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Zebrafish Gene Expression Deconvolution Using MuSiC

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.

Project Objective

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)

Script Details

deconvolution_bulk_zebrafish_to_zebrafish_pancreas.R

  • Uses pancreas-specific cell types (e.g., alpha, beta_1, duct)
  • Converts Seurat object (Control_annotated.rds) into a SingleCellExperiment
  • 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.rds downloaded 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

deconvolution_bulk_zebrafish_to_whole_zebrafish.R

  • 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.rds downloaded from the DanioCell Portal

    Citation: 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

Requirements

  • R (version ≥ 4.1)
  • CRAN & Bioconductor packages:
install.packages(c("ggplot2", "dplyr", "reshape2", "pheatmap"))
BiocManager::install(c("MuSiC", "Seurat", "SingleCellExperiment", "Matrix", "Biobase", "scuttle"))

License

This repository is shared under an academic research license. For permission to reuse or cite the work, please contact the author(s).