Making mouse transcriptomics deconvolution accessible with immunedeconv

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Article numbervbae032
JournalBioinformatics Advances
Volume4
Issue number1
DOIs
StatePublished - 2024

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