# 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 · $389,584

## Abstract

Abstract
 Brain tissue is composed of heterogeneous cell populations. Understanding the changes in brain cell
type or state composition during neurodegeneration have important implications for future treatment of
Alzheimer's disease (AD). For example, microglia cell
development
(scRNA-Seq)
brain.
including
poorly
type specific gene expression changes occur early in the
of AD. Over the past few years, the development and application of single-cell RNA sequencing
have revolutionized brain research thus enabling us to study the cellular heterogeneity of the
With the advent of scRNA-Seq, we can now identify healthy and diseased brain cell types or states
rare cell type or state populations and identify transcriptional alterations within these cell groups.
addition to the cellular complexity of the AD, the molecular complexity of the disease also remains
understood.
In
Until now, studies have identified numerous germline genomic variants associated with
susceptibility to AD. However,
from
level
identification of somatic
example,
However,
brain
healthy and diseased scRNA-Seq data
DNA alterations that are distinct
the germline, term referred as `brain somatic genomic mosaicism'. Brain somatic variants occur at a low-
allele frequency, which could only be detected using single-cell DNA sequencing. C
variants For
sporadic AD the copy number of APP gene is mosaically increased in single neuron cells.
it is challenging to characterize these somatic variants since there is a lack of AD brain or healthy
single-cell DNA sequencing data. Therefore, there is a great value in utilizing
to investigate somatic mosaicism in single brain cells and
brain cells, especially neurons, harbor diverse
omprehensive
in brain cells will explain the contribution of somatic mosaicism to AD.
in
the growing number of
identify
genomic variants that are associated with susceptibility to AD.
 In this proposal, we describe a novel deep network approach for deconvolving different cell types or
states in bulk AD sample using single-cell RNA sequencing data. Our approach will estimate not only the ratio
of cell types or states but also the ratio of somatic clonal mosaicism in AD samples using scRNA-Seq data. We
define somatic clonal mosaicism as the groups of cells, i.e. clones, harboring somatic genomic variants
such
asCNVs, SNPs, or indels. Thesesomatic genomic variants
novel multiscale resolution signal processing based algorithm named CaSpER.
will be identified from scRNA-Seq data
We will then extract
using our
cell or
clone type gene signatures from scRNA-Seq data using a generative deep learning approach called General
Adversarial Networks (GANs).
used We will also
adapt radiogenomics approaches where we correlate image features with cell type ratios. Our proposed
approach will lead to major improvements in clinical care to guide the treatment and prognosis of AD.
These cell type gene signatures identified from scRNA-Seq data will be later
to infer fractions of cell type in bulk AD...

## Key facts

- **NIH application ID:** 10117064
- **Project number:** 3R01CA241930-02S1
- **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:** $389,584
- **Award type:** 3
- **Project period:** 2019-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10117064

## Citation

> US National Institutes of Health, RePORTER application 10117064, Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC) (3R01CA241930-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10117064. Licensed CC0.

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