iChip1 {iChip} | R Documentation

## Bayesian modeling of ChIP-chip data through hidden Ising models

### Description

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

### Usage

    iChip1(enrich,burnin=2000,sampling=10000,sdcut=2,beta0=3,
           minbeta=0,maxbeta=10,normsd=0.1,verbose=FALSE)

### Arguments

`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.

### Value

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.

### Author(s)

Qianxing Mo qianxing.mo@moffitt.org

### References

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

### See Also

`iChip2`,`enrichreg`, `lmtstat`

### Examples

    # 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)

[Package iChip version 1.44.0 Index]