Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.19.537436v1?rss=1
Authors: Azuma, I., Mizuno, T., Morita, K., Kusuhara, H.
Abstract:
Background: Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome. 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 various mouse liver injury models and established dataset with corresponding bulk RNA-Seq and immune cell proportions. 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 data sets and showed that the 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.
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