Description Usage Arguments Details Value References See Also Examples

Compares the empirical and estimated distributions for different count data models

1 | ```
compareCountDist(x, plot=TRUE, ...)
``` |

`x` |
numeric vector containing the read counts. |

`plot` |
If |

`...` |
Further arguments to be passed to the plot function. |

This function serves the purpose of comparing a empirical distribution of counts with three Poisson-Tweedie distributions arising from estimating mean, dispersion and setting *a=1* for comparing against a Poisson, *a=0* for comparing against a negative binomial and estimating the shape parameter a from data too. The legend shows the values of the *a* parameter and the P-value of the likelihood ratio test on whether the expression profile follows a negative binomial distribution (*H_0:a=0*).

List with the following components:

`a` |
shape parameter estimated from the input data |

`p.value` |
P-value for the test that the data follows a negative binomial distribution, i.e., |

Esnaola M, Puig P, Gonzalez D, Castelo R and Gonzalez JR (2013). A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. BMC Bioinformatics 14: 254

1 2 3 4 5 6 | ```
# Generate 500 random counts following a Poisson Inverse Gaussian
# distribution with mean = 20 and dispersion = 5
randomCounts <- rPT(n = 500, mu = 20, D = 5, a = 0.5)
xx <- compareCountDist(randomCounts, plot=FALSE)
xx
``` |

tweeDEseq documentation built on Jan. 5, 2019, 7:06 p.m.

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