scanpy.pp.calculate_qc_metrics

scanpy.pp.calculate_qc_metrics(adata, *, expr_type='counts', var_type='genes', qc_vars=(), percent_top=(50, 100, 200, 500), layer=None, use_raw=False, inplace=False, log1p=True, parallel=None)

Calculate quality control metrics.

Calculates a number of qc metrics for an AnnData object, see section Returns for specifics. Largely based on calculateQCMetrics from scater [McCarthy et al., 2017]. Currently is most efficient on a sparse CSR or dense matrix.

Note that this method can take a while to compile on the first call. That result is then cached to disk to be used later.

Parameters:

adata AnnData

Annotated data matrix.

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 Collection[str] | str (default: ())

Keys for boolean columns of .var which identify variables you could want to control for (e.g. “ERCC” or “mito”).

percent_top Collection[int] | None (default: (50, 100, 200, 500))

List of ranks (where genes are ranked by expression) at which the cumulative proportion of expression will be reported as a percentage. This can be used to assess library complexity. Ranks are considered 1-indexed, and if empty or None don’t calculate.

E.g. percent_top=[50] finds cumulative proportion to the 50th most expressed gene.

layer str | None (default: None)

If provided, use adata.layers[layer] for expression values instead of adata.X.

use_raw bool (default: False)

If True, use adata.raw.X for expression values instead of adata.X.

inplace bool (default: False)

Whether to place calculated metrics in adata’s .obs and .var.

log1p bool (default: True)

Set to False to skip computing log1p transformed annotations.

Return type:

tuple[DataFrame, DataFrame] | None

Returns:

Depending on inplace returns calculated metrics (as DataFrame) or updates adata’s obs and var.

Observation level metrics include:

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.

pct_{expr_type}_in_top_{n}_{var_type} – for n in percent_top

E.g. “pct_counts_in_top_50_genes”. Cumulative percentage of counts for 50 most expressed genes in a cell.

total_{expr_type}_{qc_var} – for qc_var in qc_vars

E.g. “total_counts_mito”. Total number of counts for variables in qc_vars.

pct_{expr_type}_{qc_var} – for qc_var in qc_vars

E.g. “pct_counts_mito”. Proportion of total counts for a cell which are mitochondrial.

Variable level metrics include:

total_{expr_type}

E.g. “total_counts”. Sum of counts for a gene.

n_genes_by_{expr_type}

E.g. “n_genes_by_counts”. The number of genes with at least 1 count in a cell. Calculated for all cells.

mean_{expr_type}

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

n_cells_by_{expr_type}

E.g. “n_cells_by_counts”. Number of cells this expression is measured in.

pct_dropout_by_{expr_type}

E.g. “pct_dropout_by_counts”. Percentage of cells this feature does not appear in.

Example

Calculate qc metrics for visualization.

import scanpy as sc
import seaborn as sns

pbmc = sc.datasets.pbmc3k()
pbmc.var["mito"] = pbmc.var_names.str.startswith("MT-")
sc.pp.calculate_qc_metrics(pbmc, qc_vars=["mito"], inplace=True)
sns.jointplot(
    data=pbmc.obs,
    x="log1p_total_counts",
    y="log1p_n_genes_by_counts",
    kind="hex",
)

sns.histplot(pbmc.obs["pct_counts_mito"])