Learn R Programming

EBSeqHMM (version 1.6.0)

Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments

Description

The EBSeqHMM package implements an auto-regressive hidden Markov model for statistical analysis in ordered RNA-seq experiments (e.g. time course or spatial course data). The EBSeqHMM package provides functions to identify genes and isoforms that have non-constant expression profile over the time points/positions, and cluster them into expression paths.

Copy Link

Version

Version

1.6.0

License

Artistic-2.0

Maintainer

Ning Leng

Last Published

February 15th, 2017

Functions in EBSeqHMM (1.6.0)

GetDECalls

Obtain DE gene/isoform list at a certain FDR
PlotExp

Plot expression of a single gene
LikefunNBHMM

Likelihood function of the Beta-Negative Binomial HMM Model
GeneExampleData

Simulated gene level data set with 5 ordered conditions
EBSeqHMM-package

EBSeqHMM: A Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments
EBSeqHMMTest

Identify DE genes and classify them into their most likely path in an RNA-seq experiment with ordered conditions
EBTest_ext

Extented EBTest function
f0

Calculate the prior predictive distribution of the Beta-Negative Binomial model
EBHMMNBfunForMulti

Baum-Welch algorithm for multiple hidden markov chains
beta.mom

Method of moments estimation ( beta distribution )
IsoExampleList

Simulated isoform level data set with 5 ordered conditions
GetConfidentCalls

Obtain confident gene calls for classifying genes into expression paths
EBHMMNBfun

Baum-Welch algorithm for a single hidden markov chain
EBHMMNBMultiEM_2chain

Run EBSeqHMM model with a fixed expected FC
GetAllPaths

Obtain all possible gene paths for an RNA-seq experiments with ordered conditions