evident

Evident is a tool for performing effect size and power calculations on microbiome diversity data.

Installation

You can install the most up-to-date version of evident from PyPi using the following command:

pip install evident

QIIME 2

evident is also available as a QIIME 2 plugin. Make sure you have activated a QIIME 2 environment and run the same installation command as above.

To check that evident installed correctly, run the following from the command line:

qiime evident --help

You should see something like this if evident installed correctly:

Usage: qiime evident [OPTIONS] COMMAND [ARGS]...

  Description: Perform power analysis on microbiome diversity data. Supports
  calculation of effect size given metadata covariates and supporting
  visualizations.

  Plugin website: https://github.com/gibsramen/evident

  Getting user support: Please post to the QIIME 2 forum for help with this
  plugin: https://forum.qiime2.org

Options:
  --version            Show the version and exit.
  --example-data PATH  Write example data and exit.
  --citations          Show citations and exit.
  --help               Show this message and exit.

Commands:
  alpha-effect-size-by-category  Alpha diversity effect size by category.
  alpha-power-analysis           Alpha diversity power analysis.
  beta-effect-size-by-category   Beta diversity effect size by category.
  beta-power-analysis            Beta diversity power analysis.
  plot-power-curve               Plot power curve.
  visualize-results              Tabulate evident results.

Standalone Usage

evident requires two input files:

Either an alpha or beta diversity file
Sample metadata

First, open Python and import evident

import evident

Next, load your diversity file and sample metadata. For alpha diversity, this should be a pandas Series. For beta diversity, this should be an scikit-bio DistanceMatrix. Sample metadata should be a pandas DataFrame. We'll be using an alpha diversity vector for this tutorial but the commands are nearly the same for beta diversity distance matrices.

import pandas as pd

metadata = pd.read_table("data/metadata.tsv", sep="\t", index_col=0)
faith_pd = metadata["faith_pd"]

The main data structure in evident is the 'DiversityHandler'. This is the way that evident stores the diversity data and metadata for power calculations. For our alpha diversity example, we'll load the AlphaDiversityHandler class from evident. AlphaDiversityHandler takes as input the pandas Series with the diversity values and the pandas DataFrame containing the sample metadata. By default, evident will only consider metadata columns with, at max, 5 levels. To modify this behavior, provide a value for the max_levels_per_category argument. Additionally, evident will not consider any category levels represented by fewer than 3 samples. To modify this behavior, use the min_count_per_level argument.

adh = evident.AlphaDiversityHandler(faith_pd, metadata)

Next, let's say we want to get the effect size of the diversity differences between two groups of samples. We have in our example a column in the metadata "classification" comparing two groups of patients with Crohn's disease. First, we'll look at the mean of Faith's PD between these two groups.

metadata.groupby("classification").agg(["count", "mean", "std"])["faith_pd"]

which results in

                count       mean       std
classification
B1                 99  13.566110  3.455625
Non-B1            121   9.758946  3.874911

Looks like there's a pretty large difference between these two groups. What we would like to do now is calculate the effect size of this difference. Because we are comparing only two groups, we can use Cohen's d. evident automatically chooses the correct effect size to calculate - either Cohen's d if there are only two categories or Cohen's f if there are more than 2.

adh.calculate_effect_size(column="classification")

This tells us that our effect size is 1.03.

Now let's say we want to see how many samples we need to be able to detect this difference with a power of 0.8. evident allows you to easily specify arguments for alpha, power, or total observations for power analysis. We can then plot these results as a power curve to summarize the data.

from evident.plotting import plot_power_curve
import numpy as np

alpha_vals = [0.01, 0.05, 0.1]
obs_vals = np.arange(10, 101, step=10)
results = adh.power_analysis(
    "classification",
    alpha=alpha_vals,
    total_observations=obs_vals
)
plot_power_curve(results, target_power=0.8, style="alpha", markers=True)

When we inspect this plot, we can see how many samples we would need to collect to observe the same effect size at different levels of significance and power.

Interactive power curve with Bokeh

evident allows users to interactively perform effect size and power calculations using Bokeh. To create a Bokeh app, use the following command:

from evident.interactive import create_bokeh_app

create_bokeh_app(adh, "app")

This will save the necessary files into a new directory app/. Navigate to the directory containing app/ (not app/ itself) and execute this command from your terminal:

bokeh serve --show app

This should open up a browser window where you can modify the chosen column, significance, level, and observations. We also provide a command line script to generate an interactive app using some test data. You can access this script at evident/tests/make_interactive.py.

Note that because evident uses Python to perform the power calculations, it is at the moment not possible to embded this interactive app into a standalone webpage.

QIIME 2 Usage

evident provides support for the popular QIIME 2 framework of microbiome data analysis. We assume in this tutorial that you are familiar with using QIIME 2 on the command line. If not, we recommend you read the excellent documentation before you get started with evident. Note that we have only tested evident on QIIME 2 version 2021.11. If you are using a different version and encounter an error please let us know via an issue.

As with the standalone version, evident requires a diversity file and a sample metadata file. These inputs are expected to conform to QIIME 2 standards.

To calculate power, we can run the following command:

qiime evident alpha-power-analysis \
    --i-alpha-diversity faith_pd.qza \
    --m-sample-metadata-file metadata.qza \
    --m-sample-metadata-column classification \
    --p-alpha 0.01 0.05 0.1 \
    --p-total-observations $(seq 10 10 100) \
    --o-power-analysis-results results.qza

Notice how we used $(seq 10 10 100) to provide input into the --p-total-observations argument. seq is a command on UNIX-like systems that generates a sequence of numbers. In our example, we used seq to generate the values from 10 to 100 in intervals of 10 (10, 20, ..., 100).

With this results artifact, we can visualize the power curve to get a sense of how power varies with number of observations and significance level. Run the following command:

qiime evident plot-power-curve \
    --i-power-analysis-results results.qza \
    --p-target-power 0.8 \
    --p-style alpha \
    --o-visualization power_curve.qzv

You can view this visualization at view.qiime2.org directly in your browser.

Parallelization

evident provides support for parallelizing effect size calculations through joblib. Parallelization is performed across different columns when using effect_size_by_category and pairwise_effect_size_by_category. Consider parallelization if you have a lot of samples and/or a lot of different metadata categories of interest. By default, no parallelization is performed.

With Python:

from evident.effect_size import effect_size_by_category

effect_size_by_category(
    adh,
    ["classification", "cd_resection", "cd_behavior"],
    n_jobs=2
)

With QIIME 2:

qiime evident alpha-effect-size-by-category \
    --i-alpha-diversity faith_pd.qza \
    --m-sample-metadata-file metadata.qza \
    --p-columns classification sex cd_behavior \
    --p-n-jobs 2 \
    --o-effect-size-results alpha_effect_sizes.qza

Help with evident

If you encounter a bug in evident, please post a GitHub issue and we will get to it as soon as we can. We welcome any ideas or documentation updates/fixes so please submit an issue and/or a pull request if you have thoughts on making evident better.

If your question is regarding the QIIME 2 version of evident, consider posting to the QIIME 2 forum. You can open an issue on the Community Plugin Support board and tag @gibsramen if required.

gibsramen/evident_