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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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