GenoSet extends RangedSummarizedExperiment by adding some additional methods to the API. Examples include subsetting rows with a GenomicRanges and combining this with access to assays like genoset[i,j,assay].
The RleDataFrame class serves to hold a collection of Run Length Encoded vectors (Rle objects) of the same length. For example, it could be used to hold information along the genome for a number of samples, such as sequencing coverage, DNA copy number, or GC content. This class inherits from both DataFrame and SimpleRleList (one of the AtomicVector types). This means that all of the usual subsetting and applying functions will work. Also, the AtomicList functions, like mean and sum, that automatically apply over the list elements will work. The scalar mathematical AtomicList methods can make this class behave much like a matrix (see Examples).
These methods mirror the viewMeans type functions from IRanges for SimpleRleList. They differ in that they work on an RleDataFrame and an IRanges directly and also have a simplify argument. This works out to be faster (compute-wise) and also convenient.
Calculate Mirrored B-Allele Frequence (mBAF) from B-Allele Frequency (BAF) as in Staaf et al., Genome Biology, 2008. BAF is converted to mBAF by folding around 0.5 so that is then between 0.5 and 1. HOM value are then made NA to leave only HET values that can be easily segmented. Values > hom.cutoff are made NA. Then, if genotypes (usually from a matched normal) are provided as the matrix 'calls' additional HOMs can be set to NA. The argument 'call.pairs' is used to match columns in 'calls' to columns in 'baf'.
boundingIndices
(Package: genoset) :
Find indices of features bounding a set of chromosome ranges/genes
This function is similar to findOverlaps but it guarantees at least two features will be covered. This is useful in the case of finding features corresponding to a set of genes. Some genes will fall entirely between two features and thus would not return any ranges with findOverlaps. Specifically, this function will find the indices of the features (first and last) bounding the ends of a range/gene (start and stop) such that first <= start < stop <= last. Equality is necessary so that multiple conversions between indices and genomic positions will not expand with each conversion. Ranges/genes that are outside the range of feature positions will be given the indices of the corresponding first or last index rather than 0 or n + 1 so that genes can always be connected to some data.
boundingIndicesByChr
(Package: genoset) :
Find indices of features bounding a set of chromosome ranges/genes, across chromosomes
Finds subject ranges corresponding to a set of genes (query ranges), taking chromosome into account. Specifically, this function will find the indices of the features (first and last) bounding the ends of a range/gene (start and stop) such that first <= start < stop <= last. Equality is necessary so that multiple conversions between indices and genomic positions will not expand with each conversion. Ranges/genes that are outside the range of feature positions will be given the indices of the corresponding first or last index on that chromosome, rather than 0 or n + 1 so that genes can always be connected to some data. Checking the left and right bound for equality will tell you when a query is off the end of a chromosome.
Given a matrix of first/last indices, like from boundingIndicesByChr, and values for each range, convert to a Rle. This function takes the expected length of the Rle, n, so that any portion of the full length not covered by a first/last range will be a run with the value NA. This is typical in the case where data is segmented with CBS and some of the data to be segmented is NA.
calcGC
(Package: genoset) :
Calculate GC Percentage in windows
Local GC content can be used to remove GC artifacts from copynumber data (see Diskin et al, Nucleic Acids Research, 2008, PMID: 18784189). This function will calculate GC content fraction in expanded windows around a set of ranges following example in http://www.bioconductor.org/help/course-materials/2012/useR2012/Bioconductor-tutorial.pdf. Currently all ranges are tabulated, later I may do letterFrequencyInSlidingWindow for big windows and then match to the nearest.
calcGC2
(Package: genoset) :
Calculate GC Percentage in sliding window
Local GC content can be used to remove GC artifacts from copynumber data (see Diskin et al, Nucleic Acids Research, 2008, PMID: 18784189). This function will calculate GC content fraction in expanded windows around a set of ranges following example in http://www.bioconductor.org/help/course-materials/2012/useR2012/Bioconductor-tutorial.pdf. Values are as.integer( 1e4 * fraction ) for space reasons.
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