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Update data.py #9

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Update data.py #9

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7 changes: 4 additions & 3 deletions scquint/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,7 @@ def group_introns(adata, by="three_prime", filter_unique_gene_per_group=True):
adata.var["intron_group"] = adata.var.apply(
lambda intron: intron.chromosome
+ "_"
# 正链5`端上游，3`端下游，所以根据3`端分组的时候，下游的碱基序列作为start
+ (str(intron.end) if intron.strand == "+" else str(intron.start))
+ "_"
+ intron.strand,
Expand Down Expand Up @@ -200,7 +201,7 @@ def relabel(labels):
new_labels = np.array([mapping[l] for l in labels])
return new_labels


# 以伪bulk的方式，对psi值进行平滑填充
def group_normalize(X, groups, smooth=False):
groups = relabel(groups)
intron_group_summation = make_intron_group_summation_cpu(groups)
Expand All @@ -215,15 +216,15 @@ def group_normalize(X, groups, smooth=False):
def calculate_PSI(adata, smooth=False):
return group_normalize(adata.X.toarray(), adata.var.intron_group.values, smooth=smooth)


# 过滤每个AS特征中的细胞数目
def filter_min_cells_per_feature(adata, min_cells_per_feature, idx_cells_to_count=slice(None)):
print("filter_min_cells_per_feature")
idx_features = np.where((adata.X[idx_cells_to_count] > 0).sum(axis=0).A1 >= min_cells_per_feature)[0]
adata = adata[:, idx_features]
adata = filter_singletons(adata)
return adata


# 过滤每个内含子组中的细胞数目
def filter_min_cells_per_intron_group(adata, min_cells_per_intron_group, idx_cells_to_count=slice(None)):
print("filter_min_cells_per_intron_group")
intron_groups = relabel(adata.var.intron_group.values)
Expand Down