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ezancestry-0.0.7
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مشاهده بیشترتوضیحات
Easily predict and visualize genetic ancestry. Evaluate custom ancestry-informative SNP sets.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ezancestry-0.0.7 |
| نام | ezancestry |
| نسخه کتابخانه | 0.0.7 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | arvkevi |
| ایمیل نویسنده | arvkevi@gmail.com |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/ezancestry/ |
| مجوز | MIT |
# ezancestry
Easily visualize your direct-to-consumer genetics next to 2500+ samples from the 1000 genomes project. Evaluate the performance of a custom set of ancestry-informative snps (AISNPs) at classifying the genetic ancestry of the 1000 genomes samples using a machine learning model.
A subset of 1000 Genomes Project samples' single nucleotide polymorphism(s), or, SNP(s) have been parsed from the [publicly available `.bcf` files](ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/bcf_files/).
The subset of `SNPs`, AISNPs (ancestry-informative snps), were chosen from two publications:
* Set of 55 AISNPs. [Progress toward an efficient panel of SNPs for ancestry inference](https://www.ncbi.nlm.nih.gov/pubmed?db=pubmed&cmd=Retrieve&dopt=citation&list_uids=24508742). Kidd et al. 2014
* Set of 128 AISNPs. [Ancestry informative marker sets for determining continental origin and admixture proportions in common populations in America.](https://www.ncbi.nlm.nih.gov/pubmed?cmd=Retrieve&dopt=citation&list_uids=18683858). Kosoy et al. 2009 (Seldin Lab)
ezancestry ships with pretrained k-nearest neighbor models for all combinations of following:
* Kidd (55 AISNPs)
* Seldin (128 AISNPs)
* continental-level population (superpopulation)
* regional population (population)
* principal componentanalysis (PCA)
* neighborhood component analysis (NCA)
* uniform manifold approximation and projection (UMAP)
## Table of Contents
* [Installation](#installation)
* [Config](#config)
* [Usage](#usage)
* [command line tool](#command-line-interface)
* [predict](#predict)
* [plot](#plot)
* [generate-dependencies](#generate-dependencies)
* [build-model](#build-model)
* [Python API](#python-api)
* [Visualization](#visualization)
* [Contributing](#contributing)
## Installation
Install ezancestry with pip:
```shell
pip install ezancestry
```
Or clone the repository and run `pip install` from the directory:
```shell
git clone git@github.com:arvkevi/ezancestry.git
cd ezancestry
pip install .
```
## Config
The first time `ezancestry` is run it will generate a `config.ini` file and `data/` directory in your home directory under `${HOME}/.ezancestry`.
You can edit `conf.ini` to change the default settings, but it is not necessary to use ezancestry. The settings are just a utility for the user so they don't have to be verbose when interacting with the software. The settings are also keyword arguments to each of the commands in the ezancestry API, so you can always override the default settings.
These will be created in your home directory:
```shell
${HOME}/.ezancestry/conf.ini
${HOME}/.ezancestry/data/
```
Explanations of each setting is described in the Options section of the `--help` of each command, for example:
```shell
ezancestry predict --help
Usage: ezancestry predict [OPTIONS] INPUT_DATA
Predict ancestry from genetic data.
* Default arguments are from the ~/.ezancestry/conf.ini file. *
Arguments:
INPUT_DATA Can be a file path to raw genetic data (23andMe, ancestry.com,
.vcf) file, a path to a directory containing several raw genetic
files, or a (tab or comma) delimited file with sample ids as
rows and snps as columns. [required]
Options:
--output-directory TEXT The directory where to write the prediction
results file
--write-predictions / --no-write-predictions
If True, write the predictions to a file. If
False, return the predictions as a
dataframe. [default: True]
--models-directory TEXT The path to the directory where the model
files are located.
--aisnps-directory TEXT The path to the directory where the AISNPs
files are located.
--n-components INTEGER The number of components to use in the PCA
dimensionality reduction.
--k INTEGER The number of nearest neighbors to use in
the KNN model.
--thousand-genomes-directory TEXT
The path to the 1000 genomes directory.
--samples-directory TEXT The path to the directory containing the
samples.
--algorithm TEXT The dimensionality reduction algorithm to
use. Choose pca|umap|nca
--aisnps-set TEXT The name of the AISNP set to use. To start,
choose either 'Kidd' or 'Seldin'. The
default value in conf.ini is 'Kidd'. *If
using your AISNP set, this value will be the
in the namingc onvention for all the new
model files that are created*
--help Show this message and exit.
```
## Usage
ezancestry can be used as a command-line tool or as a Python library. ezancestry `predict` comes with pre-trained models when `--aisnps-set="Kidd"` (default) or `--aisnps-set="Seldin"`.
`build-model` and `generate-dependencies` are for advanced users -- they download large amounts of data and build a new model from a custom AISNPs file.
### command-line interface
There are four commands available:
1. `predict`: predict the genetic ancestry of a sample or cohort of samples using the nearest neighbors model.
2. `plot`: plot the genetic ancestry of samples using only the output of `predict`.
3. `generate-dependencies`: generate the dependencies for `build-model`.
4. `build-model`: build a nearest neighbors model from the 1000 genomes data using a custom set of AISNPs. Requires: `generate-dependencies` to be run first.
Use the commands in the following way:
#### predict
ezancestry can predict the genetic ancestry of a sample or cohort of samples using the nearest neighbors model.
The `input_data` can be a file path to raw genetic data (23andMe, ancestry.com, .vcf) file, a path to a directory containing several raw genetic files, or a (tab or comma) delimited file with sample ids as rows and snps as columns.
This writes a file, `predictions.csv` to the `output_directory` (defaults to current directory). This file contains predicted ancestry for each sample.
**Direct-to-consumer genetic data file (23andMe, ancestry.com, etc.)**:
```shell
ezancestry predict mygenome.txt
```
**Directory of direct-to-consumer genetic data files or .vcf files**:
```shell
ezancestry predict /path/to/genetic_datafiles
```
**comma-separated file with sample ids as rows and snps as columns, filled with genotypes as values**
```shell
ezancestry predict ${HOME}/.ezancestry/data/aisnps/thousand_genomes.KIDD.dataframe.csv
```
#### plot
Visualize the output of `predict` using the `plot` command. This will open a 3d scatter plot in a browser.
```shell
ezancestry plot predictions.csv
```
#### generate-dependencies
This command will download all of the data required to build a new nearest neighbors model for a custom set of AISNPs.
**This command will attempt to download all the .bcf files from The 1000 Genomes Project.** If you want to use existing models, see `predict` and `plot`.
Without any arguments this command will download all necessary data to build new models and put it in the `${HOME}/.ezancestry/data/` directory.
```shell
ezancestry generate-dependencies
```
Now you are ready to build a new model with `build-model`.
#### build-model
Test the discriminative power of your custom set of AISNPs.
This command will build all the necessary models to visualize and predict the 1000 genomes samples as well as user-uploaded samples. A model performace evaluation report will be generated for a five-fold cross-validation on the training set of the 1000 genomes samples as well as a report for the holdout set.
Create a custom AISNP file here: `~/.ezancestry/data/aisnps/custom.AISNP.txt`. The prefix of the filename, `custom`, can be whatever you want. Note that this value is used as the `aisnps-set` keyword argument for other ezancestry commands.
The file should look like this:
```
id chromosome position_hg19
rs731257 7 12669251
rs2946788 11 24010530
rs3793451 9 71659280
rs10236187 7 139447377
rs1569175 2 201021954
```
```shell
ezancestry build-model --aisnps-set=custom
```
### Python API
See the [notebook](github.com/arvkevi/ezancestry/ezancestry_library_tutorial.ipynb)
### Visualization
[http://ezancestry.herokuapp.com/](http://ezancestry.herokuapp.com/)
[Open in Streamlit](https://share.streamlit.io/arvkevi/ezancestry/streamlit-share/streamlit/app.py)
### Contributing
Contributions are welcome! Please feel free to create an issue for discussion or make a pull request.
نیازمندی
| مقدار | نام |
|---|---|
| >=3.21.2,<4.0.0 | cmake |
| >=0.30.11,<=0.30.14 | cyvcf2 |
| >=0.5.3,<0.6.0 | loguru |
| >=1.21.2,<2.0.0 | numpy |
| >=1.3.2,<2.0.0 | pandas |
| >=5.3.1,<6.0.0 | plotly |
| >=0.24.2,<0.25.0 | scikit-learn |
| >=2.3.0,<3.0.0 | snps |
| >=0.87.0,<0.88.0 | streamlit |
| >=0.5.1,<0.6.0 | umap-learn |
| >=0.4.0,<0.5.0 | typer |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7.1,<3.11 | Python |
نحوه نصب
نصب پکیج whl ezancestry-0.0.7:
pip install ezancestry-0.0.7.whl
نصب پکیج tar.gz ezancestry-0.0.7:
pip install ezancestry-0.0.7.tar.gz