This repository contains the Starlng R package, which identifies stable clusters of coexpressed genes and describes their position along the pseudotime trajectory. The package builds on top of the Monocle3 [1] and ClustAssess [2] frameworks.
A live example of the Starlng Shiny app can be found here.
Installation
Starlng can be installed from Github using the remotes package:
remotes::install_github("Core-Bioinformatics/Starlng").
The following packages are required for Starlng:
- circlize,
- ClustAssess,
- ComplexHeatmap,
- dplyr,
- DT,
- foreach,
- ggplot2,
- Gmedian,
- gprofiler2,
- HDF5Array,
- igraph,
- leidenbase,
- Matrix (>= 1.5.0),
- methods,
- monocle3,
- patchwork,
- qs2,
- qualpalr,
- RANN,
- rclipboard,
- reshape2,
- RhpcBLASctl,
- rhdf5,
- shiny,
- shinyjs,
- shinyWidgets,
- spsComps,
- stringr,
- tidyr,
- uwot,
- viridis
We suggest installing the following packages for optimal performance:
- doFuture,
- doParallel,
- ggraph,
- ggrepel,
- irlba,
- mgcv,
- testthat (>= 3.0.0),
- parallel,
- plotly,
- SeuratData,
- SharedObject
References
[1] J. Cao, M. Spielmann, X. Qiu, X. Huang, D. M. Ibrahim, A. J. Hill, F. Zhang, S. Mundlos, L. Christiansen, F. J. Steemers, C. Trapnell, and J. Shendure, “The single-cell transcriptional landscape of mammalian organogenesis,” Nature, vol. 566, p. 496–502, Feb. 2019.
[2] A. Shahsavari, A. Munteanu, and I. Mohorianu, “Clustassess: tools for assessing the robustness of single-cell clustering,” bioRxiv, 2022.