Jaakkola Maria K, Elo Laura L
Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland.
NAR Genom Bioinform. 2021 Jan 12;3(1):lqaa110. doi: 10.1093/nargab/lqaa110. eCollection 2021 Mar.
Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.
计算反卷积是一种省时且经济高效的方法，可从血液等异质组织的大量基因表达中获取细胞类型特异性信息。反卷积的目的可以是估计样本中细胞类型的比例或丰度，或者估计每种存在的细胞类型对不同基因的表达强度，或者同时进行这两项任务。在这两个独立的目标中，细胞类型比例/丰度的估计已得到广泛研究，但在定义细胞类型特异性表达谱方面关注较少。在这里，我们通过引入一种新方法Rodeo来填补这一空白，并利用不同的数据集从多个角度对其以及其他可用工具进行实证评估。
