Multi-Network Co-expression Analysis Enhances Biological Insights from Single-Cell Gene Expression

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Observational analysis enhances biological insights from cell-type-specific gene networks in Parkinson's disease, suggesting new annotations.

scCoExpNets detects changes in 95.84% of the initial gene co-expression networks after applying sparsity reduction.
For 13 controls and 14 Parkinson's disease cases, the approach generates multiple co-expression networks to enhance biological understanding.
Using expressed data from single-nucleus RNA sequencing, scCoExpNets creates cell-type-specific gene networks to facilitate rich biological insights.
Highlights the importance of detecting a robust dopaminergic module enriched with ferroptosis suppressors in independent datasets.

Gómez-Pascual et al. (2026) studied this question.

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View Full Paper

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Observational analysis enhances biological insights from cell-type-specific gene networks in Parkinson's disease, suggesting new annotations.

scCoExpNets detects changes in 95.84% of the initial gene co-expression networks after applying sparsity reduction.
For 13 controls and 14 Parkinson's disease cases, the approach generates multiple co-expression networks to enhance biological understanding.
Using expressed data from single-nucleus RNA sequencing, scCoExpNets creates cell-type-specific gene networks to facilitate rich biological insights.
Highlights the importance of detecting a robust dopaminergic module enriched with ferroptosis suppressors in independent datasets.

Gómez-Pascual et al. (2026) studied this question.

View Full Paper

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