Import Scanpy as:
import scanpy as sc
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 ~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 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 ~anndata.AnnData
object and leave the passed adata
unchanged, pass copy=True
.
Scanpy is based on anndata
, which provides the ~anndata.AnnData
class.
At the most basic level, an ~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. ~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 ~anndata.AnnData
object, call:
adata = sc.read(filename)
to initialize an ~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)