Dataset Information

Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.

ABSTRACT: Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell-cell interactions and relevant ligand-receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq is fast and scalable. It draws inferences from routine single-cell data and does not require prior knowledge about sample cell-types or multiplets. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics.

SUBMITTER: Ghaddar B

PROVIDER: S-EPMC9371920 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.

Ghaddar Bassel B De Subhajyoti S

Nucleic acids research 20220801 14

Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell-cell interactions and relevant ligand-receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, an ...[more]

PMID: 35536255

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Project description:Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data present challenges for GRN inference. In recent years, the dramatic increase in data on experimentally validated transcription factors binding to DNA has made it possible to infer GRNs by supervised methods. In this study, we address the problem of GRN inference by framing it as a graph link prediction task. In this paper, we propose a novel framework called GNNLink, which leverages known GRNs to deduce the potential regulatory interdependencies between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce a graph convolutional network-based interaction graph encoder to effectively refine gene features by capturing interdependencies between nodes in the network. Finally, the inference of GRN is obtained by performing matrix completion operation on node features. The features obtained from model training can be applied to downstream tasks such as measuring similarity and inferring causality between gene pairs. To evaluate the performance of GNNLink, we compare it with six existing GRN reconstruction methods using seven scRNA-seq datasets. These datasets encompass diverse ground truth networks, including functional interaction networks, Loss of Function/Gain of Function data, non-specific ChIP-seq data and cell-type-specific ChIP-seq data. Our experimental results demonstrate that GNNLink achieves comparable or superior performance across these datasets, showcasing its robustness and accuracy. Furthermore, we observe consistent performance across datasets of varying scales. For reproducibility, we provide the data and source code of GNNLink on our GitHub repository: https://github.com/sdesignates/GNNLink.

Dataset Information

Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.

Publications

Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.

Similar Datasets

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