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Deconvolution of Low-count RNA Sequencing Data for Tumor Cells Using Embedded Negative Binomial Distributions

ABSTRACT: A major challenge in studying tumor heterogeneity is the presence of mixed signals in gene expression data, especially when working with sparse count datasets such as microRNA and spatial transcriptomics. Estimating tumor-specific transcript proportions can provide critical insights into tumor heterogeneity and cell plasticity. However, current methods often require comprehensive single-cell reference data, which is not always obtainable, and may suffer from technical biases. To overcome these issues, we introduce DeMixNB, a semi-reference-based deconvolution method designed specifically for estimating transcript proportions from sparse expression matrices using a negative binomial model, without the reliance on high-quality external reference data. Through simulations and analyses of real microRNA sequencing and spatial transcriptomics data from breast and lung cancers, we demonstrate that DeMixNB accurately estimates tumor-specific transcript proportions and provides meaningful biological insights from real-world datasets.

ORGANISM(S): Homo sapiens

PROVIDER: GSE311690 | GEO | 2026/07/27

REPOSITORIES: GEO

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