ScoreMatrix or ScoreMatrixList object. If it is a
ScoreMatrixList object, all matrices in the ScoreMatrixList should have
the same number of
columns.

centralTend

a character that determines central tendency of meta-profile(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 meta-region 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 meta-region profiles are scaled to 0 to 1 range by
subtracting the min from profiles and dividing them by max-min.
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 centralTend of meta-region profiles. Defaults to colors from
rainbow() function.

dispersion

shows dispersion interval bands around centralTend (default:NULL). It takes
one of the character:

"se"shows standard error of the mean and 95 percent confidence interval for the mean

"sd"shows standard deviation and 2*(standard deviation)

"IQR"shows 1st and 3rd quartile and
confidence interval around the median based on the median +/- 1.57 * IQR/sqrt(n) (notches)

dispersion.col

color of bands of dispersion.
Defaults to colors from rainbow() and transparency is set to 0.5
(rainbow(length(mat), alpha = 0.5)).

ylim

same as ylim at plot function.
if NULL ylim is estimated from all meta-region profiles.

ylab

same as ylab at plot function.
Default: "average score"

xlab

same as xlab at plot function.
Default: "bases"

...

other options to plot

Value

returns the meta-region profiles invisibly as a matrix.

Note

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 non-missing 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 (5-10) can cause notches to extend beyond the interquartile range (IQR)
(Martin Krzywinski et al. Nature Methods 11, 119-120 (2014))

Examples

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))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(genomation)
Loading required package: grid
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/genomation/plotMeta.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotMeta
> ### Title: Line plot(s) for meta-region profiles
> ### Aliases: plotMeta
>
> ### ** Examples
>
> 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))
> ## No test:
> 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))
>
> ## End(No test)
>
>
>
>
>
> dev.off()
null device
1
>