q2-core

bokulich-lab

Quickstart install for:

[Fresh Install]

[Fresh Install (Apple Silicon)]

[Update Existing]

conda env create --name q2-core-moshpit-2026.7 --file https://raw.githubusercontent.com/bokulich-lab/q2-core/refs/heads/main/environment-files/q2-core-rachis-moshpit-2026.7.yml

CONDA_SUBDIR=osx-64 conda env create \
  --name q2-core-moshpit-2026.7 \
  --file https://raw.githubusercontent.com/bokulich-lab/q2-core/refs/heads/main/environment-files/q2-core-rachis-moshpit-2026.7.yml
conda activate q2-core-moshpit-2026.7
conda config --env --set subdir osx-64

Rachis (QIIME 2) plugin for computing prevalence-abundance core scores from relative-frequency feature tables.

How the core score is calculated¶

For each feature, q2-core combines how consistently the feature occurs across samples with how abundant it is:

Prevalence is the fraction of samples in which the feature’s relative abundance is strictly greater than min_rel_abundance (default: 0.001).
Mean relative abundance is calculated across all samples by default. If mean_abundance_on_presence=True, the mean instead uses only samples where the feature exceeds min_rel_abundance.
The mean abundance is transformed as log10(mean_abundance + offset), where offset (default: 1e-6) prevents taking the logarithm of zero.
Prevalence and log-transformed mean abundance are each min--max scaled to the interval [0, 1] across all features in the input table.
The final score is their product: core_score = scaled_prevalence * scaled_log_mean_abundance.

Consequently, a high score identifies features that are both widespread and relatively abundant. Features with a low value for either component receive a low score. Scores are relative to the features in the supplied table; if all features have the same value for either component, that component is set to zero for every feature.

The output contains the unscaled and scaled component values as prevalence, mean_abundance, log_mean_abundance, prevalence_scaled, and log_mean_abundance_scaled, in addition to core_score.

Installation¶

Please follow the installation instructions on the QIIME 2 Library, to install an env with the QIIME 2 or MOSHPIT distros and q2-core.

Usage¶

Python API¶

rel_frequency_table = Artifact.load("path-to-table")

core_scores = core.actions.score(
    table=rel_frequency_table,
    min_rel_abundance=0.001,
    mean_abundance_on_presence=True,
)
core_scores_artifact = core_scores.core_scores
core_scores_artifact.save("output_path_core_score")

filtered_table = feature_table.actions.filter_features(
    table=rel_frequency_table,
    metadata=core_scores_artifact.view(Metadata),
    where="core_score > 0.01",
    max_frequency="None",
)
filtered_rel_frequency_table = filtered_table.filtered_table
filtered_rel_frequency_table.save("output_path_filtered_table")

CLI¶

qiime core score \
  --i-table rel_frequency_table.qza \
  --p-min-rel-abundance 0.001 \
  --p-mean-abundance-on-presence \
  --o-core-scores core_scores.qza

qiime feature-table filter-features \
  --i-table rel_frequency_table.qza \
  --m-metadata-file core_scores.qza \
  --p-where 'core_score > 0.01' \
  --o-filtered-table filtered_rel_frequency_table.qza

Rachis (QIIME 2) plugin for computing prevalence-abundance core scores from relative-frequency feature tables.

How the core score is calculated¶

For each feature, q2-core combines how consistently the feature occurs across samples with how abundant it is:

Prevalence is the fraction of samples in which the feature’s relative abundance is strictly greater than min_rel_abundance (default: 0.001).
Mean relative abundance is calculated across all samples by default. If mean_abundance_on_presence=True, the mean instead uses only samples where the feature exceeds min_rel_abundance.
The mean abundance is transformed as log10(mean_abundance + offset), where offset (default: 1e-6) prevents taking the logarithm of zero.
Prevalence and log-transformed mean abundance are each min--max scaled to the interval [0, 1] across all features in the input table.
The final score is their product: core_score = scaled_prevalence * scaled_log_mean_abundance.

Installation¶

Please follow the installation instructions on the QIIME 2 Library, to install an env with the QIIME 2 or MOSHPIT distros and q2-core.

Usage¶

Python API¶

rel_frequency_table = Artifact.load("path-to-table")

core_scores = core.actions.score(
    table=rel_frequency_table,
    min_rel_abundance=0.001,
    mean_abundance_on_presence=True,
)
core_scores_artifact = core_scores.core_scores
core_scores_artifact.save("output_path_core_score")

filtered_table = feature_table.actions.filter_features(
    table=rel_frequency_table,
    metadata=core_scores_artifact.view(Metadata),
    where="core_score > 0.01",
    max_frequency="None",
)
filtered_rel_frequency_table = filtered_table.filtered_table
filtered_rel_frequency_table.save("output_path_filtered_table")

CLI¶

qiime core score \
  --i-table rel_frequency_table.qza \
  --p-min-rel-abundance 0.001 \
  --p-mean-abundance-on-presence \
  --o-core-scores core_scores.qza

qiime feature-table filter-features \
  --i-table rel_frequency_table.qza \
  --m-metadata-file core_scores.qza \
  --p-where 'core_score > 0.01' \
  --o-filtered-table filtered_rel_frequency_table.qza

Rachis (QIIME 2) plugin for computing prevalence-abundance core scores from relative-frequency feature tables.

How the core score is calculated¶

For each feature, q2-core combines how consistently the feature occurs across samples with how abundant it is:

Prevalence is the fraction of samples in which the feature’s relative abundance is strictly greater than min_rel_abundance (default: 0.001).
Mean relative abundance is calculated across all samples by default. If mean_abundance_on_presence=True, the mean instead uses only samples where the feature exceeds min_rel_abundance.
The mean abundance is transformed as log10(mean_abundance + offset), where offset (default: 1e-6) prevents taking the logarithm of zero.
Prevalence and log-transformed mean abundance are each min--max scaled to the interval [0, 1] across all features in the input table.
The final score is their product: core_score = scaled_prevalence * scaled_log_mean_abundance.

Installation¶

Please follow the installation instructions on the QIIME 2 Library, to install an env with the QIIME 2 or MOSHPIT distros and q2-core.

Usage¶

Python API¶

rel_frequency_table = Artifact.load("path-to-table")

core_scores = core.actions.score(
    table=rel_frequency_table,
    min_rel_abundance=0.001,
    mean_abundance_on_presence=True,
)
core_scores_artifact = core_scores.core_scores
core_scores_artifact.save("output_path_core_score")

filtered_table = feature_table.actions.filter_features(
    table=rel_frequency_table,
    metadata=core_scores_artifact.view(Metadata),
    where="core_score > 0.01",
    max_frequency="None",
)
filtered_rel_frequency_table = filtered_table.filtered_table
filtered_rel_frequency_table.save("output_path_filtered_table")

CLI¶

qiime core score \
  --i-table rel_frequency_table.qza \
  --p-min-rel-abundance 0.001 \
  --p-mean-abundance-on-presence \
  --o-core-scores core_scores.qza

qiime feature-table filter-features \
  --i-table rel_frequency_table.qza \
  --m-metadata-file core_scores.qza \
  --p-where 'core_score > 0.01' \
  --o-filtered-table filtered_rel_frequency_table.qza

Rachis (QIIME 2) plugin for computing prevalence-abundance core scores from relative-frequency feature tables.

How the core score is calculated¶

For each feature, q2-core combines how consistently the feature occurs across samples with how abundant it is:

Prevalence is the fraction of samples in which the feature’s relative abundance is strictly greater than min_rel_abundance (default: 0.001).
Mean relative abundance is calculated across all samples by default. If mean_abundance_on_presence=True, the mean instead uses only samples where the feature exceeds min_rel_abundance.
The mean abundance is transformed as log10(mean_abundance + offset), where offset (default: 1e-6) prevents taking the logarithm of zero.
Prevalence and log-transformed mean abundance are each min--max scaled to the interval [0, 1] across all features in the input table.
The final score is their product: core_score = scaled_prevalence * scaled_log_mean_abundance.

Installation¶

Please follow the installation instructions on the QIIME 2 Library, to install an env with the QIIME 2 or MOSHPIT distros and q2-core.

Usage¶

Python API¶

rel_frequency_table = Artifact.load("path-to-table")

core_scores = core.actions.score(
    table=rel_frequency_table,
    min_rel_abundance=0.001,
    mean_abundance_on_presence=True,
)
core_scores_artifact = core_scores.core_scores
core_scores_artifact.save("output_path_core_score")

filtered_table = feature_table.actions.filter_features(
    table=rel_frequency_table,
    metadata=core_scores_artifact.view(Metadata),
    where="core_score > 0.01",
    max_frequency="None",
)
filtered_rel_frequency_table = filtered_table.filtered_table
filtered_rel_frequency_table.save("output_path_filtered_table")

CLI¶

qiime core score \
  --i-table rel_frequency_table.qza \
  --p-min-rel-abundance 0.001 \
  --p-mean-abundance-on-presence \
  --o-core-scores core_scores.qza

qiime feature-table filter-features \
  --i-table rel_frequency_table.qza \
  --m-metadata-file core_scores.qza \
  --p-where 'core_score > 0.01' \
  --o-filtered-table filtered_rel_frequency_table.qza

Links

Documentation

Source Code

Stars

Last Commit

35065a8

Available Distros

2026.7: 2026.7/moshpit; 2026.7/qiime2; 2026.7/tiny