<img src="man/figures/SETAsmall.jpg?raw=true" align="right" width=250px>    ## 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](https://github.com/jo-m-lab/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) ```r if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("SETA") ``` ### From GitHub (development version) ```r 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