aIc.scale {aIc}

aIc.scale calculates the scaling invariance of a sample in a dataset for a given correction. This compares the distances of samples of the full dataset and a scaled version of the dataset. This is expected to be true if the transform is behaving rationally in compositional datasets.

Description

aIc.scale calculates the scaling invariance of a sample in a dataset for a given correction. This compares the distances of samples of the full dataset and a scaled version of the dataset. This is expected to be true if the transform is behaving rationally in compositional datasets.

Usage

aIc.scale(
  data,
  norm.method = "prop",
  zero.remove = 0.95,
  zero.method = "prior",
  distance = "euclidian",
  log = FALSE,
  group = NULL
)

Arguments

data

can be any dataframe or matrix with samples by column

norm.method

can be prop, clr, iqlr, lvha, RLE, TMM, TMMwsp

zero.remove

is a value. Filter data to remove features that are 0 across at least that proportion of samples: default 0.95

zero.method

can be any of NULL, prior, GBM or CZM. NULL will not impute or change 0 values, GBM (preferred) and CZM are from the zCompositions R package, and prior will simply add 0.5 to all counts.

distance

can be euclidian, bray, or jaccard. euclidian on log-ratio transformed data is the same as the Aitchison distance. default=euclidian

log

is a logical. log transform the RLE or TMM outputs, default=FALSE

group

is a vector containing group information. Required for clr, RLE, TMM, lvha, and iqlr based normalizations.

Value

Returns a list with the overlap between distances in the full and scaled composition in ol (expect 0), a yes/no binary decision in is.scale and the table of distances for the whole and scaled composition in dist.all and dist.scale, a plot showing a histogram of the resulting overlap in distances in plot, and the plot and axis labels in main xlab and ylab

Author(s)

Greg Gloor

Examples

data(selex)
group = c(rep('N', 7), rep('S', 7))
x <- aIc.scale(selex, group=group, norm.method='clr', zero.method='prior')
plot(x$plot, main=x$main, ylab=x$ylab, xlab=x$xlab)