Description Usage Arguments Details Value Note Author(s) References Examples

Function to detect differentially expressed genes when data are unpaired

1 | ```
SMVar.unpaired(geneNumbers, listcond, fileexport = NULL, minrep = 2, method = "BH", threshold = 0.05)
``` |

`geneNumbers` |
Vector with gene names or dataframe which contains all information about spots on the chip |

`listcond` |
list of the different conditions to be compared |

`fileexport` |
file to export the list of differentially expressed genes |

`minrep` |
minimum number of replicates to take a gene into account, |

`method` |
method of multiple tests adjustment for p.values |

`threshold` |
threshold of False Discovery Rate |

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".

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

`Stat$TestStat ` |
gives the test statistics as described in the paper |

`Stat$StudentPValue` |
gives the raw p-values |

`Stat$DegOfFreedom ` |
gives the number of degrees of freedom for the Student distribution for the test statistics |

`Stat$Cond1` |
gives the first condition considered in the log-ratio |

`Stat$Cond2` |
gives the second condition considered in the log-ratio |

`Stat$LogRatio` |
gives the logratios (listcond[[Cond2]]-listcond[[Cond1]]) |

`Stat$AdjPValue` |
gives the adjusted p-values |

If the first column of the file geneNumbers contains identical names for two different spots, these two spots are only counted once if they are both differentially expressed. By default, the correction for multiple testing is Benjamini Hochberg with a threshold of False Discovery Rate (FDR) of 5%. The FDR threshold can be changed, and it is also possible to choose the multiple test correction method ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"). To see the references for these methods, use the R-help ?p.adjust.

Guillemette Marot with contributions from Anne de la Foye

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

1 2 3 4 |

```
[1] "8 differentially expressed gene(s)"
[1] "Warning: No file is given to save the results of the analysis"
```

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