Spots clustering

A very useful feature is to classify spots based on their gene expression values. This can be used to measure the quality of the data, to select spots based on cell types or even to redefine anatomical regions.

You can do this by clicking on the "Spots classification" button in the main view. Basically, the spots (all the spots present in the dataset) are assigned a class/color/cluster (similar color represents similar expression patterns). The clusters are computed by first doing a dimensionality reduction (t-SNE or PCA) followed by clustering (KMeans) of the dimensionality reduced coordinates to finally assign one color to each cluster. The number of clusters needs to be provided in advance.

The Spot classification window allows to choose the dimensionality reduction algorithm and its settings, the normalization method, the clustering method and to define thresholds.

The computation may take few seconds but once it is completed you will see a scatter plot of the clustered spots and you will also see the colored spots on top of the tissue image in the main view (if the "Show spots" box if checked in the Visualization Settings).

A very useful feature it so select spots from the scatter plot using the right button of the mouse (lasso selection). This way you can select spots based on the gene expression similarities or simply outliers. You can also save all the clusters as selections and export the scatter plot as an image.