intermediate_stability: Intermediate Stability
In microbiome: Microbiome Analytics

Description Usage Arguments Details Value Author(s) Examples

Quantify intermediate stability with respect to a given reference point.

intermediate_stability(
  x,
  reference.point = NULL,
  method = "correlation",
  output = "scores"
)

`x`	phyloseq object. Includes abundances (variables x samples) and sample_data data.frame (samples x features) with 'subject' and 'time' field for each sample.
`reference.point`	Calculate stability of the data w.r.t. this point. By default the intermediate range is used (min + (max - min)/2). If a vector of points is provided, then the scores will be calculated for every point and a data.frame is returned.
`method`	'lm' (linear model) or 'correlation'; the linear model takes time into account as a covariate
`output`	Specify the return mode. Either the 'full' set of stability analysis outputs, or the 'scores' of intermediate stability.

Decomposes each column in x into differences between consecutive time points. For each variable and time point we calculate for the data values: (i) the distance from reference point; (ii) distance from the data value at the consecutive time point. The 'correlation' method calculates correlation between these two variables. Negative correlations indicate that values closer to reference point tend to have larger shifts in the consecutive time point. The 'lm' method takes the time lag between the consecutive time points into account as this may affect the comparison and is not taken into account by the straightforward correlation. Here the coefficients of the following linear model are used to assess stability: abs(change) ~ time + abs(start.reference.distance). Samples with missing data, and subjects with less than two time point are excluded. The absolute count data x is logarithmized before the analysis with the log10(1 + x) trick to circumvent logarithmization of zeroes.

A list with following elements: stability: estimated stability data: processed data set used in calculations

Leo Lahti leo.lahti@iki.fi

data(atlas1006)
x <- subset_samples(atlas1006, DNA_extraction_method == 'r')
x <- prune_taxa(c('Akkermansia', 'Dialister'), x)
res <- intermediate_stability(x, reference.point=NULL)