INNER CODE UNIT · Python

disp_function

scverse/PyDESeq2 · src/pydeseq2/dds.py:796

    def disp_function(self, x):
        """Return the dispersion trend function at x."""
        if self.uns["disp_function_type"] == "parametric":
            return dispersion_trend(x, self.uns["trend_coeffs"])
        elif self.uns["disp_function_type"] == "mean":
            return np.full_like(x, self.uns["mean_disp"])

    def fit_dispersion_prior(self) -> None:
        """Fit dispersion variance priors and standard deviation of log-residuals.

        The computation is based on genes whose dispersions are above 100 * min_disp.

        Note: when the design matrix has fewer than 3 degrees of freedom, the estimate of log dispersions is likely to be imprecise.
        """
        # Check that the dispersion trend curve was fitted. If not, fit it.
        if "fitted_dispersions" not in self.var:
            self.fit_dispersion_trend()

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