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Speed up iterative size factor fitting by not slicing AnnData in the objective #479

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Speed up iterative size factor fitting by not slicing AnnData in the objective #479

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22 changes: 13 additions & 9 deletions src/pydeseq2/dds.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1438,15 +1438,9 @@ def _fit_iterate_size_factors(self, niter: int = 10, quant: float = 0.95) -> Non
)

# Fit size factors using MLE
def objective(p):
def objective(p, counts, mu, alpha):
sf = np.exp(p - np.mean(p))
nll = nb_nll(
counts=self[:, self.non_zero_genes].X,
mu=self[:, self.non_zero_genes].layers["_mu_hat"]
/ self.obs["size_factors"].values[:, None]
* sf[:, None],
alpha=self.var.loc[self.non_zero_genes, "dispersions"].values,
)
nll = nb_nll(counts=counts, mu=mu * sf[:, None], alpha=alpha)
# Take out the lowest likelihoods (highest neg) from the sum
return np.sum(nll[nll < np.quantile(nll, quant)])

Expand Down Expand Up @@ -1478,8 +1472,18 @@ def objective(p):
self.fit_MAP_dispersions()
old_sf = self.obs["size_factors"].copy()

# The optimizer evaluates the objective many times, so extract its inputs
# once instead of slicing the AnnData object in every evaluation.
counts = self.X[:, self.non_zero_idx]
mu = (
self.layers["_mu_hat"][:, self.non_zero_idx] / old_sf.to_numpy()[:, None]
)
alpha = self.var["dispersions"].to_numpy()[self.non_zero_idx]

# Fit size factors using MLE
res = minimize(objective, np.log(old_sf), method="Powell")
res = minimize(
objective, np.log(old_sf), args=(counts, mu, alpha), method="Powell"
)

self.obs["size_factors"] = np.exp(res.x - np.mean(res.x))

Expand Down
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