def plot_correlated_genes(<EOL>self,<EOL>name,<EOL>n_genes=<NUM_LIT:5>,<EOL>number_of_features=<NUM_LIT:1000>,<EOL>**kwargs):
all_gene_names = np.array(list(self.adata.var_names))<EOL>if((all_gene_names == name).sum() == <NUM_LIT:0>):<EOL><INDENT>print(<EOL>"<STR_LIT>"<EOL>"<STR_LIT>")<EOL>return<EOL><DEDENT>sds = self.corr_bin_genes(<EOL>input_gene=name,<EOL>number_of_features=number_of_features)<EOL>if (n_genes + <NUM_LIT:1> > sds.size):<EOL><INDENT>x = sds.size<EOL><DEDENT>else:<EOL><INDENT>x = n_genes + <NUM_LIT:1><EOL><DEDENT>for i in range(<NUM_LIT:1>, x):<EOL><INDENT>self.show_gene_expression(sds[i], **kwargs)<EOL><DEDENT>return sds[<NUM_LIT:1>:]<EOL>
Plots gene expression patterns correlated with the input gene.

        Parameters
        ----------
        name - string
            The name of the gene with respect to which correlated gene
            expression patterns will be displayed.

        n_genes - int, optional, default 5
            The number of top ranked correlated genes to display.

        **kwargs -
            All keyword arguments in 'show_gene_expression' and 'scatter'
            are eligible.