propd: The propd Method
In propr: Calculating Proportionality Between Vectors of Compositional Data

Description Usage Arguments Value Slots Methods (by generic) Functions

Welcome to the propd method!

Let X and Y be non-zero positive feature vectors measured across N samples belonging to one of two groups, sized N1 and N2. We use VLR to denote the variance of the log of the ratio of the vectors X over Y. We define theta as the weighted sum of the within-group VLR divided by the weighted total VLR.

The propd method calculates theta. This fails in the setting of zero counts. The propd method will use a Box-Cox transformation to approximate VLR based on the parameter α, if provided. We refer the user to the vignette for more details.

Note that Group 1 always refers to the first element of the group vector argument supplied to propd.

## S4 method for signature 'propd'
show(object)

propd(counts, group, alpha, p = 100, weighted = FALSE)

setActive(propd, what = "theta_d")

setDisjointed(propd)

setEmergent(propd)

updateCutoffs.propd(object, cutoff = seq(0.05, 0.95, 0.3))

updateF(propd, moderated = FALSE, ivar = "clr")

`object`	A `propr` or `propd` object.
`counts`	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.
`group`	A character vector. Group or sub-group memberships, ordered according to the row names in `counts`.
`alpha`	A double. See vignette for details. Leave missing to skip Box-Cox transformation.
`p`	An integer. The number of permutation cycles.
`weighted`	A boolean. Toggles whether to calculate theta using `limma::voom` weights.
`propd`	A `propr` or `propd` object.
`what`	A character string. The theta type to set active.
`cutoff`	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.
`moderated`	For `updateF`, a boolean. Toggles whether to calculate a moderated F-statistic.
`ivar`	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.

Returns a propr object.

counts: A data.frame. Stores the original "count matrix" input.
alpha: A double. Stores the alpha value used for transformation.
group: A character vector. Stores the original group labels.
weighted: A logical. Stores whether the theta is weighted.
weights: A matrix. If weighted, stores the limma-based weights.
active: A character. Stores the name of the active theta type.
Fivar: ANY. Stores the reference used to moderate theta.
dfz: A double. Stores the prior df used to moderate theta.
results: A data.frame. Stores the pairwise propd measurements.
permutes: A data.frame. Stores the shuffled group labels, used to reproduce permutations of propd.
fdr: A data.frame. Stores the FDR cutoffs for propd.

show: Method to show propd object.

setActive: Build analyses and figures using a specific theta type. For example, set what = "theta_d" to analyze disjointed proportionality and what = "theta_e" to analyze emergent proportionality.

setDisjointed: A wrapper for setActive(propd, what = "theta_d").

setEmergent: A wrapper for setActive(propd, what = "theta_e").

updateCutoffs: Use the propd object to permute theta across a number of theta cutoffs. Since the permutations get saved when the object is created, calling updateCutoffs will use the same random seed each time.

updateF: Use the propd object to calculate the F-statistic from theta as described in the Erb et al. 2017 manuscript on differential proportionality. Optionally calculates a moderated F-statistic using the limma-voom method. Supports weighted and alpha transformed theta values.

propr documentation built on Dec. 16, 2019, 9:30 a.m.