Package: hummingbird 1.15.0
Eleni Adam
hummingbird: Bayesian Hidden Markov Model for the detection of differentially methylated regions
A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups.
Authors:
hummingbird_1.15.0.tar.gz
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hummingbird.pdf |hummingbird.html✨
hummingbird/json (API)
NEWS
| # Install 'hummingbird' in R: |
| install.packages('hummingbird', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
- abnormM - Sample matrix
- abnormUM - Sample matrix
- exampleSECase - Sample input data
- exampleSEControl - Sample input data
- normM - Sample matrix
- normUM - Sample matrix
- pos - Sample matrix
On BioConductor:hummingbird-1.15.0(bioc 3.20)hummingbird-1.14.0(bioc 3.19)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:c13162bdb7
Exports:hummingbirdEMhummingbirdEMinternalhummingbirdGraphhummingbirdPostAdjustmenthummingbirdPostAdjustmentInternal
Dependencies:abindaskpassBiobaseBiocGenericscrayoncurlDelayedArrayGenomeInfoDbGenomeInfoDbDataGenomicRangeshttrIRangesjsonlitelatticeMatrixMatrixGenericsmatrixStatsmimeopensslR6RcppS4ArraysS4VectorsSparseArraySummarizedExperimentsysUCSC.utilsXVectorzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| A Bayesian Hidden Markov Model for the detection of differentially methylated regions | hummingbird-package hummingbird |
| Sample matrix | abnormM |
| Sample matrix | abnormUM |
| Sample dataset | exampleHummingbird |
| Sample input data | exampleSECase |
| Sample input data | exampleSEControl |
| EM Algorithm for Fitting the Hidden Markov Model | hummingbirdEM |
| EM Algorithm (internal) | hummingbirdEMinternal |
| Observations and Predictions Graphs | hummingbirdGraph |
| Post Adjustment algorithm for the output of the EM | hummingbirdPostAdjustment |
| Post Adjustment algorithm (internal) | hummingbirdPostAdjustmentInternal |
| Sample matrix | normM |
| Sample matrix | normUM |
| Sample matrix | pos |
