scanpy.tl.marker_gene_overlap#
- scanpy.tl.marker_gene_overlap(adata, reference_markers, *, key='rank_genes_groups', method='overlap_count', normalize=None, top_n_markers=None, adj_pval_threshold=None, key_added='marker_gene_overlap', inplace=False)[source]#
Calculate an overlap score between data-deriven marker genes and provided markers
Marker gene overlap scores can be quoted as overlap counts, overlap coefficients, or jaccard indices. The method returns a pandas dataframe which can be used to annotate clusters based on marker gene overlaps.
This function was written by Malte Luecken.
- Parameters:
- adata
AnnData
The annotated data matrix.
- reference_markers
dict
[str
,set
] |dict
[str
,list
] A marker gene dictionary object. Keys should be strings with the cell identity name and values are sets or lists of strings which match format of
adata.var_name
.- key
str
(default:'rank_genes_groups'
) The key in
adata.uns
where the rank_genes_groups output is stored. By default this is'rank_genes_groups'
.- method
Literal
['overlap_count'
,'overlap_coef'
,'jaccard'
] (default:'overlap_count'
) (default:
overlap_count
) Method to calculate marker gene overlap.'overlap_count'
uses the intersection of the gene set,'overlap_coef'
uses the overlap coefficient, and'jaccard'
uses the Jaccard index.- normalize
Optional
[Literal
['reference'
,'data'
]] (default:None
) Normalization option for the marker gene overlap output. This parameter can only be set when
method
is set to'overlap_count'
.'reference'
normalizes the data by the total number of marker genes given in the reference annotation per group.'data'
normalizes the data by the total number of marker genes used for each cluster.- top_n_markers
int
|None
(default:None
) The number of top data-derived marker genes to use. By default the top 100 marker genes are used. If
adj_pval_threshold
is set along withtop_n_markers
, thenadj_pval_threshold
is ignored.- adj_pval_threshold
float
|None
(default:None
) A significance threshold on the adjusted p-values to select marker genes. This can only be used when adjusted p-values are calculated by
sc.tl.rank_genes_groups()
. Ifadj_pval_threshold
is set along withtop_n_markers
, thenadj_pval_threshold
is ignored.- key_added
str
(default:'marker_gene_overlap'
) Name of the
.uns
field that will contain the marker overlap scores.- inplace
bool
(default:False
) Return a marker gene dataframe or store it inplace in
adata.uns
.
- adata
- Returns:
Returns
pandas.DataFrame
ifinplace=True
, else returns anAnnData
object where it sets the following field:adata.uns[key_added]
pandas.DataFrame
(dtypefloat
)Marker gene overlap scores. Default for
key_added
is'marker_gene_overlap'
.
Examples
>>> import scanpy as sc >>> adata = sc.datasets.pbmc68k_reduced() >>> sc.pp.pca(adata, svd_solver='arpack') >>> sc.pp.neighbors(adata) >>> sc.tl.leiden(adata) >>> sc.tl.rank_genes_groups(adata, groupby='leiden') >>> marker_genes = { ... 'CD4 T cells': {'IL7R'}, ... 'CD14+ Monocytes': {'CD14', 'LYZ'}, ... 'B cells': {'MS4A1'}, ... 'CD8 T cells': {'CD8A'}, ... 'NK cells': {'GNLY', 'NKG7'}, ... 'FCGR3A+ Monocytes': {'FCGR3A', 'MS4A7'}, ... 'Dendritic Cells': {'FCER1A', 'CST3'}, ... 'Megakaryocytes': {'PPBP'} ... } >>> marker_matches = sc.tl.marker_gene_overlap(adata, marker_genes)