# Identify genes that are under translational control independent of cytosolic mRNA levels

### Description

This function uses analysis of partial variance (APV) to identify genes that are under translational regulation independent of cytosolic mRNA levels.

### Usage

1 2 |

### Arguments

`dataT` |
A matrix with cytosolic mRNA data. Non numerical rownames are needed. |

`dataP` |
A matrix with translational activity data. Non numerical rownames are needed. |

`phenoVec` |
A vector describing the sample classes (each class should have a unique identifier). Note that dataT, dataP and phenoVec have to have the same sample order so that column 1 in dataP is the translational data for a sample, column 1 in dataT is the cytosolic mRNA data and position 1 in phenoVec describes the sample class. |

`anotaQcObj` |
The object returned by anotaPerformQc. |

`correctionMethod` |
anota corrects p-values for multiple testing using the multtest package. Correction method can be "Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY", "ABH" or "TSBH" as implemented in the multtest package or "qvalue" as implemented in the qvalue package. Default is "BH". |

`contrasts` |
When there is more than 2 sample categories it is possible to use custom contrasts. The order of the sample classes needs to be correct and can be seen in the object generated from anotaPerformQc in the phenoClasses slot (see details section). |

`useRVM` |
Should the Random Variance Model be applied. Default is TRUE. |

`useProgBar` |
Should the progress bar be shown. Default is TRUE, show progress bar. |

### Details

The function performs APV on two or more sample categories. When more than two sample classes are compared it is possible to set custom contrasts to compare the sample classes of interest. Otherwise "treatment" contrasts are used which follow the alphabetical order of the sample classes. The order of the sample classes which the contrast matrix should follow can be found in the output of the anotaPerformQc function in the phenoClasses slot. Contrasts are supplied as a matrix where the sample classes are rows (same order as phenoClasses) and the columns are the different contrasts used. Contrasts are coded by using e.g. -1 for group a, 0 for group b and 1 for group c to compare group a and c; -2 for group a, 1 for group b and 1 for group c to compare group a to b & c. Each column of the contrast matrix should sum to 0 and to analyze orthagonal contrasts the products of all pairwise rows should sum to 0. The results will follow the order of the contrasts, i.e. the anocovaStats slot in the output object is a list with positions 1...n where 1 is the first contrast and n is the last.

A rare error can occur when data within dataT or dataP from any gene and any sample class has no variance. This is reported as "ANOVA F-TEST on essentially perfect fit...". In this case those genes that show no variance for a sample class within either dataT or dataP need to be removed before analysis. Trying a different normalization method may fix the problem.

### Value

anotaGetSigGenes creates a plot showing the fit of the inverse gamma distribution used in RVM (similar output as from anotaPerformQc). anotaGetSigGenes also returns a list object with the following slots:

`apvStats` |
A list object (each slot named from 1 to the number of contrasts) where each slot contains a matrix with statistics from the applied APV for that contrast. Columns are "apvSlope" (the common slope used in APV); "apvSlopeP" (if the slope is <0 or >1 a p-value for the slope being <0 or >1 is calculated; if the slope is >=0 & <=1 this value is set to 1); "unadjustedResidError" (the residual error before calculating the effective residual error); "apvEff" (the group effect); "apvMSerror" (the effective mean square error); "apvF" (the F-value); "residDf" (the residual degrees of freedom); "apvP" (the p-value); "apvPAdj" (the adjusted p-value). |

`apvStatsRvm` |
A summary list object (each slot named from 1 to the number of contrasts) where each slot contains a matrix with RVM statistics from the applied APV. Columns are "apvSlope" (the common slope used in APV); "apvSlopeP" (if the slope is <0 or >1 a p-value for the slope being <0 or >1 is calculated; if the slope is >=0 & <=1 this value is set to 1); "apvEff" (the group effect); "apvRvmMSerror" (the effective mean square error after RVM); "apvRvmF" (the RVM F-value); "residRvmDf" (the residual degrees of freedom after RVM); "apvRvmP" (the RVM p-value); "apvRvmPAdj" (the adjusted RVM p-value). |

`correctionMethod` |
The multiple testing correction method used to adjust the p-values. |

`usedContrasts` |
A matrix with the contrasts used. Order is same as in the statistical outputs (column wise) so that the first contrast is found in the first slot of the apvStats and the apvStatsRvm lists. |

`abList` |
A list object containing the a and b parameters from the inverse gamma fits. Same order as the contrasts. |

### Author(s)

Ola Larsson ola.larsson@ki.se, Nahum Sonenberg nahum.sonenberg@mcgill.ca, Robert Nadon robert.nadon@mcgill.ca

### See Also

`anotaPerformQc`

,
`anotaResidOutlierTest`

, `anotaPlotSigGenes`

### Examples

1 | ```
## See example for \code{\link{anotaPlotSigGenes}}
``` |