computeInfRV | R Documentation |

`InfRV`

is a useful quantity for comparing groups of features
(transcripts, genes, etc.) by inferential uncertainty.
This function provides computation of the mean InfRV over samples,
per feature, stored in `mcols(y)$meanInfRV`

.

```
computeInfRV(y, pc = 5, shift = 0.01, meanVariance, useCounts = FALSE)
```

`y` |
a SummarizedExperiment |

`pc` |
a pseudocount parameter for the denominator |

`shift` |
a final shift parameter |

`meanVariance` |
logical, use pre-computed inferential mean
and variance assays instead of |

`useCounts` |
logical, whether to use the MLE count matrix for the mean instead of mean of inferential replicates. this argument is for backwards compatability, as previous versions used counts. Default is FALSE |

InfRV is defined in Zhu et al. (2019) as:
`\max(s^2 - \mu, 0) / \mu`

, using the inferential
sample variance and sample mean. This formulation takes the
non-Poisson part of the inferential variance and scales by the
mean, which effectively stabilizes inferential uncertainty over
mean count. In practice, we also add `pc`

to the denominator and
`shift`

to the final quantity, to facilitate visualization.

This function also computes and adds the mean and variance of inferential
replicates, which can be useful ahead of `plotInfReps`

.
Note that InfRV is not used in the `swish`

statistical method (for generating test statistics, p-values
or q-values), it is just for visualization.

a SummarizedExperiment with `meanInfRV`

in the metadata columns

Anqi Zhu, Avi Srivastava, Joseph G Ibrahim, Rob Patro, Michael I Love "Nonparametric expression analysis using inferential replicate counts" Nucleic Acids Research (2019). https://doi.org/10.1093/nar/gkz622

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