2020
DOI: 10.1101/2020.09.23.310656
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A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

Abstract: The metabolic heterogeneity, and metabolic interplay between cells and their microenvironment have been known as significant contributors to disease treatment resistance. Our understanding of the intra-tissue metabolic heterogeneity and cooperation phenomena among cell populations is unfortunately quite limited, without a mature single cell metabolomics technology. To mitigate this knowledge gap, we developed a novel computational method, namely scFEA (single cell Flux Estimation Analysis), to infer single cel… Show more

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Cited by 17 publications
(28 citation statements)
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References 82 publications
(119 reference statements)
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“…The modelling of human metabolism often relies on GEMs [ 27 , [45] , [46] , [47] ] from Recon [ 37 ] and Human Metabolic Reaction series [ 48 ] and the resources are being constantly improved [ 49 ]. GEMs can be also constructed de novo from KEGG or other resources and are often updated with manual curation [ 20 , 46 , 50 ]. To enable comparison and knowledge integration across resources, standardization efforts are needed [ 44 , 51 ].…”
Section: Resources For Metabolic Modelingmentioning
confidence: 99%
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“…The modelling of human metabolism often relies on GEMs [ 27 , [45] , [46] , [47] ] from Recon [ 37 ] and Human Metabolic Reaction series [ 48 ] and the resources are being constantly improved [ 49 ]. GEMs can be also constructed de novo from KEGG or other resources and are often updated with manual curation [ 20 , 46 , 50 ]. To enable comparison and knowledge integration across resources, standardization efforts are needed [ 44 , 51 ].…”
Section: Resources For Metabolic Modelingmentioning
confidence: 99%
“…Currently, differential expression analysis followed by pathway enrichment is the most commonly used method for metabolic analysis based on scRNA-seq data [ 20 , 55 ], and uses general purpose enrichment methods [ [56] , [57] , [58] ]. Alternatively, pathway activity can be inferred from gene expression of pathway-associated genes [ 14 , 59 , 60 ].…”
Section: Modelling Approachesmentioning
confidence: 99%
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