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evident-0.4.0
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Effect size calculations for microbiome data.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | evident-0.4.0 |
| نام | evident |
| نسخه کتابخانه | 0.4.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Gibraan Rahman |
| ایمیل نویسنده | grahman@eng.ucsd.edu |
| آدرس صفحه اصلی | https://github.com/biocore/evident |
| آدرس اینترنتی | https://pypi.org/project/evident/ |
| مجوز | BSD-3-Clause |
[](https://github.com/biocore/evident/actions/workflows/main.yml)
[](https://github.com/biocore/evident/actions/workflows/q2.yml)
[](https://pypi.org/project/evident)
# Evident
Evident is a tool for performing effect size and power calculations on microbiome data.
## Installation
You can install the most up-to-date version of Evident from PyPi using the following command:
```bash
pip install evident
```
## QIIME 2
Evident is also available as a [QIIME 2](https://qiime2.org/) 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:
```bash
qiime evident --help
```
You should see something like this if Evident installed correctly:
```bash
Usage: qiime evident [OPTIONS] COMMAND [ARGS]...
Description: Perform power analysis on microbiome data. Supports
calculation of effect size given metadata covariates and supporting
visualizations.
Plugin website: https://github.com/biocore/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:
multivariate-effect-size-by-category
Multivariate data effect size by category.
multivariate-power-analysis Multivariate data power analysis.
plot-power-curve Plot power curve.
univariate-effect-size-by-category
Univariate data effect size by category.
univariate-power-analysis Univariate data power analysis.
univariate-power-analysis-repeated-measures
Univariate data power analysis for repeated
measures.
visualize-results Tabulate evident results.
```
## Standalone Usage
Evident can operate on two types of data:
* Univariate (vector)
* Multivariate (distance matrix)
Univariate data can be alpha diversity. log ratios, PCoA coordinates, etc.
Multivariate data is usually a beta diversity distance matrix.
For this tutorial we will be using alpha diversity values, but the commands are nearly the same for beta diversity distance matrices.
First, open Python and import Evident
```python
import evident
```
Next, load your diversity file and sample metadata.
```python
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 'DataHandler'.
This is the way that Evident stores the data and metadata for power calculations.
For our alpha diversity example, we'll load the `UnivariateDataHandler` class from Evident.
`UnivariateDataHandler` 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.
```python
adh = evident.UnivariateDataHandler(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.
```python
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.
```python
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.
```python
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](https://docs.bokeh.org/en/latest/).
To create a Bokeh app, use the following command:
```python
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:
```bash
bokeh serve --show app
```
This should open up a browser window with the interactive visualizations.
The "Summary" tab gives an overview of the data and the effect sizes/power.
Barplots showing the metadata effect sizes for both binary and multi-class categories (ranked in descending order) are shown.
On the right is a dynamic power curve showing the power analysis for metadata columns.
The significance level, total observation range, and chosen columns can be modified by using the control panel on the left side of the tab.
Swap to the "Data" tab using the bar on the top.
Here you can see boxplots of the data for each metadata category.
Select a column from the dropdown to change which data is shown.
You can also check the "Show scatter points" box to overlay the raw data onto the boxplots.
Note that because evident uses Python to perform the power calculations, it is at the moment *not* possible to embed 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](https://docs.qiime2.org/) 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.
To calculate power, we can run the following command:
```bash
qiime evident univariate-power-analysis \
--m-sample-metadata-file metadata.qza \
--m-sample-metadata-file faith_pd.qza \
--p-data-column faith_pd \
--p-group-column classification \
--p-alpha 0.01 0.05 0.1 \
--p-total-observations $(seq 10 10 100) \
--o-power-analysis-results results.qza
```
We provide multiple sample metadata files to QIIME 2 because they are internally merged.
You should provide a value for `--p-data-column` so Evident knows which column in the merged metadata contains the numeric values (this is only necessary for univariate analysis).
In this case, the name of the `faith_pd.qza` vector is `faith_pd` so we use that as input.
Notice how we used `$(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:
```bash
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](https://view.qiime2.org/) directly in your browser.
## Parallelization
Evident provides support for parallelizing effect size calculations through [joblib](https://joblib.readthedocs.io/en/latest/parallel.html).
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:
```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:
```bash
qiime evident univariate-effect-size-by-category \
--m-sample-metadata-file metadata.qza \
--m-sample-metadata-file faith_pd.qza \
--p-data-column faith_pd \
--p-group-columns classification sex cd_behavior \
--p-n-jobs 2 \
--o-effect-size-results alpha_effect_sizes.qza
```
## Repeated Measures
Evident supports limited analysis of repeated measures.
When your dataset has repeated measures, you can calculate `eta_squared` for univariate data.
Note that multivariate data is not supported for repeated measures analysis.
Power analysis for repeated measures implements a repeated measures ANOVA.
Additionally, when performing power analysis *only* power can be calculated (in contrast to `UnivariateDataHandler` and `MultivariateDataHandler` where alpha, significance, and observations can be calculated).
This power analysis assumes that the number of measurements per group is equal.
With Python:
```python
from evident.data_handler import RepeatedMeasuresUnivariateDataHandler
rmadh = RepeatedMeasuresUnivariateDataHandler(
faith_pd,
metadata,
individual_id_column="subject",
)
effect_size_result = rmadh.calculate_effect_size(state_column="group")
power_analysis_result = rmadh.power_analysis(
state_column="group",
subjects=[2, 4, 5],
measurements=10,
alpha=0.05,
correlation=[-0.5, 0, 0.5],
epsilon=0.1
)
```
With QIIME 2:
```
qiime evident univariate-power-analysis-repeated-measures \
--m-sample-metadata-file metadata.qza \
--m-sample-metadata-file faith_pd.qza \
--p-data-column faith_pd \
--p-individual-id-column subject \
--p-state-column group \
--p-subjects 2 4 5 \
--p-measurements 10 \
--p-alpha 0.05 \
--p-correlation -0.5 0 0.5 \
--p-epsilon 0.1
```
## 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](https://forum.qiime2.org/).
You can open an issue on the [Community Plugin Support](https://forum.qiime2.org/c/community-plugin-support/24) board and tag [@gibsramen](https://forum.qiime2.org/u/gibsramen) if required.
نحوه نصب
نصب پکیج whl evident-0.4.0:
pip install evident-0.4.0.whl
نصب پکیج tar.gz evident-0.4.0:
pip install evident-0.4.0.tar.gz