diff --git a/src/find_interaction_cluster/community_figures/fig_functions.py b/src/find_interaction_cluster/community_figures/fig_functions.py
index d7367e87eef4b4044a729f3b87a8656e8b8fd2ae..3fa685b08634ed53b85d023600b69adb8a30152f 100644
--- a/src/find_interaction_cluster/community_figures/fig_functions.py
+++ b/src/find_interaction_cluster/community_figures/fig_functions.py
@@ -77,8 +77,10 @@ def lm_maker_summary(df: pd.DataFrame, outfile: Path, target_col: str,
mod = f"mod <- glm({target_col} ~ log(community_size) + community," \
f"data=data, family=binomial(link='logit'))"
df[target_col] = df[target_col].astype(int)
- tmp = df[[target_col, 'community']].groupby('community').mean().reset_index()
- bad_groups = tmp.loc[tmp[target_col] == 0, "community"].to_list()
+ tmp = df[[target_col, 'community']].groupby('community').mean().\
+ reset_index()
+ bad_groups = tmp.loc[(tmp[target_col] == 0) | (tmp[target_col] == 1),
+ "community"].to_list()
if "C-CTRL" in bad_groups:
print("Control group as a mean value equals to 0, exiting...")
exit(1)
@@ -105,7 +107,10 @@ def lm_maker_summary(df: pd.DataFrame, outfile: Path, target_col: str,
res_df.rename({'index': 'community'}, inplace=True, axis=1)
res_df['community'] = res_df['community'].str.replace('community', '')
res_df.loc[res_df['community'] == "(Intercept)", "community"] = "C-CTRL"
- mean_df = df[[target_col, "community", "community_size"]]. \
+ good_cols = [target_col, "community", "community_size"]
+ if "community_data" in df.columns:
+ good_cols.append("community_data")
+ mean_df = df[good_cols]. \
groupby(["community", "community_size"]).mean().reset_index()
return df, res_df.merge(mean_df, how="left", on="community")
@@ -151,8 +156,11 @@ def expand_results_lm(df: pd.DataFrame, rdf: pd.DataFrame,
:return: The merged dataframe: i.e df with the stats columns
"""
p_col = "Pr(>|t|)" if test_type == "lm" else "Pr(>|z|)"
- df = df[[f"id_{feature}", target_col, "community",
- "community_size"]].copy()
+ good_cols = [f"id_{feature}", target_col, "community",
+ "community_size"]
+ if "community_data" in df.columns:
+ good_cols.append("community_data")
+ df = df[good_cols].copy()
rdf = rdf[["community", "community_size", p_col, target_col]].copy()
rdf.rename({target_col: f"mean_{target_col}", p_col: "p-adj"},
axis=1, inplace=True)
@@ -317,9 +325,11 @@ def expand_results_perm(df: pd.DataFrame, rdf: pd.DataFrame, target_col: str,
:param iteration: The number of iteration
:return: The merged dataframe: i.e df with the stats columns
"""
+ good_cols = [f"id_{feature}", target_col, "community", "community_size"]
+ if "community_data" in df.columns:
+ good_cols.append("community_data")
df = df.loc[-df["community"].isna(),
- [f"id_{feature}", target_col,
- "community", "community_size"]].copy()
+ good_cols].copy()
ctrl_df = rdf[[f"{target_col}_mean_{iteration}_ctrl",
f"{target_col}_std_{iteration}_ctrl", "community"]].copy()
rdf = rdf[["community", "community_size", target_col, "p-adj",
@@ -334,6 +344,39 @@ 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 handle_community_data(g: sns.FacetGrid, df_bar: pd.DataFrame,
+ display_size: bool):
+ """
+
+ :param g: A seaborn FacetGrid
+ :param df_bar: A dataframe with the enrichment of a \
+ nucleotide frequency for every community (without control)
+ :param display_size: True to display the size of the community above \
+ each one of them False to display nothing. (default False)
+ :return: The seaborn fascetgrid
+ """
+ com_col = "community_data"
+ if com_col in df_bar.columns:
+ df_val = df_bar[["community", com_col]].groupby("community").mean()\
+ .reset_index()
+ df_val = df_bar.drop("community_data", axis=1)\
+ .merge(df_val, how="left",
+ on="community")[["community",
+ "community_data"]].drop_duplicates()
+ ax3 = g.ax.twinx()
+ ax3.set_ylabel('community_size', color="purple")
+ if display_size:
+ ax3.spines["right"].set_position(("axes", 1.03))
+ ax3.spines["right"].set_visible(True)
+ df_val.plot(x="community", y="community_data", kind="scatter", ax=ax3,
+ legend=False, zorder=55,
+ color=(0.8, 0.2, 0.8, 0.4))
+ ax3.tick_params(axis='y', labelcolor="purple")
+ ax3.grid(False)
+ g.set(xticklabels=[])
+ return g
+
+
def display_size_fig(g: sns.FacetGrid, display_size: bool, target_col: str,
df_bar: pd.DataFrame):
"""
@@ -361,7 +404,7 @@ def display_size_fig(g: sns.FacetGrid, display_size: bool, target_col: str,
if max(sizes) - min(sizes) > 500:
ax2.set_yscale("log")
g.ax.set_xlim(xrange)
- g.set(xticklabels=[])
+ g = handle_community_data(g, df_bar, display_size)
return g
@@ -398,7 +441,7 @@ def make_barplot(df_bar: pd.DataFrame, outfile: Path, test_type: str,
target_kind = f" ({target_kind})" if target_kind else ""
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)")
+ f"(stats obtained with a {test_type} test)")
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")
@@ -571,6 +614,8 @@ def create_community_fig(df: pd.DataFrame, feature: str,
:param display_size: True to display the size of the community above \
each one of them False to display nothing. (default False)
"""
+ df.to_csv(str(outfile_ctrl).replace(".pdf", ".tmp.txt"), sep="\t",
+ index=False)
if dic_com is None:
dic_com = {} if test_type != 'permutation' \
else get_feature_by_community(df, feature)