diff --git a/src/find_interaction_cluster/community_figures/__main__.py b/src/find_interaction_cluster/community_figures/__main__.py
index e01081b74bdc737a8ddba3584afa4dc77cfbe615..259b1179f4760706674a387570e16faec9b5bc6b 100644
--- a/src/find_interaction_cluster/community_figures/__main__.py
+++ b/src/find_interaction_cluster/community_figures/__main__.py
@@ -54,7 +54,8 @@ def load_and_check_table(table: str, feature: str, target_col: str):
def create_community_figures(table: str, feature: str, target_col: str,
output: str, outfile: str, test_type: str,
target_kind: str = "", sd_community: str = "y",
- iteration: int = 10000) -> None:
+ iteration: int = 10000,
+ display_size: bool = False) -> None:
"""
Create a dataframe with a control community, save it as a table and \
as a barplot figure.
@@ -75,6 +76,8 @@ def create_community_figures(table: str, feature: str, target_col: str,
is only used if test_type = 'permutation' (default 10000).
:param sd_community: y to display the standard deviation for communities \
false else.
+ :param display_size: True to display the size of the community above \
+ each one of them False to display nothing. (default False)
"""
df = load_and_check_table(table, feature, target_col)
if not outfile.endswith(".pdf"):
@@ -83,7 +86,7 @@ def create_community_figures(table: str, feature: str, target_col: str,
sd_community = sd_community.lower() == 'y'
create_community_fig(df, feature, target_col, moutfile, test_type,
target_kind=target_kind, iteration=iteration,
- sd_community=sd_community)
+ sd_community=sd_community, display_size=display_size)
if __name__ == "__main__":
diff --git a/src/find_interaction_cluster/community_figures/fig_functions.py b/src/find_interaction_cluster/community_figures/fig_functions.py
index bc836380ced46fa0a32d6f20a086e50d3e73537f..a05c2561a6626fe7166b89b3593b1605bfdf321d 100644
--- a/src/find_interaction_cluster/community_figures/fig_functions.py
+++ b/src/find_interaction_cluster/community_figures/fig_functions.py
@@ -316,9 +316,41 @@ def expand_results_perm(df: pd.DataFrame, rdf: pd.DataFrame, target_col: str,
return pd.concat([df_ctrl, df], axis=0, ignore_index=True)
+def display_size_fig(g: sns.FacetGrid, display_size: bool, target_col: str,
+ df_bar: pd.DataFrame):
+ """
+
+ :param g: A seaborn FacetGrid
+ :param display_size: True to display the size of the community above \
+ each one of them False to display nothing. (default False)
+ :param target_col: The name of the column containing the data of interest
+ :param df_bar: A dataframe with the enrichment of a \
+ nucleotide frequency for every community (without control)
+ :return: The seaborn fascetgrid
+ """
+ xrange = g.ax.get_xlim()
+ if display_size:
+ df_size = df_bar[[f"mean_{target_col}", "community",
+ "community_size"]].drop_duplicates()
+ ax2 = g.ax.twinx()
+ ax2.set_ylabel('community_size', color="green")
+ df_size.plot(x="community", y="community_size", kind="scatter", ax=ax2,
+ legend=False, zorder=55,
+ color=(0.2, 0.8, 0.2, 0.4))
+ ax2.tick_params(axis='y', labelcolor="green")
+ ax2.grid(False)
+ sizes = df_size["community_size"].to_list()
+ if max(sizes) - min(sizes) > 500:
+ ax2.set_yscale("log")
+ g.ax.set_xlim(xrange)
+ g.set(xticklabels=[])
+ return g
+
+
def make_barplot(df_bar: pd.DataFrame, outfile: Path,
target_col: str, feature: str, target_kind: str = "",
- sd_community: Optional[str] = "sd") -> None:
+ sd_community: Optional[str] = "sd",
+ display_size: bool = False) -> None:
"""
Create a barplot showing the frequency of `nt` for every community \
of exons/gene in `df_bar`.
@@ -332,6 +364,8 @@ def make_barplot(df_bar: pd.DataFrame, outfile: Path,
:param feature: The king of feature of interest
:param sd_community: sd to display community error bar, None to display \
nothing
+ :param display_size: True to display the size of the community above \
+ each one of them False to display nothing. (default False)
"""
sns.set(context="poster")
g = sns.catplot(x="community", y=target_col, data=df_bar, kind="point",
@@ -346,7 +380,7 @@ def make_barplot(df_bar: pd.DataFrame, outfile: Path,
g.fig.suptitle(f"Mean frequency of {target_col}{target_kind}"
f"among community of {feature}s\n"
f"(stats obtained with a lm test)")
- g.set(xticklabels=[])
+ g = display_size_fig(g, display_size, target_col, df_bar)
g.ax.set_ylabel(f'Frequency of {target_col}')
df_bara = df_bar.drop_duplicates(subset="community", keep="first")
for i, p in enumerate(g2.ax.patches):
@@ -366,7 +400,8 @@ def make_barplot(df_bar: pd.DataFrame, outfile: Path,
def make_barplot_perm(df_bar: pd.DataFrame, outfile: Path,
target_col: str, feature: str,
target_kind: str = "",
- sd_community: Optional[str] = "sd") -> None:
+ sd_community: Optional[str] = "sd",
+ display_size: bool = False) -> None:
"""
Create a barplot showing the frequency of `nt` for every community \
of exons/gene in `df_bar`.
@@ -380,6 +415,8 @@ def make_barplot_perm(df_bar: pd.DataFrame, outfile: Path,
:param feature: The king of feature of interest
:param sd_community: sd to display community error bar, None to display \
nothing
+ :param display_size: True to display the size of the community above \
+ each one of them False to display nothing. (default False)
"""
sns.set(context="poster")
df_ctrl = df_bar.loc[df_bar[f"id_{feature}"] == 'ctrl', :]
@@ -404,7 +441,7 @@ def make_barplot_perm(df_bar: pd.DataFrame, outfile: Path,
g.fig.suptitle(f"Mean frequency of {target_col}{target_kind}"
f"among community of {feature}s\n"
f"(stats obtained with a permutation test)")
- g.set(xticklabels=[])
+ g = display_size_fig(g, display_size, target_col, df_bar)
g.ax.set_ylabel(f'Frequency of {target_col}')
df_bara = df_bar.drop_duplicates(subset="community", keep="first")
for i, p in enumerate(g2.ax.patches):
@@ -423,7 +460,8 @@ def make_barplot_perm(df_bar: pd.DataFrame, outfile: Path,
def barplot_creation(df_bar: pd.DataFrame, outfig: Path,
cpnt: str, test_type: str, feature: str,
- target_kind: str, sd_community: bool) -> None:
+ target_kind: str, sd_community: bool,
+ display_size: bool) -> None:
"""
Reformat a dataframe with the enrichment of a nucleotide frequency \
for every feature for every community and then create a \
@@ -441,13 +479,16 @@ def barplot_creation(df_bar: pd.DataFrame, outfig: Path,
target_col.
:param sd_community: True to display the errors bars for communities,
False else.
+ :param display_size: True to display the size of the community above \
+ each one of them False to display nothing. (default False)
"""
sd_community = "sd" if sd_community else None
if test_type == "lm":
- make_barplot(df_bar, outfig, cpnt, feature, target_kind)
+ make_barplot(df_bar, outfig, cpnt, feature, target_kind,
+ display_size=display_size)
else:
make_barplot_perm(df_bar, outfig, cpnt, feature, target_kind,
- sd_community)
+ sd_community, display_size)
def get_feature_by_community(df: pd.DataFrame, feature: str) -> Dict:
@@ -486,7 +527,8 @@ def create_community_fig(df: pd.DataFrame, feature: str,
dic_com: Optional[Dict] = None,
target_kind: str = "",
iteration: int = 10000,
- sd_community: bool = True) -> None:
+ sd_community: bool = True,
+ display_size: bool = False) -> None:
"""
Create a dataframe with a control community, save it as a table and \
as a barplot figure.
@@ -507,6 +549,8 @@ def create_community_fig(df: pd.DataFrame, feature: str,
:param iteration: The number of sub samples to create
:param sd_community: True to display the errors bars for communities,
False else.
+ :param display_size: True to display the size of the community above \
+ each one of them False to display nothing. (default False)
"""
if dic_com is None:
dic_com = {} if test_type == 'lm' \
@@ -522,4 +566,4 @@ def create_community_fig(df: pd.DataFrame, feature: str,
bar_outfile = str(outfile_ctrl).replace(".pdf", "_bar.txt")
df_bar.to_csv(bar_outfile, sep="\t", index=False)
barplot_creation(df_bar, outfile_ctrl, target_col, test_type, feature,
- target_kind, sd_community)
+ target_kind, sd_community, display_size)