Echidna

A Bayesian framework for quantifying gene dosage effect on phenotypic plasticity through integrating single-cell RNA sequencing (scRNA-seq) and bulk whole-genome sequencing (WGS) from a single or multiple time points.

Install

Echidna is available on PyPI under the name sc-echidna and bioconda under echidna.

Step 1 (optional but recommended)

Create a conda environment with a recent Python version: conda create -n "echidna-env" python=3.10.

Step 2

Ensure you have the right version of torch installed for your device. See instructions on pytorch.org.

Conda Formula

conda install bioconda::echidna

Pip Formula

pip install sc-echidna

Installation time depends on hardware, but can be finished within 5 minutes for most computers.

Tutorial

There are four example notebooks:

1. 1-single-timepoint.ipynb
2. 2-multi-timepoint.ipynb
3. 3-infer-gene-dosage.ipynb
4. 4-echidna-model.ipynb

The notebooks are meant to be run sequentially, and they build off of each other.

Notebook 1 introduces you to the package - preparing your data, setting hyperparamters, performing posterior predictive checks - with data collected from a single point in time.

In notebook 2, we look at a multi-timepoint setting, where we have paired single-cell and WGS data collected over time. The demo dataset included in ./demo_data is the same data we used for this notebook.

The saved model runs from notebook 2 will be used in notebook 3, where you will see how to infer amplifications and deletions by cluster of genes across a given genome. This notebook also shows you how to calculate and plot gene dosage effect with Echidna.

Notebook 4 is meant to show you how to do more custom work with the model. We package together many functions for your convenience, but this notebook will show you how to work directly with the model for the experiments not covered in the package. Some Pyro knowledge is assumed.

Echidna Configuration Settings

.obs Labels

Training Parameters

Model Hyperparameters