plot.aovBioCond {MAnorm2}

Plot an aovBioCond Object

Description

Given an aovBioCond object, which records the results of calling differential genomic intervals across a set of bioCond objects, this method creates a scatter plot of (conds.mean, log10(between.ms)) pairs from all genomic intervals, marking specifically the ones that show a statistical significance. See aovBioCond for a description of the two variables and the associated hypothesis testing. The mean-variance curve associated with the bioCond objects is also added to the plot, serving as a baseline to which the between.ms variable of each interval could be compared.

Usage

## S3 method for class 'aovBioCond'
plot(
  x,
  padj = NULL,
  pval = NULL,
  col = alpha(c("black", "red"), 0.04),
  pch = 20,
  xlab = "Mean",
  ylab = "log10(Var)",
  args.legend = list(x = "bottomleft"),
  args.lines = list(col = "green3", lwd = 2),
  ...
)

Arguments

x

An object of class "aovBioCond", typically a returned value from aovBioCond.

padj, pval

Cutoff of adjusted/raw p-value for selecting significant intervals. Only one of the two arguments is effectively used; pval is ignored if padj is specified. The default is equivalent to setting padj to 0.1.

col, pch

Optional length-2 vectors specifying the colors and point characters of non-significant and significant intervals, respectively. Elements are recycled if necessary.

xlab, ylab

Labels for the X and Y axes.

args.legend

Further arguments to be passed to legend.

args.lines

Further arguments to be passed to lines.

...

Further arguments to be passed to plot.

Value

The function returns NULL.

See Also

bioCond for creating a bioCond object; fitMeanVarCurve for fitting a mean-variance curve for a set of bioCond objects; aovBioCond for calling differential intervals across multiple bioConds.

Examples

data(H3K27Ac, package = "MAnorm2")
attr(H3K27Ac, "metaInfo")

## Call differential genomic intervals among GM12890, GM12891 and GM12892
## cell lines and visualize the overall analysis results.

# Perform MA normalization and construct bioConds to represent the cell
# lines.
norm <- normalize(H3K27Ac, 4, 9)
norm <- normalize(norm, 5:6, 10:11)
norm <- normalize(norm, 7:8, 12:13)
conds <- list(GM12890 = bioCond(norm[4], norm[9], name = "GM12890"),
              GM12891 = bioCond(norm[5:6], norm[10:11], name = "GM12891"),
              GM12892 = bioCond(norm[7:8], norm[12:13], name = "GM12892"))
autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY"))
conds <- normBioCond(conds, common.peak.regions = autosome)

# Variations in ChIP-seq signals across biological replicates of a cell line
# are generally of a low level, and their relationship with the mean signal
# intensities is expected to be well modeled by the presumed parametric
# form.
conds <- fitMeanVarCurve(conds, method = "parametric", occupy.only = TRUE)
summary(conds[[1]])
plotMeanVarCurve(conds, subset = "occupied")

# Perform a moderated ANOVA on these cell lines.
res <- aovBioCond(conds)
head(res)

# Visualize the overall analysis results.
plot(res, padj = 1e-6)