A class for storing count data obtained from ChIP-seq experiments by counting the number of reads lying within regions. The class extends RangedSummarizedExperiment.
This method estimates the posterior distribution of a Bayesian mixture model using Markov Chain Monte Carlo (MCMC) methods and calculates measures of this distribution. The mixture model may consist of normal components (with a fixed expectation of 0), exponential components and gamma components, which may be mirrored in order to model negative values.
This method calculates the cross correlation, i.e. the Pearson correlation between the coverages of the positive and negative strand from a DNA sequencing experiment. The cross correlation can be used as a quality measure in ChIP-seq experiments (Kharchenko et al. 2008). Cross correlation can also be used to estimate the fragment size by determining the shift (given in base pairs) that maximizes the cross correlation.
This function calculates the product of the standardized differences between two conditions in ChIP-seq data and the respective standardized differences in gene expression data. A score close to zero means that there are no (large) differences in at least one of the two data sets. If the score is positive, equally directed differences exist in both data sets. In case of a negative score, differences have unequal signs in the two data sets.
This function returns a GRangesList object asigning promoter regions to probes. The assignment of transcripts to probes and the transcriptional start sites must be given as arguments.
providing 
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