MICC_1.0-package: MICC: an R package to identify chromatin interactions from...

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

Description

This package combined Bayesian prior and a three-component mixture model to systematically remove random ligation and random collison noise. It could detect chromatin interactions at a higher sensitivity from ChIA-PET sequencing data

Details

Package:	MICC_1.0
Type:	Package
Version:	1.0
Date:	2015-03-18
License: LGPL (>=2)

InputMatrixFormatted: process input PET clusters into MICC main model input
MICCMainLearn: fit model parameters
FDRcompute: compute FDR
MICCfoutput: implement MICC model and get output

Author(s)

Chao He
Maintainer: [e-mail]

References

Dempster, P. et al. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal statistical Society, 39, 1¨C38.
Jessen, B. and Winter, A. (1935).Distribution functions and the Riemann zeta function. Transactions of the American Mathematical Society, 38, 48-88.
Li, G. et al. (2010). ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biology, 11, 1¨C13.
Newman, E. (2005). Power laws, pareto distributions and zipf¡¯s law. Contemporary physics, 46, 323¨C351.

See Also

InputMatrixFormatted, MICCMainLearn, MICCoutput.

Examples

library(MICC)

## Import data
data(TestData)

# implement the model
MICCoutput( TestData, "./TestData.txt" )

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