diff --git a/src/find_interaction_cluster/community_figures/fig_functions.py b/src/find_interaction_cluster/community_figures/fig_functions.py
index 124bf0db76fd59256cf69caf18cfedf6d497548b..20747cae31f8df8677c4f55bb701387f6bbc9dea 100644
--- a/src/find_interaction_cluster/community_figures/fig_functions.py
+++ b/src/find_interaction_cluster/community_figures/fig_functions.py
@@ -315,7 +315,8 @@ def expand_results_perm(df: pd.DataFrame, rdf: pd.DataFrame, target_col: str,
def make_barplot(df_bar: pd.DataFrame, outfile: Path,
- target_col: str, feature: str, target_kind: str = "") -> None:
+ target_col: str, feature: str, target_kind: str = "",
+ sd_community: Optional[str] = "sd") -> None:
"""
Create a barplot showing the frequency of `nt` for every community \
of exons/gene in `df_bar`.
@@ -327,11 +328,13 @@ def make_barplot(df_bar: pd.DataFrame, outfile: Path,
target_col.
:param target_col: The name of the column containing the data of interest
:param feature: The king of feature of interest
+ :param sd_community: sd to display community error bar, None to display \
+ nothing
"""
sns.set(context="poster")
g = sns.catplot(x="community", y=target_col, data=df_bar, kind="point",
- ci="sd", aspect=2.5, height=14, errwidth=0.5, capsize=.4,
- scale=0.5,
+ ci=sd_community, aspect=2.5, height=14, errwidth=0.5,
+ capsize=.4, scale=0.5,
palette=["red"] + ["darkgray"] * (df_bar.shape[0] - 1))
g2 = sns.catplot(x="community", y=target_col, data=df_bar, kind="bar",
ci="sd", aspect=2.5, height=14, errwidth=0.5, capsize=.4,
@@ -347,7 +350,9 @@ def make_barplot(df_bar: pd.DataFrame, outfile: Path,
for i, p in enumerate(g2.ax.patches):
stats = "*" if df_bara.iloc[i, :]["p-adj"] < 0.05 else ""
com = df_bara.iloc[i, :]["community"]
- csd = np.std(df_bar.loc[df_bar["community"] == com, target_col])
+ csd = 0
+ if sd_community == "sd":
+ csd = np.std(df_bar.loc[df_bar["community"] == com, target_col])
g.ax.annotate(stats,
(p.get_x() + p.get_width() / 2., p.get_height() + csd),
ha='center', va='center', xytext=(0, 10), fontsize=12,
@@ -357,7 +362,8 @@ 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 = "") -> None:
+ target_kind: str = "",
+ sd_community: Optional[str] = "sd") -> None:
"""
Create a barplot showing the frequency of `nt` for every community \
of exons/gene in `df_bar`.
@@ -369,6 +375,8 @@ def make_barplot_perm(df_bar: pd.DataFrame, outfile: Path,
target_col.
:param target_col: The name of the column containing the data of interest
:param feature: The king of feature of interest
+ :param sd_community: sd to display community error bar, None to display \
+ nothing
"""
sns.set(context="poster")
df_ctrl = df_bar.loc[df_bar[f"id_{feature}"] == 'ctrl', :]
@@ -377,13 +385,17 @@ def make_barplot_perm(df_bar: pd.DataFrame, outfile: Path,
ci="sd", aspect=2.5, height=14, errwidth=0.5, capsize=.4,
palette=["darkgray"] * (df_bar.shape[0]))
g = sns.catplot(x="community", y=target_col, data=df_bar, kind="point",
- ci="sd", aspect=2.5, height=14, errwidth=0.5, capsize=.4,
- scale=0.5, palette=["darkgray"] * (df_bar.shape[0]))
+ ci=sd_community, aspect=2.5, height=14, errwidth=0.5,
+ capsize=.4, scale=0.5,
+ palette=["darkgray"] * (df_bar.shape[0]))
xrange = g.ax.get_xlim()
+ yrange = g.ax.get_ylim()
df_ctrl.plot(x="community", y=target_col, kind="scatter", ax=g.ax,
yerr="ctrl_std", legend=False, zorder=10,
color=(0.8, 0.2, 0.2, 0.4))
g.ax.set_xlim(xrange)
+ if sd_community is None:
+ g.ax.set_ylim(yrange)
g.fig.subplots_adjust(top=0.9)
target_kind = f" ({target_kind})" if target_kind else ""
g.fig.suptitle(f"Mean frequency of {target_col}{target_kind}"
@@ -395,7 +407,9 @@ def make_barplot_perm(df_bar: pd.DataFrame, outfile: Path,
for i, p in enumerate(g2.ax.patches):
stats = "*" if df_bara.iloc[i, :]["p-adj"] < 0.05 else ""
com = df_bara.iloc[i, :]["community"]
- csd = np.std(df_bar.loc[df_bar["community"] == com, target_col])
+ csd = 0
+ if sd_community == "sd":
+ csd = np.std(df_bar.loc[df_bar["community"] == com, target_col])
g.ax.annotate(stats,
(p.get_x() + p.get_width() / 2., p.get_height() + csd),
ha='center', va='center', xytext=(0, 10), fontsize=12,
@@ -405,7 +419,7 @@ 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) -> None:
+ target_kind: str, sd_community: bool) -> None:
"""
Reformat a dataframe with the enrichment of a nucleotide frequency \
for every feature for every community and then create a \
@@ -421,11 +435,15 @@ def barplot_creation(df_bar: pd.DataFrame, outfig: Path,
:param test_type: The type of test to make (permutation or lm)
:param target_kind: An optional name that describe a bit further \
target_col.
+ :param sd_community: True to display the errors bars for communities,
+ False else.
"""
+ sd_community = "sd" if sd_community else None
if test_type == "lm":
make_barplot(df_bar, outfig, cpnt, feature, target_kind)
else:
- make_barplot_perm(df_bar, outfig, cpnt, feature, target_kind)
+ make_barplot_perm(df_bar, outfig, cpnt, feature, target_kind,
+ sd_community)
def get_feature_by_community(df: pd.DataFrame, feature: str) -> Dict:
@@ -461,7 +479,8 @@ def create_community_fig(df: pd.DataFrame, feature: str,
outfile_ctrl: Path, test_type: str,
dic_com: Optional[Dict] = None,
target_kind: str = "",
- iteration: int = 10000) -> None:
+ iteration: int = 10000,
+ sd_community: bool = True) -> None:
"""
Create a dataframe with a control community, save it as a table and \
as a barplot figure.
@@ -480,6 +499,8 @@ def create_community_fig(df: pd.DataFrame, feature: str,
:param target_kind: An optional name that describe a bit further \
target_col.
:param iteration: The number of sub samples to create
+ :param sd_community: True to display the errors bars for communities,
+ False else.
"""
if dic_com is None:
dic_com = {} if test_type == 'lm' \
@@ -495,4 +516,4 @@ 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)
+ target_kind, sd_community)