diff --git a/src/find_interaction_cluster/nt_and_community.py b/src/find_interaction_cluster/nt_and_community.py
index 07d5e40e7701ac40d133061a0cf9857afc0cbbbe..a96e351303428e77a41660cc7ec31e0c7b0ac4c7 100644
--- a/src/find_interaction_cluster/nt_and_community.py
+++ b/src/find_interaction_cluster/nt_and_community.py
@@ -152,7 +152,6 @@ def lmm_maker_summary(df: pd.DataFrame, outfile: Path, nt: str
:param outfile: A name of a file
:param nt: the nucleotide of interest
:return: the pvalue of lmm
-
"""
pandas2ri.activate()
lmm = r(
@@ -222,7 +221,8 @@ def create_ctrl_community(df: pd.DataFrame,
def lmm_with_ctrl(df: pd.DataFrame, feature: str, region: str,
- nt: str, outfile_diag: Path) -> pd.DataFrame:
+ nt: str, outfile_diag: Path
+ ) -> Tuple[pd.DataFrame, pd.DataFrame]:
"""
:param df: df: A dataframe containing the frequency of each nucleotide \
@@ -232,11 +232,12 @@ def lmm_with_ctrl(df: pd.DataFrame, feature: str, region: str,
:param nt: The nucleotide of interest
:param outfile_diag: File from which the diagnostics folder will be \
inferred
- :return: The dataframe with the p-value compared to the control \
- list of exons.
+ :return: The dataframe with ctrl exon and \
+ The dataframe with the p-value compared to the control \
+ list of feature.
"""
ndf = create_ctrl_community(df, feature, region)
- return lmm_maker_summary(ndf, outfile_diag, nt)
+ return ndf, lmm_maker_summary(ndf, outfile_diag, nt)
def get_feature_by_community(df: pd.DataFrame, feature: str) -> Dict:
@@ -418,24 +419,78 @@ def make_barplot(df_bar: pd.DataFrame, outfile: Path, nt: str, test_type: str,
sns.set()
test_name = "permutation" if test_type == "perm" else "lmm"
g = sns.catplot(x="community", y=nt, data=df_bar, kind="bar",
- ci="sd", aspect=2.5, height=12,
- palette=["red"] + ["grey"] * (df_bar.shape[0] - 1))
+ ci="sd", aspect=2.5, height=12, errwidth=0.5, capsize=.4,
+ palette=["red"] + ["lightgray"] * (df_bar.shape[0] - 1))
g.fig.suptitle(f"Mean frequency of {nt} among community of {feature}s\n"
f"(stats obtained with as {test_name} test)")
g.set(xticklabels=[])
g.ax.set_ylabel(f'Frequency of {nt}')
- if test_type == "perm":
- df_bar = df_bar.drop_duplicates(subset="community", keep="last")
+ df_bara = df_bar.drop_duplicates(subset="community", keep="first")
for i, p in enumerate(g.ax.patches):
- stats = "*" if df_bar.iloc[i, :]["p-adj"] < 0.05 else ""
- print(i, stats, df_bar.iloc[i, :]["p-adj"])
+ 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, nt])
g.ax.annotate(stats,
- (p.get_x() + p.get_width() / 2., p.get_height()),
+ (p.get_x() + p.get_width() / 2., p.get_height() + csd),
ha='center', va='center', xytext=(0, 10),
textcoords='offset points')
g.savefig(outfile)
+def expand_results_lmm(df: pd.DataFrame, rdf: pd.DataFrame,
+ nt: str, feature: str) -> pd.DataFrame:
+ """
+ Merge df and rdf together.
+
+ :param df: A dataframe containing the frequency of each nucleotide \
+ in each feature belonging to a community.
+ :param rdf: The dataframe containing the mean frequency for \
+ each community and the p-value of their enrichment compared to control \
+ exons.
+ :param nt: the nucleotide of interest
+ :param feature: The feature of interest
+ :return: The merged dataframe: i.e df with the stats columns
+ """
+ p_col = "Pr(>|t|)"
+ df = df[[f"id_{feature}", nt, "community", "community_size"]].copy()
+ rdf = rdf[["community", "community_size", p_col, nt]].copy()
+ rdf.rename({nt: f"mean_{nt}", p_col: "p-adj"}, axis=1, inplace=True)
+ df = df.merge(rdf, how="left", on=["community", "community_size"])
+ df_ctrl = df[df["community"] == "C-CTRL"]
+ df = df[df["community"] != "C-CTRL"].copy()
+ df.sort_values(f"mean_{nt}", ascending=True, inplace=True)
+ return pd.concat([df_ctrl, df], axis=0, ignore_index=True)
+
+
+def expand_results_perm(df: pd.DataFrame, rdf: pd.DataFrame,
+ nt: str, feature: str, iteration: int) -> pd.DataFrame:
+ """
+ Merge df and rdf together.
+
+ :param df: A dataframe containing the frequency of each nucleotide \
+ in each feature belonging to a community.
+ :param rdf: The dataframe containing the mean frequency for \
+ each community and the p-value of their enrichment compared to control \
+ exons.
+ :param nt: the nucleotide of interest
+ :param feature: The feature of interest
+ :param iteration: The number of iteration
+ :return: The merged dataframe: i.e df with the stats columns
+ """
+ df = df[[f"id_{feature}", nt, "community", "community_size"]].copy()
+ ctrl_val = rdf[f"{nt}_mean_{iteration}_ctrl"]
+ rdf = rdf[["community", "community_size", nt, "p-adj"]].copy()
+ rdf.rename({nt: f"mean_{nt}"}, axis=1, inplace=True)
+ df = df.merge(rdf, how="left", on=["community", "community_size"])
+ df_ctrl = pd.DataFrame({nt: ctrl_val,
+ f"mean_{nt}": [np.mean(ctrl_val)] * len(ctrl_val),
+ f"id_{feature}": ['ctrl'] * len(ctrl_val),
+ "community_size": [len(ctrl_val)] * len(ctrl_val),
+ "community": ["C-CTRL"] * len(ctrl_val)})
+ df.sort_values(f"mean_{nt}", ascending=True, inplace=True)
+ return pd.concat([df_ctrl, df], axis=0, ignore_index=True)
+
+
def create_and_save_ctrl_dataframe(df: pd.DataFrame, feature: str,
region: str, nt: str, outfile: Path,
test_type: str, df_ctrl: pd.DataFrame,
@@ -461,32 +516,34 @@ def create_and_save_ctrl_dataframe(df: pd.DataFrame, feature: str,
containing the test communities and the control community
"""
if test_type == "lmm":
- rdf = lmm_with_ctrl(df, feature, region, nt,
- outfile.parents[1] / outfile.name)
+ ndf, rdf = lmm_with_ctrl(df, feature, region, nt,
+ outfile.parents[1] / outfile.name)
+ df_bar = expand_results_lmm(ndf, rdf, nt, feature)
else:
rdf = perm_with_ctrl(df, feature, nt, df_ctrl, dic_com, iteration)
+ df_bar = expand_results_perm(df, rdf, nt, feature, iteration)
rdf.to_csv(outfile_ctrl, sep="\t", index=False)
- barplot_creation(rdf, outfile_ctrl, nt,
- test_type, feature, iteration)
+ barplot_creation(df_bar, outfile_ctrl, nt,
+ test_type, feature)
-def barplot_creation(rdf: pd.DataFrame, outfile: Path, nt: str,
- test_type: str, feature: str, iteration: int) -> None:
+def barplot_creation(df_bar: pd.DataFrame, outfile: Path, nt: str,
+ test_type: str, feature: str) -> None:
"""
Reformat a dataframe with the enrichment of a nucleotide frequency \
- for every community and then create a barplot showing those frequencies.
+ for every feature for every community and then create a \
+ barplot showing those frequencies.
- :param rdf: A dataframe with the enrichment of a \
- nucleotide frequency for every community
+ :param df_bar: A dataframe with the enrichment of a \
+ nucleotide frequency for every community and showing the frequency \
+ of each feature in each community
:param outfile: File were rdf is stored
:param nt: The nucleotide for which we are seeking enrichment
:param test_type: The kind of test make
:param feature: The king of feature of interest
- :param iteration: The number of sub samples to create
"""
- rdf = prepare_dataframe(rdf, test_type, nt, iteration)
outfig = outfile.parent / outfile.name.replace(".txt", ".pdf")
- make_barplot(rdf, outfig, nt, test_type, feature)
+ make_barplot(df_bar, outfig, nt, test_type, feature)
def create_outfiles(project: str, weight: int, global_weight: int,