# Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC)

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2020 · $469,538

## Abstract

Abstract:
Tumors are complex ecosystems composed of heterogeneous cell populations. Understanding the clonal cellular
composition of the tumor and the non-malignant cells within the tumor ecosystem provides significant insights in
the tumor recurrence, treatment, initiation, progression and metastasis. Previous studies estimated immune cell
type content in bulk tumor expression data using immune cell signatures generated from peripheral blood
mononuclear cells. However, with the advent of single-cell RNA sequencing methods, we can now also estimate
the tumor associated non-malignant and malignant cell type contents.
 In this proposal, we describe a novel deep net approach for deconvolving different cell types in bulk tumor
using single-cell sequencing data (scDEC). We will also infer tumor associated copy number variation (CNV)
clones and their signatures from single-cell RNA sequencing data using our novel multiscale resolution signal
processing based algorithm. Our approach will estimate not only the content of different immune cell types and
tumor associated non-malignant cell types but also the content of different CNV clone types in bulk tumor.
Moreover, we will discover new associations between cell type content and sample phenotype such as disease
survival, subtype and outcome. Our proposed project will lead to major improvements in clinical care to guide
the treatment and prognosis of various types of cancer.

## Key facts

- **NIH application ID:** 9987575
- **Project number:** 5R01CA241930-02
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Xiaobo Zhou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $469,538
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9987575

## Citation

> US National Institutes of Health, RePORTER application 9987575, Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC) (5R01CA241930-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9987575. Licensed CC0.

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