estimateAbundance

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,
clone = "clone_id",
copy = NULL,
group = NULL,
min_n = 30,
max_n = NULL,
uniform = TRUE,
ci = 0.95,
nboot = 200,
progress = FALSE
)

Arguments¶

data: data.frame with Change-O style columns containing clonal assignments.
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.
group: name of the data column containing group identifiers. If NULL then no grouping is performed and the group column of the output will contain the value NA for each row.
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 then no maximum is set.
uniform: if TRUE then uniformly resample each group to the same number of observations. If FALSE then allow each group to be resampled to its original size or, if specified, max_size.
ci: confidence interval to calculate; the value must be between 0 and 1.
nboot: number of bootstrap realizations to generate.
progress: if TRUE show a progress bar.

Value¶

A AbundanceCurve object summarizing the abundances.

References¶

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.

Examples¶

abund <- estimateAbundance(ExampleDb, group="sample_id", nboot=100)