Semi-reference based cell type deconvolution with application to human metastatic cancers.
Semi-reference based cell type deconvolution with application to human metastatic cancers.
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DOI：
10.1093/nargab/lqad109
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发表时间：
2023-12
影响因子：
4.6
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批量RNA-SEQ实验通常用于区分不同条件下基因表达的变化，由于它们关注的是平均转录丰度，因此经常忽略关键的细胞类型特定信息。认识细胞类型的贡献对于理解表型和疾病变异至关重要。单细胞RNA测序的出现允许对细胞异质性进行详细的检查；然而，成本和分析警告禁止对大量样本进行这样的测序。我们介绍了一种新的去卷积方法，SECRET，它使用来自单细胞RNA-SEQ的细胞类型特定的基因表达谱来准确地估计批量RNA-SEQ数据中的细胞类型比例。值得注意的是，SECRET可以适应批量数据中存在的单元格类型在引用中未表示的情况，从而在引用选择方面提供了更大的灵活性。与使用合成数据的现有方法相比，Secure已经证明了更高的准确性，并在真实的人类转移性癌症中识别了未知的组织特定细胞类型。它的多功能性使其广泛适用于各种人类癌症研究。
Bulk RNA-seq experiments, commonly used to discern gene expression changes across conditions, often neglect critical cell type-specific information due to their focus on average transcript abundance. Recognizing cell type contribution is crucial to understanding phenotype and disease variations. The advent of single-cell RNA sequencing has allowed detailed examination of cellular heterogeneity; however, the cost and analytic caveat prohibits such sequencing for a large number of samples. We introduce a novel deconvolution approach, SECRET, that employs cell type-specific gene expression profiles from single-cell RNA-seq to accurately estimate cell type proportions from bulk RNA-seq data. Notably, SECRET can adapt to scenarios where the cell type present in the bulk data is unrepresented in the reference, thereby offering increased flexibility in reference selection. SECRET has demonstrated superior accuracy compared to existing methods using synthetic data and has identified unknown tissue-specific cell types in real human metastatic cancers. Its versatility makes it broadly applicable across various human cancer studies.