Learn how to customize all aspects of your heatmap and explore your insights on a cluster by cluster basis
- Go to the Workspace, Create a Project and create an Analysis within the Project
- Select "Visualization" followed by "Heatmap"
- Select "Visualize Data" next to the file you would like to work with and you will be taken to the data table. Select the Plot tab to visualize your data. Any additional customizations are completely optional. Use the plot customization panel on the right size of your plot to configure the heatmap.
- Sort Method- Variance or Sum . "Variance" take the top N variable genes and uses those to conduct heatmap clustering. "Sum" takes the top N genes (by expression) and uses those to conduct the heatmap clustering.
- Distance Metric used to drive heatmap clustering.
- Number of genes displayed in the heatmap.
- If your file contains ensembl IDs, these can be converted to Gene Symbols
- Gene Symbols and Sample Names can be removed from the heatmap.
- Search for a specific gene and zoom in to that area of the heatmap
Series- Update the information pertaining to the X an Y axis of your heatmap. This includes
- Append any metadata (information pertaining to the samples used to generate your file such as mutational status, disease, treatment) to your heatmap. This information is taken directly from the information you have added to your BioBox library. Select the plus next to metadata in the right hand plot customization panel to add available metadata. Clicking on the circle next to the metadata property will allow you to customize the colour associated with each property. You can return to your library at any point to add more information.
- Grid - Update the plot title and location
- Visual Customizations
- Customize the colours associated with your row z legend by using the drop down menu next to series at the top of the plot customization panel. Select the circle to open the colour customization window.
To begin your analysis, double click on a cluster to explore your insights The genes from the cluster you have selected will be sent to EnrichR, KEGG, Reactome, and Gene Ontology for a gene set enrichment. You will be able to explore several categories of insights and the observations fond across all of the datasets you have on the platform.
- Use the zoom slider located to the right of the bar graph to see all of the pathways identified in your dataset
- Use the cloud icon located in the top right hand corner of the plot to export an image of the pathways identified in your data. Adjust the plot height and width to ensure all bars and pathway names are visible on export.
- Click on " Export Gene List" to export a list of genes identified in your dataset and the pathway of interest.