Usage Principles
Import Scanpy as:
import scanpy as sc
Workflow
The typical workflow consists of subsequent calls of data analysis tools
in sc.tl, e.g.:
sc.tl.tsne(adata, **tool_params) # embed the data using tSNE
where adata is an :class:`~anndata.AnnData` object. Each of these calls adds annotation to an expression matrix X, which stores n_obs observations (cells) of n_vars variables (genes). For each tool, there typically is an associated plotting function in sc.pl:
sc.pl.tsne(adata, **plotting_params)
If you pass show=False, a :class:`matplotlib.axes.Axes` instance is returned and you have all of matplotlib's detailed configuration possibilities.
To facilitate writing memory-efficient pipelines, by default, Scanpy tools operate inplace on adata and return None - this also allows to easily transition to out-of-memory pipelines. If you want to return a copy of the :class:`~anndata.AnnData` object and leave the passed adata unchanged, pass copy=True.
AnnData
Scanpy is based on :mod:`anndata`, which provides the :class:`~anndata.AnnData` class.
At the most basic level, an :class:`~anndata.AnnData` object adata stores
a data matrix (adata.X), dataframe-like annotation of observations
(adata.obs) and variables (adata.var) and unstructured dict-like
annotation (adata.uns). Values can be retrieved and appended via
adata.obs['key1'] and adata.var['key2']. Names of observations and
variables can be accessed via adata.obs_names and adata.var_names,
respectively. :class:`~anndata.AnnData` objects can be sliced like
dataframes, for example, adata_subset = adata[:, list_of_gene_names].
For more, see this blog post.
To read a data file to an :class:`~anndata.AnnData` object, call:
adata = sc.read(filename)
to initialize an :class:`~anndata.AnnData` object. Possibly add further annotation using, e.g., pd.read_csv:
import pandas as pd anno = pd.read_csv(filename_sample_annotation) adata.obs['cell_groups'] = anno['cell_groups'] # categorical annotation of type pandas.Categorical adata.obs['time'] = anno['time'] # numerical annotation of type float # alternatively, you could also set the whole dataframe # adata.obs = anno
To write, use:
adata.write(filename) adata.write_csvs(filename) adata.write_loom(filename)