#Zhang jing chu driver#
#Zhang jing chu code#
#Zhang jing chu series#

Moreover, the single-cell revolution, especially single-cell multi-omics sequencing, has expanded our understanding of such biological networks to the finest possible resolution–individual cells–providing an unprecedented opportunity to model and interpret network heterogeneity and dynamics in targeted cell types. Recent advances in novel functional genomics and transcriptomic profiling assays have enabled direct analysis of gene regulation and interactions on a genome-wide scale, allowing us to construct high-confidence GRNs and GCNs across various biological conditions. Therefore, an important goal in systems biology has been to model such regulatory relationships and gene interactions as gene regulatory networks (GRNs) and gene co-expression networks (GCNs), respectively, using network representation analysis.

Numerous studies have reported that alterations in this dynamically controlled process (e.g., changes in gene regulation or gene co-expression relationships) can lead to expression-level perturbations, phenotypical changes, and a wide range of diseases. In biology, cells maintain highly coordinated gene expression patterns via precise spatiotemporal control to dictate essential molecular functions. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. All other relevant data are within the manuscript and it supporting information files.įunding: ZD, YD, AH, CL, and JZ were supported by National Institutes of Health ( ) grants K01MH123896, R01HG012572, and R01NS128523.

#Zhang jing chu code#

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The code is freely available at. Received: ApAccepted: AugPublished: September 11, 2023Ĭopyright: © 2023 Duan et al. PLoS Comput Biol 19(9):Įditor: Jie Liu, University of Michigan, UNITED STATES (2023) iHerd: an integrative hi erarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease.

All other relevant data are within the manuscript and it supporting information files.Ĭitation: Duan Z, Dai Y, Hwang A, Lee C, Xie K, Xiao C, et al.

#Zhang jing chu driver#

This unique approach for driver gene classification can provide us with deeper molecular insights. Distinct from existing models, iHerd’s graph coarsening for hierarchical learning allows us to successfully classify network driver genes into early and late divergent genes (EDGs and LDGs), emphasizing genes with extensive network changes across and within signaling pathway levels. We showed that iHerd can effectively pinpoint novel and well-known risk genes in different diseases. To demonstrate its effectiveness, we applied iHerd on a tumor-to-normal GRN rewiring analysis and cell-type-specific GCN analysis using single-cell multiome data of the brain.

Lastly, iHerd projects separate gene embeddings onto the same latent space in its graph alignment module to calculate a rewiring index for driver gene prioritization. Then, it sequentially learns low-dimensional node representations at all hierarchical levels via efficient graph embedding.

#Zhang jing chu series#

Given a set of networks, iHerd first hierarchically generates a series of coarsened sub-graphs in a data-driven manner, representing network modules at different resolutions (e.g., the level of signaling pathways).

We propose a hierarchical graph representation learning method, called iHerd. Therefore, developing efficient and interpretable methods to quantify network changes and pinpoint driver genes across conditions is crucial. Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations.