Investigation of the usefulness of liver-specific deconvolution method by establishing a liver benchmark dataset
Published in NAR Genomics and Bioinformatics, 2024
Background Immune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it is mainly applied to blood-derived samples and tumor tissues. However, the influence of tissue-specific modeling on the estimation results has rarely been investigated. In this study, we constructed a system to evaluate the performance of the deconvolution method on liver transcriptome data.
Results We prepared seven mouse liver injury models using small-molecule compounds with known hepatotoxicity and established a dataset with corresponding liver bulk RNA-Seq and immune cell proportions, covering various immune responses. RNA-Seq expression for nine leukocyte subsets and four liver-associated cell types were obtained from the Gene Expression Omnibus (GEO) to provide a reference covering liver component cells. Here, we found that the combination of reference cell sets affects the estimation results of reference-based deconvolution methods. We established a liver tissue-specific deconvolution by optimizing the reference cell set for each cell to be estimated. We applied this model to independent datasets and showed that liver-specific modeling focusing on reference cell sets is highly extrapolatable.
Conclusions We provide an approach of liver-specific modeling when applying reference-based deconvolution to bulk RNA-Seq data and show its importance. It is expected to enable sophisticated estimation from rich tissue data accumulated in public databases and to obtain information on aggregated immune cell trafficking.
Recommended citation: Azuma, Iori, et al. "Investigation of the usefulness of liver-specific deconvolution method by establishing a liver benchmark dataset." NAR Genomics and Bioinformatics (2024): 2024-03.
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