MICCMainLearn: MICCMainLearn: main function to fit the model

In rakarnik/MICC: Model based Interaction Calling from ChIA-PET data

Description

This function is used to learn the model parameters from formatted PET clusters

Usage

MICCMainLearn(data_formatted, params.init = NULL, reltol = 1e-05, abstol = 0.001, step = 200, restart = 5, MinConfident = 5)

Arguments

data_formatted	Formatted data matrix by "InputMatrixFormatted" function
params.init	Initialized paramters, see value section for more details
reltol	Relative tolerance, default value: 1e-5
abstol	Absolute tolerance, default value: 1e-5
step	Max number of steps before convergence, default value: 200
restart	Times to restart before convergence, default value: 5
MinConfident	Minimal number of PET-count to classify true interaction PET clusters when initializing the paramters, default value: 5

Value

params	A list of model parameters theta12.par A vector of parameters for zeta distribution to describe true interaction PET clusters and random collision PET clusters. Let (par1,par2,par3,par3,theta0) = theta12.par, and distance denote genomic distance between two anchor regions of a PET cluster, then zeta paramter for true interaction PET clusters is theta1 = ( par1*distance + par3*par2*1000 ) / ( par2*1000 + distance ) + par4 / (10*distance) zeta paramter for true interaction PET clusters is theta2 = ( par1*distance + par3*par2*1000 ) / ( par2*1000 + distance ) + par4 / (10*distance) + theta0 lambda.par A vector of parameters for prior probability to describe random ligation PET clusters. Let (par1,par2,lambda0) = lambda.par, and distance denote genomic distance between two anchor regions of a PET cluster, then lambda <- lambda0 * exp( par1*log(distance) + par2 ) / ( 1 + log(distance) ) mu.par A vector of parameters for prior probability to describe random collision PET clusters. Let (par1,par2) = mu.par, and (cA, cB) denote total PET-count in the two anchor regions, then mu <- exp( par1*log(cA) + par2 ) * exp( par1*log(cB) + par2 ) / ((1+exp( par1*log(cA) + par2 ) )*(1+exp( par1*log(cB) + par2 )))
LogLik	Logliklihood after fitting the model
PostProb	A matrix with 3 columns to describe the posterior probability of each PET clusters for each state, respectively
CONVERGED!	The training is absolutely converged!
RELCONVERGED!	The training is relatively converged!
NOT CONVERGED!	The training is neither absolutely nor relatively converged!

Author(s)

Chao He

See Also

InputMatrixFormatted, MICC_1.0-package.

Examples

library(MICC)

## Import data
data(TestData)

# format the data
data_formatted <- InputMatrixFormatted(TestData)

# train the model
Par <- MICCMainLearn( data_formatted, reltol=1e-5, step=200 )

## The function is currently defined as
function (data_formatted, params.init = NULL, reltol = 1e-05, 
    abstol = 0.001, step = 200, restart = 5, MinConfident = 5) 
{
    Par <- EMIter(data_formatted, params.init = params.init, 
        reltol = reltol, abstol = abstol, step = step, restart = restart, 
        MinConfident = MinConfident)
    Par
  }

rakarnik/MICC documentation built on May 31, 2019, 10:36 a.m.