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🎨 type annotations and docstring updates
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- prepare to defien a common return type by specifying message TYPE_COLS_MSG
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@enryH
enryH committed Dec 3, 2024
1 parent 8ed860c commit ec10c71
Showing 1 changed file with 49 additions and 38 deletions.
87 changes: 49 additions & 38 deletions acore/enrichment_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,17 @@

from acore.multiple_testing import apply_pvalue_correction

TYPE_COLS_MSG = """
columns: 'terms', 'identifiers', 'foreground',
'background', foreground_pop, background_pop, 'pvalue', 'padj' and 'rejected'.
"""


def run_fisher(group1, group2, alternative="two-sided"):
def run_fisher(
group1: list[int],
group2: list[int],
alternative="two-sided",
) -> tuple[float, float]:
"""annotated not-annotated
group1 a b
group2 c d
Expand Down Expand Up @@ -66,7 +75,7 @@ def run_site_regulation_enrichment(
regulation_data,
annotation,
identifier="identifier",
groups=["group1", "group2"],
groups=("group1", "group2"),
annotation_col="annotation",
reject_col="rejected",
group_col="group",
Expand All @@ -78,9 +87,9 @@ def run_site_regulation_enrichment(
This function runs a simple enrichment analysis for significantly
regulated protein sites in a dataset.
:param regulation_data: pandas dataframe resulting from differential
:param regulation_data: pandas.DataFrame resulting from differential
regulation analysis.
:param annotation: pandas dataframe with annotations for features
:param annotation: pandas.DataFrame with annotations for features
(columns: 'annotation', 'identifier' (feature identifiers), and 'source').
:param str identifier: name of the column from annotation containing
feature identifiers.
Expand All @@ -95,8 +104,8 @@ def run_site_regulation_enrichment(
:param str method: method used to compute enrichment
(only 'fisher' is supported currently).
:param str regex: how to extract the annotated identifier from the site identifier
:return: Pandas dataframe with columns: 'terms', 'identifiers', 'foreground',
'background', 'pvalue', 'padj' and 'rejected'.
:return: pandas.DataFrame with columns: 'terms', 'identifiers', 'foreground',
'background', foreground_pop, background_pop, 'pvalue', 'padj' and 'rejected'.
Example::
Expand All @@ -108,7 +117,7 @@ def run_site_regulation_enrichment(
reject_col='rejected',
group_col='group',
method='fisher',
match="(\w+~.+)_\w\d+\-\w+"
match="(\\w+~.+)_\\w\\d+\\-\\w+"
)
"""
result = pd.DataFrame()
Expand Down Expand Up @@ -142,9 +151,9 @@ def run_up_down_regulation_enrichment(
regulation_data,
annotation,
identifier="identifier",
groups=["group1", "group2"],
groups=("group1", "group2"),
annotation_col="annotation",
reject_col="rejected",
# reject_col="rejected",
group_col="group",
method="fisher",
correction="fdr_bh",
Expand All @@ -155,9 +164,9 @@ def run_up_down_regulation_enrichment(
This function runs a simple enrichment analysis for significantly regulated proteins
distinguishing between up- and down-regulated.
:param regulation_data: pandas dataframe resulting from differential regulation
:param regulation_data: pandas.DataFrame resulting from differential regulation
analysis (CKG's regulation table).
:param annotation: pandas dataframe with annotations for features
:param annotation: pandas.DataFrame with annotations for features
(columns: 'annotation', 'identifier' (feature identifiers), and 'source').
:param str identifier: name of the column from annotation containing feature identifiers.
:param list groups: column names from regulation_data containing group identifiers.
Expand All @@ -171,7 +180,7 @@ def run_up_down_regulation_enrichment(
:param str correction: method to be used for multiple-testing correction
:param float alpha: adjusted p-value cutoff to define significance
:param float lfc_cutoff: log fold-change cutoff to define practical significance
:return: Pandas dataframe with columns:'terms', 'identifiers', 'foreground',
:return: pandas.DataFrame with columns: 'terms', 'identifiers', 'foreground',
'background', 'pvalue', 'padj' and 'rejected'.
Example::
Expand Down Expand Up @@ -286,8 +295,8 @@ def run_regulation_enrichment(
This function runs a simple enrichment analysis for significantly regulated features
in a dataset.
:param regulation_data: pandas dataframe resulting from differential regulation analysis.
:param annotation: pandas dataframe with annotations for features
:param regulation_data: pandas.DataFrame resulting from differential regulation analysis.
:param annotation: pandas.DataFrame with annotations for features
(columns: 'annotation', 'identifier' (feature identifiers), and 'source').
:param str identifier: name of the column from annotation containing feature identifiers.
:param str annotation_col: name of the column from annotation containing annotation terms.
Expand All @@ -297,21 +306,22 @@ def run_regulation_enrichment(
if feature belongs to foreground or background.
:param str method: method used to compute enrichment (only 'fisher' is supported currently).
:param str correction: method to be used for multiple-testing correction
:return: Pandas dataframe with columns: 'terms', 'identifiers', 'foreground',
'background', 'pvalue', 'padj' and 'rejected'.
:return: pandas.DataFrame with columns: 'terms', 'identifiers', 'foreground',
'background', 'foreground_pop', 'background_pop', 'pvalue', 'padj' and 'rejected'.
Example::
result = run_regulation_enrichment(
regulation_data,
annotation,
identifier='identifier',
groups=['group1',
'group2'],
annotation_col='annotation',
reject_col='rejected',
group_col='group',
method='fisher',
min_detected_in_set=2,
correction='fdr_bh',
correction_alpha=0.05,
)
"""
# ? can we remove NA features in that column?
Expand Down Expand Up @@ -364,8 +374,8 @@ def run_enrichment(
Computes enrichment of the foreground relative to a given backgroung,
using Fisher's exact test, and corrects for multiple hypothesis testing.
:param data: pandas dataframe with annotations for dataset features
(columns: 'annotation', 'identifier', 'source', 'group').
:param data: pandas.DataFrame with annotations for dataset features
(columns: 'annotation', 'identifier', 'group').
:param str foreground_id: group identifier of features that belong to the foreground.
:param str background_id: group identifier of features that belong to the background.
:param int foreground_pop: number of features in the foreground.
Expand All @@ -376,7 +386,7 @@ def run_enrichment(
:param str method: method used to compute enrichment (only 'fisher' is supported currently).
:param str correction: method to be used for multiple-testing correction.
:param float correction_alpha: adjusted p-value cutoff to define significance.
:return: Pandas dataframe with annotation terms, features,
:return: pandas.DataFrame with columns: annotation terms, features,
number of foregroung/background features in each term,
p-values and corrected p-values
(columns: 'terms', 'identifiers', 'foreground',
Expand All @@ -396,34 +406,35 @@ def run_enrichment(
method='fisher',
)
"""
if method != "fisher":
raise ValueError("Only Fisher's exact test is supported at the moment.")

result = pd.DataFrame()
df = data.copy()
terms = []
ids = []
pvalues = []
fnum = []
bnum = []
countsdf = (
df.groupby([annotation_col, group_col])
data.groupby([annotation_col, group_col])
.agg(["count"])[(identifier_col, "count")]
.reset_index()
)
countsdf.columns = [annotation_col, group_col, "count"]
for annotation in (
countsdf[countsdf[group_col] == foreground_id][annotation_col].unique().tolist()
):
for annotation in countsdf.loc[
countsdf[group_col] == foreground_id, annotation_col
].unique():
counts = countsdf[countsdf[annotation_col] == annotation]
num_foreground = counts.loc[counts[group_col] == foreground_id, "count"].values
num_background = counts.loc[counts[group_col] == background_id, "count"].values

# ! counts should always be of length one count? squeeze?
if len(num_foreground) == 1:
num_foreground = num_foreground[0]
if len(num_background) == 1:
num_background = num_background[0]
else:
num_background = 0
# ! what happens if this is not the case?
if method == "fisher" and num_foreground >= min_detected_in_set:
if num_foreground >= min_detected_in_set:
_, pvalue = run_fisher(
[num_foreground, foreground_pop - num_foreground],
[num_background, background_pop - foreground_pop - num_background],
Expand All @@ -434,11 +445,11 @@ def run_enrichment(
pvalues.append(pvalue)
ids.append(
",".join(
df.loc[
(df[annotation_col] == annotation)
& (df[group_col] == foreground_id),
data.loc[
(data[annotation_col] == annotation)
& (data[group_col] == foreground_id),
identifier_col,
].tolist()
]
)
)
if len(pvalues) > 1:
Expand Down Expand Up @@ -466,8 +477,8 @@ def run_enrichment(


def run_ssgsea(
data,
annotation,
data: pd.DataFrame,
annotation: str,
set_index: list[str],
annotation_col: str = "an notation",
identifier_col: str = "identifier",
Expand All @@ -483,8 +494,8 @@ def run_ssgsea(
described in Barbie et al., 2009:
https://www.nature.com/articles/nature08460#Sec3 (search "Single Sample" GSEA).
:param data: pandas dataframe with the quantified features (i.e. subject x proteins)
:param annotation: pandas dataframe with the annotation to be used in the enrichment
:param data: pandas.DataFrame with the quantified features (i.e. subject x proteins)
:param annotation: pandas.DataFrame with the annotation to be used in the enrichment
(i.e. CKG pathway annotation file)
:param str annotation_col: name of the column containing annotation terms.
:param str identifier_col: name of column containing dependent variables identifiers.
Expand Down Expand Up @@ -556,7 +567,7 @@ def run_ssgsea(
)
fid = uuid.uuid4()
file_path = os.path.join(outdir, str(fid) + ".gmt")
with open(file_path, "w") as out:
with open(file_path, "w", encoding="utf8") as out:
for i, row in grouped_annotations.iterrows():
out.write(
row[annotation_col]
Expand Down

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