API

Import Scanpy as:

import scanpy as sc

Note

Wrappers to external functionality are found in scanpy.external. Previously, both core and external functionality were available through scanpy.api (deprecated since 1.3.7).

Preprocessing: PP

Filtering of highly-variable genes, batch-effect correction, per-cell normalization, preprocessing recipes.

Any transformation of the data matrix that is not a tool. Other than tools, preprocessing steps usually don’t return an easily interpretable annotation, but perform a basic transformation on the data matrix.

Basic Preprocessing

For visual quality control, see highest_expr_gens() and filter_genes_dispersion() in scanpy.plotting.

pp.calculate_qc_metrics(adata[, expr_type, …])	Calculate quality control metrics.
pp.filter_cells(data[, min_counts, …])	Filter cell outliers based on counts and numbers of genes expressed.
pp.filter_genes(data[, min_counts, …])	Filter genes based on number of cells or counts.
pp.highly_variable_genes(adata[, min_disp, …])	Annotate highly variable genes [Satija15] [Zheng17].
pp.log1p(data[, copy, chunked, chunk_size])	Logarithmize the data matrix.
pp.pca(data[, n_comps, zero_center, …])	Principal component analysis [Pedregosa11].
pp.normalize_total(adata[, target_sum, …])	Normalize counts per cell.
pp.regress_out(adata, keys[, n_jobs, copy])	Regress out unwanted sources of variation.
pp.scale(data[, zero_center, max_value, copy])	Scale data to unit variance and zero mean.
pp.subsample(data[, fraction, n_obs, …])	Subsample to a fraction of the number of observations.
pp.downsample_counts(adata[, …])	Downsample counts from count matrix.

Recipes

pp.recipe_zheng17(adata[, n_top_genes, log, …])	Normalization and filtering as of [Zheng17].
pp.recipe_weinreb17(adata[, log, …])	Normalization and filtering as of [Weinreb17].
pp.recipe_seurat(adata[, log, plot, copy])	Normalization and filtering as of Seurat [Satija15].

Batch effect correction

Note that a simple batch correction method is available via pp.regress_out(). Checkout scanpy.external for more.

pp.combat(adata[, key, inplace])

ComBat function for batch effect correction [Johnson07] [Leek12] [Pedersen12].

Neighbors

pp.neighbors(adata[, n_neighbors, n_pcs, …])

Compute a neighborhood graph of observations [McInnes18].

Tools: TL

Any transformation of the data matrix that is not preprocessing. In contrast to a preprocessing function, a tool usually adds an easily interpretable annotation to the data matrix, which can then be visualized with a corresponding plotting function.

Embeddings

tl.pca(data[, n_comps, zero_center, …])	Principal component analysis [Pedregosa11].
tl.tsne(adata[, n_pcs, use_rep, perplexity, …])	t-SNE [Maaten08] [Amir13] [Pedregosa11].
tl.umap(adata[, min_dist, spread, …])	Embed the neighborhood graph using UMAP [McInnes18].
tl.draw_graph(adata[, layout, init_pos, …])	Force-directed graph drawing [Islam11] [Jacomy14] [Chippada18].
tl.diffmap(adata[, n_comps, copy])	Diffusion Maps [Coifman05] [Haghverdi15] [Wolf18].

Clustering and trajectory inference

tl.leiden(adata[, resolution, restrict_to, …])	Cluster cells into subgroups [Traag18].
tl.louvain(adata[, resolution, …])	Cluster cells into subgroups [Blondel08] [Levine15] [Traag17].
tl.dendrogram(adata, groupby[, n_pcs, …])	Computes a hierarchical clustering for the given groupby categories.
tl.dpt(adata[, n_dcs, n_branchings, …])	Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf19].
tl.paga(adata[, groups, use_rna_velocity, …])	Mapping out the coarse-grained connectivity structures of complex manifolds [Wolf19].

Marker genes

tl.rank_genes_groups(adata, groupby[, …])	Rank genes for characterizing groups.
tl.filter_rank_genes_groups(adata[, key, …])	Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories.
tl.marker_gene_overlap(adata, …[, key, …])	Calculate an overlap score between data-deriven marker genes and provided markers

Gene scores, Cell cycle

tl.score_genes(adata, gene_list[, …])	Score a set of genes [Satija15].
tl.score_genes_cell_cycle(adata, s_genes, …)	Score cell cycle genes [Satija15].

Simulations

tl.sim(model[, params_file, tmax, …])

Simulate dynamic gene expression data [Wittmann09] [Wolf18].

Plotting: PL

The plotting module scanpy.plotting largely parallels the tl.* and a few of the pp.* functions. For most tools and for some preprocessing functions, you’ll find a plotting function with the same name.

Reading

Note

For reading annotation use pandas.read_… and add it to your anndata.AnnData object. The following read functions are intended for the numeric data in the data matrix X.

Read common file formats using

read(filename[, backed, sheet, ext, …])

Read file and return AnnData object.

Read 10x formatted hdf5 files and directories containing .mtx files using

read_10x_h5(filename[, genome, gex_only])	Read 10x-Genomics-formatted hdf5 file.
read_10x_mtx(path[, var_names, make_unique, …])	Read 10x-Genomics-formatted mtx directory.

Read other formats using functions borrowed from anndata

read_h5ad(filename[, backed, chunk_size])	Read .h5ad-formatted hdf5 file.
read_csv(filename[, delimiter, …])	Read .csv file.
read_excel(filename, sheet[, dtype])	Read .xlsx (Excel) file.
read_hdf(filename, key)	Read .h5 (hdf5) file.
read_loom(filename[, sparse, cleanup, …])	Read .loom-formatted hdf5 file.
read_mtx(filename[, dtype])	Read .mtx file.
read_text(filename[, delimiter, …])	Read .txt, .tab, .data (text) file.
read_umi_tools(filename[, dtype])	Read a gzipped condensed count matrix from umi_tools.

Queries

queries.mitochondrial_genes(host, org)

Mitochondrial gene symbols for specific organism through BioMart.

Classes

AnnData is reexported from anndata.

Represent data as a neighborhood structure, usually a knn graph.

Neighbors(adata[, n_dcs])

Data represented as graph of nearest neighbors.

Settings

A convenience function for setting some default matplotlib.rcParams and a high-resolution jupyter display backend useful for use in notebooks.

set_figure_params

Set resolution/size, styling and format of figures.

An instance of the ScanpyConfig is available as scanpy.settings and allows configuring Scanpy.

_settings.ScanpyConfig(*[, verbosity, …])

Config manager for scanpy.

Some selected settings are discussed in the following.

Influence the global behavior of plotting functions. In non-interactive scripts, you’d usually want to set settings.autoshow to False.

settings.autoshow	Automatically show figures (default: True).
settings.autosave	Automatically save figures (default: False).

The default directories for saving figures, caching files and storing datasets.

settings.figdir	Directory for saving figures (default: './figures/').
settings.cachedir	Directory for cache files (default: './cache/').
settings.datasetdir	Directory for example datasets (default: './data/').

The verbosity of logging output, where verbosity levels have the following meaning: 0=’error’, 1=’warning’, 2=’info’, 3=’hint’, 4=more details, 5=even more details, etc.

settings.verbosity

Verbosity level (default: 1).

Print versions of packages that might influence numerical results.

logging.print_versions()

Versions that might influence the numerical results.

Datasets

datasets.blobs([n_variables, n_centers, …])	Gaussian Blobs.
datasets.ebi_expression_atlas(accession, *)	Load a dataset from the EBI Single Cell Expression Atlas.
datasets.krumsiek11()	Simulated myeloid progenitors [Krumsiek11].
datasets.moignard15()	Hematopoiesis in early mouse embryos [Moignard15].
datasets.pbmc3k()	3k PBMCs from 10x Genomics.
datasets.pbmc68k_reduced()	Subsampled and processed 68k PBMCs.
datasets.paul15()	Development of Myeloid Progenitors [Paul15].
datasets.toggleswitch()	Simulated toggleswitch.

Further modules

external	External API
api	Global API (deprecated)
plotting	Plotting API

Deprecated functions

pp.filter_genes_dispersion(data[, flavor, …])	Extract highly variable genes [Satija15] [Zheng17].
pp.normalize_per_cell(data[, …])	Normalize total counts per cell.