anota2seqAnalyze {anota2seq} | R Documentation

## Analysis of translational activity

### Description

Analysis of treatment effects for translated mRNA (polysome-associated mRNA or RPFs; i.e. the "P" samples in the SummarizedExperiment annotation or the samples in the dataP matrix) or total mRNA (i.e. the "T" samples in the SummarizedExperiment annotation or the samples in the dataT matrix); and/or identification of changes in translational efficiency leading to altered protein levels or buffering.

### Usage

    anota2seqAnalyze(Anota2seqDataSet, contrasts = NULL,
      correctionMethod = "BH", useProgBar = TRUE, fileStem = "ANOTA2SEQ",
      analysis = c("translation", "buffering", "translated mRNA", "total mRNA"))

### Arguments

`Anota2seqDataSet` | An object of class Anota2seqDataSet
`contrasts` | If NULL (default), the contrasts will be created automatically and may not correspond to those of interest. It is therefore possible to use custom contrasts using this parameter. The input should be a matrix with row names corresponding to treatments (i.e. those in phenoVec or the treatment column of the SummarizedExperiment annotation) and columns corresponding to the different contrasts of interest (see Details section and examples for additional information on how to set up a custom contrast matrix).
`correctionMethod` | Correction for multiple testing method. This parameter can be set to "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".
`useProgBar` | Should the progress bar be shown? Default is TRUE, show progress bar.
`fileStem` | Stem for output file name (see Value section). Default is "ANOTA2SEQ".
`analysis` | Specification of which analysis should be performed. Can be set to "translated mRNA","total mRNA", "translation", "buffering" or any combination of these. If set to translated mRNA or total mRNA, the function will perform differential expression analysis (based on default or supplied contrasts) for the specified mRNA source. If set to "translation", analysis of changes in translational efficiency leading to altered protein levels will be performed. If set to "buffering", analysis of changes in translational efficiency leading to buffering will be performed. Default is c("translated mRNA", "total mRNA", "translation", "buffering") i.e. all analyzes are performed.

### Details

The function performs APV on two or more sample categories according to default contrasts or contrasts supplied by the user. The Random Variance Model (RVM) will be applied but the results without RVM will also be available in the output.

More details about setting a contrast matrix: By default (i.e. with contrasts = NULL), the order of the sample classes which are used to calculate differences between treatments will be in alphabetical order. To change the directionality of the contrasts (e.g. treatment b vs treatment a instead of treatment a vs treatment b) or to generate a custom set of contrasts when more than 2 treatments are included, a contrast matrix can be supplied to the "contrasts" parameter described above. The row names should be specified as indicated above. The 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 in the Anota2seqDataSet object will follow the order of the contrasts (i.e. results for e.g. contrast 1 will correspond to the contrasts specified in column 1 of the contrast matrix).

### Value

An Anota2seqDataSet. anota2seqAnalyze saves its output data in the 'translatedmRNA', 'totalmRNA', 'translation' and 'buffering' slots of the Anota2seqDataSet output object when analysis is set to "translated mRNA", "total mRNA", "translation" and "buffering" respectively. This output contains statistics from the applied APV model and can be accessed using `anota2seqGetOutput` (See ?`anota2seqGetOutput` for a detailed description). Additionally, `anota2seqAnalyze` saves fold changes and translational efficiency (TE) scores in the 'deltaData' slot of the Anota2seqDataSet object. (See ?`anota2seqGetDeltaData` for a detailed description of deltaData).

`anota2seqAnalyze` creates a plot showing the fit of the inverse gamma distribution used in RVM (i.e. a similar output as compared to that from anota2seqPerformQC) for each contrast.

### See Also

`anota2seqGetOutput`, `anota2seqGetDeltaData`

### Examples

    data(anota2seq_data)
    # Initialize Anota2seqDataSet
    Anota2seqDataSet <- anota2seqDataSetFromMatrix(
        dataP = anota2seq_data_P[1:100,],
        dataT = anota2seq_data_T[1:100,],
        phenoVec = anota2seq_pheno_vec,
        dataType = "RNAseq",
        normalize = TRUE)
    # Run analysis of changes in translational efficiency leading to
    # altered protein levels and differential expression analysis on
    # translated mRNA
    ## Not run:
    Anota2seqDataSet <- anota2seqAnalyze(
        Anota2seqDataSet,
        analysis = c("translation"))

    ## End(Not run)
    # Example to build a custom contrast matrix
    # For the purpose of this example, we will use the first 6 samples of the
    # simulated data provided with the package together with the following "dummy"
    # sample classes:
    phenoVec <- c("a","a","b","b","c","c")
    contrastsEx_ads <- anota2seqDataSetFromMatrix(
        dataP = anota2seq_data_P[1:100, 1:6],
        dataT = anota2seq_data_T[1:100, 1:6],
        phenoVec = phenoVec,
        dataType = "RNAseq",
        normalize = TRUE)

    # Get the levels of the phenoVec, these will be ordered as in anota2seq
    phenoLev <- levels(as.factor(phenoVec))
    # Construct the matrix with appropriate nrow and ncol
    myContrast <- matrix(nrow =length(phenoLev),ncol=length(phenoLev)-1)
    # Set the phenoLev as rownames for your contrast matrix
    rownames(myContrast) <- phenoLev
    # Now indicate the contrasts you want to analyse as explained above
    # Compare a to c
    myContrast[,1] <- c(-1,0,1)
    # Compare a to b& c
    myContrast[,2] <- c(2,-1,-1)
    myContrast
    #   [,1] [,2]
    # a   -1    2
    # b    0   -1
    # c    1   -1
    # The custom contrast matrix can then be used as input of anota2seqAnalyze.
    contrastsEx_ads <- anota2seqAnalyze(contrastsEx_ads,
                                        analysis = "translation",
                                        contrasts = myContrast)

[Package anota2seq version 1.0.1 Index]