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利用crossWGCNA分析批量、单细胞和空间转录组学数据中的跨组织基因表达相互作用。

Cross-tissue gene expression interactions from bulk, single cell and spatial transcriptomics with crossWGCNA.

作者信息

Savino Aurora, Iannuzzi Raffaele M, Avalle Lidia, Lobascio Andrea, Iorio Francesco, Provero Paolo, Poli Valeria

机构信息

Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, Turin, 10126, Italy.

Computational Biology, Human Technopole, Palazzo Italia, Viale Rita Levi Montalcini, 1, Milan, 20157, Italy.

出版信息

BMC Genomics. 2025 Jul 1;26(1):583. doi: 10.1186/s12864-025-11747-y.

DOI:10.1186/s12864-025-11747-y
PMID:40597567
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12211745/
Abstract

BACKGROUND

Understanding the molecular interactions between cells, tissues or organs is key to understanding the functioning of a biological system as a whole.

RESULTS

Here, we propose crossWGCNA: a co-expression-based method that identifies highly interacting genes unbiasedly and that we employ to study stroma-epithelium communication in breast cancer. CrossWGCNA can be applied to bulk, single cell and spatial transcriptomics data. We validate it both in silico and experimentally, and we provide a fully documented R package allowing users to employ it.

CONCLUSIONS

The wide applicability and agnostic nature of our tool make it complementary to existing methods overcoming the limitations arising from strong baseline assumptions.

摘要

背景

理解细胞、组织或器官之间的分子相互作用是理解整个生物系统功能的关键。

结果

在此，我们提出了crossWGCNA：一种基于共表达的方法，该方法能够无偏地识别高度相互作用的基因，我们用它来研究乳腺癌中的基质-上皮细胞通讯。CrossWGCNA可应用于批量、单细胞和空间转录组学数据。我们在计算机模拟和实验中对其进行了验证，并提供了一个有完整文档记录的R包，供用户使用。

结论

我们工具的广泛适用性和无偏性使其成为现有方法的补充，克服了因强烈的基线假设而产生的局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/48c8ef57233a/12864_2025_11747_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/d0b8ccbe2dba/12864_2025_11747_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/963718e3d291/12864_2025_11747_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/b5f30d6108b3/12864_2025_11747_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/e272f130ce54/12864_2025_11747_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/48c8ef57233a/12864_2025_11747_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/d0b8ccbe2dba/12864_2025_11747_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/963718e3d291/12864_2025_11747_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/b5f30d6108b3/12864_2025_11747_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/e272f130ce54/12864_2025_11747_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b65/12211745/48c8ef57233a/12864_2025_11747_Fig5_HTML.jpg

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