Introduction | What is EMTscore? | Key objectives. | Related Bioconductor packages. | Input files. | Installation | Example walkthrough (bulk RNA-seq) | Load cell/sample annotation | Load gene expression data | Load gene signature data | Prepare gene sets and save as GMT | Compute scores using multiple methods | Prepare Data for Plotting | E score vs M score Plot | dimension 1 M score vs dimension 2 M score Plot | Combined Plot | E/M Score Distributions | Heatmap | Example Walkthrough: Single-Cell RNA-seq | Loading the single-cell datasets | Data Importing and Basic Calculations | Building Gaussian Mixture Models (GMM) in E-M space | Identifying Pathways Most Associated with the EMT Process | Visualizing the Top 10 Positively Correlated Pathways | Visualizing the Top 10 Negatively Correlated Pathways | Computing Additional Scores (e.g., Stemness and Senescence Scores) | Stemness score | Senescence score | Exploring the Relationship Between Stemness, Senescence, and EMT Scores in Single-Cell Data | Compute stemness and senescence scores for each cell using predefined gene signatures | Compute EMT Scores | E vs M scatter plot | Mscore PC1 vs Mscore PC2 scatter plot | Examine Correlation Between Stemness and Senescence | Integration and Visualization of Stemness, Senescence, and EMT Scores | Session information | References

1. Introduction of EMTscoreData | 2. Installation | Install the development version from GitHub | Install from Bioconductor (recommended once available): | 3. Download data from the EMTscoreData R package | 3.1 Browse all EMTscoreData records | 3.2 Download data using the unique identifier: | 3.3 What is inside a SingleCellExperiment? | 4. Data analysis and visualization | 4.1 Load an EMT gene set (GMT) from Zenodo | 4.2 Calculating EMT Scores for Datasets Stored in EMTscoreData