Function to detect differentially expressed genes when data are unpaired
SMVar.unpaired(geneNumbers, listcond, fileexport = NULL,
minrep = 2, method = "BH", threshold = 0.05)
Vector with gene names or dataframe which contains all information about spots on the chip
list of the different conditions to be compared
file to export the list of differentially expressed genes
minimum number of replicates to take a gene into account, minrep
must be higher than 2
method of multiple tests adjustment for p.values
threshold of False Discovery Rate
Only the number of differentially expressed genes is printed. If asked, the file giving the list of differentially expressed genes is created.
If the user creates an object when calling the function (for example "Stat=SMVar.paired(...)") then Stat contains the information for all genes, is sorted by ascending p-values and
gives the test statistics as described in the paper
gives the raw p-values
gives the number of degrees of freedom for the Student distribution for the test statistics
gives the first condition considered in the log-ratio
gives the second condition considered in the log-ratio
gives the logratios (listcond[[Cond2]]-listcond[[Cond1]])
gives the adjusted p-values
This function implements the structural model for variances described in (Jaffrezic et al., 2007).
Data must be normalized before calling the function. Matrix geneNumbers
must have one of
the following formats: "matrix","data.frame","vector","character","numeric","integer".
F. Jaffrezic, Marot, G., Degrelle, S., Hue, I. and Foulley, J. L. (2007) A structural mixed model for variances in differential gene expression studies. Genetical Research (89) 19:25
# NOT RUN {
library(SMVar)
data(ApoAIdata)
attach(ApoAIdata)
SMVar.unpaired(ApoAIGeneId,list(ApoAICond1,ApoAICond2))
# }
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