Users can enhance the datasets by adding custom annotations, such as biological or anatomical classifications of cells. Additionally, it is possible to upload gene sets and save them along the custom annotations under a username for easy access and sharing with colleagues.

For data analysis using Python and R, no coding is required. The interface provides graphical features that yield insights into cellular compositions and gene expression. Users can generate and export heatmaps and various plots (dot, track, density, violin), identify marker genes, conduct differential gene expression analysis, perform gene set enrichment analysis, and access numerous other advanced analytical functionalities. Multiple dimensionality reduction techniques, including UMAP, t-SNE, and PCA, can seamlessly be embedded in the data, along with different developmental stages.