Description Usage Arguments Details Value Author(s) Examples

Baum-Welch algorithm for a single hidden markov chain

1 2 3 4 5 6 7 8 | ```
EBHMMNBfun(Data,NgVector=NULL,Conditions, sizeFactors,
PriorFC=1.5,homo=TRUE, maxround=5,
Pi0=NULL, Tran=NULL,NoTrend=FALSE, NumTranStage=3,
FCParam=NULL, AlphaIn=NULL,BetaIn=NULL,
StateNames=c("Up","NC","Down"),
EM=TRUE, UpdateParam=TRUE, Print=TRUE,
OnlyQ=FALSE,WithinCondR=TRUE,
PenalizeLowMed=TRUE, PenalizeLowMedQt=.2,PenalizeLowMedVal=10)
``` |

`Data` |
input data, rows are genes/isoforms and columns are samples |

`NgVector` |
Ng vector; NULL for gene level data |

`Conditions` |
A factor indicates the condition (time/spatial point) which each sample belongs to. |

`sizeFactors` |
a vector indicates library size factors |

`Tran` |
initial values for transition matrices |

`Pi0` |
initial values for starting probabilities |

`NumTranStage` |
number of states |

`PriorFC` |
target FC for gridient change |

`StateNames` |
name of the hidden states |

`homo` |
whether the chain is assumed to be homogenious |

`maxround` |
max number of iteration |

`AlphaIn,BetaIn` |
If the parameters are known and the user doesn't want to estimate them from the data, user may specify them here. |

`NoTrend` |
if NoTrend=TRUE, initial transition probabilities will be set to be equal |

`FCParam` |
not in use |

`EM` |
Whether estimate the prior parameters of the beta distribution by EM |

`UpdateParam` |
Whether update starting probabilities and transition probabilities |

`OnlyQ` |
If OnlyQ=TRUE, the function will only return estimated q's. |

`WithinCondR` |
By defining WithinCondR=TRUE, estimation of r's are obtained within each condition. (WithinCondR=FALSE is not suggested here) |

`Print` |
Whether print the elapsed-time while running the test. |

`PenalizeLowMed,PenalizeLowMedQt,PenalizeLowMedVal` |
Transcripts with median quantile < = PenalizeLowMedQt will be penalized |

EBHMMNBfun() function implements the Balm-Welch algorithm that estimates the starting probabilities and transition probabilities of a single hidden Markov model. Here the emission distribution of each gene is assumed to be a Beta-Negative Binomial distribution with parameters (r_g, alpha, beta) , in which alpha and beta are shared by all the genes and r_g is gene specific. If not specified, r_g, alpha and beta will be estimated using method of moments. For isoform data, we assume isoforms from the same Ig group share the same beta^Ig. alpha is shared by all the isoforms and r_gi is isoform specific. The user also needs to specify an expected FC.

MAPTerm: the most likely path of each gene/isoform. MAPTermNum: numeric version of MAPTerm.

AllTerm: all possible expression paths considered in the model. PP: posterior probability of being each expression path.

WhichMax: index of the most likely path. Allf: prior probability of being each path.

Pi0Track: estimated starting probabilities of each iteration.

TranTrack: estimated transition probabilities of each iteration.

AlphaTrack, BetaTrack: estimated alpha and beta(s).

LLAll=PostSumForLL.Sum: log likelihood of the model.

Ning Leng

1 2 3 4 5 6 | ```
data(GeneExampleData)
CondVector <- rep(paste("t",1:5,sep=""),each=3)
Conditions <- factor(CondVector, levels=c("t1","t2","t3","t4","t5"))
Sizes <- MedianNorm(GeneExampleData)
tmp <- EBHMMNBfun(Data=GeneExampleData, sizeFactors=Sizes, Conditions=Conditions,
maxround=2, OnlyQ=TRUE)
``` |

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