pathogen-embed 3.1.0
Reduced dimension embeddings for pathogen sequences
License: MIT License

# pathogen-embed

[![PyPI version](https://badge.fury.io/py/pathogen-embed.svg)](https://pypi.org/project/pathogen-embed/)
[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/pathogen-embed/README.html)

Create reduced dimension embeddings for pathogen sequences

pathogen-embed is an open-source software to run reduced dimension embeddings (PCA, MDS, t-SNE, and UMAP) on viral populations. For more details, read [Nanduri et al.](https://bedford.io/papers/nanduri-cartography/) and check out [the corresponding GitHub repository](https://github.com/blab/cartography).

 - [Change log](CHANGES.md)
 - [Documentation](https://blab.github.io/pathogen-embed/)
 - [Source Code](https://github.com/blab/pathogen-embed/tree/main)
 - [Bug reports](https://github.com/blab/pathogen-embed/issues)

## Installation

### With pip

```
pip install pathogen-embed
```

### With Conda

```
conda install -c conda-forge -c bioconda pathogen-embed
```

## Quickstart

The following commands show an example of how to apply the pathogen-embed tools to a small set of seasonal influenza A/H3N2 hemagglutinin (HA) sequences.
To start, calculate the distance matrix between each pair of sequences in the dataset.

```bash
pathogen-distance \
  --alignment tests/data/h3n2_ha_alignment.fasta \
  --output distances.csv
```

> (Optional) For faster distance calculations without indel support, use [snp-dists](https://github.com/tseemann/snp-dists).
> This command converts the default tab-delimited output of snp-dists into the comma-delimited format expected by pathogen-embed.
>
> ```bash
> snp-dists -c -b tests/data/h3n2_ha_alignment.fasta > distances.csv
> ```

Create a t-SNE embedding from the distance matrix.
Note that the perplexity is the number of nearest neighbors to consider in the embedding calculations, so this value has to be less than or equal to the total number of samples in the input (N=50, here).

```bash
pathogen-embed \
  --alignment tests/data/h3n2_ha_alignment.fasta \
  --distance-matrix distances.csv \
  --output-dataframe tsne.csv \
  --output-figure tsne.pdf \
  --output-pairwise-distance-figure tsne_pairwise_distances.pdf \
  t-sne \
    --perplexity 45.0
```

The following figure shows the resulting embedding.

![Example t-SNE embedding of seasonal influenza A/H3N2 hemagglutinin sequences](images/example-tsne-embedding.png)

The following figure shows the distribution of pairwise Euclidean distances by corresponding pairwise genetic distance.
The equation in the figure title shows how genetic distances (*x* in the equation) scale to Euclidean distances (*y*) in the embedding.

![Distribution of Euclidean distances between pairs of genomes in a t-SNE embedding by the pairwise genetic distance between the same genomes](images/example-tsne-pairwise-distances.png)

Find clusters in the embedding.

```bash
pathogen-cluster \
  --embedding tsne.csv \
  --label-attribute tsne_label \
  --output-dataframe tsne_with_clusters.csv \
  --output-figure tsne_with_clusters.pdf
```

The following image shows the t-SNE embedding colored by clusters.
Note that the underlying clustering algorithm, [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/), allows samples to not be assigned to any cluster if there isn't a reliable cluster to place them in.
These unassigned samples receive a cluster label of "-1".

![Example t-SNE embedding of seasonal influenza A/H3N2 hemagglutinin sequences colored by the cluster label assigned by pathogen-cluster](images/example-tsne-embedding-with-clusters.png)

If you know the minimum genetic distance you want to require between clusters, you can use the equation from the pairwise distance figure above to determine the corresponding minimum Euclidean distance to pass to `pathogen-cluster`'s `--distance-threshold` argument.

## Example: Identify reassortment groups from multiple gene alignments

To identify potential reassortment groups for viruses with segmented genomes, you can calculate one distance matrix per gene and pass multiple distance matrices to `pathogen-embed`.
Internally, `pathogen-embed` sums the given distances matrices into a single matrix to use for an embedding.
The clusters in the resulting embedding represent genetic diversity in each gene individually and potential reassortment between genes.
The following example shows how to apply this approach to alignments for seasonal influenza A/H3N2 HA and NA.

Calculate a separate distance matrix per gene alignment for HA and NA.
Note that alignments must have the same sequence names in the same order.
If they do not, sort your alignments by sequence name with a tool like [seqkit](https://bioinf.shenwei.me/seqkit/) first (e.g., `seqkit sort -n alignment.fasta > alignment.sorted.fasta`).

```bash
pathogen-distance \
  --alignment tests/data/h3n2_ha_alignment.sorted.fasta \
  --output ha_distances.csv

pathogen-distance \
  --alignment tests/data/h3n2_na_alignment.sorted.fasta \
  --output na_distances.csv
```

Create a t-SNE embedding using the HA/NA alignments and distance matrices.
The t-SNE embedding gets initialized by a PCA embedding from the alignments.

```bash
pathogen-embed \
  --alignment tests/data/h3n2_ha_alignment.sorted.fasta tests/data/h3n2_na_alignment.sorted.fasta \
  --distance-matrix ha_distances.csv na_distances.csv \
  --output-dataframe tsne.csv \
  --output-figure tsne.pdf \
  --output-pairwise-distance-figure tsne_pairwise_distances.pdf \
  t-sne \
    --perplexity 45.0
```

Finally, find clusters in the embedding which represent the within and between diversity of the given HA and NA sequences.

```bash
pathogen-cluster \
  --embedding tsne.csv \
  --label-attribute tsne_label \
  --output-dataframe tsne_with_clusters.csv \
  --output-figure tsne_with_clusters.pdf
```

Compare the resulting embedding and clusters to the embedding above from only HA sequences, to get a sense of how including the NA sequences affects the results.

![Example t-SNE embedding of seasonal influenza A/H3N2 hemagglutinin and neuraminidase sequences colored by the cluster label assigned by pathogen-cluster](images/example-tsne-ha-na-embedding-with-clusters.png)

## Build documentation

Build the [Documentation](https://blab.github.io/pathogen-embed/):

```
make -C /docs html
```

Clean the docs.

```
make -C /docs clean
```

## Releasing a new version on PyPI

  1. Update `CHANGES.md` to reflect changes since the last release and set the correct version number for the new release at the top of the file.
  1. Update the version number in `setup.py`.
  1. [Create a new GitHub release](https://github.com/blab/pathogen-embed/releases/new), setting the same version number as above as the release tag and, optionally, generating release notes automatically.
  When you select "Publish release", GitHub Actions will run the [publish workflow](https://github.com/blab/pathogen-embed/blob/main/.github/workflows/publish.yml) which uses [the pypi-publish action](https://github.com/marketplace/actions/pypi-publish) to create a new release on PyPI for you.
  1. Create a pull request to bump the version of [the corresponding Bioconda package](https://github.com/bioconda/bioconda-recipes/blob/master/recipes/pathogen-embed/meta.yaml).

## Run tests during development

Clone this repository locally.

``` bash
git clone https://github.com/blab/pathogen-embed.git
cd pathogen-embed
```

Install an editable version of the package into a custom Conda environment.

``` bash
conda create -n pathogen-embed python=3.11
conda activate pathogen-embed
python3 -m pip install -e '.[dev]'
```

Run tests with cram.

``` bash
cram --shell=/bin/bash tests
```