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https://github.com/Novartis/peax
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
https://github.com/Novartis/peax
autoencoder data-visualization deep-learning epigenomics interactive-machine-learning pattern-search sequential-data
Last synced: about 2 months ago
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Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
- Host: GitHub
- URL: https://github.com/Novartis/peax
- Owner: Novartis
- License: other
- Created: 2018-09-27T12:49:07.000Z (almost 6 years ago)
- Default Branch: develop
- Last Pushed: 2022-12-14T18:38:58.000Z (over 1 year ago)
- Last Synced: 2024-04-16T11:10:09.847Z (3 months ago)
- Topics: autoencoder, data-visualization, deep-learning, epigenomics, interactive-machine-learning, pattern-search, sequential-data
- Language: Jupyter Notebook
- Homepage: http://peax.lekschas.de
- Size: 52.2 MB
- Stars: 67
- Watchers: 5
- Forks: 14
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Lists
- awesome-biological-visualizations - Peax - Interactive visual pattern exploration in epigenomic data using unsupervised deep representation learning. (Epigenomics)
- repo-5916-awesome-genome-visualization - Peax - genome-visualization/peax.png) (Epigenomics)
- awesome-genome-visualization - Peax
README
Peax
**A Visual Pattern Explorer For Epigenomic Data Using Unsupervised Deep Representation Learning**
[](https://youtu.be/FlzTdFUVE-M)
[](https://speakerdeck.com/flekschas/peax-interactive-concept-learning-for-visual-exploration-of-epigenetic-patterns)
[](https://youtu.be/oQG5DxqiXPI?t=299)
[](https://vcg.seas.harvard.edu/pubs/peax)
Epigenomic data expresses a rich body of diverse patterns that help to identify regulatory elements like promoter, enhancers, etc. But finding these patterns reliably genome wide is challenging. Peax is a tool for interactive visual pattern search and exploration of epigenomic patterns based on unsupervised representation learning with convolutional autoencoders. The visual search is driven by manually labeled genomic regions for actively learning a classifier to reflect your notion of interestingness.
**Citation:** Lekschas et al., [Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning](https://vcg.seas.harvard.edu/pubs/peax),
_Computer Graphics Forum_, 2020, doi: [10.1111/cgf.13971](https://doi.org/10.1111/cgf.13971).
**More Details:** [peax.lekschas.de](http://peax.lekschas.de)
## Installation
**Requirements:**
- [Conda](https://docs.conda.io/en/latest/) >= 4.8
**Install:**
```bash
git clone https://github.com/Novartis/peax && cd peax
make install
```
_Do not fear, `make install` is just a convenience function for setting up conda and installing npm packages._
**Notes:**
- If you're a macOS user you might need to [brew](https://brew.sh) install `libpng` and `openssl` for the [pybbi](https://github.com/nvictus/pybbi) package (see [here](https://github.com/nvictus/pybbi/issues/2)) and `xz` for pysam (if you see an error related to `lzma.h`).
## Overview
Peax consists of three main parts:
1. A server application for serving genomic and autoencoded data on the web. [[/server](server)].
2. A user interface for exploring, visualizing, and interactively labeling genomic regions. [[/ui](ui)].
3. A set of examples showing how to configure Peax and build your own. [[/examples](examples)]
## Data
We provide 6 autoencoders trained on 3 kb, 12 kb, and 120 kb window sizes (with 25,
100, and 1000 bp binning) on DNase-seq and histone mark ChIP-seq data (H3K4me1, H3K4me3, H3K27ac, H3K9ac, H3K27me3, H3K9me3, and H3K36me).
You can find detailed descriptions of the autoencoders at [zenodo.org/record/2609763](https://zenodo.org/record/2609763). When you follow the [Quick Start](#quick-start) instructions, you will automatically download the related autoencoders.
## Quick start
Peax comes with [6 autoencoders](#data) for DNase-seq and histone mark
ChIP-seq data and several example configurations for which we provide
convenience scripts to get you started as quickly as possible.
For instance, run one of the following commands to start Peax with a DNase-seq
track for 3 kb, 12 kb, and 120 kb genomic windows.
| Command | Window Size | Step Freq. | Chromosomes |
| -------------------- | ----------- | ---------- | ----------- |
| `make example-3kb` | 3 kb | 2 | 21 |
| `make example-12kb` | 12 kb | 3 | 20-21 |
| `make example-120kb` | 120 kb | 6 | 17-21 |
**Note:** The first time Peax is started it will precompute the datasets for
exploration. This can take a few minutes depending on your hardware. Also, these demos
will only prepare the above mentioned chromosomes, so don't try to search for patterns
on another chromosome. It won't work! For your own data you can freely configure this
of course.
The scripts will download test ENCODE tracks and use the matching
configuration to start the server. More examples are described in [`/examples`](examples).
## Get Started
In the following we describe how you can configure Peax for your own data.
#### Configure Peax with your data
Next, you need to configure Peax with your data to tell it which tracks you want to visualize in HiGlass and which of those tracks are encodable using an (auto)encoder.
The fastest way to get started is to copy the example config:
```
cp config.json.sample config.json
```
The config file has 10 top level properties:
| Field | Description | Dtype |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- |
| encoders | List of encoders. | list |
| datasets | List of tracks. | list |
| coords | Genome coordinates. Peax currently supports hg19, hg38, mm9, and mm10 | str |
| chroms | Chromosomes to to be searched. If omitted all chromosomes will be prepared for searching. | list |
| step_freq | Step frequency of the sliding window approach. E.g., given an encoder with window size 12 kb, a step frequency of 6 means that every 2 kb a 12 kb window will be extracted from the bigWig. | int |
| db_path | Relative path to the sqlite db for storing searches. | str |
| normalize_tracks | If `true` the y-scale of tracks within a window will be normalized to the minimum and maximum value. This is useful for exploring differential signal. | bool |
| variable_target | If `true` the window with the highest prediction probability will be shown in the query view. | bool |
| classifier | The class name of an SciKit Learn Classifier | str |
| classifier_params | A dictionary of parameters to customize the classifier | obj |
The main parts to adjust are `encoders` and `datasets`. `encoders` is a list of
(auto)encoder definitions for different datatypes.T here are two ways to
configure an (auto)encoder: (a) point to a pre-defined autoencoder or (b)
configure from scratch.
Assuming you want to use predefined encoders all you have to do is to specify the path to the encoder configuration
**Example:**
```json
{
"encoders": "examples/encoders.json"
}
```
The encoder configuration file is a dictionary with the top level keys acting
as the identifier. Given the example from above the file could look like this:
```json
{
"histone-mark-chip-seq-3kb": {},
"dnase-seq-3kb": {}
}
```
See `[encoders.json](encoders.json)` for an example. The specific definition if an
autoencoder is the same as described in the following.
To configure an autoencoder from scratch you need to provide a dictionary with
the following required format:
| Field | Description | Defaults | Dtype |
| ------------ | --------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ----- |
| autoencoder | Relative path to your pickled autoencoder model. (hdf5 file) | | str |
| encoder | Relative path to your pickled encoder model. (hdf5 file) | | str |
| decoder | Relative path to your pickled decoder model. (hdf5 file) | | str |
| content_type | Unique string describing the content this autoencoder can handle. Data tracks with the same content type will be encoded by this autoencoder. | | str |
| window_size | Window size in base pairs used for training the autoencoder. | | int |
| resolution | Resolution or bin size of the window in base pairs. | | int |
| latent_dim | Number of latent dimensions of the encoded windows. | | int |
| input_dim | Number of input dimensions for Keras. For 1D data these are 3: samples, data length (which is `window_size` / `resolution`), channels. | 3 | int |
| channels | Number of channels of the input data. This is normally 1. | 1 | int |
| model_args | List of arguments passed to a custom encoder model | 1 | int |
_Note that if you have specified an `autoencoder` you do not need to provide
separate `encoder` and `decoder` models._
**Example:**
```json
{
"encoder": "path/to/my-12kb-chip-seq-encoder.h5",
"decoder": "path/to/my-12kb-chip-seq-decoder.h5",
"content_type": "histone-mark-chip-seq",
"window_size": 12000,
"resolution": 100,
"channels": 1,
"input_dim": 3,
"latent_dim": 12
}
```
Datasets require the following format:
| Field | Description | Dtype |
| ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- |
| filepath | Relative path to the data file (bigWig or bigBed). | str |
| content_type | Unique string describing the content this dataset. If you want to search for patterns in this track you need to have an autoencoder with a matching content type. | str |
| id | A unique string identifying your track. (Optional) | str |
| name | A human readable name to be shown in HiGlass. (Optional) | str |
**Example:**
```json
{
"filepath": "data/chip-seq/my-fancy-gm12878-chip-seq-h3k27ac-fc-signal.bigWig",
"content_type": "histone-mark-chip-seq",
"uuid": "my-fancy-gm12878-chip-seq-h3k27c-track",
"name": "My Fancy GM12878 ChIP-Seq H3k27c Track"
}
```
#### Start Peax
First, start the Peax server to serve your data.
**Note:** The first time you run Peax on a new dataset all the data will be prepared!
Depending on your machine this can take some time. If you want to track the progress
activate the debugging mode using `-d`.
```bash
python start.py
```
Now go to [http://localhost:5000](http://localhost:5000).
To `start.py` script supports the following options:
```bash
usage: start.py [-h] [-c CONFIG] [--clear] [--clear-cache]
[--clear-cache-at-exit] [--clear-db] [-d] [--host HOST]
[--port PORT] [-v]
Peak Explorer CLI
optional arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
path to your JSON config file
-b BASE_DATA_DIR, --base-data-dir BASE_DATA_DIR
base directory which the config file refers to
--clear clears the cache and database on startup
--clear-cache clears the cache on startup
--clear-cache-at-exit
clear the cache on shutdown
--clear-db clears the database on startup
-d, --debug turn on debug mode
--host HOST customize the hostname
--port PORT customize the port
-v, --verbose turn verbose logging on
```
The `hostname` defaults to `localhost` and the `port` of the backend server defaults
to `5000`.
In order to speed up subsequend user interaction, Peax initially prepapres all
the data and caches that data under `/cache`. You can always remove this
directory manually or clear the cache on startup or at exist using the `--clear`
as specified above.
---
## Development
Handy commands to keep in mind:
- `make install` installs the conda environment and npm packages and builds the UI
- `make update` updates the conda environment and npm packages and rebuilds the UI
- `make build` builds the UI
- `python start.py` starts the Flask server application for serving data
- [/ui]: `npm install` installs and updates all the needed packages for the frontend
- [/ui]: `npm build` creates the production built of the frontend
- [/ui]: `npm start` starts a dev server with hot reloading for the frontend
To start developing on the server and the ui in parallel, first start the backend server
application using `./start.py` and then start the frontend server application from
`./ui` using `npm start`. Both server's watch the source code, so whenever you make
changes to the source code the servers will reload.
### Configuration
There are 2 types of configuration files. The [backend server configuration](#configure-peax-with-your-data)
defines the datasets to explore and is described in detail [above](#configure-peax-with-your-data).
Additionally, the frontend application can be configured to talk to a different backend
server and port if needed. Get started by copying the example configuration:
```bash
cd ui && cp config.json.sample config.json
```
By default the `server` is dynamically set to the hostname of the server running the
frontend application. I.e., it is assumed that the backend server application is
running on the same host as the frontend application. The `port` of the server
defaults to `5000`.
### Start the backend and frontend apps
For development the backend and frontend applications run as seperate server
applications.
```bash
# Backend server
./start.py --debug --config path/to/your/config.json
# Frontend server
cd ui && npm start
```