Skip to main content

Scalable gene regulatory network inference using tree-based ensemble regressors

Project description

arboreto Build Status Documentation Status PyPI package

The most satisfactory definition of man from the scientific point of view is probably Man the Tool-maker.

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 [1] 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 [2], 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:

  1. GRNBoost2: a novel and fast GRN inference algorithm using Stochastic Gradient Boosting Machine (SGBM) [3] regression with early-stopping regularization.

  2. GENIE3: the classic GRN inference algorithm using Random Forest (RF) or ExtraTrees (ET) regression.


  1. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE

  2. Rocklin, M. (2015). Dask: parallel computation with blocked algorithms and task scheduling. In Proceedings of the 14th Python in Science Conference (pp. 130-136).

  3. Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367-378.

  4. Marbach, D., Costello, J. C., Kuffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., … & Dream5 Consortium. (2012). Wisdom of crowds for robust gene network inference. Nature methods, 9(8), 796-804.

Get Started

Arboreto was conceived with the working bioinformatician or data scientist in mind. We provide extensive documentation and examples to help you get up to speed with the library.


BSD 3-Clause License

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

arboreto-0.1.4.tar.gz (13.3 kB view hashes)

Uploaded source

Built Distribution

arboreto-0.1.4-py2.py3-none-any.whl (16.4 kB view hashes)

Uploaded py2 py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page