From 661d27aa95beb6c7d4c3a521a02088f8719cb755 Mon Sep 17 00:00:00 2001
From: Fontrodona Nicolas <nicolas.fontrodona@ens-lyon.fr>
Date: Mon, 4 Jan 2021 12:39:10 +0100
Subject: [PATCH]
src/find_interaction_cluster/community_figures/fig_functions.py: deletion of
unused import and functions + creation of dic_com when the test type is
permutation
---
.../community_figures/fig_functions.py | 73 +------------------
1 file changed, 2 insertions(+), 71 deletions(-)
diff --git a/src/find_interaction_cluster/community_figures/fig_functions.py b/src/find_interaction_cluster/community_figures/fig_functions.py
index 62f9d73a..5e754029 100644
--- a/src/find_interaction_cluster/community_figures/fig_functions.py
+++ b/src/find_interaction_cluster/community_figures/fig_functions.py
@@ -9,8 +9,6 @@ Description:
import pandas as pd
from pathlib import Path
from typing import Dict, Tuple, List, Optional
-import sqlite3
-from ..config import ConfigGraph
from tqdm import tqdm
from rpy2.robjects import r, pandas2ri
from statsmodels.stats.multitest import multipletests
@@ -19,74 +17,6 @@ from ..radomization_test_ppi import get_pvalue
import seaborn as sns
-def get_cpnt_frequency(cnx: sqlite3.Connection, list_ft: List[str],
- feature: str, region: str = "",
- component_type: str = "nt") -> pd.DataFrame:
- """
- Get the frequency of every nucleotides for features in list_ft.
-
- :param cnx: Connection to chia-pet database
- :param list_ft: The list of exons for which we want to get
- :param feature: the kind of feature analysed
- :param region: The region of gene analysed if feature is gene
- :param component_type: The type of component to analyse; It \
- can be 'nt', 'dnt' or 'aa'.
- :return: the frequency of nucleotides for the list of exons.
-
- >>> d = get_cpnt_frequency(sqlite3.connect(ConfigGraph.db_file),
- ... ["1_1", "1_2"], "exon")
- >>> d[["id_exon", 'A', 'C', 'G', 'T']]
- ft id_exon A C G T
- 0 1_1 16.63480 34.60803 32.12237 16.63480
- 1 1_2 16.06426 26.10442 39.75904 18.07229
- >>> d = get_cpnt_frequency(sqlite3.connect(ConfigGraph.db_file),
- ... ['1', '2'], "gene")
- >>> d[["id_gene", 'A', 'C', 'G', 'T']]
- ft id_gene A C G T
- 0 1 29.49376 18.34271 18.43874 33.72479
- 1 2 31.90401 16.40251 18.79033 32.90315
- >>> d = get_cpnt_frequency(sqlite3.connect(ConfigGraph.db_file),
- ... ['1', '2'], "gene", 'exon', 'aa')
- >>> d[["id_gene", "R", "K", "D", "Q", "E"]]
- ft id_gene R K D Q E
- 0 1 4.75247 5.19300 5.95391 4.07997 6.96189
- 1 2 4.34203 6.23736 6.77708 5.21984 7.01769
- """
- query_region = ""
- if feature == "gene":
- list_ft = [int(ft) for ft in list_ft]
- if region == "":
- region = "gene"
- query_region = f"AND region = '{region}'"
- query = f"""
- SELECT ft, id_{feature}, frequency
- FROM cin_{feature}_frequency
- WHERE id_{feature} IN {tuple(list_ft)}
- AND ft_type = '{component_type}'
- {query_region}
- """
- df = pd.read_sql_query(query, cnx)
- df = df.pivot_table(index=f"id_{feature}", columns="ft",
- values="frequency").reset_index()
- df[f"id_{feature}"] = df[f"id_{feature}"].astype(str)
- return df
-
-
-def get_ft_id(cnx: sqlite3.Connection, feature: str = "exon") -> List[str]:
- """
- Return the id of every gene/exons in chia-pet database.
-
- :param cnx: A connection to chiapet database
- :param feature: The feature of interest
- :return: The list of feature id
- """
- query = f"SELECT DISTINCT id FROM cin_{feature}"
- c = cnx.cursor()
- c.execute(query)
- res = c.fetchall()
- return [str(cid[0]) for cid in res]
-
-
def get_community_table(communities: List[List[str]],
size_threshold: int, feature: str) -> pd.DataFrame:
"""
@@ -545,7 +475,8 @@ def create_community_fig(df: pd.DataFrame, feature: str,
:param iteration: The number of sub samples to create
"""
if dic_com is None:
- dic_com = get_feature_by_community(df, feature)
+ dic_com = {} if test_type == 'lm' \
+ else get_feature_by_community(df, feature)
if test_type == "lm":
ndf, rdf = lm_with_ctrl(df, target_col, outfile_ctrl)
df_bar = expand_results_lm(ndf, rdf, target_col, feature)
--
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