alakazam (version 0.2.3)

estimateAbundance: Estimates the complete clonal relative abundance distribution

Description

estimateAbundance estimates the complete clonal relative abundance distribution and confidence intervals on clone sizes using bootstrapping.

Usage

estimateAbundance(data, group, clone = "CLONE", copy = NULL, ci = 0.95, nboot = 2000)

Arguments

data

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

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 determined by the sum of copy numbers within each clonal group.

ci

confidence interval to calculate; the value must be between 0 and 1.

nboot

number of bootstrap realizations to generate.

Value

A data.frame with relative clonal abundance data and confidence intervals, containing the following columns:

• GROUP: group identifier.
• CLONE: clone identifier.
• P: relative abundance of the clone.
• LOWER: lower confidence inverval bound.
• UPPER: upper confidence interval bound.
• RANK: the rank of the clone abundance.

Details

The complete clonal abundance distribution determined 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.

Confidence intervals are derived using the standard deviation of the resampling realizations, as described in Chao et al, 2015.

References

1. Chao A. Nonparametric Estimation of the Number of Classes in a Population. Scand J Stat. 1984 11, 265270.
2. 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.
3. 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 plotAbundance for plotting of the abundance distribution. See rarefyDiversity for a similar application to clonal diversity.

Examples

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

abund <- estimateAbundance(df, "SAMPLE", nboot=100)