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dysregnet-0.0.3
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DysRegNet
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dysregnet-0.0.3 |
| نام | dysregnet |
| نسخه کتابخانه | 0.0.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Zakaria Louadi, olga lazareva |
| ایمیل نویسنده | zakaria.louadi@tum.de, olga.lazareva@tum.de |
| آدرس صفحه اصلی | https://github.com/biomedbigdata/DysRegNet_package |
| آدرس اینترنتی | https://pypi.org/project/dysregnet/ |
| مجوز | GPLv3 |
# DysRegNet package
DysRegNet, is a method for inferring patient-specific regulatory alterations (dysregulations) from gene expression profiles. DysRegNet uses linear models to account for confounders and residual-derived z-scores to assess significance.
## Installation
To install the package from PyPI please run:
`pip install dysregnet`
or you can install it from git:
`git clone https://github.com/biomedbigdata/DysRegNet_package.git && cd DysRegNet_package`
`python setup.py install`
## Data input
The inputs of the package are the following Pandas DataFrame object:
- expression_data - Gene expression matrix with the format: patients as rows (first column - patients/samples ids), and genes as columns.
- GRN - Gene Regulatory Network (GRN) with two columns in the following order ['TF', 'target'].
- meta - Metadata with the first column containing patients/samples ids and other columns for the condition and the covariates.
The patients id or samples ids must be the same in the "expression_data" and "meta". Additionally, gene names or ids must match the ones in the "GRN" DataFrame.
In the condition column of the meta DataFrame, the control samples should be encoded as 0 and case samples as 1.
GRN network should be provided a prior, You can either use an experimental validated GRN or learn it from control samples, we recommend using software like [arboreto](https://github.com/aertslab/arboreto), since you can use its output directly to DysRegNet.
## Parameters
Additionally, you can provide the following parameters:
- conCol: Column name for the condition in the meta DataFrame.
- CatCov: List of categorical variable names. They should match the name of their columns in the meta Dataframe.
- ConCov: List of continuous covariates. They should match the name of their columns in the meta Dataframe.
- zscoring: Boolean, default: True. zscoring of expression data (if needed).
- bonferroni_alpha:P-value threshold for multiple testing correction
- normaltest: Boolean. If True, Run a normality test for residuals "scipy.stats.normaltest". If residuals are not normal, the edge will not be considered in the analysis.
- normaltest_alpha: p-value threshold for normaltest (if True).
- R2_threshold: R-squared (R2) threshold from 0 to 1 (optional). If the fit is weaker, the edge will not be considered in the analysis.
- direction_condition: Boolean. If True: only include dysregulation that are relevalant for the interactions (down regulation of an activation or up regulation of a supressions). Please check the paper for more details.
## Get Started
Please note, that the functions are annotated with dockstrings for more details.
Import the package and pandas:
```python
import dysregnet
import pandas as pd
```
Define the confounding variables or the design matrix
```python
# The condition column
conCol='condition'
# categorical variable columns in meta dataframe.
# these columns will be transformed to variables for regression
CatCov=['race','gender']
# continuous variable columns in meta dataframe.
ConCov=['birth_days_to']
```
Run DysRegNet
```python
data=dysregnet.run(expression_data=expr,
meta=meta,
GRN=grn,
conCol=conCol
CatCov=CatCov,
ConCov=ConCov,
direction_condition=True,
normaltest=True,
R2_threshold=.2 )
# results table
data.get_results()
# or a binary result
data.get_results_binary()
```
The expected run time for the installation and running the demo dataset on a "normal" desktop computer is around 3~5 minutes.
## The output
The package output a DataFrame that represents patient-specific dysregulated edges. The columns represent edges and the rows patient ids.
In the result table, a value of 0 means that the edge is not significantly dysregulated (different from control samples). Otherwise, the z-score is reported, with a positive in case of activation and a negative sign in case of repression (different than the sign of the residual).
The method "get_results_binary()", outputs binarized dysregulations instead of z-scores.
## Example
A simple example for running DysRegNet:
([Notebook](https://github.com/biomedbigdata/DysRegNet_package/blob/main/test.ipynb)/[Google Colab](https://colab.research.google.com/github/biomedbigdata/DysRegNet_package/blob/main/test.ipynb)).
If you want to eun the exact demo. You will need to download the demo dataset and extract the files into test dataset/
Link: https://figshare.com/ndownloader/files/35142652
## Cite
"DysRegNet: Patient-specific and confounder-aware dysregulated network inference"
Olga Lazareva*, Zakaria Louadi*, Johannes Kersting, Jan Baumbach, David B. Blumenthal, Markus List. bioRxiv 2022.04.29.490015; doi: https://doi.org/10.1101/2022.04.29.490015. * equal first-authors
نیازمندی
| مقدار | نام |
|---|---|
| - | pandas |
| >=1.19 | numpy |
| - | scipy |
| - | statsmodels |
| - | tqdm |
| - | sklearn |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl dysregnet-0.0.3:
pip install dysregnet-0.0.3.whl
نصب پکیج tar.gz dysregnet-0.0.3:
pip install dysregnet-0.0.3.tar.gz