alakazam (version 0.2.2)

testDiversity: Pairwise test of the diversity index

Description

testDiversity performs pairwise significance tests of the diversity index ($D$) at a given diversity order ($q$) for a set of annotation groups via rarefaction and bootstrapping.

Usage

testDiversity(data, q, group, clone = "CLONE", copy = NULL, min_n = 30,
  max_n = NULL, nboot = 2000)

Arguments

data

data.frame with Change-O style columns containing clonal assignments.

q

diversity order to test.

group

name of the data column containing group identifiers.

clone

name of the data column containing clone identifiers.

copy

name of the data column containing copy numbers for each sequence. If copy=NULL (the default), then clone abundance is determined by the number of sequences. If a copy column is specified, then clone abundances is d

min_n

minimum number of observations to sample. A group with less observations than the minimum is excluded.

max_n

maximum number of observations to sample. If NULL the maximum if automatically determined from the size of the largest group.

nboot

number of bootstrap realizations to perform.

Value

• A DiversityTest object containing p-values and summary statistics.

Details

Clonal diversity is calculated using the generalized diversity index 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, 2014.

Variability in total sequence counts across unique values in the group column is corrected by repeated resampling from the estimated complete clonal distribution to a common number of sequences. The diversity index estimate ($D$) for each group is the mean value of over all bootstrap realizations.

Significance of the difference in diversity index ($D$) between groups is tested by constructing a bootstrap delta distribution for each pair of unique values in the group column. The bootstrap delta distribution is built by subtracting the diversity index $Da$ in $group-a$ from the corresponding value $Db$ in $group-b$, for all bootstrap realizations, yeilding a distribution of nboot total deltas; where $group-a$ is the group with the greater mean $D$. The p-value for hypothesis $Da != Db$ is the value of $P(0)$ from the empirical cumulative distribution function of the bootstrap delta distribution, multiplied by 2 for the two-tailed correction.

References

1. Hill M. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973 54(2):427-32.
2. Chao A. Nonparametric Estimation of the Number of Classes in a Population. Scand J Stat. 1984 11, 265270.
3. Wu Y-CB, et al. Influence of seasonal exposure to grass pollen on local and peripheral blood IgE repertoires in patients with allergic rhinitis. J Allergy Clin Immunol. 2014 134(3):604-12.
4. 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.
5. Chao A, et al. Unveiling the species-rank abundance distribution by generalizing the Good-Turing sample coverage theory. Ecology. 2015 96, 11891201.

See Also

See calcDiversity for the basic calculation and DiversityTest for the return object. See rarefyDiversity for curve generation. See ecdf for computation of the empirical cumulative distribution function.

Examples

# Load example data
file <- system.file("extdata", "ExampleDb.gz", package="alakazam")
df <- readChangeoDb(file)

# Groups under the size threshold are excluded and a warning message is issued.
testDiversity(df, "SAMPLE", q=0, min_n=30, nboot=100)