Description Usage Arguments Details Value Examples

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.

1 2 3 |

`x` |
TCC-class object. |

`FDR` |
numeric scalar specifying a false discovery rate (FDR) threshold for determining differentially expressed genes (DEGs) |

`median.lines` |
logical. If |

`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 |

`normalize` |
logical. If |

`...` |
further graphical arguments, see |

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.

A scatter plot to the current graphic device.

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 32 33 34 35 36 37 38 39 40 41 42 | ```
# 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)
``` |

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