To test a sample population of genes for over-representation of GO terms, the core function GOHyperGAll computes for all nodes in the three GO networks (BP, CC and MF) an enrichment test based on the hypergeometric distribution and returns the corresponding raw and Bonferroni corrected p-values. Subsequently, a filter function supports GO Slim analyses using default or custom GO Slim categories. Several convenience functions are provided to process large numbers of gene sets (e.g. clusters from partitioning results) and to visualize the results.
Container for storing standard intersect results created by the overLapper function. The setlist slot stores the original label sets as vectors in a list; intersectmatrix organizes the label sets in a present-absent matrix; complexitylevels represents the number of comparisons considered for each comparison set as vector of integers; and intersectlist contains the standard intersect vectors.
S4 class container for storing parameters of command-line- or R-based software. SYSargs instances are constructed by the systemArgs function from two simple tablular files: a targets file and a param file. The latter is optional for workflow steps lacking command-line software. Typically, a SYSargs instance stores all sample-level inputs as well as the paths to the corresponding outputs generated by command-line- or R-based software generating sample-level output files. Each sample level input/outfile operation uses its own SYSargs instance. The outpaths of SYSargs usually define the sample inputs for the next SYSargs instance. This connectivity is achieved by writing the outpaths with the writeTargetsout function to a new targets file that serves as input to the next systemArgs call. By chaining several SYSargs steps together one can construct complex workflows involving many sample-level input/output file operations with any combinaton of command-line or R-based software.
Container for storing Venn intersect results created by the overLapper function. The setlist slot stores the original label sets as vectors in a list; intersectmatrix organizes the label sets in a present-absent matrix; complexitylevels represents the number of comparisons considered for each comparison set as vector of integers; and vennlist contains the Venn intersect vectors.
Generate data frame containing important read alignment statistics such as the total number of reads in the FASTQ files, the number of total alignments, as well as the number of primary alignments in the corresponding BAM files.
Container for storing mappings of genes to annotation categories such as gene ontologies (GO), pathways or conserved sequence domains. The catmap slot stores a list of data.frames providing the direct assignments of genes to annotation categories (e.g. gene-to-GO mappings); catlist is a list of lists of all direct and indirect associations to the annotation categories (e.g. genes mapped to a pathway); and idconv allows to store a lookup-table for converting identifiers (e.g. array feature ids to gene ids).
Submits non-R command-line software to queueing/scheduling systems of compute clusters using run specifications defined by functions similar to runCommandline. runCluster can be used with most queueing systems since it is based on utilities from the BatchJobs package which supports the use of template files (*.tmpl) for defining the run parameters of the different schedulers. The path to the *.tmpl file needs to be specified in a conf file provided under the conffile argument.
Convenience function to perform read counting iteratively for serveral range sets, e.g. peak range sets or feature types. Internally, the read counting is performed with the summarizeOverlaps function from the GenomicAlignments package. The resulting count tables are directly saved to files.
Computes read coverage along single and multi component features based on genomic alignments. The coverage segments of component features are spliced to continuous ranges, such as exons to transcripts or CDSs to ORFs. The results can be obtained with single nucleotide resolution (e.g. around start and stop codons) or as mean coverage of relative bin sizes, such as 100 bins for each feature. The latter allows comparisons of coverage trends among transcripts of variable length. The results can be obtained for single or many features (e.g. any number of transcritpts) at once. Visualization of the coverage results is facilitated by a downstream plotfeatureCoverage function.
providing 
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