plotMVC | R Documentation |
bioCond
ObjectGiven an individual bioCond
object associated with a
mean-variance curve, plotMVC
draws a scatter plot of
observed (mean, log10(variance))
pairs from the genomic intervals
contained in the bioCond
. It also adds the mean-variance curve to
the plot. Notably, unlike plotMeanVarCurve
, here the observed
variances used for plotting are not adjusted but the mean-variance curve is
scaled based on the associated variance ratio factor (see
fitMeanVarCurve
and estimatePriorDf
for a
description of variance ratio factor).
plotMVC( cond, subset = c("all", "occupied", "non-occupied"), col = alpha("blue", 0.02), pch = 20, add = FALSE, xlab = "Mean", ylab = "log10(Var)", args.lines = list(col = "red", lwd = 2), only.add.line = FALSE, ... )
cond |
An individual |
subset |
A character string indicating the subset of genomic intervals
used for the scatter plot. Must be one of |
col, pch |
Optional vectors specifying the colors and point characters
of the genomic intervals in |
add |
Whether to add points to existing graphics (by calling
|
xlab, ylab |
Labels for the X and Y axes. |
args.lines |
Further arguments to be passed to
|
only.add.line |
A logical value. If set to |
... |
Further arguments to be passed to |
The function returns NULL
.
Tu, S., et al., MAnorm2 for quantitatively comparing groups of ChIP-seq samples. Genome Res, 2021. 31(1): p. 131-145.
bioCond
for creating a bioCond
object;
fitMeanVarCurve
for fitting a mean-variance curve on a
list of bioCond
objects; varRatio
for a formal
description of variance ratio factor; plotMeanVarCurve
for plotting a mean-variance curve on a list of bioCond
objects;
alpha
for adjusting color transparency.
data(H3K27Ac, package = "MAnorm2") attr(H3K27Ac, "metaInfo") ## Fit and plot a mean-variance curve for the GM12892 cell line (i.e., ## individual). # Perform the MA normalization and construct a bioCond to represent GM12892. norm <- normalize(H3K27Ac, 7:8, 12:13) GM12892 <- bioCond(norm[7:8], norm[12:13], name = "GM12892") # Fit a mean-variance curve by using the parametric method. GM12892 <- fitMeanVarCurve(list(GM12892), method = "parametric", occupy.only = TRUE, init.coef = c(0.1, 10))[[1]] # Draw a mean-variance scatter plot with adjusting observed variances. plotMeanVarCurve(list(GM12892), subset = "occupied") # Draw a mean-variance scatter plot with scaling the mean-variance curve. plotMVC(GM12892, subset = "occupied")
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