Dataset Information

ScapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.

ABSTRACT: Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene-cell association network for inferring single-cell pathway activity scores and identifying cell phenotype-associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pathway or gene module identification. scapGNN was developed as a systematic R package that can be flexibly extended and enhanced for existing analysis processes. It provides a new analytical platform for studying single cells at the pathway and network levels.

SUBMITTER: Han X

PROVIDER: S-EPMC10681325 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.

Han Xudong X Wang Bing B Situ Chenghao C Qi Yaling Y Zhu Hui H Li Yan Y Guo Xuejiang X

PLoS biology 20231113 11

Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene-cell association network for inferring single-cell pathway activity scores and identifying cell phenotype-associated gene mod ...[more]

PMID: 37956172

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Project description:BackgroundCell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.ResultsWe present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmaniConclusionRMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions.

Dataset Information

ScapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.

Publications

scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.

Similar Datasets

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