## image plot
revC = identical(Colv, "Rowv"),
add.expr,
## mapping data to colors
breaks,
symbreaks=min(x < 0, na.rm=TRUE) || scale!="none",
## colors
col="heat.colors",
## block sepration
colsep,
rowsep,
sepcolor="white",
sepwidth=c(0.05,0.05),
## cell labeling
cellnote,
notecex=1.0,
notecol="cyan",
na.color=par("bg"),
## level trace
trace=c("column","row","both","none"),
tracecol="cyan",
hline=median(breaks),
vline=median(breaks),
linecol=tracecol,
## Row/Column Labeling
margins = c(5, 5),
ColSideColors,
RowSideColors,
cexRow = 0.2 + 1/log10(nr),
cexCol = 0.2 + 1/log10(nc),
labRow = NULL,
labCol = NULL,
## color key + density info
key = TRUE,
keysize = 1.5,
density.info=c("histogram","density","none"),
denscol=tracecol,
symkey = min(x < 0, na.rm=TRUE) || symbreaks,
densadj = 0.25,
## plot labels
main = NULL,
xlab = NULL,
ylab = NULL,
## plot layout
lmat = NULL,
lhei = NULL,
lwid = NULL,
## extras
...
)
{
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) && (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.")
## key & density don't make sense when data is not all on the same scale
## if(scale!="none" && key==TRUE)
## {
## warning("Key cannot be plotted when scale!=\"none\".")
## key=FALSE
## }
if ( is.null(Rowv) || is.na(Rowv) )
Rowv <- FALSE
if ( is.null(Colv) || is.na(Colv) )
Colv <- FALSE
else if( Colv=="Rowv" && !isTRUE(Rowv) )
Colv <- FALSE
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")) {
## Check if Rowv and dendrogram arguments are consistent
if ( ( (!isTRUE(Rowv)) || (is.null(Rowv))) &&
(dendrogram %in% c("both","row") ) )
{
if (is.logical(Colv) && (Colv))
dendrogram <- "column"
else
dedrogram <- "none"
warning("Discrepancy: Rowv is FALSE, while dendrogram is `",
dendrogram, "'. Omitting row dendogram.")
}
}
if(!inherits(Colv, "dendrogram")) {
## Check if Colv and dendrogram arguments are consistent
if ( ( (!isTRUE(Colv)) || (is.null(Colv)))
&& (dendrogram %in% c("both","column")) )
{
if (is.logical(Rowv) && (Rowv))
dendrogram <- "row"
else
dendrogram <- "none"
warning("Discrepancy: Colv is FALSE, while dendrogram is `",
dendrogram, "'. Omitting column dendogram.")
}
}
## by default order by row/col mean
## if(is.null(Rowv)) Rowv <- rowMeans(x, na.rm = na.rm)
## if(is.null(Colv)) Colv <- colMeans(x, na.rm = na.rm)
## get the dendrograms and reordering indices
## if( dendrogram %in% c("both","row") )
## { ## dendrogram option is used *only* for display purposes
if(inherits(Rowv, "dendrogram"))
{
ddr <- Rowv ## use Rowv 'as-is', when it is dendrogram
rowInd <- order.dendrogram(ddr)
}
else if (is.integer(Rowv))
{ ## Compute dendrogram and do reordering based on given vector
hcr <- hclustfun(distfun(x))
ddr <- as.dendrogram(hcr)
ddr <- reorder(ddr, Rowv)
rowInd <- order.dendrogram(ddr)
if(nr != length(rowInd))
stop("row dendrogram ordering gave index of wrong length")
}
else if (isTRUE(Rowv))
{ ## If TRUE, compute dendrogram and do reordering based on rowMeans
Rowv <- rowMeans(x, na.rm = na.rm)
hcr <- hclustfun(distfun(x))
ddr <- as.dendrogram(hcr)
ddr <- reorder(ddr, Rowv)
rowInd <- order.dendrogram(ddr)
if(nr != length(rowInd))
stop("row dendrogram ordering gave index of wrong length")
} else {
rowInd <- nr:1
}
## if( dendrogram %in% c("both","column") )
## {
if(inherits(Colv, "dendrogram"))
{
ddc <- Colv ## use Colv 'as-is', when it is dendrogram
colInd <- order.dendrogram(ddc)
}
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))
{## Compute dendrogram and do reordering based on given vector
hcc <- hclustfun(distfun(if(symm)x else t(x)))
ddc <- as.dendrogram(hcc)
ddc <- reorder(ddc, Colv)
colInd <- order.dendrogram(ddc)
if(nc != length(colInd))
stop("column dendrogram ordering gave index of wrong length")
}
else if (isTRUE(Colv))
{## If TRUE, compute dendrogram and do reordering based on rowMeans
Colv <- colMeans(x, na.rm = na.rm)
hcc <- hclustfun(distfun(if(symm)x else t(x)))
ddc <- as.dendrogram(hcc)
ddc <- reorder(ddc, Colv)
colInd <- order.dendrogram(ddc)
if(nc != length(colInd))
stop("column dendrogram ordering gave index of wrong length")
}
else
{
colInd <- 1:nc
}
retval$rowInd <- rowInd
retval$colInd <- colInd
retval$call <- match.call()
## reorder x & cellnote
x <- x[rowInd, colInd]
x.unscaled <- x
cellnote <- cellnote[rowInd, colInd]
if(is.null(labRow))
labRow <- if(is.null(rownames(x))) (1:nr)[rowInd] else rownames(x)
else
labRow <- labRow[rowInd]
if(is.null(labCol))
labCol <- if(is.null(colnames(x))) (1:nc)[colInd] else colnames(x)
else
labCol <- labCol[colInd]
if(scale == "row") {
retval$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, "/")
}
## Set up breaks and force values outside the range into the endmost bins
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
## Calculate the plot layout
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)) { ## add middle row to layout
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)) { ## add middle column to layout
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))
## Graphics `output' -----------------------
op <- par(no.readonly = TRUE)
on.exit(par(op))
layout(lmat, widths = lwid, heights = lhei, respect = FALSE)
## draw the side bars
if(!missing(RowSideColors)) {
par(mar = c(margins[1],0, 0,0.5))
image(rbind(1:nr), col = RowSideColors[rowInd], axes = FALSE)
}
if(!missing(ColSideColors)) {
par(mar = c(0.5,0, 0,margins[2]))
image(cbind(1:nc), col = ColSideColors[colInd], axes = FALSE)
}
## draw the main carpet
par(mar = c(margins[1], 0, 0, margins[2]))
#if(scale != "none" || !symm)
# {
x <- t(x)
cellnote <- t(cellnote)
# }
if(revC)
{ ## x columns reversed
iy <- nr:1
if(exists("ddr"))
ddr <- rev(ddr)
x <- x[,iy]
cellnote <- cellnote[,iy]
}
else iy <- 1:nr
## display the main carpet
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
## fill 'na' positions with na.color
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)
}
## add labels
axis(1, 1:nc, labels= labCol, las= 2, line= -0.5, tick= 0, cex.axis= cexCol)
if(!is.null(xlab)) mtext(xlab, side = 1, line = margins[1] - 1.25)
axis(4, iy, labels= labRow, las= 2, line= -0.5, tick= 0, cex.axis= cexRow)
if(!is.null(ylab)) mtext(ylab, side = 4, line = margins[2] - 1.25)
## perform user-specified function
if (!missing(add.expr))
eval(substitute(add.expr))
## add 'background' colored spaces to visually separate sections
if(!missing(colsep))
for(csep in colsep)
rect(xleft =csep+0.5, ybottom=rep(0,length(csep)),
xright=csep+0.5+sepwidth[1], ytop=rep(ncol(x)+1,csep),
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)
## show traces
min.scale <- min(breaks)
max.scale <- max(breaks)
x.scaled <- scale01(t(x), min.scale, max.scale)
if(trace %in% c("both","column") )
{
retval$vline <- vline
vline.vals <- scale01(vline, min.scale, max.scale)
for( i in colInd )
{
if(!is.null(vline))
{
abline(v=i-0.5 + vline.vals, col=linecol, lty=2)
}
xv <- rep(i, nrow(x.scaled)) + x.scaled[,i] - 0.5
xv <- c(xv[1], xv)
yv <- 1:length(xv)-0.5
lines(x=xv, y=yv, lwd=1, col=tracecol, type="s")
}
}
if(trace %in% c("both","row") )
{
retval$hline <- hline
hline.vals <- scale01(hline, min.scale, max.scale)
for( i in rowInd )
{
if(!is.null(hline))
{
abline(h=i + hline, 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)
## the two dendrograms :
par(mar = c(margins[1], 0, 0, 0))
if( dendrogram %in% c("both","row") )
{
plot(ddr, horiz = TRUE, axes = FALSE, yaxs = "i", leaflab = "none")
}
else
plot.new()
par(mar = c(0, 0, if(!is.null(main)) 5 else 0, margins[2]))
if( dendrogram %in% c("both","column") )
{
plot(ddc, axes = FALSE, xaxs = "i", leaflab = "none")
}
else
plot.new()
## title
if(!is.null(main)) title(main, cex.main = 1.5*op[["cex.main"]])
## Add the color-key
if(key)
{
par(mar = c(5, 4, 2, 1), cex=0.75)
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(x, na.rm=TRUE) ## Again, modified to use scaled
max.raw <- max(x, na.rm=TRUE) ## or unscaled (SD 12/2/03)
}
z <- seq(min.raw, max.raw, length=length(col))
image(z=matrix(z, ncol=1),
col=col, breaks=tmpbreaks,
xaxt="n", yaxt="n")
par(usr=c(0,1,0,1))
lv <- pretty(breaks)
xv <- scale01(as.numeric(lv), min.raw, max.raw)
axis(1, at=xv, labels=lv)
if(scale=="row")
mtext(side=1,"Row Z-Score", line=2)
else if(scale=="column")
mtext(side=1,"Column Z-Score", line=2)
else
mtext(side=1,"Value", line=2)
if(density.info=="density")
{
## Experimental : also plot density of data
dens <- density(x, adjust=densadj, na.rm=TRUE)
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$y) * 0.95, col=denscol, lwd=1)
axis(2, at=pretty(dens$y)/max(dens$y) * 0.95, pretty(dens$y) )
title("Color Key\nand Density Plot")
par(cex=0.5)
mtext(side=2,"Density", line=2)
}
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$counts)])
lines(hx, hy/max(hy)*0.95, lwd=1, type="s", col=denscol)
axis(2, at=pretty(hy)/max(hy) * 0.95, pretty(hy) )
title("Color Key\nand Histogram")
par(cex=0.5)
mtext(side=2,"Count", line=2)
}
else
title("Color Key")
}
else
plot.new()
## Create a table showing how colors match to (transformed) data ranges
retval$colorTable <- data.frame(
low=retval$breaks[-length(retval$breaks)],
high=retval$breaks[-1],
color=retval$col
)
invisible( retval )
}