虚拟组织表达分析。

Virtual tissue expression analysis.

作者信息

Simeth Jakob, Hüttl Paul, Schön Marian, Nozari Zahra, Huttner Michael, Schmidt Tobias, Altenbuchinger Michael, Spang Rainer

机构信息

Institute for Statistical Bioinformatics, Faculty of Informatics and Data Science, University of Regensburg, Am Biopark 9, 93053 Regensburg, Germany.

NGS and Data Technologies Core, Leibniz Institute for Immunotherapy (LIT), c/o Universitätsklinikum Regensburg, Franz-Josef-Strauss Allee 11, 93053 Regensburg, Germany.

出版信息

Bioinformatics. 2024 Nov 28;40(12). doi: 10.1093/bioinformatics/btae709.

DOI:10.1093/bioinformatics/btae709

PMID:39589902

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11631471/

Abstract

MOTIVATION

Bulk RNA expression data are widely accessible, whereas single-cell data are relatively scarce in comparison. However, single-cell data offer profound insights into the cellular composition of tissues and cell type-specific gene regulation, both of which remain hidden in bulk expression analysis.

RESULTS

Here, we present tissueResolver, an algorithm designed to extract single-cell information from bulk data, enabling us to attribute expression changes to individual cell types. When validated on simulated data tissueResolver outperforms competing methods. Additionally, our study demonstrates that tissueResolver reveals cell type-specific regulatory distinctions between the activated B-cell-like (ABC) and germinal center B-cell-like (GCB) subtypes of diffuse large B-cell lymphomas (DLBCL).

AVAILABILITY AND IMPLEMENTATION

R package available at https://github.com/spang-lab/tissueResolver (archived as 10.5281/zenodo.14160846).Code for reproducing the results of this article is available at https://github.com/spang-lab/tissueResolver-docs archived as swh:1:dir:faea2d4f0ded30de774b28e028299ddbdd0c4f89).

摘要

动机

批量RNA表达数据广泛可得，而相比之下单细胞数据相对稀缺。然而，单细胞数据能深入洞察组织的细胞组成和细胞类型特异性基因调控，而这两者在批量表达分析中均无法体现。

结果

在此，我们展示了tissueResolver，这是一种旨在从批量数据中提取单细胞信息的算法，使我们能够将表达变化归因于单个细胞类型。在模拟数据上进行验证时，tissueResolver优于其他竞争方法。此外，我们的研究表明，tissueResolver揭示了弥漫性大B细胞淋巴瘤（DLBCL）的活化B细胞样（ABC）和生发中心B细胞样（GCB）亚型之间细胞类型特异性的调控差异。