diff --git a/src/find_interaction_cluster/nt_and_community.py b/src/find_interaction_cluster/nt_and_community.py
index 9046ae96d4159f1fec2647201cde512f5c9e23c3..07d5e40e7701ac40d133061a0cf9857afc0cbbbe 100644
--- a/src/find_interaction_cluster/nt_and_community.py
+++ b/src/find_interaction_cluster/nt_and_community.py
@@ -24,6 +24,7 @@ import multiprocessing as mp
import numpy as np
from .radomization_test_ppi import get_pvalue
from tqdm import tqdm
+import seaborn as sns
def get_nt_frequency(cnx: sqlite3.Connection, list_ft: List[str],
@@ -341,7 +342,7 @@ def perm_pvalues(df: pd.DataFrame, df_ctrl: pd.DataFrame, feature: str,
returnsorted=False)[1]
adj_regs = [list_reg[i] if adj_pvals[i] <= 0.05 else " . "
for i in range(len(list_reg))]
- df[f'mean_{iteration}_ctrl'] = mean_ctrl
+ df[f'{nt}_mean_{iteration}_ctrl'] = mean_ctrl
df[f'p-adj'] = adj_pvals
df[f'reg-adj'] = adj_regs
return df
@@ -369,6 +370,157 @@ def perm_with_ctrl(df: pd.DataFrame, feature: str,
return perm_pvalues(mean_df, df_ctrl, feature, nt, iteration, dic_com)
+def prepare_dataframe(df: pd.DataFrame, test_type: str, nt: str,
+ iteration: int) -> pd.DataFrame:
+ """
+
+ :param df: A dataframe with the enrichment of a \
+ nucleotide frequency for every community
+ :param test_type: The kind of test performed to
+ :param nt: The nucleotide of interest
+ :param iteration: The number of iteration permformed to \
+ produce the database
+ :return: The dataframe ready for barplot visualisation
+ """
+ if test_type == "lmm":
+ # removing the size parameter
+ df = df[df["community"] != "log(_size)"].copy()
+ df.rename({"Pr(>|t|)": "p-adj"}, axis=1, inplace=True)
+ df_bar = df[['community', nt, 'p-adj']]
+ df_ctrl = df_bar[df_bar["community"] == "C-CTRL"]
+ df_bar = df_bar[df_bar["community"] != "C-CTRL"].copy()
+ df_bar.sort_values(nt, ascending=True, inplace=True)
+ else:
+ df_bar = df[["community", nt, f"{nt}_mean_{iteration}_ctrl",
+ "p-adj"]].copy()
+ ctrl_val = df_bar[f"{nt}_mean_{iteration}_ctrl"]
+ df_bar.drop(f"{nt}_mean_{iteration}_ctrl", axis=1, inplace=True)
+ df_bar.sort_values(nt, ascending=True, inplace=True)
+ df_ctrl = pd.DataFrame({"community": ["C-CTRL"] * len(ctrl_val),
+ nt: ctrl_val})
+ df_bar = pd.concat([df_ctrl, df_bar], axis=0, ignore_index=True)
+ return df_bar
+
+
+def make_barplot(df_bar: pd.DataFrame, outfile: Path, nt: str, test_type: str,
+ feature: str) -> None:
+ """
+ Create a barplot showing the frequency of `nt` for every community \
+ of exons/gene in `df_bar`.
+
+ :param df_bar: A dataframe with the enrichment of a \
+ nucleotide frequency for every community
+ :param outfile: File were the figure will be 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
+ """
+ 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))
+ 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")
+ 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"])
+ g.ax.annotate(stats,
+ (p.get_x() + p.get_width() / 2., p.get_height()),
+ ha='center', va='center', xytext=(0, 10),
+ textcoords='offset points')
+ g.savefig(outfile)
+
+
+def create_and_save_ctrl_dataframe(df: pd.DataFrame, feature: str,
+ region: str, nt: str, outfile: Path,
+ test_type: str, df_ctrl: pd.DataFrame,
+ dic_com: Dict, iteration: int,
+ outfile_ctrl: Path) -> None:
+ """
+ Create a dataframe with a control community, save it as a table and \
+ as a barplot figure.
+
+ :param df: A dataframe containing the frequency of each nucleotide \
+ in each exons belonging to a community.
+ :param feature: The kind of feature analysed
+ :param region: the region of interest to extract from gene
+ :param nt: The nucleotide of interest
+ :param outfile: File used to store diagnotics
+ :param test_type: The type of test to make (permutation or lmm)
+ :param df_ctrl: A dataframe containing the frequency of each nucleotide \
+ in each exons/gene in fasterdb.
+ :param dic_com: A dictionary linking each community to the exons \
+ it contains.
+ :param iteration: The number of sub samples to create
+ :param outfile_ctrl: file used to stored the table and the figure \
+ 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)
+ else:
+ rdf = perm_with_ctrl(df, feature, nt, df_ctrl, dic_com, iteration)
+ rdf.to_csv(outfile_ctrl, sep="\t", index=False)
+ barplot_creation(rdf, outfile_ctrl, nt,
+ test_type, feature, iteration)
+
+
+def barplot_creation(rdf: pd.DataFrame, outfile: Path, nt: str,
+ test_type: str, feature: str, iteration: int) -> None:
+ """
+ Reformat a dataframe with the enrichment of a nucleotide frequency \
+ 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 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)
+
+
+def create_outfiles(project: str, weight: int, global_weight: int,
+ same_gene: bool, feature: str, nt: str, test_type: str
+ ) -> Tuple[Path, Path]:
+ """
+ Create a file used to store diagnostics and a file used to store the \
+ table containing the test communities and the control community
+
+ :param project: The name of the project of interest
+ :param weight: The minimum weight of interaction to consider
+ :param global_weight: The global weight to consider. if \
+ the global weight is equal to 0 then then density figure are calculated \
+ by project, else all projet are merge together and the interaction \
+ seen in `global_weight` project are taken into account
+ :param same_gene: Say if we consider as co-localised, exons within the \
+ same gene (True) or not (False) (default False)
+ :param nt: The nucleotide of interest
+ :param feature: The kind of feature analysed
+ :param test_type: The type of test to make (permutation or lmm)
+ :return: file used to store diagnostics and a file used to store the \
+ table containing the test communities and the control community
+ """
+
+ outfolder = "community_enrichment/nt_analysis"
+ outfile = ConfigGraph.\
+ get_community_file(project, weight, global_weight, same_gene, feature,
+ f"{nt}_stat_{test_type}.txt", outfolder)
+ outfile_ctrl = ConfigGraph.\
+ get_community_file(project, weight, global_weight, same_gene, feature,
+ f"{nt}_VS_CTRL_stat_{test_type}.txt", outfolder)
+ return outfile, outfile_ctrl
+
+
def get_stat_nt_communities(df: pd.DataFrame, project: str, weight: int,
global_weight: int, same_gene: bool,
nt: str, df_ctrl: pd.DataFrame,
@@ -402,31 +554,12 @@ def get_stat_nt_communities(df: pd.DataFrame, project: str, weight: int,
"""
logging.debug(f"{test_type} for {project}, w:{weight}, "
f"g:{global_weight} nt: {nt}")
- res = {"project": [], "nt": [], 'pval': []}
- outfile = ConfigGraph.get_community_file(project, weight,
- global_weight,
- same_gene, feature,
- f"{nt}_stat_{test_type}.txt",
- "community_enrichment")
- nfolder = outfile.parent / "nt_analysis"
- nfolder.mkdir(exist_ok=True, parents=True)
- noutfile = nfolder / outfile.name
- pval = lmm_maker(df, noutfile, nt)
- res['project'] = project
- res['nt'] = nt
- res['pval'] = pval
- outfile_ctrl = ConfigGraph.get_community_file(project, weight,
- global_weight,
- same_gene, feature,
- f"{nt}_VS_CTRL_stat_"
- f"{test_type}.txt",
- "community_enrichment")
- if test_type == "lmm":
- rdf = lmm_with_ctrl(df, feature, region, nt, outfile)
- else:
- rdf = perm_with_ctrl(df, feature, nt, df_ctrl, dic_com, iteration)
- noutfile_ctrl = nfolder / outfile_ctrl.name
- rdf.to_csv(noutfile_ctrl, sep="\t", index=False)
+ outfile, outfile_ctrl = create_outfiles(project, weight, global_weight,
+ same_gene, feature, nt, test_type)
+ res = {"project": project, "nt": nt, 'pval': lmm_maker(df, outfile, nt)}
+ create_and_save_ctrl_dataframe(df, feature, region, nt, outfile, test_type,
+ df_ctrl, dic_com, iteration, outfile_ctrl)
+
return pd.Series(res)