suggest_rarefy_depth

Calculates a rarefaction depth that balances retaining samples against
retaining total observations.

A toolkit for working with Biological Observation Matrix ('BIOM') files.
Read/write all 'BIOM' formats. Compute rarefaction, alpha diversity, and
beta diversity (including 'UniFrac'). Summarize counts by taxonomic level.
Subset based on metadata. Generate visualizations and statistical analyses.

Daniel P. Smith

rbiom

Integrated Analysis and Visualization of Microbiome Data

Alkek Center for Metagenomics and Microbiome Research 

suggest_rarefy_depth function

<dl><dt>biom</dt>
<dd>An rbiom object, or any value accepted by <code>as_rbiom()</code>.</dd></dl>

Arguments

This function selects a depth by analyzing the trade-off between dropping
samples (to increase depth) and lowering depth (to keep samples).<ol>
<li>Calculate Yields: For every distinct sample depth in the dataset,
calculate the total number of observations that would remain if the
dataset were rarefied to that level.
$$Yield_d = d \times N_{\ge d}$$
Where $d$ is the depth and $N_{\ge d}$ is the number of samples
with at least that many reads.</li>
<li>Define Threshold: Calculate 10% of the total observations in the
original un-rarefied dataset.</li>
<li>Select Depth: Find the lowest depth $d$ where the $Yield_d$
exceeds this 10% threshold.</li>
</ol>This approach prioritizes keeping as many samples as possible, provided that
doing so doesn't discard more than 90% of the dataset's total information.

Heuristic

Suggest Rarefaction Depth — suggest_rarefy_depth

<dl>

<dt>biom</dt>
<dd>An rbiom object, or any value accepted by <code>as_rbiom()</code>.</dd>

</dl>

This function selects a depth by analyzing the trade-off between dropping
samples (to increase depth) and lowering depth (to keep samples).<ol>
<li>Calculate Yields: For every distinct sample depth in the dataset,
calculate the total number of observations that would remain if the
dataset were rarefied to that level.
$$Yield_d = d \times N_{\ge d}$$
Where $d$ is the depth and $N_{\ge d}$ is the number of samples
with at least that many reads.</li>
<li>Define Threshold: Calculate 10% of the total observations in the
original un-rarefied dataset.</li>
<li>Select Depth: Find the lowest depth $d$ where the $Yield_d$
exceeds this 10% threshold.</li>
</ol>

This approach prioritizes keeping as many samples as possible, provided that
doing so doesn't discard more than 90% of the dataset's total information.

suggest_rarefy_depth: Suggest Rarefaction Depth

Description

Usage

Value

Arguments

Heuristic

See Also

Examples