Integrated Single-Cell RNA-Sequencing Data of Unwounded and Wounded Mouse Skin and Fibroblasts
November 2023
in “
Zenodo (CERN European Organization for Nuclear Research)
”
Studysummary In this study, Almet et al. (2023) compiled integrated single-cell RNA sequencing data from multiple mouse datasets to investigate how fibroblasts evolve during wound healing by examining changes in the extracellular matrix and signaling pathways.
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This document is a description of a dataset generated for a study by Almet et al. (2023), titled "Fibroblasts evolve in single-cell state to drive extracellular matrix and signaling changes across wound healing". The dataset contains integrated single-cell RNA-sequencing (scRNA-seq) data from both unwounded and wounded mouse skin and fibroblasts. The data was obtained using kallisto|bustools and velocyto, and includes raw counts, normalized counts, as well as unspliced and spliced count estimates. The data integrates information from several previous studies, including those on unwounded mice of different ages and mice with small and large wounds. The dataset can be loaded using the Python packages Scanpy or AnnData, or in R using zellkonverter.
