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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