Inferring a gene regulatory network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances in high-throughput gene profiling technology.
The Arboreto software library addresses this issue by providing a computational strategy that allows executing the class of GRN inference algorithms exemplified by GENIE3  on hardware ranging from a single computer to a multi-node compute cluster. This class of GRN inference algorithms is defined by a series of steps, one for each target gene in the dataset, where the most important candidates from a set of regulators are determined from a regression model to predict a target gene’s expression profile.
Members of the above class of GRN inference algorithms are attractive from a computational point of view because they are parallelizable by nature. In arboreto, we specify the parallelizable computation as a Dask graph , a data structure that represents the task schedule of a computation. A Dask scheduler assigns the tasks in a Dask graph to the available computational resources. Arboreto uses the Dask distributed scheduler to spread out the computational tasks over multiple processes running on one or multiple machines.
Arboreto currently supports 2 GRN inference algorithms:
- GRNBoost2: fast GRN inference algorithm using stochastic Gradient Boosting Machine  regression with early-stopping regularization, the Arboreto flagship algorithm.
- GENIE3: the popular classic GRN inference algorithm using Random Forest (RF) or ExtraTrees (ET) regression.
# import python modules import pandas as pd from arboreto.utils import load_tf_names from arboreto.algo import grnboost2 if __name__ == '__main__': # load the data ex_matrix = pd.read_csv(<ex_path>, sep='\t') tf_names = load_tf_names(<tf_path>) # infer the gene regulatory network network = grnboost2(expression_data=ex_matrix, tf_names=tf_names) network.head()
Check out more examples.
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