plot: Plot Motiv
In MotIV: Motif Identification and Validation

Description Usage Arguments Details Author(s) Examples

This functions are used to vizualise and validate motiv analysis.

	## S4 method for signature 'motiv,ANY'
plot(x, y=NULL, main=NULL, sub=NULL, ncol=0, nrow=0, top=3, bysim=TRUE, rev=FALSE, trim=0.05, cex)
	
	## S4 method for signature 'motiv,gadem'
plot(x, y, sort=FALSE, group=FALSE, main=NULL, sub=NULL, ncol=0, nrow=0, xlim=NULL, correction=TRUE,  bysim=TRUE, strand=FALSE, type="distribution", trim=0.05, col=c("blue", "red"), border=c("black", "black"), lwd=2, lty=1, nclass=20, bw="nrd0", cex=1, vcol=c("red", "green"))

`x`	An object of class `motiv`.
`y`	The GADEM type object associated with the `motiv` object.
`ncol, nrow`	A numeric value giving the the number of columns and rows to plot.
`top`	A numeric value giving the number of best matches per motif to display.
`rev`	A logical value. If `TRUE`, print reverse motif for negatif strand.
`main`	An overall title for the plot: see `title`.
`sub`	A sub title for the plot: see 'title'
`type`	What type of plot should be drawn. Possible values are : distribution to display the binding sites distribution within the peaks or distance to show the pairwise distance between motifs.
`strand`	If `TRUE`, distribution will be plot for both forward and reverse strand.
`group`	If `TRUE`, similar motifs will be grouped.
`sort`	If `TRUE`, motifs will be plot according their computed variance.
`bysim`	If `TRUE`, the 'similar' field (defined with the `combine` function) will be print instead of the original name.
`xlim`	A numeric vectors of length 2, giving the x coordinates ranges.
`correction`	If `TRUE`, corrects the position according to the alignment.
`trim`	A `numeric` value. Define the mimimun information content value for which the logo letters are shown.
`col, border, lwd, lty`	Define respectively the color, the border, the line wide and the line type of both curve and histogram. See 'par'.
`nclass`	A numerical value giving the number of class for the histogram.
`bw`	he smoothing bandwidth to be used to calculate the density. See `density`.
`cex, vcol`	A numerical value giving the amount by which plotting text should be magnified relative to the default.

A single motiv object (usualy provied by motifMatch) will plot the list of identified transcription factors for each motif. With rev=TRUE, the transcription factor logo will be print to correspond to the real alignment instead of original TF PWM.

Giving a motiv object and a gadem object with type="distribution" will show the motif repartition within gadem peaks. If strand=TRUE, a distinct distribution is made for forward and reverse strand.

A var.test is automatically made to help to distinguish centered distribution. The distribution with lowest variance is assign as "reference" distribution to compute the var.test statistic. With sort=TRUE, distribution are plot according decreasing statistic.

type="distance" indicates to compute and plot the distance between each pair of motif. It aslo provied Venn diagramm that returns the proportion of common sequences per pair of motif.

The group argument indicates to consider similar motif as a single motif.

With correction=TRUE the motif position is corrected accoring to the alignment. It means that the gap/"N" contained in the alignments are removed to give a corrected start and end position.

Eloi Mercier <emercier@chibi.ubc.ca>

#####Database and Scores#####
path <- system.file(package="MotIV")
jaspar <- readPWMfile(paste(path,"/extdata/jaspar2010.txt",sep=""))
jaspar.scores <- readDBScores(paste(path,"/extdata/jaspar2010_PCC_SWU.scores",sep=""))

#####Input#####
data(FOXA1_rGADEM)
motifs <- getPWM(gadem)
motifs.trimed <- lapply(motifs,trimPWMedge, threshold=1)
#####Analysis#####
foxa1.analysis.jaspar <- motifMatch(inputPWM=motifs,align="SWU",cc="PCC",database=jaspar,DBscores=jaspar.scores,top=5)
summary(foxa1.analysis.jaspar )

#####Filters#####
f.foxa1<-setFilter(name="", tfname="FOXA1", top=3, evalueMax=10^-5)
f.ap1 <- setFilter (tfname="AP1", top=3)
f.foxa1.ap1 <- f.foxa1 | f.ap1 
foxa1.filter <- filter(foxa1.analysis.jaspar, f.foxa1.ap1, exact=FALSE, verbose=TRUE)
foxa1.split <- split(foxa1.analysis.jaspar, c(f.foxa1, f.ap1) , drop=FALSE, exact=FALSE, verbose=TRUE)
foxa1.filter.combine <- combineMotifs(foxa1.filter, c(f.foxa1, f.ap1), exact=FALSE, name=c("FOXA1", "AP1"), verbose=TRUE)

#####Plots#####
plot(foxa1.filter.combine, ncol=2,top=5, rev=FALSE, main="FOXA", bysim=TRUE)
plot(foxa1.filter.combine ,gadem,ncol=2, type="distribution", correction=TRUE, group=FALSE, bysim=TRUE, strand=FALSE, sort=TRUE, main="FOXA", nclass=20, bw=2)
plot(foxa1.filter.combine ,gadem,type="distance", correction=TRUE, group=TRUE, bysim=TRUE, main="FOXA", strand=FALSE, xlim=c(-100,100), nclass=20, bw=8)