Description Usage Arguments Details Value See Also Examples
The function is used to access the outputs from the anota2seqAnalyze, anota2seqSelSigGenes and anota2seqRegModes stored in the Anota2seqDataSet
1 2 3 4 5  anota2seqGetOutput(object, analysis, output, selContrast, getRVM = TRUE)
## S4 method for signature 'Anota2seqDataSet'
anota2seqGetOutput(object, analysis, output,
selContrast, getRVM = TRUE)

object 
An Anota2seqDataSet 
analysis 
A character string that can be "translated mRNA", "total mRNA", "translation", "buffering" or "mRNA abundance". This parameter is not needed when the output parameter is set to "singleDf". 
output 
A character string specifying whether the output of the
anota2seqAnalyze ("full") or the anota2seqSelSigGenes ("selected") function
should be returned. This parameter can also be set to "regModes" in which
case the output of anota2seqRegModes is returned (i.e. identifiers which
were given as single regulatory mode).Lastly, the parameter can be set to "singleDf"
in this case the function returns a single data frame with apvEff and apvRvmPadj values for
translated mRNA, total mRNA, translation and buffering. Additionally,there is a column "singleRegMode"
indicating the regulatory mode the identifier belongs to and corresponds to the visualization
with the 
selContrast 
A numeric vector specifying for which contrast the output should be retrieved. The contrast number corresponds to the position of the column in the automatically generated or specified contrast matrix. 
getRVM 
TRUE or FALSE indicating whether the RVM output be retrieved. Default is TRUE. NOTE: the output of anota2seqAnalyze (accessed with output = "full") is available both with RVM (getRVM = TRUE) and without RVM (getRVM = FALSE). However, when RVM was set to TRUE, respectively FALSE, in the anota2seqRun or anota2seqSelSigGenes functions, all subsequent filtering is done on the RVM, respectively noRVM, output. 
This function is used to access outputs from the anota2seqRun, anota2seqAnalyze, anota2seqSelSigGenes and anota2seqRegModes functions stored within the Anota2seqDataSet. The output of the anota2seqAnalyze function holds analysis results on all identifiers (slope, pvalue of test on slope value, residual error, group effect, mean square error, Fvalue, residual degrees of freedom, pvalue, adjusted pvalue) and is accessed with output = "full". The output of the anota2seqSelSigGenes function is a reduced set based on filtering criteria and is accessed using output = "selected". In the case of accessing the output of anota2seqRegModes (output = "regModes"), the output will further be reduced by removal of the overlap between regulatory modes so that each gene has a unique regulatory mode. For this, we prioritize regulatory patterns so that translation > mRNA abundance > buffering (i.e. if an identifier is found as changing its translational efficiency leading to altered protein levels and mRNA abundance it will be removed from the mRNA abundance group). Lastly, to access a single data frame with merged apvEff and apvRvmPAdj results only from the analysis of translated mRNA, total mRNA, translation and buffering with indicated regulatory mode for each identifier the parameter can be set to output = "singleDf".
Returns a data.frame with the following columns:
If getRVM is FALSE: A data.frame with statistics from the applied APV for that contrast. Columns are:
apvSlope: the common slope used in APV;
apvSlopeP: for translation if the slope is <0 or >1 a pvalue for the slope being <0 or >1 is calculated; if the slope is >=0 & <=1 this value is set to 1. For buffering if the slope is <1 or >0 a pvalue for the slope being <1 or >0 is calculated; if the slope is >=1 & <=0 this value is set to 1;
unadjustedResidError: (the residual error before calculating the effective residual error);
apvEff: the group effect; log2FC. Please note that when analyzing changes in translational efficiency leading to buffering (and only for this analysis), an identifier with positive apvEff will be interpreted as being buffered down and a negative apvEff buffered up (see vignette for more details);
apvMSerror: the effective mean square error;
apvF: the Fvalue;
residDf: the residual degrees of freedom;
apvP: the pvalue;
apvPAdj: the adjusted pvalue.
if getRVM is TRUE: A data.frame with RVM statistics from the applied APV. Columns are:
apvSlope: the common slope used in APV;
apvSlopeP: for translation if the slope is <0 or >1 a pvalue for the slope being <0 or >1 is calculated; if the slope is >=0 & <=1 this value is set to 1. For buffering if the slope is <1 or >0 a pvalue for the slope being <1 or >0 is calculated; if the slope is >=1 & <=0 this value is set to 1;
apvEff: the group effect; log2FC; Please note that when analyzing changes in translational efficiency leading to buffering (and only for this analysis), an identifier with positive apvEff will be interpreted as being buffered down and a negative apvEff buffered up (see vignette for more details)
apvRvmMSerror: the effective mean square error after RVM;
apvRvmF: the RVM Fvalue;
residRvmDf: the residual degrees of freedom after RVM;
apvRvmP: the RVM pvalue;
apvRvmPAdj: the adjusted RVM pvalue.
The output of anota2seqSelSigGenes contains a matrix with the same columns and with a subset of rows corresponding to filtered identifiers.
When anota2seqRegModes has been run on the object, an additional column named singleRegMode is added indicating the allocated regulatory mode of each identifier.
While in the output of anota2seqSelSigGenes (accessed with output = "selected") a same identifier can be selected in several regulatory modes (e.g. both translation up and mRNA abundance up), the output of anota2seqRegModes (accessed with output = "regModes") shows a single regulatory mode allocated to each identifier according to the priority rule explained in the Details section.
anota2seqRun
,
anota2seqAnalyze
, anota2seqSelSigGenes
,
anota2seqRegModes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  # Initialize the Anota2seqDataSet
Anota2seqDataSet < anota2seqDataSetFromMatrix(
dataP = anota2seq_data_P[1:100,],
dataT = anota2seq_data_T[1:100,],
phenoVec = anota2seq_pheno_vec,
dataType = "RNAseq",
normalize = TRUE)
# Perform analysis of changes in translational efficiency leading to altered
# protein levels
Anota2seqDataSet < anota2seqAnalyze(Anota2seqDataSet, analysis = "translation",
useProgBar = FALSE)
# Filter
Anota2seqDataSet < anota2seqSelSigGenes(Anota2seqDataSet, selContrast = 1,
maxPAdj = .2, analysis = "translation")
# Get output for analysis of changes in translational efficiency leading to
# altered protein levels
translationResults < anota2seqGetOutput(Anota2seqDataSet,
output= "full",
analysis="translation",
selContrast = 1,
getRVM = TRUE)
# Get the selected output for analysis of changes in translational efficiency
# leading to altered protein levels
translationResultsSig < anota2seqGetOutput(Anota2seqDataSet,
output= "selected",
analysis="translation",
selContrast = 1,
getRVM = TRUE)

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