alphaDiversity - Calculate clonal alpha diversity
alphaDiversity takes in a data.frame or AbundanceCurve and computes
diversity scores (
D) over an interval of diversity orders (
alphaDiversity(data, min_q = 0, max_q = 4, step_q = 0.1, ci = 0.95, ...)
- data.frame with Change-O style columns containing clonal assignments or a AbundanceCurve generate by estimateAbundance object containing a previously calculated bootstrap distributions of clonal abundance.
- minimum value of
- maximum value of
- value by which to increment
- confidence interval to calculate; the value must be between 0 and 1.
- additional arguments to pass to estimateAbundance. Additional arguments are ignored if a AbundanceCurve is provided as input.
A DiversityCurve object summarizing the diversity scores.
Clonal diversity is calculated using the generalized diversity index (Hill numbers) proposed by Hill (Hill, 1973). See calcDiversity for further details.
Diversity is calculated on the estimated complete clonal abundance distribution. This distribution is inferred by using the Chao1 estimator to estimate the number of seen clones, and applying the relative abundance correction and unseen clone frequency described in Chao et al, 2015.
To generate a smooth curve,
D is calculated for each value of
max_q incremented by
variability in total sequence counts across unique values in the
is corrected by repeated resampling from the estimated complete clonal distribution to a
common number of sequences.
The diversity index (
D) for each group is the mean value of over all resampling
realizations. Confidence intervals are derived using the standard deviation of the
resampling realizations, as described in Chao et al, 2015.
Of note, the complete clonal abundance distribution is inferred by using the Chao1 estimator to estimate the number of seen clones, and then applying the relative abundance correction and unseen clone frequencies described in Chao et al, 2015.
- Hill M. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973 54(2):427-32.
- Chao A. Nonparametric Estimation of the Number of Classes in a Population. Scand J Stat. 1984 11, 265270.
- Chao A, et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol Monogr. 2014 84:45-67.
- Chao A, et al. Unveiling the species-rank abundance distribution by generalizing the Good-Turing sample coverage theory. Ecology. 2015 96, 11891201.
# Group by sample identifier in two steps abund <- estimateAbundance(ExampleDb, group="sample_id", nboot=100) div <- alphaDiversity(abund, step_q=1, max_q=10) plotDiversityCurve(div, legend_title="Sample")
# Grouping by isotype rather than sample identifier in one step div <- alphaDiversity(ExampleDb, group="c_call", min_n=40, step_q=1, max_q=10, nboot=100) plotDiversityCurve(div, legend_title="Isotype")