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Mouse Wound Models and Preparation of Single-Cell Suspensions
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Making mouse transcriptomics deconvolution accessible with immunedeconv.

Lorenzo Merotto1, Gregor Sturm2,3, Alexander Dietrich4

  • 1Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck 6020, Austria.

Bioinformatics Advances
|March 11, 2024
PubMed
Summary
This summary is machine-generated.

This study enhances transcriptome deconvolution for mouse RNA sequencing data, enabling accurate immune cell quantification. The updated immunedeconv R package offers customizability for diverse research needs.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcriptome deconvolution estimates cell-type abundances from bulk RNA sequencing data.
  • Existing methods for murine transcriptomics are limited and difficult to use.
  • Accurate quantification of cellular composition in mouse models is crucial for research.

Purpose of the Study:

  • To extend the immunedeconv R package for mouse transcriptomics deconvolution.
  • To enable quantification of murine immune-cell types using multiple methods.
  • To provide customizability of cell signatures for specific tissues and cell types.

Main Methods:

  • Extended the immunedeconv R package to support mouse transcriptomics.
  • Implemented 13 different deconvolution methods for murine data.
  • Enabled user-defined cell signature customization within the package.

Main Results:

  • Facilitated the deconvolution of mouse transcriptomics data.
  • Enabled quantification of murine immune-cell types.
  • Provided a flexible platform for custom cell signature annotation.

Conclusions:

  • The enhanced immunedeconv package simplifies the study of immune cell composition in mouse models.
  • This tool opens new avenues for investigating cellular composition in various tissues and organisms.
  • The R package offers a user-friendly and adaptable solution for transcriptome deconvolution.