Description Usage Arguments Value Examples

Main function. For most users, this function is all what they need for the analysis.

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`countmat` |
A count matrix. Rows are genes, and columns are samples. Each element/count is the number of reads mapped to a gene in a sample. |

`classlab` |
The class labels. A vector whose elements are of value 1 or 2. |

`depth` |
Sequencing depth. A vector of sequencing depth. Users are encouraged to provide estimated values from edgeR, DESeq, PoissonSeq, or other software that they prefer. If no values are provided, depth estimated by total counts will be used. |

`c.tukey.beta` |
The c value for beta in Tukey's biweight function. The default value, 4, is typically regarded as appropriate and should work for most datasets. |

`c.tukey.phi` |
The c value for phi in Tukey's biweight function. The default value, 4, is typically regarded as appropriate and should work for most datasets. |

`phi.ini` |
The initial value for phi to start search. If phi.in == 'adaptive' (the default value), the algorithm will adaptively choose a value (check Algorithm 1 for details). Otherwise, a positive numeric value (such as 0.5) should be given. |

A List that contains the following elements:
`tab`

: This is a data frame that contains the main results of this package. It has the following columns:
`phi1`

: the estimated dispersion of sample group 1.
`phi2`

: the estimated dispersion of sample group 2.
`beta1`

: the estimated (log) expression of sample group 1.
`beta2`

: the estimated (log) expression of sample group 2.
`statistic.phi`

: the z statistic for DD.
`statistic.beta`

: the z statistic for DE.
`p.value.phi`

: the p value for DD.
`p.value.beta`

: the p value for DE.
`fdr.phi`

: the FDR for DD.
`fdr.beta`

: the FDR for DE.
`log.depth`

: A vector of the log sequencing depths.
`countmat`

: This is the count matrix that the user provided.
`classlab`

: This is the vecgtor of class labels that the user provided.
`phi.ini`

: The initial searching value of the dispersion parameter.
`mumat`

: This is the (estimated) mu (expected expression) matrix, of the same size as countmat.
In another word, E(countmat)=mumat.
`phimat`

: This is the (estimated) phi matrix, of the same size as countmat.
In another word, counmat ~ negative binomial(mumat, phimat).

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