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deepaccess-0.1.3
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مشاهده بیشترتوضیحات
A package for training and interpreting an ensemble of neural networks for chromatin accessibility
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepaccess-0.1.3 |
| نام | deepaccess |
| نسخه کتابخانه | 0.1.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jennifer Hammelman |
| ایمیل نویسنده | jhammelm@mit.edu |
| آدرس صفحه اصلی | https://github.com/gifford-lab/deepaccess-package |
| آدرس اینترنتی | https://pypi.org/project/deepaccess/ |
| مجوز | - |
# deepaccess-package
[](https://badge.fury.io/py/deepaccess)
[](https://conda.anaconda.org/bioconda)
This is the code for training and interpretation of an ensemble of convolutional neural networks for multi-task classification. Instructions for downloading and getting started with the current release are available at [https://cgs.csail.mit.edu/deepaccess-package/](https://cgs.csail.mit.edu/deepaccess-package/). deepaccess is available via [pip](https://pypi.org/project/pip/) and [bioconda](https://bioconda.github.io/). The DeepAccess model trained on ATAC-seq data from 10 mouse cell types is available as a [zenodo record](https://zenodo.org/record/4908895#.YL6YpR0pDfY).
## Dependencies
* [bedtools](https://bedtools.readthedocs.io/en/latest/) (v2.29.2)
To run DeepAccess with regions (bedfile format) you must install bedtools and add it to your path. Bedtools binaries are available [here](https://github.com/arq5x/bedtools2/releases).
After installation, you can add bedtools to your path via the terminal or modifying your ~/.bashrc
```
export PATH="/path/to/bedtools:$PATH"
```
## Installation
deepaccess is available on the Python Package Index (PyPI) and can be installed with pip:
```
pip install deepaccess
```
and via bioconda:
```
conda install -c bioconda deepaccess
```
## Training
To train a DeepAccess model for a new task
```
usage: deepaccess train [-h] -l LABELS [LABELS ...]
-out OUT [-ref REFFASTA]
[-g GENOME] [-beds BEDFILES [BEDFILES ...]]
[-fa FASTA] [-fasta_labels FASTA_LABELS]
[-f FRAC_RANDOM] [-nepochs NEPOCHS]
[-ho HOLDOUT] [-seed SEED] [-verbose]
optional arguments:
-h, --help show this help message and exit
-l LABELS [LABELS ...], --labels LABELS [LABELS ...]
-out OUT, --out OUT
-ref REFFASTA, --refFasta REFFASTA
-g GENOME, --genome GENOME
genome chrom.sizes file
-beds BEDFILES [BEDFILES ...], --bedfiles BEDFILES [BEDFILES ...]
-fa FASTA, --fasta FASTA
-fasta_labels FASTA_LABELS, --fasta_labels FASTA_LABELS
-f FRAC_RANDOM, --frac_random FRAC_RANDOM
-nepochs NEPOCHS, --nepochs NEPOCHS
-ho HOLDOUT, --holdout HOLDOUT
chromosome to holdout
-seed SEED, --seed SEED
-verbose, --verbose Print training progress
```
### Arguments
| Argument | Description | Example |
| --------- | ----------- | -------- |
| -h, --help | show this help message and exit | NA |
| -l --labels | list of labels for each bed file | C1 C2 C3 |
| -out --out | output folder name | myoutput |
| -ref --ref | reference fasta; required with bed input | mm10.fa |
| -g --genome | genome chromosome sizes; required with bed input | default/mm10.chrom.sizes |
| -beds --bedfiles | list of bed files; one of beds or fa input required | C1.bed C2.bed C3.bed |
| -fa --fasta | fasta file; one of beds or fa input required | C1C2C3.fa |
| -fasta_labels --fasta_labels | text file containing tab delimited labels (0 or 1) for each fasta line with one column for each class | C1C2C3.txt |
| -f --frac_random | for bed file input fraction of random outgroup regions to add to training | 0.1 |
| -nepochs --nepochs | number of training iterations | 1 |
| -ho --holdout | chromosome name to hold out (only with bed input) | chr19 |
| -verbose --verbose | print training and evaluation progress | NA |
| -seed --seed | set tensorflow seed | 2021 |
## Interpretation
To run interpretation of a DeepAccess model
```
usage: deepaccess interpret [-h] -trainDir TRAINDIR
[-fastas FASTAS [FASTAS ...]]
[-l LABELS [LABELS ...]] [
-c COMPARISONS [COMPARISONS ...]]
[-evalMotifs EVALMOTIFS]
[-evalPatterns EVALPATTERNS]
[-p POSITION] [-saliency]
[-subtract] [-bg BACKGROUND] [-vis]
optional arguments:
-h, --help show this help message and exit
-trainDir TRAINDIR, --trainDir TRAINDIR
-fastas FASTAS [FASTAS ...], --fastas FASTAS [FASTAS ...]
-l LABELS [LABELS ...], --labels LABELS [LABELS ...]
-c COMPARISONS [COMPARISONS ...], --comparisons COMPARISONS [COMPARISONS ...]
-evalMotifs EVALMOTIFS, --evalMotifs EVALMOTIFS
-evalPatterns EVALPATTERNS, --evalPatterns EVALPATTERNS
-p POSITION, --position POSITION
-saliency, --saliency
-subtract, --subtract
-bg BACKGROUND, --background BACKGROUND
-vis, --makeVis
```
### Arguments
| Argument | Description | Example |
| --------- | ----------- | -------- |
| -h, --help | show this help message and exit | NA |
| -trainDir --trainDir | directory containing trained DeepAccess model | test/ASCL1vsCTCF |
| -fastas --fastas | list of fasta files to evaulate | test/ASCL1vsCTCF/test.fa |
| -l --labels | list of labels for each bed file | C1 C2 C3 |
| -c --comparisons | list of comparisons between different labels | ASCL1vsCTCF ASCL1vsNone runs differential EPE between ASCL1 and CTCF and EPE on ASCL1; C1,C2vsC3 runs differential EPE for (C1 and C2) vs C3 |
| -evalMotifs --evalMotifs | PWM or PCM data base of DNA sequence motifs | default/HMv11_MOUSE.txt |
| -evalPatterns --evalPatterns | fasta file containing DNA sequence patterns | data/ASCL1_space.fa |
| -bg --bg | fasta file containning background sequences | default/backgrounds.fa |
| -saliency --saliency | calculate per base nucleotide importance | NA |
| -subtract --subtract | use subtraction instead of ratio for EPE / DEPE | False |
| -vis --makeVis | to be used with saliency to make plot visualizing results | NA |
نیازمندی
| مقدار | نام |
|---|---|
| >=2.4 | tensorflow |
| >=2.4.3 | keras |
| >=1.6.2 | scipy |
| >=3.3.3 | matplotlib |
| >=1.19.0 | numpy |
| >=0.24.1 | scikit-learn |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl deepaccess-0.1.3:
pip install deepaccess-0.1.3.whl
نصب پکیج tar.gz deepaccess-0.1.3:
pip install deepaccess-0.1.3.tar.gz