Fit the expression values profile with a mixture of normal components ignoring outliers

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Description

firPrior performs a clustering of expression values for each gene profile using the mclust function ignoring the outliers (detected by the first step of the SpeCond prcedure) present in the SpecificOutlierStep1 argument . This results to a mixture of normal distribution components (from 1 to 3 components) fitting the expression values.

Usage

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fitNoPriorWithExclusion(expressionMatrix, specificOutlierStep1 = FALSE, 
param.detection = NULL, lambda = 1, beta = 0)

Arguments

expressionMatrix

the expression value matrix, genes*conditions

specificOutlierStep1

the list of outliers detected by the first step procedure, result of the getSpecificOutliersStep1 function or an attritube of the SpeCond result object. These outliers won't be taken into account for the mixture normal modelling performed by this function

param.detection

the matrix of parameters as obtained by getDefaultParameter or createParamterMatrix. It must contain positive values for "lambda" and "beta". If NULL, the function getDefaultParameter will be used

lambda

positive value, it influences the choice of models by affecting the selection of one, two or three normal distributions, thus introducing some weight on the effect of number of parameters to be defined. The default is 1, the model uses the BIC value taking into account the log-likelihood value

beta

Should be equal to 0; prior is put on the variance determination of the normal distribution

Value

fit2

list of the gene as first attributes, for each gene a list of three attributes:

G

number of normal components fitting the data

NorMixParam

the parameters of each normal component: proportion, mean and standard deviation for the gene

classification

the normal component id in which the expression values of the gene are attributed

Author(s)

Florence Cavalli, florence@ebi.ac.uk

See Also

fitPrior, SpeCond

Examples

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library(SpeCond)
data(expressionSpeCondExample)
##Perform the SpeCond analysis step by step
param.detection=getDefaultParameter()
param.detection

fit1=fitPrior(expressionSpeCondExample, param.detection=param.detection)

specificOutlierStep1=getSpecificOutliersStep1(expressionSpeCondExample,
 fit=fit1$fit1, param.detection, multitest.correction.method="BY", 
prefix.file="run1_Step1", print.hist.pv=FALSE)

fit2=fitNoPriorWithExclusion(expressionSpeCondExample, 
specificOutlierStep1=specificOutlierStep1,
param.detection=param.detection)

##then use getSpecificResult()