MLM.beadarray: Multi-level Mixed model

Description Usage Arguments Author(s) References Examples

View source: R/MLM.beadarray.R

Description

Function for differential expression analysis of bead array data using the Multi-level Mixed model of Kim and Lin (2011).

Usage

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MLM.beadarray(sig, stderr, nbeads, groups, var.equal = FALSE, max.iteration = 20,
epsilon = 1e-06, method = "REML")

Arguments

sig

The summarized and normalized average intensities

stderr

The standard errors of the means

nbeads

The number of beads used for summarization

groups

This refers to the groups to be compared. For filtering, group refers to the number of cloumns of sig. However, for the differential expression analysis, the user should define the group variable as appropriate for his/her data.

var.equal

i.e assuming equal variance for the variance of the array random effects

max.iteration

The maximum number of iteration to perform

epsilon

control limit for convergence

method

Allows one to choose between restricted maximum likelihood (REML) or maximum likelihood (ML) estimations

Author(s)

Ryung S. Kim and Juan Lin

References

Kim, R.S. and Lin, J. (2011). Multi-level mixed effects models for bead arrays. Bioinformatics, 27(5):633-640.

Examples

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require(beadarrayExampleData)
data(exampleSummaryData)
exampleSummaryDatalog2 <- channel(exampleSummaryData, "G")[1:40,]

exampleSummaryDataNorm <- normaliseIllumina(exampleSummaryDatalog2, 
method = "quantile", transform = "none")[1:40,]

    eSet <- na.omit(exprs(exampleSummaryDataNorm))[1:40,]
    
    seSet <- na.omit(se.exprs(exampleSummaryDataNorm))[1:40,]
    
    nSet <- na.omit(attributes(exampleSummaryDataNorm)$assayData$nObservations)[1:40,]
    
     stderrs<-seSet/sqrt(nSet)

##define group variable as appropriate for your data
group1 <- c(1:6)
group2 <- c(7:12)
 fit1 <- MLM.beadarray(eSet, stderrs, nSet, list(group1,group2), var.equal = TRUE,
 max.iteration = 20, method = "ML")
   
df<-length(group1)+length(group2)-2
fit1$pvalue<-2*(1-pt(abs(fit1$t.statistics),df))
fit1$PvalADjust<-p.adjust(fit1$pvalue, method ="fdr", n = length(fit1$pvalue))
length(which(fit1$PvalADjust<0.05)) 

beadarrayFilter documentation built on May 29, 2017, 7:09 p.m.