SpeCond
performs a full conditionspecific detection from an expression matrix
1 2 3 4 5 
expressionMatrix 
an ExpressionSet object or a matrix of expression values (in log2); columns are the conditions, rows are genes (or probe sets) 
param.detection 
the parameter matrix for the detection must contain the values for "lambda", "beta", "per", "md", "mlk" ,"rsd' and "pv" for the two steps of the procedure. Can be obtained by 
multitest.correction.method 
the multitest correction method. The default is "BY", for the possible values see 
prefix.file 
a prefix added to the histogram file (if produced). It will be used to link to the result html pages generated by other functions using the result object of this function (if no other prefix value is implemented). The default is "A". It is useful to change the prefix when you perform a new analysis with different parameters as you may want to compare the results 
print.hist.pv 
a logical (TRUE/FALSE) value indicating whether a histogram of (nonadjusted) pvalues is to be printed; the default is FALSE 
Optional parameters:
fit1 
the result of 
fit2 
the result of 
specificOutlierStep1 
the list of outliers detected by the first step procedure, result of the

condition.factor 
this argument can be used if expressionMatrix is an ExpressionSet object; a factor object of length equal to the number of columns (samples) of the ExpressionSet object specifying which sample(s) belong to which condition (condition.factor levels); can be extracted from the phenoData 
condition.method 
this argument can be used if expressionMatrix is an ExpressionSet object; the method (mean or max) to summarise the samples by conditions (defined by the condition.factor vector) 
SpeCond uses the Mclust
function to obtain the mixture of normal distributions uses by the detection procedure.
If expressionMatrix
is an ExpressionSet object it is necessary to obtain an expression value matrix. This is obtain by the getMatrixFromExpressionSet
function. This take into consideration if condition.factor
is not NULL the transformation of the expression values for the several samples of each condition to one expression values for each condition for each gene.
If print.hist.pv
is TRUE the histogramme of the nonadjusted pvalues is plotted. It is a way to check the normal distribution fitting. If the histogramme is relatively flat the normal distribution(s) fits properly the data.
An object of class sp_list
prefix.file 
the prefix used for this analysis. It will be used by default in the function 
fit1 
the fitting parameters used by the detection in the first step of the procedure: the result of the 
fit2 
the fitting parameters used by the detection in the second step of the procedure: the result of the 
specificOutliersStep1 
the condition(s) for which the expression value of the gene is detected as outlier in the first step of the procedure. If NULL, no expression value has been detected in the first step. The second fitting ignores these expression values 
specificResult 
a list of 7 attributes containing al the specific results. This object result of 
Florence Cavalli, florence@ebi.ac.uk
C.Fraley and A. E. Raftery, Modelbased clustering, discriminant analysis, and density estimation, Journal of the American Statistical Association, Vol. 97, pages 611631 (2002).
C. Fraley and A. E. Raftery, MCLUST Version 3 for R: Normal Mixture Modeling and Modelbased Clustering, Technical Report No. 504, Department of Statistics, University of Washington, September 2006.
Mclust
, fitPrior
, fitNoPriorwithExclusion
,
getSpecificOutliersStep1
, getSpecificResult
1 2 3 4 5 6 7  library(SpeCond)
data(expressionSpeCondExample)
dim(expressionSpeCondExample)
##Perform the condition specific detection analysis with SpeCond()
generalResult=SpeCond(expressionSpeCondExample, param.detection=NULL,
multitest.correction.method="BY", prefix.file="E", print.hist.pv=TRUE,
fit1=NULL, fit2=NULL, specificOutlierStep1=NULL)

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