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

Award Number: R21CA264353
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2021
PERIOD OF PERFORMANCE END DATE: 08/31/2025

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = -$7,362 )
2026	2022	UNIVERSITY OF SOUTH CAROLINA	1600 HAMPTON ST	COLUMBIA	SC	29208	RICHLAND	USA	Cancer Biology Research	000	2	12/31/2025	NON-COMPETING CONTINUATION	-$7,362
														Subtotal = -$7,362

Issue Date FY: 2024 ( Subtotal = $0 )
2024	2022	UNIVERSITY OF SOUTH CAROLINA	1600 HAMPTON ST	COLUMBIA	SC	29208	RICHLAND	USA	Cancer Biology Research	000	2	7/26/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2022 ( Subtotal = $169,120 )
2022	2022	UNIVERSITY OF SOUTH CAROLINA	1600 HAMPTON ST # 414	COLUMBIA	SC	29208	RICHLAND	USA	Cancer Biology Research	000	2	9/13/2022	NON-COMPETING CONTINUATION	$169,120
														Subtotal = $169,120

Issue Date FY: 2021 ( Subtotal = $187,928 )
2021	2021	UNIVERSITY OF SOUTH CAROLINA	1600 HAMPTON ST # 414	COLUMBIA	SC	29208	RICHLAND	USA	Cancer Biology Research	000	1	8/24/2021	NEW	$187,928
														Subtotal = $187,928

Grand Total All Awards = $349,686

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

Award Number: R21CA264353

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/01/2021

PERIOD OF PERFORMANCE END DATE: 08/31/2025

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