diff --git a/doubletdetection/doubletdetection.py b/doubletdetection/doubletdetection.py
index f0793f0..a254fe4 100644
--- a/doubletdetection/doubletdetection.py
+++ b/doubletdetection/doubletdetection.py
@@ -192,7 +192,7 @@ def fit(self, raw_counts):
         (self._num_cells, self._num_genes) = self._raw_counts.shape
         if self.normalizer is None:
             # Memoize these; default normalizer treats these invariant for all synths
-            self._lib_size = np.sum(raw_counts, axis=1).A1
+            self._lib_size = np.asarray(np.sum(raw_counts, axis=1)).ravel()
             self._normed_raw_counts = self._raw_counts.copy()
             inplace_csr_row_normalize_l1(self._normed_raw_counts)
 
@@ -298,14 +298,14 @@ def _one_fit(self):
             )
         else:
             # Follows doubletdetection.plot.normalize_counts, but uses memoized normed raw_counts
-            synth_lib_size = np.sum(self._raw_synthetics, axis=1).A1
+            synth_lib_size = np.asarray(np.sum(self._raw_synthetics, axis=1)).ravel()
             aug_lib_size = np.concatenate([self._lib_size, synth_lib_size])
             normed_synths = self._raw_synthetics.copy()
             inplace_csr_row_normalize_l1(normed_synths)
             aug_counts = sp_sparse.vstack((self._normed_raw_counts, normed_synths))
             scaled_aug_counts = aug_counts * np.median(aug_lib_size)
             if self.pseudocount != 1:
-                aug_counts = np.log(scaled_aug_counts.A + 0.1)
+                aug_counts = np.log(scaled_aug_counts.toarray() + 0.1)
             else:
                 aug_counts = np.log1p(scaled_aug_counts)
             del scaled_aug_counts