DABEST-Python

Recent Version Update

On 20 March 2023, we officially released DABEST v2023.02.14 for Python. This new version provided the following new features:

1. Repeated measures. Augments the prior function for plotting (independent) multiple test groups versus a shared control; it can now do the same for repeated-measures experimental designs. Thus, together, these two methods can be used to replace both flavors of the 1-way ANOVA with an estimation analysis.

2. Proportional data. Generates proportional bar plots, proportional differences, and calculates Cohen’s h. Also enables plotting Sankey diagrams for paired binary data. This is the estimation equivalent to a bar chart with Fisher’s exact test.

3. The $\Delta\Delta$ plot. Calculates the delta-delta ($\Delta\Delta$) for 2 × 2 experimental designs and plots the four groups with their relevant effect sizes. This design can be used as a replacement for the 2 × 2 ANOVA.

4. Mini-meta. Calculates and plots a weighted delta ($\Delta$) for meta-analysis of experimental replicates. Useful for summarizing data from multiple replicated experiments, for example by different scientists in the same lab, or the same scientist at different times. When the observed values are known (and share a common metric), this makes meta-analysis available as a routinely accessible tool.

Contents

• About
• Installation
• Usage
• How to cite
• Bugs
• Contributing
• Acknowledgements
• Testing
• DABEST in other languages

About

DABEST is a package for Data Analysis using Bootstrap-Coupled ESTimation.

Estimation statistics are a simple framework that avoids the pitfalls of significance testing. It employs familiar statistical concepts such as means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment or intervention, rather than succumbing to a false dichotomy engendered by P values.

An estimation plot comprises two key features.

1. It presents all data points as a swarm plot, ordering each point to display the underlying distribution.

2. It illustrates the effect size as a bootstrap 95% confidence interval on a separate but aligned axis.

DABEST powers estimationstats.com, allowing everyone access to high-quality estimation plots.

Installation

This package is tested on Python 3.6, 3.7, 3.8 and 3.10. It is highly recommended to download the Anaconda distribution of Python in order to obtain the dependencies easily.

You can install this package via pip.

To install, at the command line run

conda config --add channels conda-forge
conda install dabest

or –>

pip install dabest

You can also clone this repo locally.

Then, navigate to the cloned repo in the command line and run

pip install .

Usage

import pandas as pd
import dabest

# Load the iris dataset. This step requires internet access.
iris = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")

# Load the above data into `dabest`.
iris_dabest = dabest.load(data=iris, x="species", y="petal_width",
                          idx=("setosa", "versicolor", "virginica"))

# Produce a Cumming estimation plot.
iris_dabest.mean_diff.plot();

Please refer to the official tutorial for more useful code snippets.

How to cite

Moving beyond P values: Everyday data analysis with estimation plots

Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang

Nature Methods 2019, 1548-7105. 10.1038/s41592-019-0470-3

Paywalled publisher site; Free-to-view PDF

Bugs

Please report any bugs on the Github issue tracker.

Contributing

All contributions are welcome; please read the Guidelines for contributing first.

We also have a Code of Conduct to foster an inclusive and productive space.

A wish list for new features

If you have any specific comments and ideas for new features that you would like to share with us, please read the Guidelines for contributing, create a new issue using Feature request template or create a new post in our Google Group.

Acknowledgements

We would like to thank alpha testers from the Claridge-Chang lab: Sangyu Xu, Xianyuan Zhang, Farhan Mohammad, Jurga Mituzaitė, and Stanislav Ott.

Testing

To test DABEST, you need to install pytest and nbdev.

• Run pytest in the root directory of the source distribution. This runs the test suite in the folder dabest/tests/mpl_image_tests.
• Run nbdev_test in the root directory of the source distribution. This runs the value assertion tests in parent folder dabest/tests

The test suite ensures that the bootstrapping functions and the plotting functions perform as expected.

For detailed information, please refer to the test folder

DABEST in other languages

DABEST is also available in R (dabestr) and Matlab (DABEST-Matlab).