plot_ma {ideal} | R Documentation

## MA-plot from base means and log fold changes

### Description

MA-plot from base means and log fold changes, in the ggplot2 framework, with additional support to annotate genes if provided.

### Usage

    plot_ma(res_obj, FDR = 0.05, point_alpha = 0.2, sig_color = "red",
      annotation_obj = NULL, hlines = NULL, title = NULL,
      xlab = "mean of normalized counts - log10 scale", ylim = NULL,
      add_rug = TRUE, intgenes = NULL, intgenes_color = "steelblue",
      labels_intgenes = TRUE, labels_repel = TRUE)

### Arguments

`res_obj` | A `DESeqResults` object
`FDR` | Numeric value, the significance level for thresholding adjusted p-values
`point_alpha` | Alpha transparency value for the points (0 = transparent, 1 = opaque)
`sig_color` | Color to use to mark differentially expressed genes. Defaults to red
`annotation_obj` | A `data.frame` object, with row.names as gene identifiers (e.g. ENSEMBL ids) and a column, `gene_name`, containing e.g. HGNC-based gene symbols. Optional
`hlines` | The y coordinate (in absolute value) where to draw horizontal lines, optional
`title` | A title for the plot, optional
`xlab` | X axis label, defaults to "mean of normalized counts - log10 scale"
`ylim` | Vector of two numeric values, Y axis limits to restrict the view
`add_rug` | Logical, whether to add rug plots in the margins
`intgenes` | Vector of genes of interest. Gene symbols if a `symbol` column is provided in `res_obj`, or else the identifiers specified in the row names
`intgenes_color` | The color to use to mark the genes on the main plot.
`labels_intgenes` | Logical, whether to add the gene identifiers/names close to the marked plots
`labels_repel` | Logical, whether to use `geom_text_repel` for placing the labels on the features to mark

### Details

The genes of interest are to be provided as gene symbols if a `symbol` column is provided in `res_obj`, or else b< using the identifiers specified in the row names

### Value

An object created by `ggplot`

### Examples

    library(airway)
    data(airway)
    airway
    dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
                                                 colData = colData(airway),
                                                 design=~cell+dex)
    # subsetting for quicker run, ignore the next two commands if regularly using the function
    gene_subset <- c(
      "ENSG00000103196",  # CRISPLD2
      "ENSG00000120129",  # DUSP1
      "ENSG00000163884",  # KLF15
      "ENSG00000179094",  # PER1
      rownames(dds_airway)[rep(c(rep(FALSE,99), TRUE), length.out=nrow(dds_airway))]) # 1% of ids
    dds_airway <- dds_airway[gene_subset,]

    dds_airway <- DESeq2::DESeq(dds_airway)
    res_airway <- DESeq2::results(dds_airway)

    plot_ma(res_airway, FDR = 0.05, hlines = 1)

    plot_ma(res_airway, FDR = 0.1,
            intgenes = c("ENSG00000103196",  # CRISPLD2
                         "ENSG00000120129",  # DUSP1
                         "ENSG00000163884",  # KLF15
                         "ENSG00000179094")  # PER1
           )

[Package ideal version 1.8.1 Index]