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• Click a PCA data node
• Click the Batch removal section in the toolbox
• Click Harmony

You will be promoted prompted to pick some attribute(s) for analysis. The Harmony dialog is similar to the General linear model batch removal. To set up the model, you need to choose which attributes should be considered. For example, in the case of one dataset that has different cell types from multiple batches,  the batch may have divergent impacts on different cell types.  Here, batch is the attribute Sample name  and cell type is the attribute Cell type (Figure 2).  

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Diversity clustering penalty (theta): Default theta=2. Higher value of penalty will have stronger correction, which results in better mixing . Zero penalty encourages means no mixing.correction. The range of this value is from 0 to positive infinity. 

Number of clusters (nclust): Number of clusters in model. Set this to the distinct count of cell types. nclust=1 equivalent to simple linear regression. Use 0 to enable Seurat’s RunHarmony() default setting.

Width of soft kmeans clusters (sigma): The range of this value is from 0 to positive infinity. When set it to 0, an observation will be assigned to 1 cluster (hard clustering). When the value is greater than 0, the observation will be potentially belong to multiple clusters (soft clustering, or fuzzy clustering). Default sigma=0.1. Sigma scales the distance from a cell to cluster centroids. Larger values of sigma result in cells observations assigned to more clusters. Smaller values of sigma make soft kmeans cluster approach hard clustering.

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