A Statistical Framework for Co-Mediators of Zero-Inflated Single-Cell RNA-Seq Data
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914066019123200 |
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| author | Ahn, Seungjun Chen, Li van Gerwen, Maaike Roussos, Panos Li, Zhigang |
| author_facet | Ahn, Seungjun Chen, Li van Gerwen, Maaike Roussos, Panos Li, Zhigang |
| contents | Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity, enabling detailed molecular profiling at the individual cell level. However, integrating high-dimensional single-cell data into causal mediation analysis remains challenging due to zero inflation and complex mediator structures. We propose a novel mediation framework leveraging zero-inflated negative binomial models to characterize cell-level mediator distributions and beta regression for zero-inflation proportions. The model can identify expression level as well as expressed proportion that could mediate disease-leading causal pathway. Extensive simulation studies demonstrate improved power and controlled false discovery rates. We further illustrate the utility of this approach through application to ROSMAP single-cell transcriptomic data, uncovering biologically meaningful mediation effects that enhance understanding of disease mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Statistical Framework for Co-Mediators of Zero-Inflated Single-Cell RNA-Seq Data Ahn, Seungjun Chen, Li van Gerwen, Maaike Roussos, Panos Li, Zhigang Methodology Genomics Quantitative Methods Applications Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity, enabling detailed molecular profiling at the individual cell level. However, integrating high-dimensional single-cell data into causal mediation analysis remains challenging due to zero inflation and complex mediator structures. We propose a novel mediation framework leveraging zero-inflated negative binomial models to characterize cell-level mediator distributions and beta regression for zero-inflation proportions. The model can identify expression level as well as expressed proportion that could mediate disease-leading causal pathway. Extensive simulation studies demonstrate improved power and controlled false discovery rates. We further illustrate the utility of this approach through application to ROSMAP single-cell transcriptomic data, uncovering biologically meaningful mediation effects that enhance understanding of disease mechanisms. |
| title | A Statistical Framework for Co-Mediators of Zero-Inflated Single-Cell RNA-Seq Data |
| topic | Methodology Genomics Quantitative Methods Applications |
| url | https://arxiv.org/abs/2507.06113 |