plot.aovBioCond  R Documentation 
aovBioCond
ObjectGiven 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 meanvariance 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.
## 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), ... )
x 
An object of class 
padj, pval 
Cutoff of adjusted/raw pvalue for selecting
significant intervals. Only one of the two arguments is effectively
used; 
col, pch 
Optional length2 vectors specifying the colors and point characters of nonsignificant 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

args.lines 
Further arguments to be passed to

... 
Further arguments to be passed to

The function returns NULL
.
bioCond
for creating a bioCond
object;
fitMeanVarCurve
for fitting a meanvariance curve for
a set of bioCond
objects; aovBioCond
for
calling differential intervals across multiple bioCond
s.
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 ChIPseq 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 = 1e6)
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