hummingbird-package: A Bayesian Hidden Markov Model for the detection of...
In hummingbird: Bayesian Hidden Markov Model for the detection of differentially methylated regions
Description
Details
Author(s)
References
Description
A package for identifying differentially methylated regions (DMRs) between
case and control groups using whole genome bisulfite sequencing (WGBS) or
reduced representative bisulfite sequencing (RRBS) experiment data.
The hummingbird package uses a Bayesian hidden Markov model (HMM) for
detecting DMRs. It fits a Bayesian HMM for one chromosome at a time. The
final output of hummingbird are the detected DMRs with start and end
positions in a given chromosome, directions of the DMRs (hyper- or hypo-),
and the numbers of CpGs in these DMRs.
The hummingbird package implements the algorithm described in the
publication below.
Details
The main functions of the package are:
hummingbirdEM, hummingbirdPostAdjustment and hummingbirdGraph.
Author(s)
Eleni Adam, Tieming Ji, Desh Ranjan
Maintainer: Eleni Adam <eadam002@odu.edu>
References
Ji (2019) A Bayesian hidden Markov model for detecting differentially
methylated regions. Biometrics 75(2):663-673.
hummingbird documentation built on April 18, 2021, 6 p.m.
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Description Details Author(s) References
Description
A package for identifying differentially methylated regions (DMRs) between case and control groups using whole genome bisulfite sequencing (WGBS) or reduced representative bisulfite sequencing (RRBS) experiment data.
The hummingbird package uses a Bayesian hidden Markov model (HMM) for detecting DMRs. It fits a Bayesian HMM for one chromosome at a time. The final output of hummingbird are the detected DMRs with start and end positions in a given chromosome, directions of the DMRs (hyper- or hypo-), and the numbers of CpGs in these DMRs.
The hummingbird package implements the algorithm described in the publication below.
Details
The main functions of the package are: hummingbirdEM, hummingbirdPostAdjustment and hummingbirdGraph.
Author(s)
Eleni Adam, Tieming Ji, Desh Ranjan
Maintainer: Eleni Adam <eadam002@odu.edu>
References
Ji (2019) A Bayesian hidden Markov model for detecting differentially methylated regions. Biometrics 75(2):663-673.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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