Preprocessing QC
Source: src/single_cell_python_tools/preprocessing/_qc.py
These helpers are exported through sctl.pp.
annotate_QC_genes
sctl.pp.annotate_QC_genes(adata, organism="human", **parameters)
Annotate technical QC gene groups in adata.var, including mitochondrial,
ribosomal, hemoglobin, MALAT1, and related marker sets.
calculate_qc_metrics
sctl.pp.calculate_qc_metrics(adata, **parameters)
Calculate Scanpy QC metrics after QC gene groups have been annotated.
plot_QC_metrics_scatter
sctl.pp.plot_QC_metrics_scatter(adata)
Plot scatter summaries for QC metrics.
plot_QC_metrics_violin
sctl.pp.plot_QC_metrics_violin(adata)
Plot violin summaries for QC metrics.
plot_qc_metrics
sctl.pp.plot_qc_metrics(adata, **parameters)
Plot annotated technical gene groups and top highly expressed genes.
annotate_n_view_adata_raw_counts
sctl.pp.annotate_n_view_adata_raw_counts(adata, **parameters)
Run QC gene annotation, calculate QC metrics, and produce raw-count QC views.
basic_filitering
sctl.pp.basic_filitering(
adata,
filter_cells_min_counts=0,
filter_cells_min_genes=200,
filter_genes_min_cells=3,
filter_genes_min_counts=0,
**parameters,
)
Apply basic cell and gene count filters. The function name keeps the current
source spelling, basic_filitering.
filter_cells_by_anotated_QC_gene
sctl.pp.filter_cells_by_anotated_QC_gene(
adata,
filter_ncount=True,
n_genes_bycounts=2500,
filter_pct_mt=True,
percent_mt=5,
over_percent_mt=0,
filter_pct_ribo=False,
percent_ribo=100,
over_percent_ribo=0,
filter_pct_hb=False,
percent_hb=100,
over_percent_hb=0,
filter_pct_malat1=False,
percent_malat1=100,
over_percent_malat1=0,
**parameters,
)
Filter cells by annotated QC metrics. The function name keeps the current source
spelling, filter_cells_by_anotated_QC_gene.
remove_genes
sctl.pp.remove_genes(
adata,
remove_MALAT1=False,
remove_MT=False,
remove_HB=False,
remove_RP_SL=False,
remove_MRP_SL=False,
**parameters,
)
Remove configured technical gene groups from the active AnnData object.