dba.plotMA: Generate MA and scatter plots of differential binding...
In DiffBind: Differential Binding Analysis of ChIP-Seq Peak Data

Description Usage Arguments Author(s) See Also Examples

Generates MA and scatter plots of differential binding analysis results.

dba.plotMA(DBA, contrast=1, method=DBA$config$AnalysisMethod, 
           th=DBA$config$th, bUsePval=DBA$config$bUsePval, 
           fold=0, bNormalized=TRUE,
           factor="", bFlip=FALSE, bXY=FALSE, dotSize=.45, 
           bSignificant=TRUE, bSmooth=TRUE, bLoess=TRUE,
           xrange, yrange, ...)

DBA

DBA object, on which dba.analyze should have been successfully run.

`contrast`	number of contrast to report on. See `dba.show(DBA, bContrast=TRUE)` to get contrast numbers. Alternatively, an MA plot can be generated without a specific contrast, plotting one set of samples against another. In this case, `contrast` should be a list on length one or two. Each element of the list should be either a logical sample mask, or a vector of sample numbers. If the second set of samples is jot specified (list is length one), all the samples other than those specified will be used for the second group. The list elements should be named; these names will be used as labels for the sample groups in the plot.
`method`	method or vector of methods to plot results for: `DBA_DESEQ2` `DBA_DESEQ2_BLOCK` `DBA_EDGER` `DBA_EDGER_BLOCK`
`th`	significance threshold; all sites with FDR (or p-values, see `bUsePval`) less than or equal to this value will be colored red in the plot
`bUsePval`	logical indicating whether to use FDR (`FALSE`) or p-value (`TRUE`) for thresholding.
`fold`	will only include sites with fold change greater than this as significant (colored red).
`bNormalized`	logical indicating whether to plot normalized data using normalization factors computed by differential analysis method (`TRUE`) or raw read counts (`FALSE`).
`factor`	string to be prepended to plot main title; e.g. factor name.
`bFlip`	logical indicating that order of groups in contrast should be "flipped", allowing control of which sample group will have positive and which will have negative fold changes.
`bXY`	logical indicating whether to draw MA plot (`FALSE`) or XY scatter plot (`TRUE`).
`dotSize`	size of points on plot (`cex`).
`bSignificant`	Logical indicating if points corresponding to significantly differentially bound sites (based on `contrast`, `th`, `bUsePval`, and `fold` parameters) should be overlaid in red.
`bSmooth`	logical indicating that basic plot should be plotted using `smoothScatter`. Note that overlaid significant sites will be not plotted using a smoothing function.
`bLoess`	logical indicating that a MA plot should include a fitted loess curve.
`xrange`	vector of length 2 containing the desired minimum and maximum concentrations to plot.
`yrange`	vector of length 2 containing the desired minimum and maximum fold changes to plot.
`...`	passed to underlying plotting functions.

Rory Stark

dba.analyze

data(tamoxifen_analysis)

# default MA plot
dba.plotMA(tamoxifen)

# Show different normalizations
tamoxifen <- dba.normalize(tamoxifen,method=DBA_ALL_METHODS, 
                           library=DBA_LIBSIZE_PEAKREADS, background=FALSE)
tamoxifen <- dba.analyze(tamoxifen, method=DBA_ALL_METHODS)

par(mfrow=c(3,2))
dba.plotMA(tamoxifen,th=0,bNormalized=FALSE,sub="NON-NORMALIZED")
dba.plotMA(tamoxifen,th=0,bNormalized=FALSE,sub="NON-NORMALIZED")

dba.plotMA(tamoxifen,method=DBA_DESEQ2,bNormalized=TRUE,
           sub="DESeq2_RLE-RiP")
dba.plotMA(tamoxifen,method=DBA_EDGER,bNormalized=TRUE,
           sub="edgeR_TMM-RiP")

tamoxifen <- dba.normalize(tamoxifen, method=DBA_ALL_METHODS,
                           normalize=DBA_NORM_LIB, background=FALSE) 
tamoxifen <- dba.analyze(tamoxifen,method=DBA_ALL_METHODS)

dba.plotMA(tamoxifen,method=DBA_DESEQ2,bNormalized=TRUE, 
           sub="DESeq2_LIB-FULL")
dba.plotMA(tamoxifen,method=DBA_EDGER,bNormalized=TRUE,
           sub="edgeR_LIB-FULL")
           
# MA plots of samples without a contrast
data(tamoxifen_counts)
par(mfrow=c(2,2))
dba.plotMA(tamoxifen,list(Resistant=tamoxifen$masks$Resistant,
                          Responsive=tamoxifen$masks$Responsive),
                          bNormalized=FALSE)
dba.plotMA(tamoxifen,list(MCF7=tamoxifen$masks$MCF7),
                          bNormalized=FALSE)
dba.plotMA(tamoxifen, list(Sample1=1), bNormalized=FALSE)
dba.plotMA(tamoxifen, list(Random=sample(1:11,5)), bNormalized=FALSE)

#XY plots (with raw and normalized data)
data(tamoxifen_analysis)
par(mfrow=c(1,2))
dba.plotMA(tamoxifen,bXY=TRUE,bSmooth=FALSE,bNormalized=FALSE, 
           sub="NON_NORMALIZED")
dba.plotMA(tamoxifen,bXY=TRUE,bSmooth=FALSE,bNormalized=TRUE,
           sub="DESeq2-RLE-Background")