# scCOSMIX: A Mixed‐Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single‐Cell RNA‐Seq

## TL;DR

This paper introduces scCOSMiX, a new method for analyzing gene interactions in single-cell RNA-seq data that accounts for individual-level correlations.

## Contribution

The novel contribution is a mixed-effects framework that models coexpression and transcriptional interactions with subject-level random effects.

## Key findings

- scCOSMiX outperforms existing methods in capturing dynamic gene interactions in scRNA-seq data.
- The framework is applicable across different scRNA-seq experimental protocols, as demonstrated on GSE266919 and GSE108989 datasets.

## Abstract

Advancements in single‐cell RNA‐sequencing (scRNA‐seq) technologies generate a wealth of gene expression data that provide exciting opportunities for studying gene‐gene interactions systematically at individual cell resolution. Genetic interactions within a cell are tightly regulated and often highly dynamic in response to internal cellular signals and external stimuli. Evidence of these dynamic interactions can often be observed in scRNA‐seq data by examining conditional co‐expression changes. Existing approaches for studying these dynamic interaction changes in scRNA‐seq data do not address the multi‐subject hierarchical design commonly considered in single‐cell experiments. In this paper, we propose a Mixed‐effects framework for differential Coexpression and transcriptional interaction modeling in Single‐Cell RNA‐seq (scCOSMiX) to account for the cell‐cell correlation from the same individual. The proposed copula‐based approach allows the zero‐inflation, marginal, and association parameters to be modeled as functions of covariates with subject‐level random effects, to enable analyses to be tailored to the data under consideration. A series of simulation analyses were conducted to evaluate and compare the performance of scCOSMiX to other existing approaches. We applied the proposed method to both droplet and plate‐based scRNA‐seq data sets GSE266919 and GSE108989 to illustrate its applicability across distinct scRNA‐seq experimental protocols.