Description Usage Arguments Details Value Author(s) Examples

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

1 2 3 4 5 6 7 |

`Data` |
input data, rows are genes 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. |

`AllTran` |
initial values for transition matrices |

`AllPi0` |
initial values for starting probabilities |

`Terms` |
Terms |

`FCV` |
candidate values for expected FC. Default is 2. If user wants to search through a list of candidate FCs, he/she may define FCV as a vector. e.g. By defining FCV=seq(1.4,2,.2), the function will search over (1.4 1.6 1.8 2.0). Note that searching over a number of candidate FCs will increase the running time. |

`sizeFactors` |
a vector indicates library size factors |

`NumTranStage` |
number of states in two chains |

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

`UpdateRd` |
max number of iteration |

`UpdatePI` |
whether update the mixture proportion of two chains |

`PIBound` |
upper bound of the mixture proportion of the two chains |

`Qtrm,QtrmCut` |
Transcripts with Qtrm th quantile < = QtrmCut will be removed before testing. The default value is Qtrm = 0.75 and QtrmCut=10. By default setting, transcripts that have >75% of the samples with expression less than 10 won't be tested. |

`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 |

EBSeqHMMTest() function applies EBSeqHMM model with differentexpected FC's and select the optimal FC that maximizes the log likelohood. EBSeqHMMTest() calls EBHMMNBMultiEM_2chain() function which implements the EBSeqHMM model to perform statistical analysis in an RNA-seq experiment with ordered conditions based on a fixed expected FC. EBSeqHMMTest() runs EBHMMNBMultiEM_2chain() with varying FCs (default is seq(1.4,2,.2)). And it will return the results of the model with optimal FC. 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.

Pi0Out: estimated starting probabilities of each iteration.

TranOut: estimated transition probabilities of each iteration.

Pi: estimated probability of being each chain.

Alpha, Beta: estimated alpha and beta(s).

LLSum: log likelihood of the model.

QList: estimated q's.

MgAllPP: marginal PP for all paths.

MgAllMAPChar: Most likely path based on MgAllPP.

MgAllMaxVal: Highest PP based on MgAllPP.

PPMatW: Posterior probabilities of being each of the chains.

FCLikelihood: log likelihood of each FC.

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)
EBSeqHMMGeneOut <- EBSeqHMMTest(Data=GeneExampleData, sizeFactors=Sizes, Conditions=Conditions,
UpdateRd=2)
``` |

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