Decoupler : automated scRNA-seq clusters annotation
AftAfter data integration and clusters identification using Data integration task. We can perform automated cell type annotation from marker genes using decoupler tool
) and PanglaoDB database
Main functions of data annotation using Decoupler :
- Performs Over-Representation Analysis (ORA) on the input scRNA-seq data using the given marker gene information.
- Evaluates cell type enrichments in different clusters based on marker gene expression patterns.
- Calculates the Activity-by-Cluster (ACT) scores based on the ORA results.
- Scales and processes the ACT scores for visualization.
- Annotate clusters: Ranks and annotates clusters based on significant cell type enrichments using the ACT scores.
Steps to follow
- Ensure that the "gws_scomix" version 0.1.2 brick is loaded
- Then, create a new experiment.
- Import "Clusters annotation" task available in the "gws_scomix" brick.
- Add the integrated_data.h5ad file generated by "Data integration" task available in the "gws_scomix" brick as input ressource file
- Run your experiment.
Description of output files
This task will generate :
- clusters_annotation.h5ad: This file contains the processed and annotated scRNA-seq data after performing data integration and clustering analysis.
- clusters_annotation.png: This PNG image file displays visualizations of the clustered cells for both 'Control' and 'Patient' conditions. The UMAP plots are side-by-side, where each cell is color-coded based on its assigned cell type annotations. The plots offer insights into the cellular heterogeneity and distribution of cell types within different samples or conditions.