celda

Celda implements Bayesian hierarchical models for bi-clustering single-cell RNA-sequencing (scRNA-seq) data into gene modules and cell subpopulations.

Key Features:

• Bi-clustering: Simultaneously clusters genes into gene modules and cells into cell subpopulations.
• DecontX: Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information.
• Visualization: Includes a variety of scRNA-seq data visualization functions.

Scientific Applications:

• Single-cell RNA-sequencing analysis: Clustering scRNA-seq data to identify gene modules and cell subpopulations.
• Contamination removal: Estimating and removing RNA contamination from individual cells in scRNA-seq experiments.

Methodology:

Celda uses Bayesian hierarchical models and a novel Bayesian method (DecontX) for computational estimation of RNA contamination.