COFFEE: consensus single cell-type specific inference for gene regulatory networks.

IF 5.3 2区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Musaddiq K Lodi, Anna Chernikov, Preetam Ghosh
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引用次数: 0

Abstract

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.

COFFEE:基因调控网络的共识性单细胞类型特异性推断。
基因调控网络(GRN)的推断对于了解生物过程的调控机制至关重要。基因调控网络可以用图中的边来表示,因此可以通过计算来推断 scRNA-seq 数据。与批量 RNA-seq 和微阵列数据上的单个算法实施相比,一种整合多个 GRN 的边以创建一个复合 GRN 的众智方法已证明性能有所提高。为了将这种方法扩展到 scRNA-seq 数据,我们提出了 COFFEE(COnsensus single cell-type speciFic inFerence for gEnE regulatory networks),这是一种基于 Borda 投票的共识算法,它整合了 10 种成熟 GRN 推断方法的信息。我们的结论是,与基线方法相比,COFFEE 在合成数据集、策划数据集和实验数据集上的性能都有所提高。此外,我们还展示了 COFFEE 的改进版,可以利用它来提高更新的特定细胞类型 GRN 推断方法的性能。总之,我们的研究结果表明,经过相关修改的基于共识的方法对于单细胞水平的 GRN 推断仍然很有价值。虽然 COFFEE 以 10 种算法为基准,但它是一种灵活的策略,可以根据用户的偏好纳入任何一组 GRN 推断算法。COFFEE 的 Python 实现可在 GitHub 上找到:https://github.com/lodimk2/coffee。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.30
自引率
3.40%
发文量
1601
期刊介绍: ACS Applied Nano Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics and biology relevant to applications of nanomaterials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important applications of nanomaterials.
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