Add adapt = False (prior_scale=1) option to lfc_shrink()

pydeseq2/ds.py

@@ -330,7 +330,7 @@ def run_wald_test(self) -> None:
             self.statistics.loc[self.dds.new_all_zeroes_genes] = 0.0
             self.p_values.loc[self.dds.new_all_zeroes_genes] = 1.0
 
-    def lfc_shrink(self, coeff: Optional[str] = None) -> None:
+    def lfc_shrink(self, coeff: Optional[str] = None, adapt: bool = True) -> None:
         """LFC shrinkage with an apeGLM prior :cite:p:`DeseqStats-zhu2019heavy`.
 
         Shrinks LFCs using a heavy-tailed Cauchy prior, leaving p-values unchanged.
@@ -343,6 +343,9 @@ def lfc_shrink(self, coeff: Optional[str] = None) -> None:
             If the desired coefficient is not available, it may be set from the
             :class:`pydeseq2.dds.DeseqDataSet` argument ``ref_level``.
             (default: ``None``).
+        adapt: bool
+            Whether to use the MLE estimates of LFC to adapt the prior. If False, the
+            prior scale is set to 1. (``default=True``)
         """
         if self.contrast[1] == self.contrast[2] == "":
             # The factor being tested is continuous
@@ -390,8 +393,10 @@ def lfc_shrink(self, coeff: Optional[str] = None) -> None:
 
         # Set priors
         prior_no_shrink_scale = 15
-        prior_var = self._fit_prior_var(coeff_idx=coeff_idx)
-        prior_scale = np.minimum(np.sqrt(prior_var), 1)
+        prior_scale = 1
+        if adapt:
+            prior_var = self._fit_prior_var(coeff_idx=coeff_idx)
+            prior_scale = np.minimum(np.sqrt(prior_var), 1)
 
         design_matrix = self.design_matrix.values
 

tests/data/single_factor/r_test_lfc_shrink_no_apeAdapt_res.csv

@@ -0,0 +1,11 @@
+"","baseMean","log2FoldChange","lfcSE","pvalue","padj"
+"gene1",8.54131729397935,0.591975666632662,0.283593478291824,0.028605778832833,0.06413253606192
+"gene2",21.2812387436367,0.528482010716568,0.149036804392133,0.000329116482202811,0.00164558241101406
+"gene3",5.01012348853472,-0.589690914974718,0.290216684048141,0.03206626803096,0.06413253606192
+"gene4",100.51796142035,-0.406910458525142,0.118064287344954,0.000512946548270916,0.00170982182756972
+"gene5",27.1424502740787,0.570819621477817,0.153693224107584,0.000168688601657692,0.00164558241101406
+"gene6",5.4130427476525,0.00154719287124319,0.297994906704438,0.996252928924723,0.996252928924723
+"gene7",28.2940230404605,0.13158763131402,0.148675587725712,0.370439870361461,0.411599855957179
+"gene8",40.3583444203556,-0.266280574143726,0.135444467327823,0.0472273333199321,0.0787122221998868
+"gene9",37.1661826339853,-0.20919599744192,0.132384213293892,0.110392490019143,0.143143368673419
+"gene10",11.5893249023836,0.366288276213398,0.239712866785749,0.114514694938736,0.143143368673419

tests/test_pydeseq2.py

@@ -242,6 +242,51 @@ def test_lfc_shrinkage(counts_df, metadata, tol=0.02):
     ).max() < tol
 
 
+def test_lfc_shrinkage_no_apeAdapt(counts_df, metadata, tol=0.02):
+    """Test that the outputs of the lfc_shrink function match those of the original
+    R package (starting from the same inputs), up to a tolerance in relative error.
+    """
+
+    test_path = str(Path(os.path.realpath(tests.__file__)).parent.resolve())
+    r_res = pd.read_csv(
+        os.path.join(test_path, "data/single_factor/r_test_res.csv"), index_col=0
+    )
+    r_shrunk_res = pd.read_csv(
+        os.path.join(
+            test_path, "data/single_factor/r_test_lfc_shrink_no_apeAdapt_res.csv"
+        ),
+        index_col=0,
+    )
+
+    r_size_factors = pd.read_csv(
+        os.path.join(test_path, "data/single_factor/r_test_size_factors.csv"),
+        index_col=0,
+    ).squeeze()
+
+    r_dispersions = pd.read_csv(
+        os.path.join(test_path, "data/single_factor/r_test_dispersions.csv"),
+        index_col=0,
+    ).squeeze()
+
+    dds = DeseqDataSet(counts=counts_df, metadata=metadata, design_factors="condition")
+    dds.deseq2()
+    dds.obsm["size_factors"] = r_size_factors.values
+    dds.varm["dispersions"] = r_dispersions.values
+    dds.varm["LFC"].iloc[:, 1] = r_res.log2FoldChange.values * np.log(2)
+
+    res = DeseqStats(dds)
+    res.summary()
+    res.SE = r_res.lfcSE * np.log(2)
+    res.lfc_shrink(coeff="condition_B_vs_A", adapt=False)
+    shrunk_res = res.results_df
+
+    # Check that the same LFC are found (up to tol)
+    assert (
+        abs(r_shrunk_res.log2FoldChange - shrunk_res.log2FoldChange)
+        / abs(r_shrunk_res.log2FoldChange)
+    ).max() < tol
+
+
 def test_iterative_size_factors(counts_df, metadata, tol=0.02):
     """Test that the outputs of the iterative size factor method match those of the
     original R package (starting from the same inputs), up to a tolerance in relative