dysregnet 0.0.2

DysRegNet

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 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, 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:

import dysregnet
import pandas as pd

Define the confounding variables or the design matrix

# 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

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 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/Google Colab).

Cite