Description Usage Arguments Value Author(s) References See Also Examples

Function iChip1 implements the algorithm of modeling ChIP-chip data through a standard hidden Ising model.

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`enrich` |
A vector containing the probe enrichment measurements. The measurements must be sorted, firstly by chromosome and then by genomic position. The measurements could be log2 ratios of the intensities of IP-enriched and control samples for a single replicate, or summary statistics such as t-like statistics or mean differences for multiple replicates. We suggest to use the empirical Bayesian t-statistics implemented in the limma package for multiple replicates. Note, binding probes must have a larger mean value than non-binding probes. |

`burnin` |
The number of MCMC burn-in iterations. |

`sampling` |
The number of MCMC sampling iterations. The posterior probability of binding and non-binding state is calculated based on the samples generated in the sampling period. |

`sdcut` |
A value used to set the initial state for each probe. The enrichment measurements of a enriched probe is typically several standard deviations higher than the global mean enrichment measurements. |

`beta0` |
The initial parameter used to control the strength of interaction between probes, which must be a positive value. A larger value of beta represents a stronger interaction between probes. The value for beta0 could not be too small (e.g. < 1.0). Otherwise, the Ising system may not be able to reach a super-paramagnetic state. |

`minbeta` |
The minimum value of beta allowed. |

`maxbeta` |
The maximum value of beta allowed. |

`normsd` |
iChip1 uses a Metropolis random walk proposal for sampling from the posterior distributions of the model parameters. The proposal distribution is a normal distribution with mean 0 and standard deviation specified by normsd. |

`verbose` |
A logical variable. If TRUE, the number of completed MCMC iterations is reported. |

A list with the following elements.

`pp` |
The posterior probabilities of probes in the binding/enriched state. There is a strong evidence to be a binding/enriched probe if the probe has a posterior probability close to1. |

`beta` |
The posterior samples of the interaction parameter of the Ising model. |

`mu0` |
The posterior samples of the mean measurement of the probes in the non-binding/non-enriched state. |

`mu1` |
The posterior samples of the mean measurement of the probes in the binding/enriched state. |

`lambda` |
The posterior samples of the precision of the enrichment measurements of the probes. |

Qianxing Mo qianxing.mo@moffitt.org

Qianxing Mo, Faming Liang. (2010). A hidden Ising model for ChIP-chip
data analysis. *Bioinformatics* 26(6), 777-783.

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# oct4 and p53 data are log2 transformed and quantile-normalized intensities
# Analyze the Oct4 data (average resolution is about 280 bps)
data(oct4)
### sort oct4 data, first by chromosome then by genomic position
oct4 = oct4[order(oct4[,1],oct4[,2]),]
# calculate the enrichment measurements --- the limma t-statistics
oct4lmt = lmtstat(oct4[,5:6],oct4[,3:4])
# Apply the standard Ising model to the ChIP-chip data
oct4res = iChip1(enrich=oct4lmt,burnin=1000,sampling=5000,sdcut=2,
beta0=3,minbeta=0,maxbeta=10,normsd=0.1)
# check the enriched regions detected by the Ising model using
# posterior probability (pp) cutoff at 0.9 or FDR cutoff at 0.01
enrichreg(pos=oct4[,1:2],enrich=oct4lmt,pp=oct4res$pp,cutoff=0.9,
method="ppcut",maxgap=500)
enrichreg(pos=oct4[,1:2],enrich=oct4lmt,pp=oct4res$pp,cutoff=0.01,
method="fdrcut",maxgap=500)
# Analyze the p53 data (average resolution is about 35 bps)
# uncommenting the following code for running
# data(p53)
# must sort the data first
# p53 = p53[order(p53[,1],p53[,2]),]
# p53lmt = lmtstat(p53[,9:14],p53[,3:8])
# p53res = iChip1(p53lmt,burnin=1000,sampling=5000,sdcut=2,beta0=3,
# minbeta=0,maxbeta=10,normsd=0.1)
# enrichreg(pos=p53[,1:2],enrich=p53lmt,pp=p53res$pp,cutoff=0.9,
# method="ppcut",maxgap=500)
# enrichreg(pos=p53[,1:2],enrich=p53lmt,pp=p53res$pp,cutoff=0.01,
# method="fdrcut",maxgap=500)
``` |

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