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Deprecate .A attributes of sparse matrix in Sijie july24 fix #691

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Jul 10, 2024
Merged

Deprecate .A attributes of sparse matrix in Sijie july24 fix #691

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1c9df72
deprecate sparse matrix .A attributes
Sichao25 Jul 9, 2024
d196c16
revert unnecessary changes on marix.A
Sichao25 Jul 9, 2024
87422f0
revert more unnecessary changes on matrix.A
Sichao25 Jul 9, 2024
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2 changes: 1 addition & 1 deletion dynamo/preprocessing/cell_cycle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ def group_corr(adata: anndata.AnnData, layer: Union[str, None], gene_list: list)
cor = (
einsum_correlation(
np.array(expression_matrix.toarray().T, dtype="float"),
np.array(avg_exp.toarray().ravel(), dtype="float"),
np.array(avg_exp.A1, dtype="float"),
)
if issparse(expression_matrix)
else einsum_correlation(
Expand Down
2 changes: 1 addition & 1 deletion dynamo/preprocessing/deprecated.py
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Expand Up @@ -209,7 +209,7 @@ def _disp_calc_helper_NB_legacy(
nzGenes = (rounded > lowerDetectedLimit).sum(axis=0)
nzGenes = nzGenes > min_cells_detected

nzGenes = nzGenes.toarray().ravel() if issparse(rounded) else nzGenes
nzGenes = nzGenes.A1 if issparse(rounded) else nzGenes
if layer.startswith("X_"):
x = rounded[:, nzGenes]
else:
Expand Down
10 changes: 5 additions & 5 deletions dynamo/preprocessing/external/sctransform.py
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Expand Up @@ -140,7 +140,7 @@ def gmean(
assert np.all(X.sum(0) > 0)
assert np.all(X.data > 0)
X.data[:] = np.log(X.data + eps)
res = np.exp(X.mean(axis).toarray().flatten()) - eps
res = np.exp(X.mean(axis).A.flatten()) - eps

assert np.all(res > 0)
return res
Expand Down Expand Up @@ -230,7 +230,7 @@ def sctransform_core(
X.eliminate_zeros()
gene_names = np.array(list(adata.var_names))
cell_names = np.array(list(adata.obs_names))
genes_cell_count = X.sum(0).toarray().flatten()
genes_cell_count = X.sum(0).A.flatten()
genes = np.where(genes_cell_count >= min_cells)[0]
genes_ix = genes.copy()

Expand All @@ -251,11 +251,11 @@ def sctransform_core(
genes_step1 = genes
genes_log_gmean_step1 = genes_log_gmean

umi = X.sum(1).toarray().flatten()
umi = X.sum(1).A.flatten()
log_umi = np.log10(umi)
X2 = X.copy()
X2.data[:] = 1
gene = X2.sum(1).toarray().flatten()
gene = X2.sum(1).A.flatten()
log_gene = np.log10(gene)
umi_per_gene = umi / gene
log_umi_per_gene = np.log10(umi_per_gene)
Expand Down Expand Up @@ -443,5 +443,5 @@ def sctransform(
X_squared = adata.X.copy()
X_squared.data **= 2
variance = X_squared.mean(0) - np.square(adata.X.mean(0))
adata.var["sct_score"] = variance.toarray().ravel() if sp.issparse(variance) else variance.ravel()
adata.var["sct_score"] = variance.A1
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can you please double check if this line would work properly if variance is a sparse matrix

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since you've validated variance is not sparse, i've no additional comments

adata.var["use_for_pca"] = get_gene_selection_filter(adata.var["sct_score"], n_top_genes=n_top_genes)