Statistical Methods for Cellular Deconvolution with Single-Cell OmicsCai, Manqi (2024) Statistical Methods for Cellular Deconvolution with Single-Cell Omics. Doctoral Dissertation, University of Pittsburgh. (Unpublished)
AbstractCellular fractions confound the analyses of bulk omics data since tissues are mixtures of myriad cells. To deconfound tissue-level analyses, cellular deconvolution is used to estimate the proportions of cell types within bulk data and enable downstream analyses at cell-type resolution. The advent of single-cell technologies has catalyzed improvements in deconvolution references. In this thesis, we first introduce an ensemble learning algorithm, EnsDeconv, to synthesize the estimates of cell-type deconvolution from various deconvolution scenarios. It is designed to combine selecting references, deconvolution methods, and data preprocessing. EnsDeconv incorporates cell type-specific optimizations to provide accurate and robust deconvolution results, as benchmarked on several large real bulk datasets in transcriptomics and epigenomics. Reference is the most important factor in deconvolution. In Chapter 2, we improve the reference for DNA methylation with the emerging single-cell DNA methylation (scDNAm). Confronting the inherent challenges presented by the ultra-high dimensionality and excessive missingness of current scDNAm techniques, we present a novel workflow, scMD. It enables the construction of refined references from scDNAm data, surpassing conventional sorted-cell or RNA-imputed references in accuracy and precision. Chapter 3 refines scMD with a latent multinomial cell-type allocation model and a binomial DNA methylation model, applicable to high-throughput sequencing and array-based experiments. This approach is implemented as an Expectation-Maximization (EM) algorithm. By integrating a joint modeling framework for heterogeneous tissue samples alongside reference scDNAm data, the methodology facilitates the concurrent estimation of tissue composition and updated cell-type-specific methylation profiles. Share
Details
MetricsMonthly Views for the past 3 yearsPlum AnalyticsActions (login required)
|
![[feed]](/images/feed-icon-32x32.png){kind=icon}