Clustering (differential features)

Use a heatmap to visualize gene expression data across different samples

The clustering analysis type is useful for identifying gene expression patterns between different samples or groups in your experiment. Within your heat map, each row corresponds to a gene and each column corresponds to a sample with the color at each intersection representing the expression level of that gene.

To run a clustering analysis in Pluto, click and scroll down to on the sidebar until you reach "Clustering (differential features)"

Then choose what comparison you'd like to make between the groups within your experiment. You can also choose whether you'd like to analyze all your samples or specific groups of samples and make a selection.




Then press run analysis to generate your plot. After your plot appears, you can go to the plot tab in the same sidebar and make adjustments to your plot titles and the gradient color scheme used for the heatmap.

Screenshot 2023-08-31 at 11.25.38