# scDECO: A novel statistical framework to identify differential co-expression gene combinations systematically using single-cell RNA sequencing data

> **NIH NIH R21** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2022 · $169,120

## Abstract

Project Summary
Recent single-cell RNA sequencing (scRNAseq) studies have revealed complex tumor
ecosystems characterized by intricate interactions between heterogeneous cell types and
diverse transcriptional programs. Differential co-expression (DC) analysis is emerging as
a crucial complement to the standard differential expression analysis (DE) for gene
profiling data. DC analysis can detect correlation changes between pairs of genes across
different modulating conditions. However, most DC analysis approaches are originally
designed for use on either microarray or bulk RNAseq data. There is an urgent need to
develop advanced DC analytical techniques that are tailored to the characteristics of
single-cell data, study design and biological objectives.
 In Aim 1, we will develop a novel, flexible Bayesian model-based framework
named scDECO to improve the accuracy of identifying DC gene combinations using
scRNAseq data. Using data generated from various scRNAseq experiment protocols, we
will evaluate the proposed scDECO algorithm and perform benchmarking analyses to
compare our proposed approaches to current approaches. These analyses will provide a
better understanding of the advantages and limitations of these methods.
 In Aim2, we will implement the scDECO algorithm using scRNAseq datasets from
 melanoma and prostate circulating tumor cells. By identifying sets of clinically relevant
 DC gene pairs using single-cell data, the findings can promote understanding of the
 transcriptional co-regulatory processes in cancer stem-like cells and other cells in the
 tumor microenvironment. Furthermore, the proposed framework has the potential to
improve clinical disease severity prediction by incorporating gene co-expression
information into risk score calculation. The predictive performance of the proposed
 algorithm will be further evaluated using both scRNAseq and bulk RNAseq data. Finally,
 in Aim3, freely available R/Bioconductor software packages will be distributed. The
 R/Bioconductor environments are both very commonly used by biomedical researchers.
 Ultimately, this proposed framework will accelerate studies seeking to understand the
 differential co-regulatory transcriptional activities in tumors.

## Key facts

- **NIH application ID:** 10474599
- **Project number:** 5R21CA264353-02
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Yen-Yi Ho
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $169,120
- **Award type:** 5
- **Project period:** 2021-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10474599

## Citation

> US National Institutes of Health, RePORTER application 10474599, scDECO: A novel statistical framework to identify differential co-expression gene combinations systematically using single-cell RNA sequencing data (5R21CA264353-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10474599. Licensed CC0.

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