iSeq1.0Wraps the iSeq R package that implements the methods described in "A fully Bayesian hidden Ising model for ChIP-seq data analysis" by Qianxing MoLauri LylyiSeqSample CSV file, with fields: chromosome name, region middle position, strand (either 1 for forward or 2 for reverse)Control CSV file with similar format as treatment file.Either iSeq1 or iSeq2. iSeq1 implements the method that models the bin-based tag counts using Poisson-Gamma distribution and the hidden states of the bins using a standard 1D Ising model. iSeq2 is similar but uses a hidden high-order Ising model.
The maximum length of the genomic window/bin into which sequence tags are aggregated.The minimum length of the genomic window/bin into which sequence tags are aggregated.The tag count cutoff value for triggering bin size change. For example, suppose L_i and C_i are the length and tag count for bin i, respectively. If C_i >= ntagcut, the length for bin i+1 will be min(L_i/2,minlen); if C_i < ntagcut, the length for bin i+1 will be max(2*L_i, maxlen). Note, by default, the bin sizes decrease/increase by a factor of 2. Thus, the user should let maxlen = (2^n)*minlen.gap: gap is the average length of the sequenced DNA fragments. If the distance between two nearest bins is greater than 'gap', a bin with 0 tag count is inserted into the two bins for modeling.burnin: The number of MCMC burn-in iterations.sampling: The number of MCMC sampling iterations. The posterior probability of enriched and non-enriched state is calculated based on the samples generated in the sampling period.ctcut: A value used to set the initial state for each window/bin. If tag count of a bin is greater than quantile(Y[,4],probs=ctcut), its state will be set to 1, otherwise -1. For typical ChIP-seq data, because the major regions are non-enriched, a good value for ctcut could be in the interval (0.9, 0.99).a0: The scale hyper-parameter of the Gamma prior, alpha0.b0: The rate hyper-parameter of the Gamma prior, beta0.a1: The scale hyper-parameter of the Gamma prior, alpha1.b1: The rate hyper-parameter of the Gamma prior, beta1.k0: The initial parameter used to control the strength of interaction between neighboring bins, which must be a positive value (k0>0). A larger value of kappa represents a stronger interaction between neighboring bins.Unused for iSeq2. mink: The minimum value of k(kappa) allowed.Unused for iSeq2. maxk: The maximum value of k(kappa) allowed.Unused for iSeq2. normsd: iSeq1 uses a Metropolis random walk proposal for sampling from the posterior distributions of the model parameter kappa. The proposal distribution is a normal distribution with mean 0 and standard deviation specified by normsdverbose: A logical variable. If TRUE, the number of completed MCMC iterations is reported.The cutoff value (a scalar) used to call enriched bins. If use posterior probability as a criterion (method="ppcut"), a bin is said to be enriched if its pp is greater than the cutoff. If use FDR as a criterion (method="fdrcut"), bins are said to be enriched if the bin-based FDR is less than the cutoff. The FDR is calculated using a direct posterior probability approach (Newton et al., 2004). The default value 0.5 is applicable to ppcut, a more likely default for fdrcut would be 0.05.'ppcut' or 'fdrcut', depending on whether cutoff is applied to posterior probability values or false discovery rate.The criterion used to merge enriched bins. If the genomic distance of adjacent bins is less than maxgap, the bins will be merged into the same enriched region.