aIc (version 1.0)

aIc.scale: 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
)

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

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.

Author

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)