Description Usage Arguments References Examples

Estimate posterior probabilities to belong to the count component according to a zero-inflated negative binomial (ZINB) model. Internally, edgeR is used for the estimation of the NB component.

1 2 3 |

`counts` |
A count matrix with feature-wise expression values. Values in this matrix must be integers. |

`design` |
Design matrix specifying the experimental design. |

`maxit` |
The number of iterations for the EM-algorithm. 200 by default, but larger may be useful for large datasets (many samples). Convergence of the posterior probabilities can be checked by following the distribution of posterior probabilities over iterations with |

`normalization` |
The normalization method to use. Can be one of |

`colData` |
Only applicable if |

`designFormula` |
Only applicable if |

`normFactors` |
A vector of user-supplied global normalization factors for every sample. The normalization factors should be sorted according to the samples in the count matrix. |

`plot` |
Logical. Should the BCV plot be plotted in every iteration? |

`plotW` |
Logical. Should the distribution of posterior probabilities for all zeros in the count matrix be plotted in every iteration? |

`designZI` |
The design for the zero-excess model. If |

`llOffset` |
Offset added to likelihood to avoid taking the log of 0. Defaults to $1e-6$. |

Robinson MD and Oshlack A (2010). "A scaling normalization method for differential expression analysis of RNA-seq data." Genome Biology, 11, pp. 25.

Love MI, Huber W and Anders S (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome Biology, 15, pp. 550.

McMurdie PJ and Holmes S (2013). "phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data." PLoS ONE, 8(4), pp. e61217.

1 2 3 4 | ```
data(islamEset,package="zingeR")
islam=exprs(islamEset)[1:2000,]
design=model.matrix(~pData(islamEset)[,1])
zeroWeights=zeroWeightsLS(counts=islam, design=design, maxit=200)
``` |

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