Welcome to the propr package!
To learn more about calculating proportionality, see Details.
To learn more about visualizing proportionality, see
visualize.
To learn more about ALDEx2 package integration, see
aldex2propr.
To learn more about differential proportionality, see
propd.
To learn more about compositional data analysis, and its relevance to biological count data, see the bundled vignette.
# S4 method for propr
show(object)propr(counts, metric = c("rho", "phi", "phs", "cor", "vlr"),
ivar = "clr", select, symmetrize = FALSE, alpha, p = 100)
phit(counts, ...)
perb(counts, ...)
phis(counts, ...)
corr(counts, ...)
# S4 method for propr
subset(x, subset, select)
# S4 method for propr
[(x, i = "all", j, tiny = FALSE)
simplify(object)
updateCutoffs.propr(object, cutoff, ncores)
A propr or propd object.
A data.frame or matrix. A "count matrix" with subjects as rows and features as columns. Note that this matrix does not necessarily have to contain counts.
A character string. The proportionality metric to calculate. Choose from "rho", "phi", or "phs".
A numeric scalar. Specifies reference feature(s) for additive log-ratio transformation. The argument will also accept feature name(s) instead of the index position(s). Set to "iqlr" to use inter-quartile log-ratio transformation. Ignore to use centered log-ratio transformation.
Optional. Use this to subset the final proportionality matrix without altering the result. Use this argument to rearrange feature order.
A logical. If TRUE, forces symmetry
by reflecting the "lower left triangle".
A double. See vignette for details. Leave missing to skip Box-Cox transformation.
An integer. The number of permutation cycles.
Arguments passed to the wrapped method.
A propr or propd object.
Subsets via object@counts[subset, ].
Use this argument to rearrange subject order.
For backwards compatibility.
Operation used for the subset indexing. Select from "==", "=", ">", ">=", "<", "<=", "!=", or "all". For backwards compatibility.
Provide a numeric value to which to compare the
proportionality measures in the @matrix slot.
For backwards compatibility.
A logical scalar. Toggles whether to pass the indexed
result through simplify.
For backwards compatibility.
For updateCutoffs, a numeric vector.
this argument provides the FDR cutoffs to test.
For graph functions, a numeric scalar. This argument
indicates the maximum theta to include in the figure.
For graph functions, a large integer will instead
retrieve the top N pairs as ranked by theta.
An integer. The number of parallel cores to use.
Returns a propr object.
countsA data.frame. Stores the original "count matrix" input.
alphaA double. Stores the alpha value used for transformation.
metricA character string. The metric used to calculate proportionality.
ivarA vector. The reference used to calculate proportionality.
logratioA data.frame. Stores the transformed "count matrix".
matrixA matrix. Stores the proportionality matrix.
pairsA vector. Indexes the proportional pairs of interest.
resultsA data.frame. Stores the pairwise propr measurements.
permutesA list. Stores the shuffled transformed "count matrix"
instances, used to reproduce permutations of propr.
fdrA data.frame. Stores the FDR cutoffs for propr.
show: Method to show propr object.
subset: Method to subset propr object.
[: Method to subset propr object.
phit:
A wrapper for propr(counts, metric = "phi", ...).
perb:
A wrapper for propr(counts, metric = "rho", ...).
phis:
A wrapper for propr(counts, metric = "phs", ...).
corr:
A wrapper for propr(counts, metric = "cor", ...).
simplify:
This convenience function takes an indexed propr object
and subsets the object based on that index. Then, it populates the
@pairs slot of the new object with an updated version
of the original index. You can call simplify from within the
[ method using the argument tiny.
updateCutoffs:
Use the propr object to permute proportionality
across a number of cutoffs. Since the permutations get saved
when the object is created, calling updateCutoffs
will use the same random seed each time.
Let D represent a number of features measured across N samples.
This function calculates proportionality from
a data set with N rows and D columns.
One can think of phi as
analogous to a distance matrix, except that it has no symmetry unless forced.
One can think of rho as
analogous to a correlation matrix.
One can think of phs as
either a naturally symmetric variant of phi or a monotonic variant of rho.
Also, one can use corr
to calculate correlation from log-ratio transformed data.
This function depends on a reference and uses the centered log-ratio
transformation by default. The user may also specify any number of
features (by index or name) to use as a reference instead.
Alternatively, ivar = "iqlr" will transform data using the
geometric mean of features with variances that fall in the
inter-quartile range of all per-feature variances (based on
the ALDEx2 package).
The propr method calculates proportionality. This fails in
the setting of zero counts. The propr method
will use a Box-Cox transformation to approximate VLR based on
the parameter \(\alpha\), if provided. We refer the user to
the vignette for more details.