Iliski, a software for robust calculation of transfer functions

Ali-Kemal Aydin, William D Haselden, Julie Dang, Patrick J Drew, Serge Charpak and Davide Boido

PLOS Computational Biology, 2021, vol. 17, issue 6, 1-14

Abstract: Understanding the relationships between biological processes is paramount to unravel pathophysiological mechanisms. These relationships can be modeled with Transfer Functions (TFs), with no need of a priori hypotheses as to the shape of the transfer function. Here we present Iliski, a software dedicated to TFs computation between two signals. It includes different pre-treatment routines and TF computation processes: deconvolution, deterministic and non-deterministic optimization algorithms that are adapted to disparate datasets. We apply Iliski to data on neurovascular coupling, an ensemble of cellular mechanisms that link neuronal activity to local changes of blood flow, highlighting the software benefits and caveats in the computation and evaluation of TFs. We also propose a workflow that will help users to choose the best computation according to the dataset. Iliski is available under the open-source license CC BY 4.0 on GitHub (https://github.com/alike-aydin/Iliski) and can be used on the most common operating systems, either within the MATLAB environment, or as a standalone application.Author summary: Iliski is a software helping the user to find the relationship between two sets of data, namely transfer functions. Although transfer functions are widely used in many scientific fields to link two signals, their computation can be tricky due to data features such as multisource noise, or to specific shape requirements imposed by the nature of the signals, e.g. in biological data. Iliski offers a user-friendly graphical interface to ease the computation of transfer functions for both experienced and users with no coding skills. It proposes several signal pre-processing methods and allows rapid testing of different computing approaches, either based on deconvolution or on optimization of multi-parametric functions. This article, combined with a User Manual, provides a detailed description of Iliski functionalities and a thorough description of the advantages and drawbacks of each computing method using experimental biological data. In the era of Big Data, scientists strive to find new models for patho-physiological mechanisms, and Iliski fulfils the requirements of rigorous, flexible, and fast data driven hypothesis testing.

Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008614 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 08614&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1008614

DOI: 10.1371/journal.pcbi.1008614

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().