TY - JOUR
T1 - Making mouse transcriptomics deconvolution accessible with immunedeconv
AU - Merotto, Lorenzo
AU - Sturm, Gregor
AU - Dietrich, Alexander
AU - List, Markus
AU - Finotello, Francesca
N1 - Publisher Copyright:
© The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85187532918&partnerID=8YFLogxK
U2 - 10.1093/bioadv/vbae032
DO - 10.1093/bioadv/vbae032
M3 - Article
AN - SCOPUS:85187532918
SN - 2635-0041
VL - 4
JO - Bioinformatics Advances
JF - Bioinformatics Advances
IS - 1
M1 - vbae032
ER -