plot.mva: M vs. A Plot
In gnyamundanda/sma: Statistical Microarray Analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

For a single slide, this function produces a scatter plot of log intensity ratios M = log_2(R/G) versus average log intensities A = log_2(R*G)/2, where R and G represent the fluorescence intensities in the red and green channels respectively.

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plot.mva(x, layout, norm="p", pout=TRUE, image.id=1, extra.type="tci",
crit1=0.025,crit2=crit1, nclass=10, labs=NULL, plot.type="n",
col.ex=NULL,pch=".", ...)

`x`	A list with at least 4 elements. Each element of the list being a matrix with p rows for p genes and n columns for n slides. The first element 'R' contains the raw red intensities, the second element 'G' contains the raw green intensities, the third element 'Rb' contains the background red intensities and the 4th element 'Gb' contains the background green intensities. This data structure can be generated by an interactive function `init.data`.
`layout`	a list specifying the dimensions of the spot matrix and the grid matrix. This can be generated by calling `init.grid`.
`norm`	character string, one of "n", "m", "l", "p" or "s". This argument specifies the type of normalization method to be performed: "n" no normalization between the 2 channels; "m" `median` normalization, which sets the median of log intensity ratios to zero; "l" global `lowess` normalization; "p" print-tip group lowess normalization and "s" scaled print-tip group lowess normalization.
`pout`	if TRUE, an M vs. A plot will be produced. Otherwise, the function returns the normalized log intensity ratios M and the mean log intensities A for each gene.
`image.id`	integer value; the index of the slide which is considered.
`extra.type`	a character string, one of "t", "p", "tci","pci" or "lci". This argument specifies the type of plot to be drawn. The possible types are: * "t" for text, * "p" for points, * "tci" for highlighting a certain proportion of extreme ‘M’ values by text, * "pci" for highlighting a certain proportion of extreme ‘M’ values by points, * "lci" for including 2 intensity dependent lines where a prespecified proportion of points have more extreme ‘M’ values.
`crit1`	The number of points to be highlighted on the M vs A plot. If crit1 < 1, the crit1*100% spots with the smallest M values will be highlighted. If crit1 >= 1, the crit spots with the smallest M values are highlighted.
`crit2`	Similar to "crit1". If crit2 < 1, the crit2*100% spots with the largest M values will be highlighted. If crit2 >= 1, the crit2 spots with the smallest M values are highlighted.
`nclass`	A single number giving the approximate number of intensity dependent groups to consider.
`labs`	one or more character strings or expressions specifying the text to be written. If this string is not specified, by default the index of the vector ‘M’ will be used.
`plot.type`	a character string, this argument is either "n", "r" or "b". The different number of plots to be included are: * "n" for normalised M vs A plot, * "r" for unnormalised M vs A plot, and * "b" both unnormalised and normalised M vs A plots.
`col.ex`	The colour used for the highlighting extreme points, lines or text.
`pch`	The plotting character for points
`...`	graphical parameters may also be supplied as arguments to the function (see `par`).

M vs. A plots tend to be more revealing than their log R vs. log G counterparts in terms of identifying spot artifacts and detecting intensity dependent patterns in the log ratios. They are also very useful for normalization.

A plot is created on the current graphics device. The top plot is based on unnormalized log ratios and the bottom plot is based on normalized log ratios.

Yee Hwa Yang, yeehwa@stat.berkeley.edu
Sandrine Dudoit, sandrine@stat.berkeley.edu
Natalie Roberts, nroberts@wehi.edu.au

S. Dudoit, Y. H. Yang, M. J. Callow, and T. P. Speed. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments (Statistics, UC Berkeley, Tech Report \# 578).

ma.func, plot.smooth.line, stat.ma, lowess, plot.

data(MouseArray)
# mouse.setup <- init.grid()
# mouse.data <- init.data() ## see \emph{init.data}
mouse.lratio <- stat.ma(mouse.data, mouse.setup)

## Look at the normalized second data sets in the list using points to
## highlight large positive or large negative ratios.
plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="pci",
plot.type="n") 

## Look at the both unnormalized and normalized first data sets in the
## list using text to highlight large positive or negative ratios.
## plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="tci", plot.type="b")