Single-Cell Analysis in Python.

API¶

Import Scanpy as:

import scanpy as sc

Note

Wrappers to external functionality are found in scanpy.external.

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_genes() and filter_genes_dispersion() in scanpy.plotting.

`pp.calculate_qc_metrics`(adata, *[, …])	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[, layer, …])	Annotate highly variable genes [Satija15] [Zheng17] [Stuart19].
`pp.log1p`()	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 (mostly) unwanted sources of variation.
`pp.scale`()	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¶

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

pp.combat(adata[, key, covariates, 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, neighbors_key, copy])	Diffusion Maps [Coifman05] [Haghverdi15] [Wolf18].

Compute densities on embeddings.

tl.embedding_density(adata[, basis, …])

Calculate the density of cells in an embedding (per condition).

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].

Data integration¶

tl.ingest(adata, adata_ref[, obs, …])

Map labels and embeddings from reference data to new data.

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.

plotting

Plotting API

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_visium`(path[, genome, count_file, …])	Read 10x-Genomics-formatted visum dataset.

Read other formats using functions borrowed from anndata

`read_h5ad`(filename[, backed, as_sparse, …])	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.

Get object from `AnnData`: `get`¶

The module sc.get provides convenience functions for getting values back in useful formats.

`get.obs_df`(adata[, keys, obsm_keys, layer, …])	Return values for observations in adata.
`get.var_df`(adata[, keys, varm_keys, layer])	Return values for observations in adata.
`get.rank_genes_groups_df`(adata, group, *[, …])	`scanpy.tl.rank_genes_groups()` results in the form of a `DataFrame`.

Queries¶

This module provides useful queries for annotation and enrichment.

`queries.biomart_annotations`(org, attrs, *[, …])	Retrieve gene annotations from ensembl biomart.
`queries.gene_coordinates`(org, gene_name, *)	Retrieve gene coordinates for specific organism through BioMart.
`queries.mitochondrial_genes`(org, *[, …])	Mitochondrial gene symbols for specific organism through BioMart.
`queries.enrich`() ))	Get enrichment for DE results.

Metrics¶

Collections of useful measurements for evaluating results.

`metrics.confusion_matrix`(orig, new[, data, …])	Given an original and new set of labels, create a labelled confusion matrix.
`metrics.gearys_c`()	Calculate Geary’s C, as used by VISION.
`metrics.morans_i`()	Calculate Moran’s I Global Autocorrelation Statistic.

Classes¶

AnnData is reexported from anndata.

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

Neighbors(adata[, n_dcs, neighbors_key])

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([scanpy, dpi, dpi_save, …])

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.

`autoshow`	Automatically show figures if `autosave == False` (default `True`).
`autosave`	Automatically save figures in `figdir` (default `False`).

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

`figdir`	Directory for saving figures (default `'./figures/'`).
`cachedir`	Directory for cache files (default `'./cache/'`).
`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.

verbosity

Verbosity level (default warning)

Print versions of packages that might influence numerical results.

`logging.print_header`(*[, file])	Versions that might influence the numerical results.
`logging.print_versions`(*[, file])	Print print versions of imported packages

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.pbmc3k_processed`()	Processed 3k PBMCs from 10x Genomics.
`datasets.pbmc68k_reduced`()	Subsampled and processed 68k PBMCs.
`datasets.paul15`()	Development of Myeloid Progenitors [Paul15].
`datasets.toggleswitch`()	Simulated toggleswitch.
`datasets.visium_sge`([sample_id, …])	Processed Visium Spatial Gene Expression data from 10x Genomics.

Further modules¶