computeInfRV.RdInfRV 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)a SummarizedExperiment
a pseudocount parameter for the denominator
a final shift parameter
logical, use pre-computed inferential mean
and variance assays instead of counts and
computed variance from infReps. If missing,
will use pre-computed mean and variance when present
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
a SummarizedExperiment with meanInfRV in the metadata columns
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.
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