细胞类型组成是健康状况的重要指标。我们提出了一种用于单细胞RNA测序数据中推断细胞类型特异基因主题分布的引导主题模型（GTM-decon）。GTM-decon能够自动地从单细胞RNA-seq数据中推断出细胞类型特异的基因主题分布，用于解析批量转录组。与最先进的方法相比，GTM-decon在解析模拟和真实批量数据方面表现出竞争力。此外，通过对疾病转录组进行解析的实验表明，GTM-decon能够推断出单个细胞类型中的多个细胞类型特异的基因主题分布，从而捕捉亚细胞类型变异。GTM-decon还可以利用来自单细胞或批量数据的表型标签来推断表型特异的基因分布。在嵌套引导设计中，GTM-decon从批量乳腺癌转录组中鉴定了细胞类型特异性差异表达基因。© 2023. BioMed Central Ltd., part of Springer Nature.

Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.© 2023. BioMed Central Ltd., part of Springer Nature.