# Stabilized marker gene identification and functional annotation from single-cell transcriptomic data

## TL;DR

scSCOPE is a new tool that improves the identification of consistent and functionally relevant marker genes in single-cell RNA sequencing data.

## Contribution

scSCOPE introduces a stabilized LASSO and co-expression approach for more consistent and functional marker gene identification in scRNAseq.

## Key findings

- scSCOPE outperforms conventional methods in identifying consistent cell type-specific marker genes across datasets.
- The tool provides in-depth molecular insights through gene co-expression and pathway analyses.
- It improves cell type annotation and supports functional investigations of cell heterogeneity.

## Abstract

With the rapid emergence of single-cell transcriptomics datasets, reproducible marker genes and functional annotation of cell type or state is becoming increasingly important. Conventional methods that rely on differential gene expression (DEG) analysis lack both consistency across datasets and functional annotations of selected markers. Here, we present scSCOPE, an R-based platform that utilizes stabilized LASSO (Least Absolute Shrinkage and Selection Operator) feature selection, bootstrapped co-expression networks, and pathway enrichments to identify reproducible and functionally relevant marker genes and associated pathways in scRNAseq datasets. Using 9 scRNAseq datasets from human and mouse immune cells generated by different sequencing technologies, we show that scSCOPE outperforms other conventional methods by automatically identifying cell type-specific marker genes and pathways with the highest consistency across all datasets. scSCOPE’s gene co-expression and pathway analyses also provide in-depth molecular insights into the functionality of identified marker genes. We anticipate that scSCOPE will greatly improve cell type annotation and accelerate the design of experimental validation and functional investigations on cell heterogeneity.

With the growing number of single-cell transcriptomics datasets, it has become increasingly important to identify consistent markers that define cell types and their associated functions. However, existing methods rely heavily on analyzing individual gene expression, which can vary between experiments and fail to capture the bigger picture of how genes work together. In our study, we present scSCOPE, a computational tool designed to address these limitations. Unlike traditional methods, scSCOPE not only evaluates gene expression but also incorporates gene co-expression. This approach enables the identification of marker genes and pathways of cell types of interest with the highest consistency across all datasets and provides in-depth molecular insights into the functionality of identified marker genes. We anticipate that scSCOPE will greatly improve cell type annotation and accelerate the design of experimental validation and functional investigations on cell heterogeneity.