Introduction

AftAfter data integration and clusters identification using Data integration task. We can perform automated cell type annotation from marker genes using decoupler tool (https://decoupler-py.readthedocs.io/en/latest/) and PanglaoDB database (https://panglaodb.se/).

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

1. Ensure that the "gws_scomix" version 0.1.2 brick is loaded
2. Then, create a new experiment.
3. Import "Clusters annotation" task available in the "gws_scomix" brick.
4. Add the integrated_data.h5ad file generated by "Data integration" task available in the "gws_scomix" brick as input ressource file
5. 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.