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Address a potential problem while creating a training dataset with dense array inputs #78

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Address a potential problem while creating a training dataset with dense array inputs #78

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b281c89
Update data_utils.py part 1
HelloWorldLTY Jul 10, 2024
c29f4f3
Update pertdata.py part 2
HelloWorldLTY Jul 10, 2024
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9 changes: 7 additions & 2 deletions gears/data_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,7 +81,12 @@ def get_dropout_non_zero_genes(adata):
for i, j in conditions2index.items():
condition2mean_expression[i] = np.mean(adata.X[j], axis = 0)
pert_list = np.array(list(condition2mean_expression.keys()))
mean_expression = np.array(list(condition2mean_expression.values())).reshape(len(adata.obs.condition.unique()), adata.X.toarray().shape[1])
# to handle the non-sparse data input
try :
adata.X = adata.X.toarray()
mean_expression = np.array(list(condition2mean_expression.values())).reshape(len(adata.obs.condition.unique()), adata.X.shape[1])
except:
mean_expression = np.array(list(condition2mean_expression.values())).reshape(len(adata.obs.condition.unique()), adata.X.shape[1])
ctrl = mean_expression[np.where(pert_list == 'ctrl')[0]]

## in silico modeling and upperbounding
Expand Down Expand Up @@ -408,4 +413,4 @@ def get_genes_from_perts(self, perts):
gene_list = [p.split('+') for p in np.unique(perts)]
gene_list = [item for sublist in gene_list for item in sublist]
gene_list = [g for g in gene_list if g != 'ctrl']
return np.unique(gene_list)
return np.unique(gene_list)
9 changes: 7 additions & 2 deletions gears/pertdata.py
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Original file line number Diff line number Diff line change
Expand Up @@ -594,8 +594,13 @@ def create_cell_graph_dataset(self, split_adata, pert_category,
# Create cell graphs
cell_graphs = []
for X, y in zip(Xs, ys):
cell_graphs.append(self.create_cell_graph(X.toarray(),
y.toarray(), de_idx, pert_category, pert_idx))
try:
cell_graphs.append(self.create_cell_graph(X.toarray(),
y, de_idx, pert_category, pert_idx))
except:
y = y.toarray()
cell_graphs.append(self.create_cell_graph(X.toarray(),
y, de_idx, pert_category, pert_idx))

return cell_graphs

Expand Down