Direct comparison of bulk gene expression profiles is complicated by distinct cell type mixtures in each sample that obscure whether observed differences are actually caused by changes in the expression levels themselves or are simply a result of differing cell type compositions. Single-cell technology has made it possible to measure gene expression in individual cells, achieving higher resolution at the expense of increased noise. If carefully incorporated, such single-cell data can be used to deconvolve bulk samples to yield accurate estimates of the true cell type proportions, thus enabling one to disentangle the effects of differential expression and cell type mixtures. Here, we propose a generative model and a likelihood-based inference method that uses asymptotic statistical theory and a novel optimization procedure to perform deconvolution of bulk RNA-seq data to produce accurate cell type proportion estimates. We show the effectiveness of our method, called RNA-Sieve, across a diverse array of scenarios involving real data and discuss extensions made uniquely possible by our probabilistic framework, including a demonstration of well-calibrated confidence intervals.

中文翻译：

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使用单细胞参考对大量基因表达数据进行基于似然的反卷积

每个样本中不同的细胞类型混合物使大量基因表达谱的直接比较变得复杂，这使得观察到的差异实际上是由表达水平本身的变化引起的，还是仅仅是不同细胞类型组成的结果。单细胞技术使测量单个细胞中的基因表达成为可能，以增加噪声为代价实现更高的分辨率。如果仔细合并，此类单细胞数据可用于对大量样本进行反卷积，以准确估计真实细胞类型比例，从而使人们能够解开差异表达和细胞类型混合物的影响。这里，我们提出了一种生成模型和一种基于似然的推理方法，该方法使用渐近统计理论和一种新颖的优化程序来执行批量 RNA-seq 数据的反卷积，以产生准确的细胞类型比例估计。我们展示了我们的方法（称为 RNA-Sieve）在涉及真实数据的各种场景中的有效性，并讨论了我们的概率框架唯一可能实现的扩展，包括校准良好的置信区间的演示。