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