iChip2: Bayesian modeling of ChIP-chip data through hidden Ising...

In iChip: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models

Description

Function iChip2 implements the method of modeling ChIP-chip data through a high-order hidden Ising model.

Usage

iChip2(Y,burnin=2000,sampling=10000,winsize=2,sdcut=2,beta=2.5,verbose=FALSE)

Arguments

Y	A n by 2 matrix or data frame. The first column of Y contains the chromosome IDs; the second column of Y contains the probe enrichment measurements. Y must be sorted, firstly by chromosome and then by genomic position. The probe enrichment 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.
winsize	The parameter to control the order of interactions between probes. For example, winsize = 2, means that probe i interacts with probes i-2,i-1,i+1 and i+2. A balance between high sensitivity and low FDR could be achieved by setting winsize = 2.
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.
beta	The 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. In general, high resolution array such as Affymetrix tiling arrays have relatively stronger probe interactions than low resolution array such as Agilent tiling arrays. For the second order Ising model (winsize = 2), the critical value of beta is around 1.0. For low resolution array data (e.g. 280 bp resolution), beta could be set to close to the critical value; For high resolution array data (e.g. 35 bp resolution), beta could be set to a value between 2 to 4. In general, choosing a large value of beta amounts to using a more stringent criterion for detecting enriched regions in ChIP-chip experiments.
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 to 1.
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.
lambda0	The posterior samples of the precision of the enrichment measurements of the probes in the non-binding/non-enriched state.
lambda1	The posterior samples of the precision of the enrichment measurements of the probes in the binding/enriched state.

Author(s)

Qianxing Mo [e-mail]

References

Qianxing Mo, Faming Liang. (2010). Bayesian modeling of ChIP-chip data through a high-order Ising model. Biometrics 64(4), 1284-94.

See Also

iChip1, 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])

# prepare the data used for the Ising model

oct4Y = cbind(oct4[,1],oct4lmt)

# Apply the second-order Ising model to the ChIP-chip data

oct4res=iChip2(Y=oct4Y,burnin=1000,sampling=5000,winsize=2,sdcut=2,beta=1.25)

# 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])
# p53Y = cbind(p53[,1],p53lmt)
# p53res=iChip2(Y=p53Y,burnin=1000,sampling=5000,winsize=2,sdcut=2,beta=2.5)

# 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)

iChip documentation built on Nov. 8, 2020, 8:19 p.m.