# Appendix: Shotgun Metagenomics (MOSHPIT cocoa)

A shotgun metagenomics dataset reaches q2-gglasso and q2-classo as a feature
table like any other. The MOSHPIT cocoa fermentation study is the example. No
feature table ships with this book for that study, and none of the commands
below were run.

## The dataset

Cocoa bean fermentation, sampled as 14 shotgun metagenomes: two seed types
(*Forasteiro* and *Hybrid*) at seven timepoints (0, 24, 48, 72, 96, 120 and
144 hours). Sequencing is Illumina HiSeq X Ten whole-genome shotgun, deposited
under BioProject PRJNA552479.

$2 \times 7 = 14$ gives exactly one sample per condition, with no within-cell
replication, and 14 samples is a small number in an absolute sense regardless of
how the conditions are arranged.

## Where a feature table comes from

MOSHPIT is the QIIME 2 distribution for shotgun metagenomics. Three routes
through it end in a `FeatureTable[Frequency]`:

- **Read-based taxonomic profiling with Kraken2, refined by Bracken.** Kraken2
  assigns reads to taxa against a k-mer database; Bracken redistributes them to a
  chosen rank and produces per-sample abundance estimates. The result is a table
  of taxa × samples.
- **Read-based profiling with Kaiju**, which classifies against a protein
  database instead. Same shape of output, different assignment behaviour.
- **Assembly and binning.** Assemble the reads, bin the contigs into MAGs,
  dereplicate the bins across samples, then estimate per-sample abundance of each
  MAG — typically as TPM. The features are genome bins rather than named taxa.

Action names for those steps change between releases. Take them from the MOSHPIT
documentation for the release you are running.

## Compatibility with q2-gglasso and q2-classo

Both plugins consume a `FeatureTable` and neither inspects its provenance:

- `qiime gglasso transform-features` accepts
  `FeatureTable[Composition | Frequency | Design]`. A Bracken, Kaiju or MAG table
  registered as `FeatureTable[Frequency]` goes straight in, and the rest of the
  chain — `calculate-covariance`, `solve-problem`, `summarize` — is unchanged from
  [Data Preparation](../02_lowdim_gglasso/01_data_preparation.md).
- `qiime classo transform-features` likewise takes the table as `--i-features` and
  the workflow proceeds as in
  [Data Preparation](../03_lowdim_classo/02_data_preparation.md).

Three caveats attach to shotgun-derived tables specifically:

1. **TPM is not a count.** MAG-abundance tables are already normalized for genome
   length and depth. The compositional argument for a log-ratio transform still
   applies — TPM values are relative by construction — but `--p-pseudo-count` and
   `--p-coef`, which are written as count-like floors, no longer have an obvious
   scale. `--p-coef` (q2-classo) is registered as a `Float`, so you can set it
   below the smallest non-zero TPM value in your table. `--p-pseudo-count`
   (q2-gglasso) is registered as an `Int`, so it has a hard floor of 1 and
   cannot take the fractional value a TPM table usually needs. For such tables
   prefer `qiime gglasso transform-features --p-transformation mclr`, which
   log-transforms only the positive entries and needs no floor at all. `mclr` is
   a q2-gglasso option; `qiime classo transform-features` implements `clr` only,
   so on the classo side tune `--p-coef`.
2. **`--i-taxonomy` is still required by `qiime gglasso transform-features` and
   still unread.** For a MAG table there may be no natural taxonomy to pass; any
   valid `FeatureData[Taxonomy]` will satisfy the input. See
   [Troubleshooting](../90_reference/04_troubleshooting.md).
3. **`qiime classo add-taxa` does need a real taxonomy**, because trac builds its
   aggregation matrix from the hierarchy. Bracken output at a fixed rank is flat
   and gives the tree nothing to aggregate over; a MAG table needs taxonomic
   assignments attached first.

## Data availability and sample size

**No feature table is published for this study.** Producing one means running
assembly and binning, or holding a local Kraken2 or Kaiju database — large
reference resources that are impractical to bundle with a tutorial and slow to
build from scratch. No files for this study appear in
[Download the Tutorial Data](../00_getting_started/03_download_data.md).

**Fourteen samples cannot support covariance estimation.** The empirical
covariance matrix of $n$ samples has rank at most $n - 1$, so with $n = 14$ every
estimate lives in at most 13 dimensions while a Bracken table at species level has
features in the thousands. A graphical lasso would still return a network, because
that is what a penalized estimator does. The network would be almost entirely the
penalty. The same applies to the log-contrast models — with one sample per
condition, cross-validation folds contain one or two samples and the resulting
error estimate is not usable.

Shotgun tables are compatible with both plugins. That compatibility is not a
demonstration that they can be analyzed at this sample size. If you have a
shotgun dataset with tens to hundreds of samples, the workflows in
[the high-dimensional Atacama chapters](../04_highdim_atacama/00_index.md)
transfer directly — 16S rather than shotgun, but identical in every command.

```{note}
None of the MOSHPIT steps, tables or models described has been run against
QIIME 2 2026.7 or any other release. No outputs, runtimes or database sizes are
quoted.
```