Executable Network Models of Integrated Multiomics Data

Multiomics profiling
provides a holistic picture of a condition
being examined and captures the complexity of signaling events, beginning
from the original cause (environmental or genetic), to downstream
functional changes at multiple molecular layers. Pathway enrichment
analysis has been used with multiomics data sets to characterize signaling
mechanisms. However, technical and biological variability between
these layered data limit an integrative computational analyses. We
present a Boolean network-based method, multiomics Boolean Omics Network
Invariant-Time Analysis (mBONITA), to integrate
omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform
topology-based pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular
measurements by combining observed fold-changes and variance, with
a measure of node (i.e., gene or protein) influence over signaling,
and a measure of the strength of evidence for that gene across data
sets. We used mBONITA to integrate multiomics
data sets from RAMOS B cells treated with the immunosuppressant drug
cyclosporine A under varying O2 tensions to identify pathways
involved in hypoxia-mediated chemotaxis. We compare mBONITA’s performance with 6 other pathway analysis methods designed
for multiomics data and show that mBONITA identifies
a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at https://github.com/Thakar-Lab/mBONITA.

多组学表征（multiomics profiling）可为所研究的生物学状态提供全景式刻画，并完整捕捉信号传导事件的复杂层级：从最初的诱因（环境或遗传因素）出发，延伸至多个分子层面的下游功能变化。通路富集分析（Pathway Enrichment Analysis）已被应用于多组学数据集以解析信号传导机制。然而，这些分层组学数据间存在的技术与生物学异质性，制约了整合计算分析的开展。
我们提出了一种基于布尔网络（Boolean network）的方法——多组学布尔组学网络不变时间分析（multiomics Boolean Omics Network Invariant-Time Analysis，缩写为mBONITA），用于整合可量化多个分子层面的组学数据集。mBONITA借助先验知识网络开展基于拓扑结构的通路分析。此外，该方法通过整合观测到的差异倍数与方差、节点（即基因或蛋白质）对信号传导的影响力，以及该基因在各数据集间的证据强度指标，可识别在不同分子检测层面均呈现稳定调控的基因。
我们利用mBONITA整合了在不同氧分压条件下经免疫抑制剂环孢素A处理的RAMOS B细胞的多组学数据集，以识别参与缺氧介导趋化作用的通路。我们将mBONITA的性能与另外6种专为多组学数据设计的通路分析方法进行了对比，结果显示mBONITA可识别出在所有组学层面均存在调控证据的一组通路。
mBONITA可通过https://github.com/Thakar-Lab/mBONITA免费获取。