Given the clustering of the samples in good and poor prognosis associated to the signature, for each gene in the signature the test for the null hypothesis of equality of the expression levels is performed. Additional statistics are provided.

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`aSignatureFinder` |
(structure) as results from the function signatureFinder(). |

`permutationReplications` |
(integer) number of replications of the permutation test (default: 1000). |

`cpuCluster` |
structure as result from the NCPUS() function |

`stopCpuCluster` |
flag to control if the channel to the cpu-cluster has to be closed. |

The t-test for testing the differential expression of the genes in the signature is performed according to the procedure of Dudoit et al. (2002). The test statistics is the Welch's one and the null distribution is obtained through a permutation scheme.

The function returns the same variable in the input aSignatureFinder structure and

`groupMedian` |
real matrix with as many rows as length(aSignatureFinder$signature) and two columns containing the medians of each gene with respect to the good and poor prognosis group |

`medianAbsDifference` |
a list of real with as many elements as length(aSignatureFinder$signature) where each entry is the absolute difference of the medians computed in each group |

`groupMean` |
real matrix with as many rows as length(signature$signature) and two columns containing the means of each gene with respect to the good and poor prognosis group |

`meanAbsDifference` |
a list of real with as many elements as length(aSignatureFinder$signature) where each entry is the absolute difference of the means computed in each group |

`meanDifferenceTValue` |
a list of real with as many elements as length(aSignatureFinder$signature) where each entry is the value of the test statistic |

`meanDifferencePValue` |
a list of real with as many elements as length(aSignatureFinder$signature) where each entry is the p-value of the test statistic |

Stefano M. Pagnotta and Michele Ceccarelli

Dudoit S. et al.: Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments Statistica Sinica, 12, pp. 111–139, 2002.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
# find the signature starting from the gene SELP for the Non Small Cell Lung Cancer
# set the working data
data(geNSCLC)
geData <- geNSCLC
data(stNSCLC)
stData <- stNSCLC
# set the dimension of the cpu's cluster
# (use a value different from 2 depending on the number of cpu avalilable)
aMakeCluster <- makeCluster(2)
# set the starting gene to SELP
geneSeed <- which(colnames(geData) == "SELP")
# run ...
ans <- signatureFinder(geneSeed, logFilePrefix = "test",
cpuCluster = aMakeCluster, stopCpuCluster = FALSE)
ans
ans <- testGE(ans, cpuCluster = aMakeCluster)
ans$groupMean
ans$meanDifferencePValue
#####################
#library(gplots)
#barplot2(t(ans$groupMean), beside = TRUE,
# main = paste("Signature starting from:", ans$startingSignature),
# legend = paste(colnames(ans$groupMedian), "prognosis group"))
``` |

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