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

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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 tissue using convolutional neural networks (CNNs).