INNER CODE UNIT · Python

use_for_mean

scverse/PyDESeq2 · src/pydeseq2/dds.py:462

            use_for_mean = gene_dispersions > 10 * self.min_disp
            mean_disp = trim_mean(gene_dispersions[use_for_mean], proportiontocut=0.001)
            return (
                2 * np.arcsinh(np.sqrt(mean_disp * normed_counts))
                - np.log(mean_disp)
                - np.log(4)
            ) / np.log(2)
        else:
            raise NotImplementedError(
                f"Found fit_type '{self.vst_fit_type}'. Expected 'parametric' or 'mean'."
            )

    def deseq2(self, fit_type: Literal["parametric", "mean"] | None = None) -> None:
        """Perform dispersion and log fold-change (LFC) estimation.

        Wrapper for the first part of the PyDESeq2 pipeline.

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