aIc.coherent {aIc} R Documentation

## 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.

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)



[Package aIc version 1.0 Index]