A-T.30: QUASAR: Integrating domain adaptation and bootstrapping for accurate bulk RNA‑seq cell-type Deconvolution in infected organs
Heterogeneous tissue samples from infected organs consist of diverse cell types whose proportions and transcriptional profiles change dynamically during pathogen invasion. RNA sequencing (RNA-seq) of mixed-cell populations captures average gene expression across all cells, obscuring cell type-specific responses, while single cell RNA-seq (scRNA-seq) provides higher resolution but suffers from technical biases, high costs, and limited applicability to large cohorts. In silico cell type deconvolution bridges this gap by estimating the proportions and expression profiles of cell types within RNA-seq data, using reference expression signatures. Although numerous deconvolution methods exist, their application to infectious disease research is limited, partly due to missing matched single-cell references under infection conditions and sensitivity to gene expression shifts between conditions.
We present Quasar, a novel cell deconvolution tool tailored for infectious disease transcriptomics. Quasar adapts the reference to match the condition of the bulk samples before estimating proportions. Bootstrapping assesses uncertainty. We tested Quasar on single cell datasets from peripheral blood mononuclear cells (PBMCs) of individuals with COVID‑19 and dengue infections, using pseudo‑bulks generated from held‑out test cells to benchmark performance against established methods.
In simulation studies based on single-cell data from infected individuals, Quasar achieves accurate cell-type proportion estimates across multiple cell types and performs as well as or better than existing methods for certain cell types. Future work will assess Quasar’s ability to estimate cell-type-specific expression profiles and evaluate its performance in capturing condition-specific expression changes in bulk samples when the reference and target conditions differ.
Co-authors: Whitney Tam, Cynthia Bullerjahn, Andreas Beineke, Michael Altenbuchinger, Klaus Jung
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