Merge pull request #596 from Sichao25/debug

Remove duplicate size factors calculation

dynamo/preprocessing/Preprocessor.py

@@ -452,6 +452,7 @@ def preprocess_adata_seurat_wo_pca(
         self.standardize_adata(adata, tkey, experiment_type)
         self._filter_cells_by_outliers(adata)
         self._filter_genes_by_outliers(adata)
+        self._calc_size_factor(adata)
         self._normalize_by_cells(adata)
         self._select_genes(adata)
         self._norm_method(adata)
@@ -787,6 +788,7 @@ def preprocess_adata_monocle_pearson_residuals(
         self._force_gene_list(adata)
 
         X_copy = adata.X.copy()
+        self._calc_size_factor(adata)
         self._normalize_by_cells(adata)
         adata.X = X_copy
         self._normalize_selected_genes(adata)

dynamo/preprocessing/normalization.py

@@ -168,26 +168,29 @@ def get_sz_exprs(
         corresponding to the size factor.
     """
 
-    if layer == "raw":
-        CM = adata.raw.X
-        szfactors = adata.obs[layer + "Size_Factor"].values[:, None]
-    elif layer == "X":
-        CM = adata.X
-        szfactors = adata.obs["Size_Factor"].values[:, None]
-    elif layer == "protein":
-        if "protein" in adata.obsm_keys():
-            CM = adata.obsm[layer]
-            szfactors = adata.obs["protein_Size_Factor"].values[:, None]
+    try:
+        if layer == "raw":
+            CM = adata.raw.X
+            szfactors = adata.obs[layer + "Size_Factor"].values[:, None]
+        elif layer == "X":
+            CM = adata.X
+            szfactors = adata.obs["Size_Factor"].values[:, None]
+        elif layer == "protein":
+            if "protein" in adata.obsm_keys():
+                CM = adata.obsm[layer]
+                szfactors = adata.obs["protein_Size_Factor"].values[:, None]
+            else:
+                CM, szfactors = None, None
         else:
-            CM, szfactors = None, None
-    else:
-        CM = adata.layers[layer]
-        szfactors = adata.obs[layer + "_Size_Factor"].values[:, None]
+            CM = adata.layers[layer]
+            szfactors = adata.obs[layer + "_Size_Factor"].values[:, None]
 
-    if total_szfactor is not None and total_szfactor in adata.obs.keys():
-        szfactors = adata.obs[total_szfactor][:, None]
-    elif total_szfactor is not None:
-        main_warning("`total_szfactor` is not `None` and it is not in adata object.")
+        if total_szfactor is not None and total_szfactor in adata.obs.keys():
+            szfactors = adata.obs[total_szfactor][:, None]
+        elif total_szfactor is not None:
+            main_warning("`total_szfactor` is not `None` and it is not in adata object.")
+    except KeyError:
+        raise KeyError(f"Size factor for layer {layer} is not in adata object. Please run `dynamo.tl.calc_sz_factor`.")
 
     return szfactors, CM
 
@@ -231,22 +234,23 @@ def normalize(
         An updated anndata object that are updated with normalized expression values for different layers.
     """
 
+    layers = DKM.get_available_layer_keys(adata, layers)
+
     if recalc_sz:
         if "use_for_pca" in adata.var.columns and keep_filtered is False:
             adata = adata[:, adata.var.loc[:, "use_for_pca"]]
 
         adata.obs = adata.obs.loc[:, ~adata.obs.columns.str.contains("Size_Factor")]
 
-    layers = DKM.get_available_layer_keys(adata, layers)
-
-    calc_sz_factor(
-        adata,
-        layers=layers,
-        locfunc=np.nanmean,
-        round_exprs=False,
-        method=sz_method,
-        scale_to=scale_to,
-    )
+    if np.count_nonzero(adata.obs.columns.str.contains("Size_Factor")) < len(layers):
+        calc_sz_factor(
+            adata,
+            layers=layers,
+            locfunc=np.nanmean,
+            round_exprs=False,
+            method=sz_method,
+            scale_to=scale_to,
+        )
 
     excluded_layers = DKM.get_excluded_layers(
         X_total_layers=X_total_layers,

dynamo/tools/clustering.py

@@ -12,7 +12,7 @@
 
 from ..configuration import DKM
 from ..dynamo_logger import main_info
-from ..preprocessing.normalization import normalize
+from ..preprocessing.normalization import calc_sz_factor, normalize
 from ..preprocessing.QC import filter_genes_by_outliers as filter_genes
 from ..preprocessing.pca import pca
 from ..preprocessing.transform import log1p
@@ -651,6 +651,7 @@ def scc(
 
     filter_genes(adata, min_cell_s=min_cells)
     adata.uns["pp"] = {}
+    calc_sz_factor(adata, layers="X")
     normalize(adata, layers="X")
     log1p(adata)
     pca(adata, n_pca_components=30, pca_key="X_pca")

tests/test_preprocess.py

@@ -13,7 +13,7 @@
 from dynamo.preprocessing import Preprocessor
 from dynamo.preprocessing.cell_cycle import get_cell_phase
 from dynamo.preprocessing.deprecated import _calc_mean_var_dispersion_sparse_legacy
-from dynamo.preprocessing.normalization import normalize
+from dynamo.preprocessing.normalization import calc_sz_factor, normalize
 from dynamo.preprocessing.transform import log1p, is_log1p_transformed_adata
 from dynamo.preprocessing.utils import (
     convert_layers2csr,
@@ -449,6 +449,7 @@ def test_normalize():
     adata.uns["pp"] = dict()
 
     # Call the function
+    calc_sz_factor(adata)
     normalized = normalize(
         adata=adata,
         # norm_method=np.log1p,