Usage Principles¶

Input¶

adata: AnnData object of shape n_obs × n_vars. Rows correspond to cells and columns to genes.
search_list: List of optional numbers of cell types for the estimation.

Output¶

estimated_k: Estimated number of cell types.

ASTER can also be seamlessly integrated with EpiScanpy, a widely-used Python library for epigenomics single cell analysis:

import episcanpy.api as epi
import epiaster as aster
# Load the single-cell chromatin accessibility data as an AnnData object (adata)
# Run ASTER
estimated_k = aster.estimate_k(adata, search_list)

AnnData¶

ASTER supports episcanpy and anndata, which provides the AnnData class.

At the most basic level, an AnnData object adata stores a data matrix adata.X, annotation of observations adata.obs and variables adata.var as pd.DataFrame and unstructured annotation adata.uns as dict. Names of observations and variables can be accessed via adata.obs_names and adata.var_names, respectively. 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 object, call:

import episcanpy.api as epi
adata = epi.read(filename)

to initialize an 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)