rapids_singlecell.pp.calculate_qc_metrics

rapids_singlecell.pp.calculate_qc_metrics(adata, *, expr_type='counts', var_type='genes', qc_vars=None, log1p=True, layer=None)

Calculates basic qc Parameters. Calculates number of genes per cell (n_genes) and number of counts per cell (n_counts). Loosely based on calculate_qc_metrics from scanpy [Wolf et al. 2018]. Updates obs and var with columns with qc data.

Parameters:

adata AnnData

AnnData object

expr_type str (default: 'counts')

Name of kind of values in X.

var_type str (default: 'genes')

The kind of thing the variables are.

qc_vars str | list (default: None)

Keys for boolean columns of var which identify variables you could want to control for (e.g. Mito). Run flag_gene_family first

log1p bool (default: True)

Set to False to skip computing log1p transformed annotations.

layer str (default: None)

If provided, use layers for expression values instead of X.

Return type:

None

Returns:

adds the following columns in obs :

total_{var_type}_by_{expr_type}

E.g. ‘total_genes_by_counts’. Number of genes with positive counts in a cell.

total_{expr_type}

E.g. ‘total_counts’. Total number of counts for a cell.

for qc_var in qc_vars

total_{expr_type}_{qc_var}

number of counts per qc_var (e.g total counts mitochondrial genes)

pct_{expr_type}_{qc_var}

Proportion of counts of qc_var (percent of counts mitochondrial genes)

adds the following columns in var :

total_{expr_type}

E.g. ‘total_counts’. Sum of counts for a gene.

n_genes_by_{expr_type}

E.g. ‘n_cells_by_counts’. Number of cells this expression is measured in.

mean_{expr_type}

E.g. “mean_counts”. Mean expression over all cells.

pct_dropout_by_{expr_type}

E.g. ‘pct_dropout_by_counts’. Percentage of cells this feature does not appear in.