GraphComm: A Graph-based Deep Learning Method to Predict Cell-Cell Communication in single-cell RNAseq data
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| Publicado en: | bioRxiv (Dec 21, 2024) |
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| Otros Autores: | , , , |
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Cold Spring Harbor Laboratory Press
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| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3131948446 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2023.04.26.538432 |2 doi | |
| 035 | |a 3131948446 | ||
| 045 | 0 | |b d20241221 | |
| 100 | 1 | |a So, Emily | |
| 245 | 1 | |a GraphComm: A Graph-based Deep Learning Method to Predict Cell-Cell Communication in single-cell RNAseq data | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Dec 21, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Interactions between cells coordinate various functions across cell-types in health and disease states. Novel single-cell techniques enable deep investigation of cellular crosstalk at single-cell resolution. Cell-cell communication (CCC) is mediated by underlying gene-gene networks, however most current methods are unable to account for complex interactions within the cell as well as incorporate the effect of pathway and protein complexes on interactions. This results in the inability to infer overarching signalling patterns within a dataset as well as limit the ability to successfully explore other data types such as spatial cell dimension. Therefore, to represent transcriptomic data as intricate networks, complementing gene expression with information from cells to ligands and receptors for relevant cell-cell communication inference, we present GraphComm - a new graph-based deep learning method for predicting cell-cell communication in single-cell RNAseq datasets. GraphComm improves CCC inference by capturing detailed information such as cell location and intracellular signalling patterns from a database of more than 30,000 protein interaction pairs. With this framework, GraphComm is able to predict biologically relevant results in datasets previously validated for CCC, datasets that have undergone chemical or genetic perturbations and datasets with spatial cell information.Competing Interest StatementBHK is a shareholder and paid consultant for Code Ocean Inc. ES is paid consultant for Code Ocean Inc.Footnotes* Figure 4F replaced with new results; Extended Data Figure 1 replaced with new results. | |
| 653 | |a Gene expression | ||
| 653 | |a Cell interactions | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Transcriptomics | ||
| 653 | |a Communication | ||
| 653 | |a Signal transduction | ||
| 653 | |a Intracellular signalling | ||
| 700 | 1 | |a Sikander Hayat | |
| 700 | 1 | |a Nair, Sisira Kadambat | |
| 700 | 1 | |a Wang, Bo | |
| 700 | 1 | |a Haibe-Kains, Benjamin | |
| 773 | 0 | |t bioRxiv |g (Dec 21, 2024) | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3131948446/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2023.04.26.538432v5 |