STARLNG: STability Analysis of coReguLated Nests of Genes

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

or 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
• qs
• qs2
• qualpalr
• RANN
• rclipboard
• reshape2
• RhpcBLASctl
• rhdf5
• shiny
• shinyjs
• shinyWidgets
• spsComps
• tidyr
• stringr
• viridis

We suggest installing the following packages for optimal performance:

• doFuture
• doParallel
• irlba
• testthat (>= 3.0.0)
• parallel
• plotly
• SharedObject

Citing Starlng

To be added.

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.