stat.ChurSap {sma}R Documentation

Apply Sapir and Churchills single slide method

Description

Applies Sapirs and Churchills single slide method.

Usage

stat.ChurSap(RG,layout,pp=0.95,norm="p", pout=TRUE, image.id=1,...)

Arguments

RG a list with 4 elements, each represents a matrix with p rows for p genes and n columns for n slides.
The first element "R" contains the raw red intensities from slide i=1,...,n .
Similarly, the second element "G" contains the raw green intensities.
The third element "Rb" contains the background red intensities and
the fourth element "Gb" contains the background green intensities.
This list structure can be generated by the 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.
pp Posterior probability of being differentially expressed. Defaults to 0.95
image.id Specifies image to which Chen's method will be applied.
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. The default method is set to print-tip normalization.
pout if TRUE, an M vs. A plot will be produced with limits due to Churchill and Sapir at the specified posterior probability level. If FALSE, return a list with pertinant information
... additional graphical parameters

Value

List containing the following components:

limits the positive value of the limit at the posterior probability value of pp
theta parameters estimated by EM algorithm, specifically a mixing proportion and a variance
pp Posterior probabilities of being differentially expressed given observed data for each gene.

Author(s)

Ben Bolstad, bolstad@stat.berkeley.edu

References

Sapir and Churchill(2000), Estimating the posterior probability of differential gene expression from microarray data . http://www.jax.org/research/churchill/

See Also

stat.Chen,stat.Newton

Examples

data(MouseArray)
##mouse.setup <- init.grid() 
##mouse.data <- init.data() ## see \emph{init.data}
stat.ChurSap(mouse.data,mouse.setup,pp=0.95,image.id=3)

[Package sma version 0.5.15 Index]