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)[source]#
Calculate quality control metrics.
Calculates a number of qc metrics for an AnnData object, see section
Returns
for specifics. Largely based oncalculateQCMetrics
from scater [McCarthy17]. 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
] (default:()
) Keys for boolean columns of .var which identify variables you could want to control for (e.g. “ERCC” or “mito”).
- percent_top
Optional
[Collection
[int
]] (default:(50, 100, 200, 500)
) Which proportions of top genes to cover. If empty or None don’t calculate. Values are considered 1-indexed, percent_top=[50] finds cumulative proportion to the 50th most expressed gene.
- layer
Optional
[str
] (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.
- adata
- Return type:
- 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 variabes 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"])