Virtual Tissue Expression Analysis.

Bioinformatics (Oxford, England) Pub Date : 2024-11-26 DOI:10.1093/bioinformatics/btae709

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

{"title":"Virtual Tissue Expression Analysis.","authors":"Jakob Simeth, Paul Hüttl, Marian Schön, Zahra Nozari, Michael Huttner, Tobias Schmidt, Michael Altenbuchinger, Rainer Spang","doi":"10.1093/bioinformatics/btae709","DOIUrl":null,"url":null,"abstract":"Motivation: Bulk RNA expression data is widely accessible, whereas single-cell data is relatively scarce in comparison. However, single-cell data offers 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.Code for reproducing the results of this paper is available at https://github.com/spang-lab/tissueResolver-docs1.Supplementary material: Supplementary material and additional analyses available online.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btae709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Motivation: Bulk RNA expression data is widely accessible, whereas single-cell data is relatively scarce in comparison. However, single-cell data offers 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.Code for reproducing the results of this paper is available at https://github.com/spang-lab/tissueResolver-docs1.

Supplementary material: Supplementary material and additional analyses available online.

查看原文本刊更多论文

虚拟组织表达分析

动机大量 RNA 表达数据可广泛获取，而单细胞数据相对较少。然而，单细胞数据提供了对组织细胞组成和细胞类型特异性基因调控的深刻洞察，而这两者在大量表达分析中都是隐性的：在此，我们介绍了组织解析器（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。在对模拟数据进行验证时，tissueResolver 的表现优于其他竞争方法。此外，我们的研究还表明，tissueResolver揭示了弥漫大B细胞淋巴瘤（DLBCL）的活化B细胞样（ABC）亚型和生殖中心B细胞样（GCB）亚型之间的细胞类型特异性调控区别：用于重现本文结果的 R 软件包可从 https://github.com/spang-lab/tissueResolver.Code 获取：https://github.com/spang-lab/tissueResolver-docs1.Supplementary material：补充材料和附加分析可在线获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Bioinformatics (Oxford, England)

自引率

0.00%

发文量