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cmonkey-plotting.R
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cmonkey-plotting.R
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###################################################################################
## cMonkey - version 4, Copyright (C) David J Reiss, Institute for Systems Biology
## This software is provided AS IS with no warranty expressed or implied. Neither
## the authors of this software nor the Institute for Systems Biology shall be held
## liable for anything that happens as a result of using this software
###################################################################################
## resids.in.out <- function( cluster ) {
## out <- rep( 1, 2 ); names( out ) <- c( "in", "out" )
## if ( length( cluster$nrows ) <= 0 || is.null( cluster$nrows ) || cluster$nrows <= 1 ) return( out )
## if ( cluster$ncol > 1 )
## out[ "in" ] <- residual.submatrix( ratios[ cluster$rows, cluster$cols ] )
## if ( sum( ! attr( ratios, "cnames" ) %in% cluster$cols ) > 1 )
## out[ "out" ] <- residual.submatrix( ratios[ cluster$rows, attr( ratios, "cnames" )[ ! attr( ratios, "cnames" ) %in% cluster$cols ] ] )
## return( out )
## }##,
###############################
plotCluster <- function( cluster, imag=F, cond.labels=F, o.genes=NULL, col.func=if(imag) topo.colors else rainbow,
rats.names=names( ratios ), main=NULL, range.r=NULL, no.par=F, sort=F, box.plot=F, ... ) { ##, title=NULL, for.figure=F ) {##regulators=NULL,
if ( length( cluster$rows ) <= 0 ) { warning( "Trying to plot a cluster with no rows!" ); return() }
k <- cluster$k
if ( is.null( main ) ) main <- paste( sprintf( "Cluster: %04d %s; resid: %s; r/c: %d/%d", k, organism,
paste( sprintf( "%.2f", cluster$resid[ rats.names ] ), collapse=" " ),
length( cluster$rows ), length( cluster$cols ) ) )
rats <- get.cluster.matrix( unique( c( cluster$rows, o.genes ) ), cluster$cols, matrices=rats.names )
cols.b <- colnames( rats )[ colnames( rats ) %in% cluster$cols ]
if ( sort ) {
o1 <- order( apply( rats[ cluster$rows, cols.b, drop=F ], 2, mean, na.rm=T ) )
cols.b <- cols.b[ o1 ]
rats <- rats[ ,cols.b, drop=F ]
}
if ( all( is.na( rats ) ) ) { plot( 0, 0, typ="n", min=main, ... ); return() }
if ( is.vector( rats ) ) { rats <- t( rats ); rownames( rats ) <- cluster$rows }
if ( imag ) {
grey.image <- function( mat, n.gray=32, x=1:nrow(mat), y=1:ncol( mat ), col=gray((0:n.gray)/n.gray), ... )
image( x, y, mat, col=col, ... )
grey.image( t( rats ), col=col.func( 256 ) )
return()
}
##if ( renorm ) rats <- standardize.row( rats, NULL, 0 )
##if ( "ylim" %in% names( list( ... ) ) ) range.r <- ylim
##!else
if ( is.null( range.r ) ) ##range.r <- range( rats, na.rm=T )
range.r <- range( rats[ rats != min( rats, na.rm=T ) & rats != max( rats, na.rm=T ) ], na.rm=T )
if ( cond.labels && cluster$ncols < 100 ) range.r[ 1 ] <- range.r[ 1 ] * 1.5
if ( ! no.par ) ##&& ! cond.labels )
par( mar=rep(2.0,4), mgp=c(3,1,0)*0.5 )##; on.exit( par( old.pars ) )
plot( 1:length( cols.b ), ylim=range.r, xlab=NA, ylab=NA, main=main, typ="n", xaxs="i", ... )
if ( length( rats.names ) > 1 ) { ## Shade different ratios matrices backgrounds
ind <- 0.5
rts <- NULL
for ( i in 1:length( rats.names ) ) {
col <- sapply( col2rgb( i+1 ) / 255 + 0.9, function( cc ) min( cc, 1 ) )
col <- rgb( col[ 1 ], col[ 2 ], col[ 3 ] )
rect( ind, range.r[1]+0.05, ind+sum( colnames( rats ) %in% colnames( ratios[[ rats.names[ i ] ]] ) ),
range.r[2]-0.05, col=col, dens=NA )
ind <- ind + sum( colnames( rats ) %in% colnames( ratios[[ rats.names[ i ] ]] ) )
rts <- cbind( rts, rats[ ,cols.b[ cols.b %in% colnames( ratios[[ rats.names[ i ] ]] ), drop=F ] ] )
}
rats <- rts; rm( rts )
cols.b <- colnames( rats )
}
if ( exists( "col.rug" ) ) {
if ( is.integer( col.rug ) ) colmap <- col.func( max( col.rug ) )[ col.rug[ cols.b ] ]
else colmap <- col.rug[ cols.b ]
} else if ( all( deparse( col.func ) == deparse( rainbow ) ) ) {
colmap <- col.func( length( cols.b ) ) ##rep( 'black', length( cols.b ) )
} else {
colmap <- col.func( cols.b )
}
##colmap <- rug1[ cols.b, 2 ]
##!else
if ( box.plot ) {
colMeans <- apply( rats[ cluster$rows, ,drop=F ], 2, mean, na.rm=T )
colSd <- apply( rats[cluster$rows, ,drop=F ], 2, sd, na.rm=T )
matlines( 1:length( cols.b ), cbind( colMeans - 2 * colSd, colMeans + 2 * colSd ), lty=1, col='lightgrey' )
boxplot( as.data.frame( rats[ cluster$rows, ,drop=F ] ), ylim=range.r,
names=NA, main=main, col=colmap, outline=FALSE, border=FALSE, add=T, xaxs="i", xaxt="n", ... )
if ( sort ) lines( 1:length( cols.b ), colMeans, lty=1, lwd=1, col='red' )
} else {
cmap <- col.func( cluster$nrows )
matlines( 1:length( cols.b ), t( rats[ cluster$rows, ,drop=F ] ), ylim=range.r, xlab=NA, ylab=NA, main=main,
col=cmap, lty=1, ... ) ##typ="l",
if ( exists( "col.rug" ) ) for ( i in unique( col.rug ) )
rug( which( cols.b %in% names( which( col.rug == i ) ) ), col=colmap[ which( col.rug == i )[ 1 ] ] )
}
if ( cond.labels ) {
tmp.y <- rep( range.r[1] * 0.85, cluster$ncols )
cols <- if ( box.plot ) colmap else 'black'
text( 1:cluster$ncols, tmp.y, cols.b, srt=90, col=cols, ... )
}
if ( names( dev.cur() ) == "devSVG" ) {
par( family="Arial" )
for ( c in 1:length( cols.b ) ) {
setSVGShapeToolTip( cols.b[ c ] )
rect( c, range.r[1], c+1, range.r[2], col=NA, border=NA )
}
}
if ( ! is.null( o.genes ) ) {
matlines( 1:length( cols.b ), t( rats[ o.genes, , drop=F ] ), lty=1, lwd=3, col=2:6 )
legend( "bottomright", legend=o.genes, lty=1, lwd=3, col=2:6, cex=0.7, bty="n" )
}
}
###############################
plotCluster.all.conds <- function( cluster, imag=F, cond.labels=F, o.genes=NULL, rats.names=names( ratios ),
range.r=NULL, sort=F, box.plot=F, col.func=if( imag ) topo.colors else rainbow, ... ) {
if ( length( cluster$rows ) <= 0 ) { warning( "Trying to plot a cluster with no rows!" ); return() }
k <- cluster$k
main <- paste( sprintf( "Cluster: %04d %s; resid: %s; r/c: %d/%d", k, organism,
paste( sprintf( "%.2f", cluster$resid[ rats.names ] ), collapse=" " ),
length( cluster$rows ), length( cluster$cols ) ) )
rats <- get.cluster.matrix( unique( c( cluster$rows, o.genes ) ), NULL, matrices=rats.names ) ##sort( cluster$cols )
cols.b <- c( colnames( rats )[ colnames( rats ) %in% cluster$cols ],
colnames( rats )[ ! colnames( rats ) %in% cluster$cols ] )
##cols.b <- attr( ratios, "cnames" )[ attr( ratios, "cnames" ) %in% cluster$cols ]
##cols.b <- c( cols.b, attr( ratios, "cnames" )[ ! attr( ratios, "cnames" ) %in% cluster$cols ] )
##rats <- get.cluster.matrix( cluster$rows, cols.b, matrices=rats.names )
if ( sort ) {
inClust <- colnames( rats )[ colnames( rats ) %in% cluster$cols ]
o1 <- order( apply( rats[ cluster$rows, inClust ,drop=F ], 2, mean, na.rm=T ) )
outClust <- colnames( rats )[ ! colnames( rats ) %in% cluster$cols ]
o2 <- order( apply( rats[ cluster$rows, outClust ,drop=F ], 2, mean, na.rm=T ) )
cols.b <- c( inClust[ o1 ], outClust[ o2 ] )
}
len.b <- length( cols.b )
rats <- rats[ ,cols.b ,drop=F ]
par( mar=rep(2.0,4), mgp=c(3,1,0)*0.5 )
if ( all( is.na( rats ) ) ) { plot( 0, 0, typ="n", main=main, ... ); return() }
if ( is.vector( rats ) ) { rats <- t( rats ); rownames( rats ) <- cluster$rows }
if ( imag ) {
grey.image( t( rats ), col=col.func( 256 ) )
lines( rep( cluster$ncols + 0.5, 2 ), c( -999, 9999 ), col=2, lwd=3, lty=2 )
return()
}
if ( is.null( range.r ) )
range.r <- range( rats[ rats != min( rats, na.rm=T ) & rats != max( rats, na.rm=T ) ], na.rm=T )
if ( cond.labels && len.b < 100 ) range.r[ 1 ] <- range.r[ 1 ] * 1.5
plot( 1:len.b, xlim=c( 0.95, len.b+0.05 ), ylim=range.r, xlab=NA, ylab=NA, main=main, typ="n", xaxs="i", ... )
if ( length( ratios ) > 1 ) { ## Shade different ratios matrices backgrounds
ind <- 0.5
rts.in <- rts.out <- NULL
for ( in.out in 1:2 ) {
if ( in.out == 1 ) cols <- cols.b[ cols.b %in% cluster$cols ]
else if ( in.out == 2 ) cols <- cols.b[ ! cols.b %in% cluster$cols ]
for ( i in 1:length( ratios ) ) {
col <- sapply( col2rgb( i+1 ) / 255 + 0.9, function( cc ) min( cc, 1 ) )
col <- rgb( col[ 1 ], col[ 2 ], col[ 3 ] )
rect( ind, range.r[1]+0.01, ind+sum( cols %in% colnames( ratios[[ i ]] ) ), range.r[2]-0.01, col=col,
dens=NA )
ind <- ind + sum( cols %in% colnames( ratios[[ i ]] ) )
if ( in.out == 1 ) rts.in <- cbind( rts.in, rats[ ,cols[ cols %in% colnames( ratios[[ i ]] ) ] ,drop=F ] )
else if ( in.out == 2 ) rts.out <- cbind( rts.out, rats[ ,cols[ cols %in% colnames( ratios[[ i ]] ) ] ,drop=F ] )
}
}
rats <- cbind( rts.in, rts.out )
cols.b <- colnames( rats )
rm( rts.in, rts.out )
}
if ( exists( "col.rug" ) ) {
if ( is.integer( col.rug ) ) colmap <- col.func( max( col.rug ) )[ col.rug[ cols.b ] ]
else colmap <- col.rug[ cols.b ]
} else if ( all( deparse( col.func ) == deparse( rainbow ) ) ) {
colmap <- col.func( length( cols.b ) ) ##rep( 'black', length( cols.b ) )
} else {
colmap <- col.func( cols.b )
}
##colmap <- rug1[ cols.b, 2 ]
##!else
if ( box.plot ) {
##if ( exists( "rug1" ) ) colmap <- rug1[ cols.b, 2 ]
##!else colmap <- rep( 'black', length( cols.b ) )
## if ( exists( "col.rug" ) ) colmap <- col.func( max( col.rug ) )[ col.rug[ cols.b ] ]
## else if ( all( deparse( col.func ) == deparse( rainbow ) ) ) colmap <- col.func( len.b ) ##rep( 'black', length( cols.b ) )
## else colmap <- col.func( cols.b )
colMeans <- apply( rats[ cluster$rows, ,drop=F ], 2, mean, na.rm=T )
colSd <- apply( rats[cluster$rows, ,drop=F ], 2, sd, na.rm=T )
matlines( 1:length( cols.b ), cbind( colMeans - 2 * colSd, colMeans + 2 * colSd ), lty=1, col='lightgrey' )
boxplot( as.data.frame( rats[ cluster$rows, ,drop=F ] ), ylim=range.r,
names=NA, main=main, col=colmap, outline=FALSE, border=FALSE, add=T, xaxs="i", xaxt="n", ... )
if ( sort ) lines( 1:length( cols.b ), colMeans, lty=1, lwd=1, col='red' ) ## This hides the boxes if not sorted
} else {
cmap <- col.func( cluster$nrows )
matlines( 1:len.b, t( rats[ cluster$rows, ,drop=F ] ), ylim=range.r, col=cmap, main=main, ##type="l",
xlab=NA, ylab=NA, lty=1, ... )
if ( exists( "col.rug" ) ) for ( i in unique( col.rug ) )
rug( which( cols.b %in% names( which( col.rug == i ) ) ), col=colmap[ which( col.rug == i )[ 1 ] ] )
}
##lines( rep( cluster$ncols + 0.5, 2 ), range.r, col=2, lwd=3, lty=2 )
cols.in <- colnames( rats )[ colnames( rats ) %in% cluster$cols ]
lines( rep( length( cols.in ) + 0.5, 2 ), range.r, col=2, lwd=3, lty=2 )
if ( ! is.null( o.genes ) ) {
matlines( 1:len.b, t( rats[ o.genes, , drop=F ] ), lty=1, lwd=3, col=2:6 )
legend( "bottomright", legend=o.genes, lty=1, lwd=3, col=2:6, cex=0.7, bty="n" )
}
if ( cond.labels ) { ## || len.b < 100 ) {
tmp.y <- rep( range.r[1] * 0.85, len.b ) ##(1:len.b / 1:len.b ) * range.r[1] * 0.85
cols <- if ( box.plot ) colmap else 'black'
text( 1:len.b, tmp.y, cols.b, srt=90, col=cols, ... ) ##cex=0.7 )
}
if ( names( dev.cur() ) == "devSVG" ) {
par( family="Arial" )
for ( c in 1:length( cols.b ) ) {
setSVGShapeToolTip( cols.b[ c ] )
rect( c, range.r[1], c+1, range.r[2], col=NA, border=NA ) ##, xpd=NA )
}
}
}
plotCluster.motif <- function( cluster, seqs=cluster$seqs, layout=NULL, colors=NULL, motif.e.cutoff=Inf,
no.plotCluster=T, addl.text=NULL, ... ) {
if ( length( cluster$rows ) <= 0 ) { warning( "Trying to plot a cluster with no rows!" ); return() }
if ( names( dev.cur() ) == "devSVG" ) par( family="Arial" )
if ( any( ! cluster$rows %in% attr( ratios, "rnames" ) ) ) {
cluster$rows <- cluster$rows[ cluster$rows %in% attr( ratios, "rnames" ) ]
cluster$nrows <- length( cluster$rows )
warning( cluster$k, ": Some cluster rows are not in the ratios. Will plot without these rows.\n" )
}
if ( any( ! cluster$cols %in% attr( ratios, "cnames" ) ) ) {
cluster$cols <- cluster$cols[ cluster$cols %in% attr( ratios, "cnames" ) ]
cluster$ncols <- length( cluster$cols )
warning( cluster$k, ": Some cluster cols are not in the ratios. Will plot without these cols.\n" )
}
seq.types <- cluster$seq.type
if ( length( seq.types ) == 1 ) {
n.pssm.plot <- 3
} else {
n.pssm.plot <- 6
}
if ( is.null( layout ) ) {
if ( length( seq.types ) == 1 ) {
layout <- matrix( c( 1, 1, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 2, 2, 2, 2, 2,
1, 1, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 2, 2, 2, 2, 2,
3, 3, 3, 3, 4, 4, 4, 4, 7, 7, 7, 7, 7, 7, 7, 7, 7,
5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7 ), ncol=17, byrow=T )
} else { ## Allow for up to 3 seq.type motifs (2 each type)
layout <- matrix( c( 1, 1, 1, 1, 1, 1, 1, 1, 11, 2, 2, 2, 2, 2, 2, 2, 2,
1, 1, 1, 1, 1, 1, 1, 1, 11, 2, 2, 2, 2, 2, 2, 2, 2,
1, 1, 1, 1, 1, 1, 1, 1, 11, 2, 2, 2, 2, 2, 2, 2, 2,
1, 1, 1, 1, 1, 1, 1, 1, 11, 2, 2, 2, 2, 2, 2, 2, 2,
3, 3, 3, 3, 4, 4, 4, 4, 10, 10, 10, 10, 10, 10, 10, 10, 10,
5, 5, 5, 5, 6, 6, 6, 6, 10, 10, 10, 10, 10, 10, 10, 10, 10,
7, 7, 7, 7, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10,
8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10 ), ncol = 17, byrow = T )
}
if ( no.plotCluster ) { ## Dont include panel with plot of cluster with only in-cols (plotCluster())
layout[ layout == 1 | layout == max( layout ) ] <- 2
layout <- layout - 1
layout[ ,1 ][ layout[ ,1 ] == 1 ] <- max( layout ) + 1
}
}
layout( layout )
k <- cluster$k
if ( ! is.null( ratios ) ) {
args <- list( ... )
args <- args[ names( args ) != "p.val.shade.cutoff" ] ## To prevent the warning:
args$cluster <- cluster ## 'In plot.window(...) : "p.val.shade.cutoff" is not a graphical parameter'
do.call( plotCluster.all.conds, args ) ##o.genes=o.genes, ... )
if ( ! no.plotCluster ) do.call( plotCluster, args ) ##o.genes=o.genes, ... )
}
rows <- cluster$rows
if ( is.null( colors ) ##&& exists( "gene.coords" )
|| ! is.null( cluster$cog.code ) ) {
tmp.lett <- 1:26
names( tmp.lett ) <- LETTERS
##if ( exists( "gene.coords" ) && ! is.null( gene.coords$gene.code ) ) coo <- gene.coords$gene.code[ rows ]
##!else
if ( ! is.null( cluster$cog.code ) ) coo <- cluster$cog.code[ rows ]
else coo <- 1:length( rows )
tmp <- unique( tmp.lett[ coo ] )
names( tmp ) <- names( tmp.lett[ coo ][ ! duplicated( tmp.lett[ coo ] ) ] )
cols <- rainbow( length( tmp ) )
names( cols ) <- names( tmp )
cols <- cols[ names( tmp.lett[ coo ] ) ]
cols[ is.na( names( cols ) ) ] <- "darkgrey"
names( cols ) <- rows
} else {
cols <- rainbow( length( rows ) )
names( cols ) <- rows
}
colors <- cols
##if ( length( seq.types ) > 1 ) seq.types <- seq.types[ 1:3 ]
n.plotted <- 1
for ( seq.type in seq.types ) {
if ( n.plotted > n.pssm.plot ) break
##if ( is.na( seq.type ) ||
if ( is.null( cluster[[ seq.type ]]$e.val ) ||
all( is.na( cluster[[ seq.type ]]$e.val ) ) || is.null( cluster[[ seq.type ]]$motif.out ) || ##min( cluster$e.val, na.rm=T ) > min.e.val ||
##all( is.na( cluster[[ seq.type ]]$p.clust ) ) ||
is.null( cluster[[ seq.type ]]$motif.out$pssms ) ) next
## for ( i in 1:2 ) plot( 1, 1, typ="n", axes=F, xaxt="n", yaxt="n", xlab="", ylab="" )
## } else {
pssm <- cluster[[ seq.type ]]$motif.out$pssms
for ( ppp in 1:min( floor( n.pssm.plot / length( seq.types ) ), length( pssm ) ) ) {
if ( n.plotted > n.pssm.plot ) break
##opar <- par( mar=rep(0.5,4), mgp=c(3,1,0)*0.5 )##; on.exit( par( old.pars ) )
if ( cluster[[ seq.type ]]$motif.out$e.values[ ppp ] > motif.e.cutoff ) next
viewPssm( pssm[[ ppp ]], mot.ind=ppp, main.title=sprintf( "%s PSSM #%d; e=%.3g", seq.type, ppp,
cluster[[ seq.type ]]$motif.out$e.values[ ppp ] ), cex.main=0.9 )
n.plotted <- n.plotted + 1
##par( opar )
}
## if ( length( pssm ) < 2 ) for ( i in ( length( pssm ) + 1 ):2 )
## plot( 1, 1, typ="n", axes=F, xaxt="n", yaxt="n", xlab="", ylab="" )
##}
##if ( length( seq.types ) == 1 ) plot( 1, 1, typ="n", axes=F, xaxt="n", yaxt="n", xlab="", ylab="" )
}
while ( n.plotted <= n.pssm.plot ) {
plot( 1, 1, typ="n", axes=F, xaxt="n", yaxt="n", xlab="", ylab="" )
n.plotted <- n.plotted + 1
}
suppressWarnings( cluster <- plotCluster.network( cluster, ... ) ) ## suppress printing of confusing internal igraph warnings
##seqs <- cluster$seqs
if ( is.null( seqs ) ) { seqs <- rep( "", length( cluster$rows ) ); names( seqs ) <- cluster$rows }
if ( ! is.null( seq.type ) && ! is.null( seqs ) && length( seqs ) > 0 )
plotClusterMotifPositions( cluster, seqs, colors=colors, ... ) ##o.genes=o.genes,
##p.val.shade.cutoff=p.val.shade.cutoff, ... )
else plot( 1, 1, typ="n", axes=F, xaxt="n", yaxt="n", xlab="", ylab="" )
try ( {
par( mar=rep(0.5,4), mgp=c(2,1,0)*0.5 )##; on.exit( par( old.pars ) )
plot( c( 0.5, 2.5 ), c( -1, 1 ), type="n", tck=0.01, cex.lab=0.2, cex.sub=0.2, cex.axis=0.2, axes=F )
if ( names( dev.cur() ) == "devSVG" ) {
par( family="Arial" )
setSVGShapeToolTip( title=paste( "Cluster:", sprintf( "%04d", k ), organism, cmonkey.version ),
desc1=sprintf( "resid = %s; genes = %d; conds = %d",
paste( sprintf( "%.2f", cluster$resid ), collapse=" " ),
length( cluster$rows ), length( cluster$cols ) ) )
setSVGShapeURL( paste( "http://www.genome.ad.jp/dbget-bin/www_bget?",
paste( organism, ":", cluster$rows, sep="", collapse="+" ), sep="" ) )
rect( 0.5, -1, 3.25, +1, col="lightgreen", border=NA )
}
} )
##if ( names( dev.cur() ) != "devSVG" ) { ## TODO: this dies on the SVG setting - fix it!?
if ( ! is.null( cluster$name ) ) text( 0.65, 0, cluster$name, srt=90, xpd=NA, cex=1 )
else if ( ! is.null( addl.text ) ) text( 0.65, 0, addl.text, srt=90, xpd=NA, cex=1 )
text( 1.5, 0, sprintf( "%s iter=%d", date.run, iter ), srt=90, xpd=NA, cex=1 )
text( 2.35, 0, paste( "cMonkey Version", cmonkey.version, organism ), srt=90, xpd=NA, cex=1 )
##}
invisible( cluster )
##if ( ! is.null( layout ) && exists( "plotCluster.custom" ) ) plotCluster.custom( cluster, seqs=cluster$seqs, ... )
}
plotCluster.network <- function( cluster, network="all", o.genes=NULL, colors=NULL, cex=0.7, no.legend=F, ... ) {
if ( length( cluster$rows ) <= 0 ) { warning( "Trying to plot a cluster with no rows!" ); return() }
require( igraph )
rows <- cluster$rows
if ( is.null( cluster$network ) ) {
if ( network == "all" ) network <- names( networks )
for ( i in network ) {
if ( ! i %in% names( networks ) ) next
tmp.net <- networks[[ i ]][ networks[[ i ]]$protein1 %in% rows & networks[[ i ]]$protein2 %in% rows, ]
tmp.net <- cbind( tmp.net, net=rep( i, nrow( tmp.net ) ) )
cluster$network <- if ( ! is.null( cluster$network ) ) rbind( cluster$network, tmp.net ) else tmp.net
}
}
network <- cluster$network
nrows <- rows; if ( ! is.null( o.genes ) ) nrows <- c( nrows, o.genes )
if ( is.null( cluster$cog.code ) && "cog.code" %in% names( genome.info ) )
cluster$cog.code <- genome.info$cog.code[ rows ]
if ( is.null( cluster$colors ) ) {
if ( is.null( colors ) ##&& exists( "gene.coords" )
|| "cog.code" %in% names( genome.info ) ) {
tmp.lett <- 1:26
names( tmp.lett ) <- LETTERS
##if ( exists( "gene.coords" ) && ! is.vector( gene.coords ) && ! is.null( gene.coords$gene.code ) )
## coo <- gene.coords$gene.code[ rows ]
##!else
if ( ! is.null( cluster$cog.code ) ) coo <- cluster$cog.code[ rows ]
else coo <- 1:length( rows )
tmp <- unique( tmp.lett[ coo ] )
names( tmp ) <- names( tmp.lett[ coo ][ ! duplicated( tmp.lett[ coo ] ) ] )
cols <- rainbow( length( tmp ) )
names( cols ) <- names( tmp )
cols <- cols[ names( tmp.lett[ coo ] ) ]
cols[ is.na( names( cols ) ) ] <- "darkgrey"
names( cols ) <- rows
cluster$colors <- cols
} else {
cols <- rainbow( length( rows ) )
names( cols ) <- rows
cluster$colors <- cols
}
}
colors <- cluster$colors
if ( is.null( network ) || nrow( network ) <= 0 )
network <- data.frame( protein1=nrows, protein2=nrows, combined_score=jitter( rep( 1/50, length( nrows ) ) ),
net=rep( "none", length( nrows ) ) )
not.in <- nrows[ ! nrows %in% network$protein1 & ! nrows %in% network$protein2 ]
for ( i in not.in ) network <- rbind( network, data.frame( protein1=i, protein2=i, combined_score=0, net="none" ) )
gr <- graph.edgelist( as.matrix( network[ ,1:2 ] ), directed=F )
net.wts <- as.numeric( network$combined_score ); names( net.wts ) <- as.character( network$net )
for ( n in unique( names( net.wts ) ) ) {
if ( n == "none" ) next
net.wts[ names( net.wts ) == n ] <- net.wts[ names( net.wts ) == n ] /
max( net.wts[ names( net.wts ) == n ], na.rm=T )
}
gr.layout <- layout.fruchterman.reingold( gr, niter=3000, weights=net.wts / 5 ) ##network$combined_score / 5000 )
gr.layout <- layout.norm( gr.layout, -1, 1, -1, 1 )
edge.colors <- character(); curves <- rep( 0, nrow( network ) )
nets <- unique( as.character( network$net ) )
if ( "none" %in% nets ) {
nets <- unique( c( "none", nets ) )
inds <- c( 1, 1:( length( nets ) - 1 ) ); inds <- inds[ inds != 0 ]; inds <- inds[ 1:length( nets ) ]
net.colors <- t( col2rgb( inds, T ) ) / 255
net.colors[ 1, 4 ] <- 0
} else {
net.colors <- t( col2rgb( 1:length( nets ), T ) ) / 255
}
rownames( net.colors ) <- nets
for ( i in 1:nrow( network ) ) {
net <- as.character( network$net )[ i ]
shade <- net.wts[ i ] ##network$combined_score[ i ] / 1000
nodes <- c( as.character( network$protein1 )[ i ], as.character( network$protein2 )[ i ] )
sub.net <- subset( network,protein1 %in% nodes & protein2 %in% nodes )
sub.nets <- unique( as.character( sub.net$net ) )
curve.it <- max( 0, nrow( sub.net ) - 2 ) / 2 * 0.33
col <- net.colors[ net, ]
col2 <- col; col2[ col2 == 0 ] <- 1 - shade
edge.colors[ i ] <- if ( names( dev.cur() ) != "X11" ) rgb( col[ 1 ], col[ 2 ], col[ 3 ], shade ) else
rgb( col2[ 1 ], col2[ 2 ], col2[ 3 ] )
curves[ i ] <- curve.it * floor( which( sub.nets == net ) / 2 ) *
( if ( which( sub.nets == net ) %% 2 == 0 ) -1 else 1 )
}
if ( all( curves == 0 ) ) curves <- FALSE ## same but faster to draw
if ( ! no.legend ) {
labels <- try( get.long.names( get.vertex.attribute( gr, "name" ), short=T ), silent=T )
if ( class( labels ) == "try-error" ) labels <- get.vertex.attribute( gr, "name" )
labels[ is.na( labels ) | labels == "" ] <- get.vertex.attribute( gr, "name" )[ is.na( labels ) | labels == "" ]
} else labels <- NA ## Assume it's for the thumbnail (so no vertex labels needed either)
plot( gr, layout=gr.layout, margin=0, rescale=F, edge.curved=curves,
vertex.color=colors[ get.vertex.attribute( gr, "name" ) ],
vertex.frame.color=colors[ get.vertex.attribute( gr, "name" ) ], vertex.label.cex=cex, vertex.size=7,
vertex.label=labels,
vertex.label.family=if ( names( dev.cur() ) != "pdf" ) "Arial" else "sans",
edge.color=edge.colors, edge.width=round( net.wts ) + 1 )
if ( ! no.legend && length( nets[ nets != "none" ] ) > 0 )
legend( "bottomright", legend=nets[ nets != "none" ], col=1:length( nets[ nets != "none" ] ),
lty=1, lwd=2, bty="n", cex=0.5 )
if ( names( dev.cur() ) == "devSVG" ) {
names <- cluster$gene.coords ##get.long.names( cluster$k )
for ( i in 1:nrow( gr.layout ) ) {
gene <- get.vertex.attribute( gr, "name" )[ i ]
setSVGShapeToolTip( title=gene, desc1=ifelse( is.na( names[ gene ] ), "", names[ gene ] ) )
setSVGShapeURL( paste( "http://www.genome.ad.jp/dbget-bin/www_bget?", organism, ":", gene, sep="" ) )
points( gr.layout[ i, 1 ], gr.layout[ i, 2 ], col="#FF000001", cex=10/3 )
}
}
cluster
}
col.let <- c( "A", "C", "G", "T" )
viewPssm <- function( pssm, e.val=NA, mot.ind=NA, use.char=T, main.title=NA, ... ) {
if ( is.null( pssm ) ) return()
getEntropy <- function( pssm ) {
pssm[ pssm == 0 ] <- 0.00001
entropy <- apply( pssm, 1, function( i ) -sum( i * log2( i ) ) )
return( entropy )
}
char.coords = list( T=list( x=c( 0.45, 0.55, 0.55, 1, 1, 0, 0, 0.45 ), y=c( 0, 0, 0.9, 0.9, 1, 1, 0.9, 0.9 ), color=2 ),
A=list( x=c( 0, 0.1, 0.28, 0.72, 0.68, 0.32, 0.5, 0.9, 1, 0.55, 0.45, 0 ), y=c( 0, 0, 0.4, 0.4, 0.5, 0.5, 0.9, 0, 0, 1, 1, 0 ), color=3 ),
C=list( x=c( 1, 1, 0.85, 0.55, 0.45, 0.15, 0, 0, 0.15, 0.45, 0.55, 0.85, 1.0, 1.0, 0.9, 0.9, 0.8, 0.55, 0.45, 0.2, 0.1, 0.1, 0.2, 0.45, 0.55, 0.8, 0.9, 0.9 ), y=c( 0.6, 0.7, 0.9, 1, 1, 0.9, 0.65, 0.35, 0.1, 0, 0, 0.1, 0.35, 0.4, 0.4, 0.35, 0.2, 0.1, 0.1, 0.2, 0.42, 0.58, 0.8, 0.9, 0.9, 0.8, 0.65, 0.6 ), color=4 ),
G=list( x=c( 1, 1, 0.85, 0.55, 0.45, 0.15, 0, 0, 0.15, 0.45, 0.55, 0.85, 1.0, 1.0, 0.7, 0.7, 0.9, 0.8, 0.55, 0.45, 0.2, 0.1, 0.1, 0.2, 0.45, 0.55, 0.8, 0.9, 0.9 ), y=c( 0.6, 0.7, 0.9, 1, 1, 0.9, 0.65, 0.35, 0.1, 0, 0, 0.1, 0.35, 0.5, 0.5, 0.4, 0.4, 0.2, 0.1, 0.1, 0.2, 0.42, 0.58, 0.8, 0.9, 0.9, 0.8, 0.65, 0.6 ), color="orange" ) )##,
draw.char <- function( char=col.let, rect=c( 0, 0, 1, 1 ) ) {
## rect is (x,y,width,height)
if ( rect[ 4 ] <= 1e-5 ) return()
x <- char.coords[[ char ]]$x * rect[ 3 ] + rect[ 1 ]
y <- char.coords[[ char ]]$y * rect[ 4 ] + rect[ 2 ]
color <- char.coords[[ char ]]$color
polygon( x, y, col=color, border=color )
}
win.size <- nrow( pssm )
par( mar=rep(0.5,4)+0.1, mgp=c(3,1,0)*0.75 )##; on.exit( par( old.pars ) )
if ( any( pssm <= 0 ) ) pssm <- pssm + 1e-10
if ( any( pssm > 1 ) ) pssm <- t( apply( pssm, 1, function( i ) i / ( sum( i ) + 1e-10 ) ) )
entr <- getEntropy( pssm )
scale.e <- (2 - entr) / 2
##scale.e[ scale.e < 0.05] <- 0.05
x.range <- c(0.5, win.size + 0.5 )
y.range <- c(0,1)
plot( x.range, y.range, type="n", tck=0.01, cex.lab=0.2, cex.sub=0.2, cex.axis=0.2, axes=F )
if ( ! is.na( main.title[ 1 ] ) ) {
if ( ! is.na( mot.ind ) ) title( main.title, col.main=mot.ind+1, xpd=NA, ... )
else title( main.title, xpd=NA, ... )
} else if ( ! is.na( mot.ind ) || ! is.na( e.val ) ) {
if ( ! is.na( mot.ind ) ) {
if ( ! is.na( e.val ) ) tmp.tit <- sprintf( "PSSM #%d; E=%.3g", mot.ind, e.val )
else tmp.tit <- tmp.tit <- sprintf( "PSSM #%d", mot.ind )
title( tmp.tit, col.main=mot.ind+1, xpd=NA, ... )
} else title( tmp.tit, xpd=NA, ... )
}
pssm.sc <- scale.e * pssm
for (j in 1:win.size) {
inds <- sort( pssm.sc[ j, ], index=T )$ix
for (i in 1:4) {
ind <- inds[ i ]
if ( i == 1 ) {
if ( ! use.char ) {
rect( (j-0.5), 0, (j+0.5), pssm.sc[j,ind], col=colMap[ind])
if (pssm[j,ind] > 0.05) text(j, 0 + pssm.sc[j,ind]/2, colLet[ind])
} else {
draw.char( col.let[ ind ], c( (j-0.4), 0, 0.9, pssm.sc[j,ind]-0.01 ) )
}
prev.h <- pssm.sc[j,ind]
} else {
if ( ! use.char ) {
rect( (j-0.5), prev.h, (j+0.5), (pssm.sc[j,ind]+prev.h), col=colMap[ind])
if (pssm.sc[j,ind] > 0.05 ) {
if (i == 2) text(j, prev.h + 0.5 * pssm.sc[j,ind], colLet[ind], col=8)
else text(j, prev.h + 0.5 * pssm.sc[j,ind], colLet[ind])
}
} else {
draw.char( col.let[ ind ], c( (j-0.4), prev.h, 0.9, pssm.sc[j,ind]-0.01 ) )
}
prev.h <- prev.h + pssm.sc[j,ind]
}
}
if ( win.size < 10 )
text( 1:win.size, rep( -0.01, win.size ), as.character( 1:win.size ), cex=0.7, adj=c(0.5,1), xpd=NA )
else if ( win.size < 20 ) text( seq( 1, win.size, 2 ), rep( -0.01, win.size ),
as.character( seq( 1, win.size, 2 ) ), cex=0.7, adj=c(0.5,1), xpd=NA )
else text( seq( 1, win.size, 5 ), rep( -0.01, win.size ),
as.character( seq( 1, win.size, 5 ) ), cex=0.7, adj=c(0.5,1), xpd=NA )
}
}
## Note: to get list of all positions of motif #1 in SEARCHED sequences, this now works:
## posns=unlist(sapply(e$meme.scores$upstream[1:e$k.clust],function(i)i$meme.out[[1]]$posns$start))
## And this will give the posns for motif #1 in all SCANNED sequences (in-clust):
## posns=unlist(sapply(e$meme.scores$upstream[1:e$k.clust],function(i)i$pv.ev[[1]]$posns))
## So the 'out.posns' returned here is now commented out! And I guess we don't need 'no.plot' either.
## This is all moved to new func in cmonkey-postproc.R -- "plot.all.clusterMotifPositions"
###############################
plotClusterMotifPositions <- function( cluster, seqs=cluster$seqs, long.names=T, shade=T, p.val.shade.cutoff=999,
colors=NULL, sort.by="p.value", o.genes=NULL, no.key=F, ##no.plot=F,
short.names=organism == "sce", seq.type=cluster$seq.type[ 1 ], ... ) {
if ( length( cluster$rows ) <= 0 ) { warning( "Trying to plot a cluster with no rows!" ); return() }
k <- cluster$k
rows <- cluster$rows
if ( ! is.null( o.genes ) ) rows <- c( rows, o.genes )
motif.out <- NULL
if ( ! is.null( seq.type ) && seq.type %in% names( cluster ) ) motif.out <- cluster[[ seq.type ]]$motif.out
is.dup.seq <- get.dup.seqs( cluster$seqs )
p.clust <- cluster$p.clust ##[ seq.type ]
e.clust <- cluster$e.val ##[ seq.type ]
motif.info <- NULL
if ( ( ! all( is.na( p.clust ) ) || ! all( is.na( e.clust ) ) ) && ! is.null( motif.out ) &&
! is.null( motif.out$pv.ev ) ) motif.info <- subset( motif.out$pv.ev[[ 2 ]], gene %in% rows )
##if ( no.plot ) {
## out.posns <- list()
## out.posns$starts <- out.posns$ends <- out.posns$mots <- integer()
## out.posns$p.vals <- out.posns$e.vals <- numeric()
##}
if ( is.null( colors ) ##&& exists( "gene.coords" )
|| "cog.code" %in% names( genome.info ) ) {
tmp.lett <- 1:26
names( tmp.lett ) <- LETTERS
##if ( exists( "gene.coords" ) && ! is.vector( gene.coords ) && ! is.null( gene.coords$gene.code ) )
## coo <- gene.coords$gene.code[ rows ]
##!else
if ( ! is.null( cluster$cog.code ) ) coo <- cluster$cog.code[ rows ]
else coo <- 1:length( rows )
tmp <- unique( tmp.lett[ coo ] )
names( tmp ) <- names( tmp.lett[ coo ][ ! duplicated( tmp.lett[ coo ] ) ] )
cols <- rainbow( length( tmp ) )
names( cols ) <- names( tmp )
cols <- cols[ names( tmp.lett[ coo ] ) ]
cols[ is.na( names( cols ) ) ] <- "darkgrey"
names( cols ) <- rows
} else {
cols <- rainbow( length( rows ) )
names( cols ) <- rows
}
cluster$colors <- colors <- cols
no.motif <- FALSE
p.values <- motif.widths <- pssm <- NULL ##diagrams <- NULL
if ( ! is.null( motif.out ) && ! is.null( motif.info ) && nrow( motif.info ) > 0 &&
( ! all( is.na( p.clust ) ) || ! all( is.na( e.clust ) ) ) ) {
p.values <- motif.out$p.values[ rows ]
motif.widths <- sapply( motif.out$pssms, nrow, simplify=T )
pssm <- motif.out$pssms
} else {
no.motif <- TRUE
p.values <- numeric( length( rows ) )
motif.widths <- 0
}
seqs <- seqs[ rows ]
names( seqs ) <- names( p.values ) <- rows ##names( diagrams ) <-
seq.lengths <- nchar( seqs ); seq.lengths[ seq.lengths == 2 ] <- NA ## NA seqs become nchar==2 for some reason!
if ( any( seq.lengths[ ! is.na( seq.lengths ) ] > median( seq.lengths, na.rm=T ) ) ) {
seqs <- substr( seqs, 1, median( seq.lengths, na.rm=T ) )
seq.lengths <- nchar( seqs )
}
maxlen <- max( seq.lengths, na.rm=T )
##print(k);print(rows);print(seqs);print(is.dup.seq);print(seq.lengths);print(maxlen)
if ( ! is.null( seq.type ) && ( maxlen == 0 || is.infinite( maxlen ) ) )
maxlen <- diff( motif.upstream.search[[ seq.type ]] )
inds <- integer()
if ( no.motif && ( sort.by == "p.value" || sort.by == TRUE ) ) sort.by <- "gene.name"
if ( sort.by == "gene.name" ) inds <- sort( rows, decreasing=T, index=T )$ix
else if ( sort.by == "p.value" || sort.by == TRUE ) inds <- order( p.values[ rows ], decreasing=T, na.last=F )
else if ( sort.by == "resid" ) inds <- order( row.scores[ rows, k ], decreasing=T )
else if ( sort.by == "total" ) inds <- order( rr.scores[ rows, k ], decreasing=T )
if ( length( inds ) < length( rows ) ) inds <- c( (1:length( rows ))[ ! 1:length( rows ) %in% inds ], inds )
x.range <- c( -maxlen*0.08, maxlen*1.15 ) ##c( -maxlen*0.15, maxlen*1.08 )
y.range <- c( 0.5, length( rows ) + 1 )
##if ( ! no.plot ) {
plot( x.range, y.range, type="n", axes=F, xlab="sequence position", ylab="" )
cexes <- 1.0
if ( ! no.key ) axis( side=1, pos=0.6, tck=0.01, mgp=c(0.1,0.1,0.1),
labels=c( -1, seq( -100, -maxlen, -100 ) ),
at=seq( maxlen, 0, -100 ) + motif.upstream.scan[[ seq.type ]][ 1 ], ... )
if ( max( seq.lengths, na.rm=T ) > 0 )
sapply( maxlen - c( 0, motif.upstream.search[[ seq.type ]] ) + motif.upstream.scan[[ seq.type ]][ 1 ],
function( i ) lines( rep( i, 2 ), c( -999, 999 ), col="lightgray", lty=2 ) )
colmap <- rainbow( length( rows ) )
##}
mots.used <- numeric()
if ( is.list( motif.widths ) ) {
if ( length( motif.widths ) <= 0 ) motif.widths <- 0
else {
for ( i in 1:length( motif.widths ) ) if ( is.null( motif.widths[[ i ]] ) ) motif.widths[[ i ]] <- 0
motif.widths <- unlist( motif.widths )
}
}
lwd <- 3
if ( length( rows ) > 20 ) lwd <- 1
else if ( length( rows ) > 10 ) lwd <- 2
if ( no.key ) lwd <- 1
if ( ! no.motif ) {
tmp.mot.info <- subset( motif.info, gene %in% rows )
tmp.mot.info <- subset( tmp.mot.info, posns <= diff( motif.upstream.scan[[ seq.type ]] ) )
p.min <- quantile( log10( tmp.mot.info$pvals ), 0.1, na.rm=T )
if ( is.na( p.min ) ) p.min <- -5
p.max <- quantile( log10( tmp.mot.info$pvals ), na.rm=T, 0.9 ) ##+ 1
if ( is.na( p.max ) ) p.max <- log10( p.val.shade.cutoff )
}
for ( j in 1:length( rows ) ) {
jj <- inds[ j ]
cur.gene <- rows[ jj ]
seq.len <- seq.lengths[ jj ]
if ( ! is.null( rows ) ) {
label <- rows[ jj ]
if ( ! is.null( colors ) ) rect( maxlen+5, j-0.18, maxlen*1.195, j+0.18, col=colors[ label ],
border=colors[ label ], lwd=3 )
}
if ( ! no.motif ) {
rects <- NULL
mot.info <- subset( tmp.mot.info, gene == cur.gene ) ##motif.out$mast.info[[ cur.gene ]]
if ( nrow( mot.info ) > 0 ) {
mots <- mot.info$mots
starts <- mot.info$posns ##is still f-ed up (bugs in get.mast.pvals())
widths <- motif.widths[ abs( mots ) ]
for ( i in 1:length( mots ) ) {
mot <- mots[ i ]
if ( is.na( mot ) || is.na( seq.len ) ) next
start <- starts[ i ]
if ( start > seq.len ) next
end <- start + widths[ i ]
##if ( no.plot ) {
## out.posns$starts <- c( out.posns$starts, start )
## out.posns$ends <- c( out.posns$ends, end )
## out.posns$mots <- c( out.posns$mots, abs( mot ) )
## out.posns$p.vals <- c( out.posns$p.vals, mot.info$pvals[ i ] )
## out.posns$e.vals <- c( out.posns$e.vals, motif.out$e.values[ abs( mot ) ] )
## if ( no.plot ) next
##}
mots.used <- unique( c( mots.used, abs( mot ) ) )
col <- abs( mot ) + 1
if ( shade ) {
if ( ! is.null( mot.info ) ) p.val <- mot.info$pvals[ i ]
else p.val <- 1e-5
if ( is.na( p.val ) || p.val > p.val.shade.cutoff || p.val > 1 ) next ##p.val <- 10
else if ( p.val <= 0 ) p.val <- 1e-5
## Map range p.val of -5 to log10(p.val.shade.cutoff) (dark to light)
## e.g. for red: -5 -> (1.0,0,0) log10(p.val.shade.cutoff) -> (1.0,1.0,1.0)
p.val <- log10( p.val )
col <- col2rgb( palette()[ col ] ) / 255
col[ col > 0 ] <- 1
tmp <- if ( p.val < 10 ) min( 1, max( 0, ( p.val - p.min ) / ( p.max - p.min ) ) ) else 0.99
if ( names( dev.cur() ) != "X11" ) {
alpha <- tmp
} else {
col[ col == 0 ] <- tmp
alpha <- 0
}
col[ col < 0 ] <- 0
col[ col > 1 ] <- 1 #0.9
col <- rgb( col[ "red", 1 ], col[ "green", 1 ], col[ "blue", 1 ], 1-alpha )
}
start.1 <- start + maxlen - seq.len
end.1 <- end + maxlen - seq.len
if ( names( dev.cur() ) == "devSVG" ) {
par( family="Arial" )
setSVGShapeToolTip( title=sprintf( "Motif # %2d", abs( mot ) ),
desc1=paste( ifelse ( mot < 0, "Rev.", "For." ), "strand,",
start - maxlen, "to", end-maxlen ),
desc2=sprintf( "p-value = %.2g", 10^p.val ) )
}
if ( ! is.null( mot.info ) ) {
if ( names( dev.cur() ) == "devSVG" ) {
if ( mot > 0 ) rect( start.1, j+0.01, end.1, j+0.3, col=col, border=col )
else if ( mot < 0 ) rect( start.1, j-0.3, end.1, j-0.01, col=col, border=col )
} else {
if ( mot > 0 ) rects <- rbind( rects, c( start.1, j+0.01, end.1, j+0.3, col=col, border=col ) )
else if ( mot < 0 ) rects <- rbind( rects, c( start.1, j-0.3, end.1, j-0.01, col=col, border=col ) )
}
} else {
if ( names( dev.cur() ) == "devSVG" ) {
if ( mot > 0 ) rect( start.1, j+0.01, end.1, j+0.3, border=col )
else if ( mot < 0 ) rect( start.1, j-0.3, end.1, j-0.01, border=col )
} else {
if ( mot > 0 ) rects <- rbind( rects, c( start.1, j+0.01, end.1, j+0.3, col=NA, border=col ) )
else if ( mot < 0 ) rects <- rbind( rects, c( start.1, j-0.3, end.1, j-0.01, col=NA, border=col ) )
}
}
}
}
if ( ! is.null( rects ) ) rect( rects[ ,1 ], rects[ ,2 ], rects[ ,3 ], rects[ ,4 ], col=rects[ ,5 ],
border=rects[ ,6 ] ) ## Faster over remote x11 than doing each individually
}
##if ( no.plot ) next
## Make gene start be at the right
slen <- seq.lengths[ jj ]; if ( all( seq.lengths[ ! is.na( seq.lengths ) ] ) == 0 ) slen <- 50
lines( c( maxlen - slen, maxlen ), c( j, j ), lwd=lwd + as.integer( rows[ jj ] %in% o.genes ), col=colmap[jj] )
## New: if there is a masked region of sequence ("N"s), then grey it out!
if ( grepl( "N", seqs[ jj ] ) ) {
locs <- which( strsplit( seqs[ jj ], "" )[[ 1 ]] == "N" )
diff.locs <- c( diff( locs ), 999 )
##locs <- unique( c( locs[ 1 ], locs[ locs %% 5 == 0 ], locs[ length( locs ) ] ) )
##sapply( locs, function( jjj ) lines( c( jjj, jjj + 1 ) + maxlen - slen, c( j, j ),
## lwd=lwd + as.integer( rows[ jj ] %in% o.genes ), col="gray" ) )
for ( i in 1:sum( diff.locs > 1 ) ) {
if ( i == 1 ) lines( c( locs[ 1 ], locs[ diff.locs > 1 ][ i ] ) + maxlen - slen, c( j, j ),
lwd=lwd + as.integer( rows[ jj ] %in% o.genes ), col="gray", lty=2 )
else lines( c( locs[ which( diff.locs > 1 ) + 1 ][ i - 1 ], locs[ diff.locs > 1 ][ i ] ) + maxlen - slen, c( j, j ),
lwd=lwd + as.integer( rows[ jj ] %in% o.genes ), col="gray", lty=2 )
}
}
if ( ! is.null( rows ) ) {
label <- rows[ jj ]
col <- "black"
if ( exists( "all.tfs" ) && label %in% all.tfs ) col <- "tomato3"
if ( names( dev.cur() ) == "devSVG" || ! long.names && ! no.key ) {
label <- substr( label, 1, 80 )
text( maxlen*1.2, j, labels=label, adj=c(1,0.5), col=col, xpd=NA, ... ) ##cex=0.7,
}
if ( long.names || names( dev.cur() ) == "devSVG" ) {
g.name <- toupper( label )
##gene.coords <- cluster$gene.coords
##if ( exists( "gene.coords" ) && is.vector( gene.coords ) && is.character( gene.coords ) ) {
if ( ! is.null( cluster$gene.coords ) ) g.name <- cluster$gene.coords[ label ]
##} else {
##if ( exists( "gene.coords" ) && ! is.null( gene.coords$gene.func ) )
## g.name <- gene.coords$gene.func[ g.name ]
## if ( exists( "gene.coords" ) && ! is.null( gene.coords$gene.name ) &&
## gene.coords$gene.name[ label ] != "-" && ! is.na( gene.coords$gene.name[ label ] ) &&
## toupper( gene.coords$gene.name[ label ] ) != toupper( g.name ) ) {
## ll <- gene.coords$gene.name[ label ]
## ##if ( nchar( ll ) > 60 ) ll <- substr( ll, nchar( ll ), nchar( ll ) - 60 )
## g.name <- paste( ll, ": ", g.name, sep="" )
## }
## }
g.name[ is.na( g.name ) ] <- label[ is.na( g.name ) ]
if ( is.na( g.name ) ) g.name <- label
if ( names( dev.cur() ) == "devSVG" ) {
par( family="Arial" )
setSVGShapeURL( paste( "http://www.genome.ad.jp/dbget-bin/www_bget?", organism, ":", label, sep="" ) )
if ( ! is.na( g.name ) ) setSVGShapeToolTip( label, g.name )
else setSVGShapeToolTip( label )
rect( maxlen*1.2, j-0.18, maxlen, j+0.18, col=NA, border=NA, xpd=NA )
} else if ( ! is.na( g.name ) ) { ##|| for.figure ) {
lab <- label
if ( toupper( g.name ) != toupper( label ) && g.name != "" ) {
g.name <- gsub( "^[:\\s+]+", "", gsub( "\\s+$", "", g.name, perl=T ), perl=T ) ##maxlen/8
if ( names( dev.cur() ) == "X11" ) g.name <- strtrim( g.name, 40 ) else gname <- strtrim( g.name, 60 )
if ( label != "" ) lab <- paste( g.name, ": ", label, sep="" ) ##label, " (", g.name, ")", sep="" )
else lab <- g.name
}
if ( nchar( lab ) > 60 ) lab <- substr( lab, nchar( lab ) - 60, nchar( lab ) )
if ( ! no.key ) text( maxlen*1.2, j, labels=lab, adj=c(1,0.5), col=col, xpd=NA, ... ) ##cex=0.9,
}
}
if ( ( ! all( is.na( p.clust ) ) || ! all( is.na( e.clust ) ) ) && ! no.key )
text( -maxlen*0.07, j, labels=sprintf( "%.2f", p.values[ label ] ),
xpd=NA, col=if ( label %in% names( which( ! is.dup.seq ) ) ) "black" else "blue", ... )
}
}
if ( ! no.key && ( ! all( is.na( p.clust ) ) || ! all( is.na( e.clust ) ) ) ) {##&& ! for.figure ) ##! no.plot &&
text( -maxlen*0.15, length(rows)+0.9, labels=sprintf( "log10(P) %s", seq.type ), pos=4, ... ) ##cex=0.7 )
mots.used <- sort( unique( mots.used ) )
if ( length( mots.used ) > 1 ) {
##text( maxlen*0.03, length(rows)+0.9, "Motif legend:", xpd=NA, adj=c(0,0.5), ... )
sapply( 1:length( mots.used ), function( j )
text( maxlen*0.24 + (j+0)*maxlen*0.03, length(rows)+0.9, as.character( mots.used[ j ] ),
col=mots.used[ j ] + 1, xpd=NA, adj=c(0,0.5), ... ) )
}
n.unique.seqs <- sum( ! is.dup.seq )
text( maxlen*1.2, length(rows)+0.9, sprintf( "log10(P.clust)=%.2f; %d seqs; %d uniq", p.clust[ seq.type ],
length( seqs ), n.unique.seqs ), xpd=NA, adj=c(1,0.5), ... )
}
##invisible( out.posns )
}
plotClust <- function( k, cluster=NULL, w.motifs=T, all.conds=T, title=NULL, o.genes=NULL, dont.plot=F,
network="all", short.names=organism == "sce", seq.type=names( mot.weights ), ... ) {
if ( ! dont.plot ) opar <- par( no.readonly=T )
if ( ! is.null( cluster ) ) {
if ( ! dont.plot ) plotCluster.motif( cluster, seqs=cluster$seqs, p.val.shade.cutoff=1, o.genes=o.genes,
no.plotCluster=all.conds, ... )
return( invisible( cluster ) )
}
c <- get.clust( k, varNorm=F )
rows <- get.rows( k ); if ( ! is.null( o.genes ) ) rows <- c( rows, o.genes )
if ( length( rows ) <= 0 ) { warning( "Trying to plot a cluster with no rows!" ); return() }
if ( ! w.motifs && ! dont.plot ) {
if ( all.conds ) plotCluster.all.conds( c, o.genes=o.genes, ... )
else plotCluster( c, o.genes=o.genes, ... )
} else {
c$seq.type <- seq.type
for ( st in seq.type ) {
c[[ st ]] <- list()
c[[ st ]]$motif.out <- meme.scores[[ st ]][[ k ]]
tmp <- cluster.pclust( k, st )
c[[ st ]]$e.val <- tmp$e.vals
c[[ st ]]$p.clust <- tmp$p.clusts
c[[ st ]]$motif.out$pssms <- lapply( c[[ st ]]$motif.out$meme.out, "[[", "pssm" )
c[[ st ]]$motif.out$e.values <- c[[ st ]]$e.val
if ( ! is.null( c[[ st ]]$motif.out$pv.ev ) ) { ##&& ! is.null( meme.scores[[ st ]]$all.pv ) ) { ## Replicate old pv.ev list of 2 data frames
if ( "gene" %in% colnames( c[[ st ]]$motif.out$pv.ev[[ 1 ]] ) ) c[[ st ]]$motif.out$pv.ev[[ 2 ]] <- c[[ st ]]$motif.out$pv.ev[[ 1 ]]
##!else if ( "gene" %in% colnames( c[[ st ]]$motif.out$pv.ev[[ 2 ]] ) ) c[[ st ]]$motif.out$pv.ev[[ 2 ]] <- c[[ st ]]$motif.out$pv.ev[[ 2 ]]
if ( ! is.null( meme.scores[[ st ]]$all.pv ) ) {
tmp <- cbind( p.value=meme.scores[[ st ]]$all.pv[ ,k ],
e.value=if ( "all.ev" %in% names( meme.scores[[ st ]] ) ) meme.scores[[ st ]]$all.ev[ ,k ] else NA )
} else {
pv.ev <- meme.scores[[ st ]][[ k ]]$pv.ev[[ 1 ]]
if ( ncol( pv.ev ) <= 2 ) pv.ev <- meme.scores[[ st ]][[ k ]]$pv.ev[[ 2 ]]
tmp <- as.matrix( pv.ev[ ,2:ncol( pv.ev ) ] )
rownames( tmp ) <- pv.ev[ ,1 ]; colnames( tmp ) <- c( "p.value", "posns", "mots" )
}
c[[ st ]]$motif.out$pv.ev[[ 1 ]] <- tmp
c[[ st ]]$motif.out$p.values <- log10( c[[ st ]]$motif.out$pv.ev[[ 1 ]][ ,"p.value" ] )
names( c[[ st ]]$motif.out$p.values ) <- rownames( c[[ st ]]$motif.out$pv.ev[[ 1 ]] )
}
}
}
if ( ! is.na( mot.iters[ 1 ] ) && ! no.genome.info ) {
## Filtering is used for plotting masked subseqs (which are not input to MEME) as dashed lines
c$seqs <- get.sequences( rows, distance=motif.upstream.scan[[ seq.type[ 1 ] ]],
seq.type=seq.type[ 1 ], filter=T, uniq=F ) ##, remove.repeats=T, mask=T, blast=T ) ##[ rows ]
tmp <- c$seqs[ rows ]
if ( ! is.null( tmp ) ) names( tmp ) <- rows
attr( tmp, "start.stops" ) <- attr( c$seqs, "start.stops" ); c$seqs <- tmp; rm( tmp )
} else c$seqs <- NULL
if ( ! is.na( net.iters[ 1 ] ) ) {
if ( network == "all" ) network <- names( networks )
for ( i in network ) {
if ( ! i %in% names( networks ) ) next
tmp.net <- networks[[ i ]][ networks[[ i ]]$protein1 %in% rows & networks[[ i ]]$protein2 %in% rows, ]
tmp.net <- cbind( tmp.net, net=rep( i, nrow( tmp.net ) ) )
c$network <- if ( ! is.null( c$network ) ) rbind( c$network, tmp.net ) else tmp.net
}
}
c$gene.coords <- get.long.names( rows, short=short.names )
if ( "cog.code" %in% names( genome.info ) ) c$cog.code <- genome.info$cog.code[ rows ]
if ( ! is.null( title ) ) c$name <- title
##cluster<<-c
if ( ! dont.plot ) {
plotCluster.motif( c, seqs=c$seqs, p.val.shade.cutoff=1, o.genes=o.genes, no.plotCluster=all.conds, ... )
if ( ! "layout" %in% names( list( ... ) ) ) par( opar )
}
invisible( c )
}
plotScores <- function( k, o.genes=NULL, b.genes=NULL, recompute=F ) {
row.memb <- apply( row.membership == k, 1, any )
opar <- par( no.readonly=T )
rows <- get.rows( k )
if ( recompute ) {
tmp <- get.all.scores( k, force.row=T, force.col=T, force.motif=T, force.net=T )
rs <- tmp$r; ms <- tmp$m; ns <- tmp$n
}
tmp.scale <- round( attr( ratios, "nrow" ) / length( rows ) / 4 )
layout( matrix( c( 1, 2, 3, 4, 4, 5, 4, 4, 6 ), 3, 3, byrow=T ) )
if ( ! recompute ) rs <- row.scores[ ,k, drop=T ]
rs[ rs < -220 ] <- min( rs[ rs > -220 ], na.rm=T )
h <- try( hist( rs, breaks=20, main=paste( "Cluster", k ), xlab="Ratios scores" ), silent=T )
if ( class( h ) != "try-error" ) {
try( hist( rep( rs[ rows ], tmp.scale ), breaks=h$breaks, col="red", border="red", add=T ), silent=T )
try( hist( rs, breaks=h$breaks, add=T ), silent=T )
}
if ( ! recompute ) ms <- ns <- NULL
if ( ! is.null( mot.scores ) && ! all( is.na( mot.scores[ ,k ] ) ) && ! no.genome.info ) {
if ( ! recompute ) ms <- mot.scores[ ,k, drop=T ]; ms[ ms < -20 ] <- min( ms[ ms > -20 ], na.rm=T )
h <- try( hist( ms, breaks=20, main=NULL, xlab="Motif scores" ), silent=T )
if ( class( h ) != "try-error" ) {
try( hist( rep( ms[ rows ], tmp.scale * 3 ), breaks=h$breaks, col="red", border="red", add=T ), silent=T )
try( hist( ms, breaks=h$breaks, add=T ), silent=T )
}
} else plot( 1, 1, typ="n", axes=F, xaxt="n", yaxt="n", xlab="", ylab="" )
if ( ! is.null( net.scores ) && ! all( net.scores[ ,k ] == 0 ) ) {
if ( ! recompute ) ns <- net.scores[ ,k, drop=T ]; ns[ ns < -20 ] <- min( ns[ ns > -20 ], na.rm=T ); ns <- -log10( -ns )
ns[ is.infinite( ns ) ] <- max( ns[ ! is.infinite( ns ) ], na.rm=T ) + 0.1
h <- try( hist( ns, breaks=20, main=NULL, xlab="-log10(-Network scores)" ), silent=T )
if ( class( h ) != "try-error" ) {
try( hist( rep( ns[ rows ], tmp.scale / 3 ), breaks=h$breaks, col="red", border="red", add=T ), silent=T )
try( hist( ns, breaks=h$breaks, add=T ), silent=T )
}
} else plot( 1, 1, typ="n", axes=F, xaxt="n", yaxt="n", xlab="", ylab="" )
if ( ! is.null( ms ) && ! all( is.na( ms ) ) && ! all( ns == 0 ) ) {
plot( rs, ms, typ="n", main=paste( "Cluster", k ), xlab="Ratios scores", ylab="Mot scores" ) ##,log="xy")
text( rs, ms, label=1:length(rs), col=row.memb+1, cex=0.5 )
} else if ( ! is.null( ns ) && ! all( ns == 0 ) ) {
plot( rs, ns, typ="n", main=paste( "Cluster", k ), xlab="Ratios scores", ylab="Net scores" )
text( rs, ns, label=1:length(rs), col=row.memb+1, cex=0.5 )
} else {
plot( rs, jitter(rep(0,length(rs))), typ="n", main=paste( "Cluster", k ), xlab="Ratios scores", ylab="" )
text( rs, jitter(rep(0,length(rs))), label=1:length(rs), col=row.memb+1, cex=0.5 )
}
if ( ! is.null( o.genes ) ) text( rs[ o.genes ], ms[ o.genes ],
label=which( attr( ratios, "rnames" ) %in% o.genes ), col="green", cex=0.5 )