def run_tsne(self, X=None, metric='<STR_LIT>', **kwargs):
if(X is not None):<EOL><INDENT>dt = man.TSNE(metric=metric, **kwargs).fit_transform(X)<EOL>return dt<EOL><DEDENT>else:<EOL><INDENT>dt = man.TSNE(metric=self.distance,<EOL>**kwargs).fit_transform(self.adata.obsm['<STR_LIT>'])<EOL>tsne2d = dt<EOL>self.adata.obsm['<STR_LIT>'] = tsne2d<EOL><DEDENT>
Wrapper for sklearn's t-SNE implementation.

        See sklearn for the t-SNE documentation. All arguments are the same
        with the exception that 'metric' is set to 'precomputed' by default,
        implying that this function expects a distance matrix by default.