transform.madata: Micro Array experiment data transformation

In maanova: Tools for analyzing Micro Array experiments

Description

This is the function to transform the Micro Array experiment data based on the given method.

Usage

## S3 method for class 'madata'
transform(`_data`,method=c("shift","glowess","rlowess","linlog","linlogshift"),
       lolim, uplim, f=0.1, iter=3, degree=1, cg=0.3, cr=0.3, n.bin=10,
       draw=c("screen", "dev", "off"), ...)

Arguments

_data	An object of class madata.
method	The smoothing method.
lolim	Low shift limit. If this argument is missing, the negative of the minimum element in the pmt data is used.
uplim	High shift limit. If this argument is missing, the minimum element in the pmt data is used. lolim and uplim are applicable only if the method is "shift" or "linlogshift".
f	The smoother span. This gives the proportion of points in the plot which influence the smooth at each value. Larger values give more smoothness. It is equivalent to the "span" parameter in loess.
iter	The number of robustifying iterations which should be performed. Using smaller values of iter will make lowess run faster.
degree	The degree of the polynomials to be used in loess, up to 2. This is used when method is "glowess" or "rlowess".
cg	Percentage of genes to be transformed linearly for the green channel.
cr	Percentage of genes to be transformed linearly for the red channel.
n.bin	Number of bins for calculating the variance after linlogShift.
draw	Where to plot the transformation plots. "off" means no plot. "screen" means to display the plots on screen then x11() (in Unix/Windows) or macintosh() (in Mac) will be called inside the function. "dev" means to output the plots to the current device. User can use this option to output the plot to a file. Default option is "screen".
...	Ignored at this point.

Details

The smoothing methods include:

shift – the calculation of offset is based on the minimum sum of absolute deviations (SAD). Each array will have its own offset value. The data after shift cannot be smaller than 1.

glowess – Intensity-based lowess. This method is to smooth the scatter plot of Ratio (R/G) versus Intensity (R*G). The formula in the fitting is ratio ~ intensity.

rlowess – Joint lowess. This method is to smooth the scatter plot of Ratio versus Intensity and grid locations. It is the joint of intensity-based lowess and spatial loess. You have to have the grid location for every spot in order to use this method. The formula in fitting is ratio ~ intesity + row + col.

linlog – Linear-log transformation.

linlogshift – Linear-log shift transformation.

Previously, intensity lowess was called global lowess and joint lowess was called regional lowess. So I use "glowess" and "rlowess" in the method. Although the method names doesn't make too much sense, I will keep them for the reason of backward compatibility.

If you have replicated spots and want to collapse them in read.madata by providing avgreps=1 or 2, you will lose grid information and joint lowess will be unavailable.

Note that this function is only working for two-dye array.

Value

The return value is an object of class madata. Compared with the input object, the following fields are changed:

• Field data is the transformed data.

• Field TransformMethod will be the transformation method applied.

Author(s)

Hao Wu

References

Kerr and Churchill(2001), Statistical design and the analysis of gene expression microarrays, Genetical Research, 77:123-128.

Kerr, Martin and Churchill(2000), Analysis of variance for gene expression microarray data, Journal of Computational Biology, 7:819-837.

Cui, Kerr and Churchill(2002), Data transformations for cDNA Microarray data, submitted, find manuscript in www.jax.org/research/churchill.

See Also

loess

Examples

# load in data
data(kidney)
# do regional loess on raw data
## Not run: 
raw.lowess <- transform.madata(kidney.raw, method="rlowess")
graphics.off()
#do shift without displaying the plot 
data1.shift <- transform.madata(kidney.raw, method="shift", lolim=-50, 
     uplim=50,draw="off")

# do global lowess and output the plots to a postscript file
postscript(file="glowess.ps")
data1.glowess <- transform.madata(kidney.raw, method="glowess", draw="dev")
graphics.off()

# do linear-log
data1.linlog <- transform.madata(kidney.raw, method="linlog")
graphics.off()

# do linear-log shift
data1.linlogshift <- transform.madata(kidney.raw, method="linlogshift", 
  lolim=-50, uplim=50)
graphics.off()

## End(Not run)

maanova documentation built on Nov. 8, 2020, 8:21 p.m.