This repository contains R scripts and resources for deconvolving bulk RNA-seq gene expression data in zebrafish, using MuSiC (Multi-subject Single-cell deconvolution). The goal is to investigate how environmental exposures influence gene expression at the cell-type level, using single-cell RNA-seq references from both pancreas-specific and whole-zebrafish sources.
To estimate cell type proportions in bulk RNA-seq samples of zebrafish and assess how they shift in response to environmental exposures. This is achieved through multimodal analysis by integrating:
- Bulk RNA-seq gene counts (exposed vs control)
- Single-cell RNA-seq reference data (annotated by cell type)
- Uses pancreas-specific cell types (e.g., alpha, beta_1, duct)
- Converts Seurat object (
Control_annotated.rds) into aSingleCellExperiment - Performs MuSiC deconvolution and outputs:
- Jitter plot comparing MuSiC vs NNLS estimates
- Heatmap of estimated proportions
- Stacked bar plot of cell type distributions
- Reference Dataset:
Control_annotated.rdsdownloaded from the Broad Institute's Single Cell Portal (SCP1549)Citation: Singh, S. P., Janjuha, S., Hartmann, T., Kayisoglu, O., Konantz, J., Birke, S., ... & Lickert, H. (2022). A single-cell atlas of de novo β-cell regeneration reveals the contribution of hybrid β/δ cells to diabetes recovery in zebrafish. Development, 149(2), dev199853. https://doi.org/10.1242/dev.199853
- Uses DanioCell 2023 dataset as the scRNA-seq reference (
Daniocell2023_SeuratV4.rds) - Converts both bulk and single-cell data into
ExpressionSet - Performs MuSiC deconvolution using the entire cellular atlas
- Outputs the same set of visualizations and CSV matrices
- Reference Dataset:
Daniocell2023_SeuratV4.rdsdownloaded from the DanioCell PortalCitation: White, D. T., Freudenberg, J., Riva, A., Snyder, D. T., Lai, S. L., Ang, K. C., ... & Burgess, S. M. (2023). A zebrafish single-cell transcriptome atlas reveals shared and distinct cellular responses to injury. bioRxiv. https://doi.org/10.1101/2023.03.29.534734
- R (version ≥ 4.1)
- CRAN & Bioconductor packages:
install.packages(c("ggplot2", "dplyr", "reshape2", "pheatmap"))
BiocManager::install(c("MuSiC", "Seurat", "SingleCellExperiment", "Matrix", "Biobase", "scuttle"))This repository is shared under an academic research license. For permission to reuse or cite the work, please contact the author(s).