anota2seq-plots {anota2seq} | R Documentation

## Visualization of anota2seq results

### Description

These functions generate graphical outputs aiming to provide an overview of the extents of different regulatory modes of gene expression. The graphical outputs consist of:

1. For all selected contrasts, P-value and FDR density plots indicating the distribution of P-values and FDRs for all analyzes found in the Anota2seqDataSet object.

2. Fold change plots where total mRNA log2FC is compared to translated mRNA (e.g. polysome-associated mRNA or RPF) log2FC (for all selected contrasts).

3. Per identifier and per treatment fitted regression curves between total and translated mRNA for all samples which helps evaluating regulation of single identifiers.

### Usage

    anota2seqPlotFC(object, visualizeRegModes = "all",
      selContrast, contrastName = NULL, fileStem = "ANOTA2SEQ_FoldchangePlot", plotToFile = TRUE, myYlim = NULL, myXlim = NULL, ...)

    anota2seqPlotPvalues(object, useRVM = TRUE, selContrast, contrastName = NULL,
      myBw = 0.05, plotToFile = TRUE, fileStem = "ANOTA2SEQ_pvalue_density", ...)

    anota2seqPlotGenes(object, selContrast, analysis, geneNames = NULL,
    plotToFile = TRUE, fileStem = "ANOTA2SEQ_significantGenes_plot")

    ## S4 method for signature 'Anota2seqDataSet'
    anota2seqPlotFC(object,
      visualizeRegModes = "all", selContrast, contrastName = NULL, fileStem = "ANOTA2SEQ_FoldchangePlot",
      plotToFile = TRUE, myYlim = NULL, myXlim = NULL, ...)

    ## S4 method for signature 'Anota2seqDataSet'
    anota2seqPlotPvalues(object,
      useRVM = TRUE, selContrast, contrastName = NULL, myBw = 0.05, plotToFile = TRUE,
      fileStem = "ANOTA2SEQ_pvalue_density", ...)

    ## S4 method for signature 'Anota2seqDataSet'
    anota2seqPlotGenes(object,
      selContrast, analysis, geneNames = NULL,
      plotToFile = TRUE, fileStem = "ANOTA2SEQ_significantGenes_plot")

### Arguments

`object` | An object of class Anota2seqDataSet. Should contain output of the anota2seqAnalyze function. To visualize significant identifiers in the fold change plot (`anota2seqPlotFC`) and for regression plots (`anota2seqPlotGenes`), the object should also contain the output of anota2seqSelSigGenes and/or anota2seqRegModes (see Details).
`visualizeRegModes` | Can be set to "all" (default, genes which were selected by `anota2seqSelSigGenes` are colored according to their allocated regulatory mode (change in mRNA abundance or change in translational efficiency leading to altered protein levels or buffering), "none" (no colors), "translation" (only identifiers regulated by change in translational efficiency leading to altered protein levels are colored), "buffering" (only identifiers regulated by change in translational efficiency leading to buffering are colored). The object will be required to contain different outputs depending on the value given to this parameter (see Details).
`selContrast` | Which contrast(s) should be considered? Descriptions of the contrasts can be found in the output from the anota2seqAnalyze object in the usedContrasts slot. Indicate the contrast(s) by a numeric vector of the column number(s).
`contrastName` | Custom name annotation for the selected contrast(s). Provide a character name for each selected contrast, this name will be used as plot title in the anota2seqPlotPvalues plots or as xlab and ylab annotation in the anota2seqPlotFC plots.
`fileStem` | if plotToFile is TRUE, this stem will be added in front of the output filename(s).
`plotToFile` | Boolean. If set to TRUE (default) the function will output the plot as a PDF file. If set to FALSE plots will be plotted to the R default graphics device.
`myXlim` | Specify the x-axis limits used for the scatterplots(s) in the anota2seqPlotFC function. Default is NULL.
`myYlim` | Specify the y-axis limits used for the scattersplot(s) in the anota2seqPlotFC function. Default is NULL
`...` | Graphical parameters that are passed to the par() function.
`useRVM` | Should the density plot be performed on RVM p-values/FDR (default) or no-RVM p-values/FDR?
`myBw` | Smoothing bandwidth parameter for used in the stats::density function. Within one plot, the same bandwidth will be used for all density curves.
`analysis` | For which analysis should anota2seqPlotGenes be performed. Can be set to "translation" or "buffering".
`geneNames` | When anota2seqPlotGenes performs the individual gene plots they will be named by the original row names supplied to the anota2seqAnalyze function. geneNames allows the user to add additional names when plotting to e.g. include gene symbols. Input is a matrix with one column where the original row names match the row names of the input matrix and the desired new names are given in column 1. Default is NULL i.e. no additional names.

### Details

`anota2seqPlotFC`: if visualizeRegModes is set to "none", the Anota2seqDataSet object is only required to contains an output of anota2seqAnalyze. If visualizeRegModes is set to "translation" or "buffering", the object should contain the output of anota2seqAnalyze and anota2seqSelSigGenes for the corresponding analysis. Finally, if visualizeRegModes is set to "all" (default), anota2seqRegModes should have been called on the Anota2seqDataSet object so that identifiers can be colored according to the three regulatory modes (i.e. change in mRNA abundance or change in translational efficiency leading to altered protein levels or buffering).

`anota2seqPlotGenes`: requires an Anota2seqDataSet object containing the output of `anota2seqAnalyze` and `anota2seqSelSigGenes` for translation and/or buffering. In the graphical output of `anota2seqPlotGenes`, the results for each significant gene is displayed on a separate row. The first graph shows all samples and per treatment regression lines using the common slope with different colors for each treatment. The magnitude of the common slope is indicated. The second graph shows key statistics for the identifier without the RVM model for all contrasts analyzed when running anota2seqAnalyze. The third graph is similar to the second but with RVM statistics instead.

### Value

No value is returned. These functions generate a graphical outputs as described above.

### Examples

    ## Not run:
    data(anota2seq_data)
    # Initialize the Anota2seqDataSet
    Anota2seqDataSet <- anota2seqDataSetFromMatrix(dataP = anota2seq_data_P[1:1000,],
                                          dataT = anota2seq_data_T[1:1000,],
                                          phenoVec = anota2seq_pheno_vec,
                                          dataType = "RNAseq",
                                          normalize = TRUE)
    # Perform anota2seqRun function (performQC is set to FALSE here to limit running
    # time of this example but the model assumptions should be assessed (see help of
    # anota2seqPerformQC))
    Anota2seqDataSet <- anota2seqRun(Anota2seqDataSet,
                                     performQC = FALSE,
                                     performROT = FALSE)
    # Visualize results
    anota2seqPlotPvalues(Anota2seqDataSet, plotToFile = FALSE, selContrast = 1)
    anota2seqPlotFC(Anota2seqDataSet, plotToFile = FALSE, selContrast = 1)
    anota2seqPlotGenes(Anota2seqDataSet, plotToFile = TRUE, analysis = "translation",
               selContrast = 1)

    ## End(Not run)

[Package anota2seq version 1.2.0 Index]