aIc.coherent {aIc}

Calculate the subcompositional coherence of samples in a dataset for a given correction.

Description

'aIc.coherent' compares the correlation coefficients of features in common of the full dataset and a subset of the dataset. This is expected to be false for all compositional datasets and transforms.

Usage

aIc.coherent(
  data,
  norm.method = "prop",
  zero.remove = 0.95,
  zero.method = "prior",
  log = FALSE,
  group = NULL,
  cor.test = "spearman"
)

Arguments

data

can be any dataframe or matrix with samples by column

norm.method

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

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 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 prop, RLE or TMM outputs, default=FALSE

group

is a vector containing group information. Required for clr, RLE,

cor.test

is either the pearson or spearman method (default)

Value

Returns a list with the correlation in cor, a yes/no binary decision in is.coherent, the x and y values for a scatterplot of the correlations in the full and subcompositions, 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.coherent(selex, group=group, norm.method='clr', zero.method='prior')
plot(x$plot[,1], x$plot[,2], main=x$main, ylab=x$ylab, xlab=x$xlab)