NetBID2 provides comprehensive hidden driver analysis

08 June 2023

Presentation Record(Previous Presentation will be showed here if the video is not released for this talk)

Dr. Jiyang Yu

Dissecting molecular (re)wiring and underpinning hidden drivers within and between cells/systems toward clinical translation. Biological processes operate through molecular networks at the cellular level and cell–cell communication networks at the tissue/organ level. Deciphering the wiring and rewiring of these networks under normal and pathologic conditions is a fundamental goal of biomedical research. Our lab is focused on developing data-driven systems biology algorithms to integrate bulk, single-cell, and spatial omics data to decipher these (re)wiring events and hidden drivers underpinning biological processes in health and disease. Ultimately, we aim to translate the hidden driver discoveries into therapeutic targets, biomarkers, and combination therapies for cancer and other disorders.

Computational Biology Department Comprehensive Cancer Center St. Jude Graduate School of Biomedical Sciences

Other softwares developed by Dr. Yu and his team:

scMINER, Single-cell mutual information network engineered ranger (scMINER) is a toolbox for single-cell analysis based on mutual information.

NetBID, NetBID is a data-driven network-based algorithm for Bayesian inference of drivers.

SJARACNe, SJARACNe is a scalable tool for gene network reverse engineering from bulk or single-cell gene expression data.

ScreenBEAM, ScreenBEAM is a meta-analysis tool for functional genomic screens via Bayesian hierarchical modeling.

Many signaling and other genes known as “hidden” drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID.

The paper introduces NetBID2, a comprehensive algorithm and toolkit designed to identify hidden drivers that play a role in phenotypes such as tumorigenesis through post-translational modification or other mechanisms. These drivers may not exhibit genetic or epigenetic alterations or show differential expression at the mRNA or protein levels, making them challenging to detect using conventional genomics or differential expression approaches.NetBID2 addresses this limitation by reverse-engineering context-specific interactomes and integrating network activity inferred from large-scale multi-omics data. It enables the identification of hidden drivers that would otherwise remain undetected. The toolkit has undergone significant improvements from its previous version, offering versatile data visualization and sophisticated statistical analyses. These enhancements greatly aid researchers in result interpretation through end-to-end multi-omics data analysis.The paper showcases the capabilities of NetBID2 through three examples of hidden drivers. To facilitate analysis, real-time interactive visualization, and cloud-based data sharing, the authors provide NetBID2 Viewer, Runner, and Cloud apps. These apps support 145 context-specific gene regulatory and signaling networks across normal tissues as well as pediatric and adult cancers. Users can access these resources to conduct end-to-end analysis, visualize results interactively, and share data via the cloud.The authors emphasize that NetBID2 is freely available to the research community at https://jyyulab.github.io/NetBID. By leveraging NetBID2, researchers can overcome the limitations of traditional analyses and gain insights into hidden drivers that contribute to complex phenotypes, enhancing our understanding of diseases like cancer and facilitating the development of targeted therapies.

This paper is very important important:

1, Addressing the limitation of traditional approaches: The paper highlights the limitations of conventional genomics or differential expression approaches in identifying hidden drivers that contribute to complex phenotypes. By introducing NetBID2, a comprehensive algorithm and toolkit, the paper provides a novel solution to overcome this limitation and enables researchers to uncover these hidden drivers.

2, Integration of multi-omics data: NetBID2 integrates large-scale multi-omics data to infer network activity and context-specific interactomes. This integration allows researchers to gain a more holistic understanding of the molecular mechanisms underlying phenotypes, including diseases like cancer. By leveraging diverse data types, NetBID2 provides a comprehensive analysis framework for studying complex biological systems.

3, Empowering researchers with visualization and statistical analyses: The paper emphasizes the improvements made in NetBID2, including versatile data visualization and sophisticated statistical analyses. These enhancements facilitate result interpretation and data analysis, empowering researchers to gain meaningful insights from their multi-omics datasets. The provided NetBID2 Viewer, Runner, and Cloud apps further streamline the analysis process and enable real-time interactive visualization and data sharing.

4, Examples of hidden driver identification: The paper demonstrates the power of NetBID2 by presenting three examples of hidden drivers. These examples showcase the capability of NetBID2 to identify drivers that are not evident through traditional approaches. By highlighting specific cases, the paper illustrates the potential impact of NetBID2 in uncovering important molecular drivers in various biological contexts.

From this paper, researchers can learn the following:

1, Methodology for detecting hidden drivers: The paper introduces the methodology and algorithmic framework of NetBID2, providing researchers with insights into how they can identify hidden drivers that contribute to phenotypes. Researchers can understand the process of reverse-engineering context-specific interactomes and integrating multi-omics data to infer network activity.

2, Data integration and analysis techniques: The paper highlights the importance of integrating multi-omics data and provides guidance on how to leverage diverse data types effectively. Researchers can learn about the statistical analyses and visualization techniques employed in NetBID2 to gain a comprehensive understanding of their data.

3, Application of NetBID2 in different contexts: By presenting examples across normal tissues and pediatric and adult cancers, the paper demonstrates the versatility of NetBID2 in various biological contexts. Researchers can learn about the potential applications of NetBID2 in their own studies, particularly in uncovering hidden drivers related to complex diseases.

4, Overall, this paper is important as it introduces a novel approach to overcome the limitations of traditional analyses and provides researchers with a comprehensive toolkit for identifying hidden drivers. It offers insights into data integration, analysis techniques, and the potential impact of uncovering hidden drivers in various biological contexts.

这篇论文具有多个重要之处：

解决传统方法的局限性：论文强调了传统基因组学或差异表达方法在发现对复杂表型有贡献的隐藏驱动因素方面的局限性。通过引入NetBID2，一种全面的算法和工具包，论文提供了一种新的解决方案，克服了这种局限性，使研究人员能够发现这些隐藏的驱动因素。

多组学数据的整合：NetBID2整合了大规模的多组学数据，以推断网络活动和特定背景下的相互作用组。这种整合使研究人员能够更全面地理解底层复杂表型的分子机制，包括癌症等疾病。通过利用多样化的数据类型，NetBID2为研究复杂生物系统提供了全面的分析框架。

为研究人员提供可视化和统计分析能力：论文强调了NetBID2的改进之处，包括灵活的数据可视化和复杂的统计分析。这些改进有助于结果解释和数据分析，使研究人员能够从多组学数据中获得有意义的见解。提供的NetBID2 Viewer、Runner和Cloud应用进一步简化了分析过程，实现了实时交互式可视化和数据共享。

隐藏驱动因素识别的示例：论文通过提供三个隐藏驱动因素的示例展示了NetBID2的能力。这些示例展示了NetBID2识别通过传统方法无法明显观察到的驱动因素的能力。通过具体案例，论文阐明了NetBID2在揭示各种生物背景中重要分子驱动因素的潜在影响。

从这篇论文中，研究人员可以学到以下内容：

检测隐藏驱动因素的方法论：论文介绍了NetBID2的方法论和算法框架，为研究人员提供了如何发现对表型有贡献的隐藏驱动因素的洞见。研究人员可以了解反向工程特定背景下的相互作用组和整合多组学数据推断网络活动的过程。

数据整合和分析技术：论文强调了整合多组学数据的重要性，并提供了如何有效利用不同数据类型的指导。研究人员可以了解NetBID2中使用的统计分析和可视化技术，从而全面了解他们的数据。

NetBID2在不同背景中的应用：通过展示正常组织、儿童和成人癌症的例子，论文展示了NetBID2在各种生物背景中的适用性。研究人员可以了解NetBID2在他们自己研究中的潜在应用，特别是在发现与复杂疾病相关的隐藏驱动因素方面。

总的来说，这篇论文的重要性在于引入了一种新的方法来克服传统分析的局限性，并为研究人员提供了一个全面的工具包，用于发现隐藏的驱动因素。论文提供了有关数据整合、分析技术和在各种生物背景中揭示隐藏驱动因素的潜在影响的见解。

在这个领域中，还存在一些值得注意的挑战，包括但不限于以下几点：

1, 数据质量和一致性：多组学数据的整合涉及不同实验平台和技术的数据，可能存在数据质量和一致性的问题。这包括批次效应、噪声、偏差等。研究人员需要解决这些问题，并采取措施确保数据的准确性和可靠性。

2, 复杂性和维度灾难：多组学数据通常是高维、复杂的，包含大量的特征和样本。处理这些数据可能面临维度灾难的挑战，需要合适的降维、特征选择和集成方法，以便有效地分析和提取有意义的信息。

3, 算法的可解释性：在使用算法进行网络推断和隐藏驱动因素识别时，算法的可解释性是一个关键问题。研究人员需要开发方法来解释算法的结果，以便理解网络的结构和功能，并验证隐藏驱动因素的合理性。

4, 缺乏标准化和共享数据集：在多组学数据的研究中，缺乏标准化的数据格式和共享的数据集是一个挑战。这限制了研究人员之间的比较和复现性。建立标准化的数据格式和共享数据集，将促进研究结果的可重复性和可验证性。

5, 网络的可靠性和准确性：网络推断是多组学数据分析的核心步骤之一，但构建准确和可靠的网络仍然是一个挑战。研究人员需要开发更精确和鲁棒的网络推断算法，并评估网络的可靠性和准确性。

6, 跨领域合作和跨平台整合：多组学数据的分析需要跨越不同学科和领域的合作。这包括生物学、计算机科学、统计学等领域的专家。同时，需要整合不同平台和工具，使得多组学数据分析能够无缝地进行。

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