plot {TCC} | R Documentation

## Plot a log fold-change versus log average expression (so-called M-A plot)

### Description

This function generates a scatter plot of log fold-change (i.e., M = log2(G2) - log2(G1) on the y-axis between Groups 1 vs. 2) versus log average expression (i.e., A = (log2(G1) + log2(G2)) / 2 on the x-axis) using normalized count data.

### Usage

    ## S3 method for class 'TCC'
    plot(x, FDR = NULL, median.lines = FALSE, floor = 0,
         group = NULL, col = NULL, col.tag = NULL, normalize = TRUE, ...)

### Arguments

`x` | TCC-class object.
`FDR` | numeric scalar specifying a false discovery rate (FDR) threshold for determining differentially expressed genes (DEGs)
`median.lines` | logical. If `TRUE`, horizontal lines specifying the median M values for non-DEGs (black) and DEGs (red) are drawn.
`floor` | numeric scalar specifying a threshold for adjusting low count data.
`group` | numeric vector consists two elements for specifying what two groups should be drawn when data contains more than three groups.
`col` | vector specifying plotting color.
`col.tag` | numeric vector spacifying the index of `col` for coloring the points of the genes.
`normalize` | logical. If `FALSE`, the coordinates of M-A plot are calculated from the raw data.
`...` | further graphical arguments, see `plot.default`.

### Details

This function generates roughly three different M-A plots depending on the conditions for TCC-class objects. When the function is performed just after the `new` method, all the genes (points) are treated as non-DEGs (the default is black; see Example 1). The `simulateReadCounts` function followed by the `plot` function can classify the genes as true non-DEGs (black), true DEGs. (see Example 2). The `estimateDE` function followed by the `plot` function generates estimated DEGs (magenta) and the remaining estimated non-DEGs (black).

Genes with normalized counts of 0 in any one group cannot be plotted on the M-A plot because those M and A values cannot be calculated (as \log 0 is undefined). Those points are plotted at the left side of the M-A plot, depending on the minimum A (i.e., log average expression) value. The x coordinate of those points is the minimum A value minus one. The y coordinate is calculated as if the zero count was the minimum observed non zero count in each group.

### Value

A scatter plot to the current graphic device.

### Examples

    # Example 1.
    # M-A plotting just after constructing the TCC class object from
    # hypoData. In this case, the plot is generated from hypoData
    # that has been scaled in such a way that the library sizes of
    # each sample are the same as the mean library size of the
    # original hypoData. Note that all points are in black. This is
    # because the information about DEG or non-DEG for each gene is
    # not indicated.
    data(hypoData)
    group <- c(1, 1, 1, 2, 2, 2)
    tcc <- new("TCC", hypoData, group)
    plot(tcc)

    normalized.count <- getNormalizedData(tcc)
    colSums(normalized.count)
    colSums(hypoData)
    mean(colSums(hypoData))

    # Example 2.
    # M-A plotting of DEGES/edgeR-normalized simulation data.
    # It can be seen that the median M value for non-DEGs approaches
    # zero. Note that non-DEGs are in black, DEGs are in red.
    tcc <- simulateReadCounts()
    tcc <- calcNormFactors(tcc, norm.method = "tmm", test.method = "edger",
                           iteration = 1, FDR = 0.1, floorPDEG = 0.05)
    plot(tcc, median.lines = TRUE)

    # Example 3.
    # M-A plotting of DEGES/edgeR-normalized hypoData after performing
    # DE analysis.
    data(hypoData)
    group <- c(1, 1, 1, 2, 2, 2)
    tcc <- new("TCC", hypoData, group)
    tcc <- calcNormFactors(tcc, norm.method = "tmm", test.method = "edger",
                           iteration = 1, FDR = 0.1, floorPDEG = 0.05)
    tcc <- estimateDE(tcc, test.method = "edger", FDR = 0.1)
    plot(tcc)

    # Changing the FDR threshold
    plot(tcc, FDR = 0.7)

[Package TCC version 1.20.1 Index]