利用immunedeconv实现小鼠转录组学反卷积分析

Making mouse transcriptomics deconvolution accessible with immunedeconv.

作者信息

Merotto Lorenzo, Sturm Gregor, Dietrich Alexander, List Markus, Finotello Francesca

机构信息

Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck 6020, Austria.

Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck 6020, Austria.

出版信息

Bioinform Adv. 2024 Feb 28;4(1):vbae032. doi: 10.1093/bioadv/vbae032. eCollection 2024.

SUMMARY

Transcriptome deconvolution has emerged as a reliable technique to estimate cell-type abundances from bulk RNA sequencing data. Unlike their human equivalents, methods to quantify the cellular composition of complex tissues from murine transcriptomics are sparse and sometimes not easy to use. We extended the immunedeconv R package to facilitate the deconvolution of mouse transcriptomics, enabling the quantification of murine immune-cell types using 13 different methods. Through immunedeconv, we further offer the possibility of tweaking cell signatures used by deconvolution methods, providing custom annotations tailored for specific cell types and tissues. These developments strongly facilitate the study of the immune-cell composition of mouse models and further open new avenues in the investigation of the cellular composition of other tissues and organisms.

AVAILABILITY AND IMPLEMENTATION

The R package and the documentation are available at https://github.com/omnideconv/immunedeconv.

摘要

转录组反卷积已成为一种从大量RNA测序数据中估计细胞类型丰度的可靠技术。与人类的同类方法不同，从小鼠转录组学数据中量化复杂组织细胞组成的方法很少，而且有时使用起来并不容易。我们扩展了immunedeconv R包，以促进小鼠转录组学的反卷积，从而能够使用13种不同方法对小鼠免疫细胞类型进行量化。通过immunedeconv，我们还提供了调整反卷积方法所使用细胞特征的可能性，为特定细胞类型和组织提供定制注释。这些进展极大地促进了对小鼠模型免疫细胞组成的研究，并进一步为研究其他组织和生物体的细胞组成开辟了新途径。