SETA: Ecological Compositional Analysis of scRNA-seq Data

SETA aims to make compositional analysis user friendly and easy to understand by breaking down a full analysis into educational pieces. SETA includes vignettes for visualization of compositional analysis of single-cell RNA-seq data, aiming to make it easier to perform sample-level unsupervised analysis of single-cell data via sample embeddings and distances.

Project Status

• Based on a non-Bioconductor-compliant package, SETA

Planned Features

• proportionality networks
• sample-level trajectories
• vegan ecological latent space methods and metrics (like unifrac)

To Do List

• Compositional Space Calculation
  • Latent space methods (RDA, PLS-DA, tensors!) - vegan and otherwise
  • Add trajectory capabilities
• Compositional Transforms
  • ILR with balances
  • ideas welcome
• Methods for Cell Type Trees
  • Addition of metadata to tree objects
• Analysis Methods
  • Build proportionality or Pearson correlation networks of cell type compositions
  • Tensors and complex modeling
• Vignettes
  • Proportionality Networks
  • Multi-view tensor sample-level analysis

Installation

From Bioconductor (recommended)

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("SETA")

From GitHub (development version)

install.packages("remotes")
remotes::install_github("CellDiscoveryNetwork/SETA")

Contributions are welcome. Please open an issue or pull request with any suggestions or enhancements.

License

Released under an MIT open-source license