Description Usage Arguments Value Note Examples
Function calculates metaprofile(s) from a ScoreMatrix or a ScoreMatrixList, then produces a line plot or a set of line plots for metaregion profiles
1 2 3 4 5 
mat 

centralTend 
a character that determines central tendency of metaprofile(s). It takes "mean" (default) or "median". 
overlay 
If TRUE multiple profiles will be overlayed in the same plot (Default:TRUE). If FALSE, and mat is a ScoreMatrixList, consider using par(mfrow=c(1,length(mat))) to see the plots from all matrices at once. 
winsorize 
Numeric vector of two, defaults to c(0,100). This vector determines the upper and lower percentile values to limit the extreme values. For example, c(0,99) will limit the values to only 99th percentile, everything above the 99 percentile will be equalized to the value of 99th percentile.This is useful for visualization of matrices that have outliers. 
profile.names 
a character vector for names of the profiles. The order should be same as the as the order of ScoreMatrixList. 
xcoords 
a numeric vector which designates relative base positions of the metaregion profiles. For example, for a 2001 column ScoreMatrix, xcoord=1000:1000 indicates relative positions of each column in the score matrix. If NULL (Default), xcoords equals to 1:ncol(mat) 
meta.rescale 
if TRUE metaregion profiles are scaled to 0 to 1 range by subtracting the min from profiles and dividing them by maxmin. If dispersion is not NULL, then dispersion will be scaled as well. 
smoothfun 
a function to smooth central tendency and dispersion bands (Default: NULL), e.g. stats::lowess. 
line.col 
color of lines for 
dispersion 
shows dispersion interval bands around

dispersion.col 
color of bands of 
ylim 
same as 
ylab 
same as 
xlab 
same as 
... 
other options to 
returns the metaregion profiles invisibly as a matrix.
Score matrices are plotted according to ScoreMatrixList order. If ScoreMatrixList contains more than one matrix then they will overlap each other on a plot, i.e. the first one is plotted first and every next one overlays previous one(s) and the last one is the topmost.
Missing values in data slow down plotting dispersion around central tendency.
The reason is that dispersion is plotted only for nonmissing values,
for each segment that
contains numerical values graphics::polygon
function is used to plot dispersion bands.
There might be a situation, when in a column of ScoreMatrix is only one
numeric number and the rest are NAs, then at corresponding position
only central tendency will be plotted.
Notches show the 95 percent confidence interval for the median according to an approximation based on the normal distribution. They are used to compare groups  if notches corresponding to adjacent base pairs on the plot do not overlap, this is strong evidence that medians differ. Small sample sizes (510) can cause notches to extend beyond the interquartile range (IQR) (Martin Krzywinski et al. Nature Methods 11, 119120 (2014))
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  data(cage)
data(promoters)
scores1=ScoreMatrix(target=cage,windows=promoters,strand.aware=TRUE)
data(cpgi)
scores2=ScoreMatrix(target=cpgi,windows=promoters,strand.aware=TRUE)
# create a new ScoreMatrixList
x=new("ScoreMatrixList",list(scores1,scores2))
plotMeta(mat=x,overlay=TRUE,main="my plotowski")
# plot dispersion nd smooth central tendency and variation interval bands
plotMeta(mat=x, centralTend="mean", dispersion="se", winsorize=c(0,99),
main="Dispersion as interquartile band", lwd=4,
smoothfun=function(x) stats::lowess(x, f = 1/5))

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