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added list option for sample_identifier in load #190

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merged 3 commits into from
Jan 31, 2023
Merged

added list option for sample_identifier in load #190

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83c1b0b
added list option for sample_identifier in load
johannesostner Jan 25, 2023
3a506f0
Remove useless empty line
Zethson Jan 25, 2023
54b7f30
Fix nox
Zethson Jan 26, 2023
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2 changes: 1 addition & 1 deletion pertpy/plot/_coda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@ def __stackbar( # pragma: no cover
if show_legend:
ax.legend(loc="upper left", bbox_to_anchor=(1, 1), ncol=1)
ax.set_xticks(r)
ax.set_xticklabels(level_names, rotation=45)
ax.set_xticklabels(level_names, rotation=45, ha="right")
ax.set_ylabel("Proportion")

return ax
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24 changes: 19 additions & 5 deletions pertpy/tools/_base_coda.py
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Expand Up @@ -1350,24 +1350,24 @@ def import_tree(
def from_scanpy(
adata: AnnData,
cell_type_identifier: str,
sample_identifier: str,
sample_identifier: str | list[str],
covariate_uns: str | None = None,
covariate_obs: list[str] | None = None,
covariate_df: pd.DataFrame | None = None,
) -> AnnData:
"""
Creates a compositional analysis dataset from a single anndata object, as it is produced by e.g. scanpy.

The anndata object needs to have a column in adata.obs that contains the cell type assignment,
and one column that specifies the grouping into samples.
Covariates can either be specified via a key in adata.uns, or as a separate DataFrame.
The anndata object needs to have a column in adata.obs that contains the cell type assignment.
Further, it must contain one column or a set of columns (e.g. subject id, treatment, disease status) that uniquely identify each (statistical) sample.
Further covariates (e.g. subject age) can either be specified via addidional column names in adata.obs, a key in adata.uns, or as a separate DataFrame.

NOTE: The order of samples in the returned dataset is determined by the first occurence of cells from each sample in `adata`

Args:
adata: An anndata object from scanpy
cell_type_identifier: column name in adata.obs that specifies the cell types
sample_identifier: column name in adata.obs that specifies the sample
sample_identifier: column name or list of column names in adata.obs that uniquely identify each sample
covariate_uns: key for adata.uns, where covariate values are stored
covariate_obs: list of column names in adata.obs, where covariate values are stored. Note: If covariate values are not unique for a value of sample_identifier, this covaariate will be skipped.
covariate_df: DataFrame with covariates
Expand All @@ -1377,6 +1377,20 @@ def from_scanpy(

"""

if type(sample_identifier) == str:
sample_identifier = [sample_identifier]

if covariate_obs:
covariate_obs += sample_identifier
else:

covariate_obs = sample_identifier

# join sample identifiers
if type(sample_identifier) == list:
adata.obs["scCODA_sample_id"] = adata.obs[sample_identifier].agg('-'.join, axis=1)
sample_identifier = "scCODA_sample_id"

# get cell type counts
groups = adata.obs.value_counts([sample_identifier, cell_type_identifier])
count_data = groups.unstack(level=cell_type_identifier)
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