aIc (version 1.0)

aIc.singular: aIc.singular tests for singular data. This is expected to be true if the transform is behaving rationally in compositional datasets and also true in the case of datasets with more features than samples.

Description

aIc.singular tests for singular data. This is expected to be true if the transform is behaving rationally in compositional datasets and also true in the case of datasets with more features than samples.

Usage

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

Value

Returns a list with a yes/no binary decision in

is.singular and the covariance matrix in cov.matrix

Arguments

data

can be any dataframe or matrix with samples by column

norm.method

can be prop, clr, 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.

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.singular(selex, group=group, norm.method='clr', zero.method='prior')