aIc (version 1.0)

aIc.coherent: 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"
)

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.

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)

Author

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)