diffexp: Differential Exposure Analysis
In signeR: Empirical Bayesian approach to mutational signature discovery

Description Usage Arguments Value Examples

DiffExp : Identify signatures with significantly different activities among sample groups.

## S4 method for signature 'SignExp,character'
DiffExp(signexp_obj, labels,
    method=kruskal.test, contrast="all", quant=0.5, cutoff=0.05,
    p.adj= "BH",plot_to_file=FALSE, file="Diffexp_boxplot.pdf",
    colored=TRUE, relative = FALSE, ...)

`signexp_obj`	a SignExp object returned by signeR function.
`labels`	sample labels used to define sample groups.
`method`	algorithm used to compare each signature exposures among sample groups. Default is kruskal.test, which leads to the use of Kruskal-Wallis Rank Sum Test.
`contrast`	defines which sample groups will be considered in the analysis. Default is "all", which leads the algorithm to evaluates the null hypothesis of exposure levels beeing constant in all groups. Instead, if this parameter contains a list of group labels, the algorithm will evaluate the null hypothesis of exposure levels beeing constant among those groups.
`quant`	the p-values quantile which, after log-transform, will be used as DES (Differental Exposure Score). Deafult is 0.5, which means the median log-transformed p-value will be considered as DES.
`p.adj`	correction method for p-values adjust at the post-hoc tests performed when there are more then two group labels. See p.adjust for options.
`cutoff`	threshold for p-values quantile for signatures to be considered as showing differential exposure.
`plot_to_file`	Whether to save the plot to the file parameter. Default is FALSE.
`file`	Output file to export p-values boxplot.
`colored`	Boolean variable, if TRUE boxplots of differentially exposed signatures will be colored in green, cutoff line will be colored in red and line segments showing the transformed p-value quantile used for DE evaluation will be colored in blue. Otherwise the plot will be black & white.
`relative`	Whether tests should be performed on absolute or relative signature contributions to each sample mutations. Default is FALSE (absolute contributions will be tested).
`...`	additional parameters for test algorithm defined by the method parameter.

A list with the following items:

`Pvquant`	boolean array with one entry for each signature, indicating whether it shows differential exposure.
`Pvalues`	matrix containing all computed p-values, with one row for each signature.
`MostExposed`	for each differentially exposed signature, this array contains the label of the group where it showed higher levels of exposure. Contains NA for signatures not showing differential exposure.
`Differences`	List of matrices, exported only when there are more than two groups in the analysis and any signature is found to be differentially active. Each matrix corresponds to one of the highlighted signatures and show the results of comparisons among groups, with the significant ones marked as TRUE.

# assuming signatures is the return value of signeR()

# labels vector, one for each sample
my_labels <- c("a","a","b","b")

diff_exposure <- DiffExp(signatures$SignExposures,labels=my_labels)

# see also
vignette(package="signeR")