iSeq1: Bayesian modeling of ChIP-seq data through hidden Ising...

In iSeq: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models

Description

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.

Usage

iSeq1(Y,gap=300,burnin=500,sampling=2000,ctcut=0.95,a0=1,b0=1,a1=5,b1=1,
  k0=3,mink=0,maxk=10,normsd=0.1,verbose=FALSE)

Arguments

Y	Y should be a data frame containing the first 4 columns of the data frame returned by function 'mergetag()'. The columns 1-4 of Y are chromosome IDs, start position of the bin, end position of the bin, tag counts in the bins. For one-sample analysis, the tag counts can be the number of forward and reverse tags falling in the bins. For two-sample analysis, tag counts are the adjusted counts of ChIP samples, which are obtained by subtracting the control tag counts from corresponding ChIP tag counts for each bin. If the user provides his/her own Y, Y must be firstly sorted by the chromosome ID, then by the start position, and then by the end position.
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 neighboring 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.
mink	The minimum value of k(kappa) allowed.
maxk	The maximum value of k(kappa) allowed.
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 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 bins in the enriched state.
kappa	The posterior samples of the interaction parameter of the Ising model.
lambda0	The posterior samples of the model parameter lambda0
lambda1	The posterior samples of the model parameter lambda1.

Author(s)

Qianxing Mo [e-mail]

References

Qianxing Mo. (2012). A fully Bayesian hidden Ising model for ChIP-seq data analysis. Biostatistics 13(1), 113-28.

See Also

iSeq2,peakreg,mergetag,plotreg

Examples

 data(nrsf)
 chip = rbind(nrsf$chipFC1592,nrsf$chipFC1862,nrsf$chipFC2002)
 mock = rbind(nrsf$mockFC1592,nrsf$mockFC1862,nrsf$mockFC2002)
 tagct = mergetag(chip=chip,control=mock,maxlen=80,minlen=10,ntagcut=10)
 tagct22 = tagct[tagct[,1]=="chr22",]
 res1 = iSeq1(Y=tagct22[,1:4],gap=200,burnin=200,sampling=500,
 ctcut=0.95,a0=1,b0=1,a1=5,b1=1,k0=3,mink=0,maxk=10,normsd=0.1,verbose=FALSE)

 reg1 = peakreg(tagct22[,1:3],tagct22[,5:6]-tagct22[,7:8],res1$pp,0.5,
        method="ppcut",maxgap=200)

 reg2 = peakreg(tagct22[,1:3],tagct22[,5:6]-tagct22[,7:8],res1$pp,0.05,
         method="fdrcut",maxgap=200)

 ID = (reg1[1,4]):(reg1[1,5])
 plotreg(tagct22[ID,2:3],tagct22[ID,5:6],tagct22[ID,7:8],peak=reg1[1,6])

iSeq documentation built on Nov. 8, 2020, 8:03 p.m.