arXiv subjects

Panos Roussos

Publications and source records attributed to Panos Roussos.

3 recordsLinked to original sources

arXiv2026

A Quasi-Regression Method for the Mediation Analysis of Zero-Inflated Single-Cell Data

Recent advances in single-cell technologies have advanced our understanding of gene regulation and cellular heterogeneity at single-cell resolution. Single-cell data contain both gene expression levels and the proportion of expressing cells, which makes them structurally different from bulk data. Currently, methodological work on causal mediation analysis for single-cell data remains limited and often requires specific distributional assumptions. To address this challenge, we present QuasiMed, a mediation framework specialized for single-cell data. Our proposed method comprises three steps, including (i) screening mediator candidates through penalized regression and marginal models (similar to sure independence screening), (ii) estimation of indirect effects through the average expression and the proportion of expressing cells, (iii) and hypothesis testing with multiplicity control. The key benefit of QuasiMed is that it specifies only the mean functions of the mediation models through a quasi-regression framework, thereby relaxing strict distributional assumptions. The method performance was evaluated through the real-data-inspired simulations, and demonstrated high power, false discovery rate control, and computational efficiency. Lastly, we applied QuasiMed to ROSMAP single-cell data to illustrate its potential to identify mediating causal pathways. R package is freely available on GitHub repository at https://github.com/sjahnn/QuasiMed.

stat.ME↗

arXiv2025

A Statistical Framework for Co-Mediators of Zero-Inflated Single-Cell RNA-Seq Data

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.

stat.ME↗

arXiv2019

Transcriptomic Causal Networks identified patterns of differential gene regulation in human brain from Schizophrenia cases versus controls

Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, which work in a coordinated way. However, the vast majority of studies focus on the differential expression of one individual gene at a time. Here, we aim to provide insight into the underlying relationships of the genes expressed in the human brain in cases with schizophrenia (SCZ) and controls. We introduced a novel approach to identify differential gene regulatory patterns and identify a set of essential genes in the brain tissue. Our method integrates genetic, transcriptomic, and Hi-C data and generates a transcriptomic-causal network. Employing this approach for analysis of RNA-seq data from CommonMind Consortium, we identified differential regulatory patterns for SCZ cases and control groups to unveil the mechanisms that control the transcription of the genes in the human brain. Our analysis identified modules with a high number of SCZ-associated genes as well as assessing the relationship of the hubs with their down-stream genes in both, cases and controls. In addition, the results identified essential genes for brain function and suggested new genes putatively related to SCZ.

q-bio.GN↗