diff --git a/tests/test_functions.py b/tests/test_functions.py
index 943dc255971e67b9871af42fde5b881c3edfba5b..a0f2f1ad3d9630cc613012398e9aa045d13fb2a6 100644
--- a/tests/test_functions.py
+++ b/tests/test_functions.py
@@ -4,6 +4,7 @@ import pandas as pd
import numpy as np
+# pylint: disable=C0103
def find_path(filename: str) -> str:
"""Find the path to a file.
diff --git a/tests/test_match_reptrans_explvl.py b/tests/test_match_reptrans_explvl.py
index ae3bb896aeecb7c5448e5da0540c29cbf94242b5..abef23b98d6aae2bf75b1aaf7ba5ddca4c2d96ca 100644
--- a/tests/test_match_reptrans_explvl.py
+++ b/tests/test_match_reptrans_explvl.py
@@ -223,11 +223,7 @@ class TestMatchReptrans:
"at least one row contain NA values"
def test_output_tsv():
- """
- This function test if a tsv file is generated from a pandas
- dataframe in the right format.
- """
-
+ """Test if a tsv file is generated from a df in the right format."""
dict_repr_test = {
'ENSMUSG00000079415': 'ENSMUST00000112933',
"ENSMUSG00000024691": "ENSMUST00000025595",
@@ -251,7 +247,7 @@ class TestMatchReptrans:
ref_path = tFun.find_path("test_ref_output.tsv")
output_path = tFun.find_output()
- with open(ref_path, 'r') as t1, open(output_path, 'r') as t2:
+ with open(ref_path, 'r', encoding="utf-8") as t1, open(output_path, 'r') as t2:
fileRef = t1.readlines()
fileOutput = t2.readlines()
diff --git a/transcript_sampler/find_reptrans.py b/transcript_sampler/find_reptrans.py
index e24746e280bb09c4fb6d100f2b739ebc012161b1..46af0ba6d35fde24fea74d108e80c18dfd66f85c 100644
--- a/transcript_sampler/find_reptrans.py
+++ b/transcript_sampler/find_reptrans.py
@@ -1,10 +1,11 @@
"""Find representative transcripts."""
-
import logging
+from typing import Union
LOG = logging.getLogger(__name__)
+# pylint: disable=R0912,R0915
class FindRepTrans:
"""Find representative transcripts."""
@@ -12,7 +13,7 @@ class FindRepTrans:
"""Initiate."""
@staticmethod
- def attributes_converter(attributes: str) -> list:
+ def attributes_converter(attributes):
"""Attributes converter function.
This funtion converts the "unstructured" ;-seperated part of
@@ -23,7 +24,7 @@ class FindRepTrans:
Input:
attributes = str() # the unstructured part of the entry
Output:
- attributes = list() # cleaned list with the \
+ attributes = list() # cleaned list with the
characteristics described above
"""
attributes = (
@@ -96,7 +97,7 @@ class FindRepTrans:
ValueError: If an unexpected entry is encountered in the GTF file.
"""
# setting default variables
- rep_transcripts = dict()
+ rep_transcripts: dict = {}
cur_g_id = ""
# [transcript_id, transcript_support_level, transcript_length]
cur_best_trans = ["", 100, 0]
@@ -122,11 +123,11 @@ class FindRepTrans:
if cur_g_id != attributes[1]:
LOG.error("Exon from an unexpected gene")
raise ValueError("Exon from an unexpected gene")
- elif (
+ if (
self.find_in_attributes(
attributes, "transcript_id"
- ) != cur_tID
- ):
+ ) != cur_t_ID
+ ):
LOG.error("Exon from an unexpected transcript")
raise ValueError("Exon from an unexpected transcript")
@@ -148,10 +149,10 @@ class FindRepTrans:
raise ValueError("Transcript from an unexpected gene")
# finding the transcript id and the support level
- cur_tID = self.find_in_attributes(
+ cur_t_ID = self.find_in_attributes(
attributes, "transcript_id"
)
- t_supp_lvl = self.find_in_attributes(
+ t_supp_lvl: Union[int, str] = self.find_in_attributes(
attributes, "transcript_support_level"
)
@@ -161,7 +162,7 @@ class FindRepTrans:
if t_supp_lvl == "NA":
t_supp_lvl = 100
else:
- if t_supp_lvl.isdigit():
+ if isinstance(t_supp_lvl, str) and t_supp_lvl.isdigit():
t_supp_lvl = int(t_supp_lvl)
else:
t_supp_lvl = 100
@@ -169,11 +170,11 @@ class FindRepTrans:
# decides if the transcript has potential to become the
# representative transcript
if t_supp_lvl < cur_best_trans[1] or cur_best_trans[0] == "":
- cur_best_trans = [cur_tID, t_supp_lvl, 0]
+ cur_best_trans = [cur_t_ID, t_supp_lvl, 0]
pot_best_trans = False
ignor_trans = False
elif t_supp_lvl == cur_best_trans[1]:
- pot_best_trans = [cur_tID, t_supp_lvl, 0]
+ pot_best_trans = [cur_t_ID, t_supp_lvl, 0]
else:
ignor_trans = True
@@ -203,7 +204,7 @@ class FindRepTrans:
if cur_g_id in rep_transcripts:
if (rep_transcripts[cur_g_id][1] > cur_best_trans[1] or
(rep_transcripts[cur_g_id][1] == cur_best_trans[1] and
- rep_transcripts[cur_g_id][2] < cur_best_trans[2])):
+ rep_transcripts[cur_g_id][2] < cur_best_trans[2])):
rep_transcripts[cur_g_id] = cur_best_trans
else:
rep_transcripts[cur_g_id] = cur_best_trans
diff --git a/transcript_sampler/match_reptrans_explvl.py b/transcript_sampler/match_reptrans_explvl.py
index 5bc73833a9a9d32fb5e1b646ace9c185af6e426d..654f8dc95c04ac41822508b82e04bbe85277e114 100644
--- a/transcript_sampler/match_reptrans_explvl.py
+++ b/transcript_sampler/match_reptrans_explvl.py
@@ -1,4 +1,4 @@
-"""Match representative transcript with expression level"""
+"""Match representative transcript with expression level."""
# Made by Hugo Gillet #
import logging
@@ -40,12 +40,15 @@ class MatchReptransExplvl:
return df_gtf
@staticmethod
- def dict_repr_trans_to_df(dict_reprTrans: "dict[str, str]") -> pd.DataFrame:
+ def dict_repr_trans_to_df(
+ dict_repr_trans: "dict[str, str]"
+ ) -> pd.DataFrame:
"""
- Convert a dictionary of genes and their representative transcript into a DataFrame.
+ Convert a dict of genes and their representative transcript into a df.
Args:
- dict_reprTrans (dict): {'Gene': ['transcriptA', 'transcriptB'], ...}
+ dict_repr_trans (dict):
+ {'Gene': ['transcriptA', 'transcriptB'], ...}
Returns:
Pandas DataFrame with 'Gene' and 'Transcript' as columns.
@@ -55,22 +58,32 @@ class MatchReptransExplvl:
TypeError: Keys should be strings.
TypeError: Values should be strings.
"""
- if not isinstance(dict_reprTrans, dict):
+ if not isinstance(dict_repr_trans, dict):
LOG.error("Only dictionaries are allowed")
raise TypeError("Only dictionaries are allowed")
- if not all(isinstance(key, str) for key in dict_reprTrans.keys()):
+ if not all(
+ isinstance(key, str) for key in dict_repr_trans.keys()
+ ):
LOG.error("Keys should be strings")
raise TypeError("Keys should be strings")
- if not all(isinstance(value, str) for value in dict_reprTrans.values()):
+ if not all(
+ isinstance(value, str) for value in dict_repr_trans.values()
+ ):
LOG.error("Values should be strings")
raise TypeError("Values should be strings")
- df_reprTrans = pd.DataFrame.from_dict(dict_reprTrans, orient="index", columns=["reprTranscript"])
- df_reprTrans = df_reprTrans.reset_index()
- df_reprTrans.columns = ["Gene", "reprTrans"]
- df_reprTrans["reprTrans"] = df_reprTrans["reprTrans"].str.replace(r"\.[1-9]", "", regex=True)
+ df_repr_trans = pd.DataFrame.from_dict(
+ dict_repr_trans, orient="index", columns=["reprTranscript"]
+ )
+ df_repr_trans = df_repr_trans.reset_index()
+ column_names = ["Gene", "reprTrans"]
+ df_repr_trans.columns = pd.Index(column_names)
+ # pylint: disable=E1136,E1137
+ df_repr_trans["reprTrans"] = df_repr_trans["reprTrans"].str.replace(
+ r"\.[1-9]", "", regex=True
+ )
- return df_reprTrans
+ return df_repr_trans
@staticmethod
def tsv_or_csv_to_df(input_txt: str) -> pd.DataFrame:
@@ -99,85 +112,111 @@ class MatchReptransExplvl:
@staticmethod
def expr_level_by_gene(
- df_exprTranscript: pd.DataFrame, df_output_gtf_selection: pd.DataFrame
+ df_expr_transcript: pd.DataFrame, df_output_gtf_selection: pd.DataFrame
) -> pd.DataFrame:
- """
- Find the gene of each transcript given by the expression level CSV/TSV file
- and sum the expression level of all transcripts from the same gene.
-
+ """Sum expression levels.
+
+ Find the gene of each transcript given by the expression level
+ CSV/TSV file and sum the expression level of all transcripts
+ from the same gene.
+
Args:
- df_exprTranscript (pd.DataFrame): Pandas DataFrame containing transcripts and their expression levels,
- generated by the "tsv_or_csv_to_df" function.
- df_output_gtf_selection (pd.DataFrame): Pandas DataFrame containing genes and transcripts,
- generated by the "transcripts_by_gene_inDf" function.
-
+ df_expr_transcript (pd.DataFrame):
+ Pandas DataFrame containing transcripts and their
+ expression levels, generated by the
+ "tsv_or_csv_to_df" function.
+ df_output_gtf_selection (pd.DataFrame):
+ Pandas DataFrame containing genes and transcripts,
+ generated by the "transcripts_by_gene_inDf" function.
+
Returns:
- Pandas DataFrame having 'Gene' and sum of its transcript expression levels.
-
+ Pandas DataFrame having 'Gene' and sum of its
+ transcript expression levels.
+
Raises:
None
"""
- df_merged = pd.merge(df_output_gtf_selection, df_exprTranscript, how="inner", on="Transcript")
- df_sum = df_merged.groupby("Gene")["Expression_level"].sum().reset_index()
+ df_merged = pd.merge(
+ df_output_gtf_selection, df_expr_transcript,
+ how="inner", on="Transcript")
+ df_sum = df_merged.groupby("Gene")["Expression_level"].sum(
+ ).reset_index()
return df_sum
@staticmethod
def match_by_gene(
- df_reprTranscript: pd.DataFrame, df_expressionLevel_byGene: pd.DataFrame
+ df_repr_transcript: pd.DataFrame,
+ df_expression_level_by_gene: pd.DataFrame
) -> pd.DataFrame:
"""
Find matching genes between the two DataFrames.
-
+
Args:
- df_reprTranscript (pd.DataFrame): Pandas DataFrame containing genes and their representative transcripts,
- generated by the "dict_repr_trans_to_df()" function.
- df_expressionLevel_byGene (pd.DataFrame): Pandas DataFrame containing genes and their expression levels,
- generated by the "transcript_by_gene_inDf()" function.
-
+ df_repr_transcript (pd.DataFrame): Pandas DataFrame
+ containing genes and their representative transcripts,
+ generated by the "dict_repr_trans_to_df()" function.
+ df_expression_level_by_gene (pd.DataFrame): Pandas DataFrame
+ containing genes and their expression levels,
+ generated by the "transcript_by_gene_inDf()" function.
+
Returns:
- Pandas DataFrame having representative transcripts and their expression levels.
-
+ Pandas DataFrame having representative transcripts and
+ their expression levels.
+
Raises:
None
"""
- df_merged = pd.merge(df_reprTranscript, df_expressionLevel_byGene, how="inner", on="Gene")
+ df_merged = pd.merge(
+ df_repr_transcript, df_expression_level_by_gene,
+ how="inner", on="Gene"
+ )
df_clean = df_merged.loc[:, ["reprTrans", "Expression_level"]]
return df_clean
def match_repr_transcript_expression_level(
- self, exprTrans: str, dict_reprTrans: dict, gtf_file: str,
+ self, expr_trans: str, dict_repr_trans: dict, gtf_file: str,
):
- """
- Combine functions to replace transcripts from an expression level CSV/TSV file with representative transcripts.
+ """Replace expression level with representative transcripts.
+
+ Combine functions to replace transcripts from an expression level
+ CSV/TSV file with representative transcripts.
Args:
- exprTrans (str): CSV or TSV file containing transcripts and their expression level.
- dict_reprTrans (dict): Dictionary of genes and their representative transcripts.
+ expr_trans (str): CSV or TSV file containing transcripts
+ and their expression level.
+ dict_repr_trans (dict): Dictionary of genes
+ and their representative transcripts.
gtf_file (str): Path to the GTF file.
Returns:
- Pandas DataFrame of representative transcripts and their expression level.
+ Pandas DataFrame of representative transcripts
+ and their expression level.
Raises:
None
"""
df_gene_transcript = self.gtf_to_df(gtf_file)
- df_exprTrans = self.tsv_or_csv_to_df(exprTrans)
- df_reprTrans = self.dict_repr_trans_to_df(dict_reprTrans)
- df_expr_level_by_gene = self.expr_level_by_gene(df_exprTrans, df_gene_transcript)
- df_match = self.match_by_gene(df_reprTrans, df_expr_level_by_gene)
- df_match.rename(columns={"reprTrans": "id", "Expression_level": "level"}, inplace=True)
+ df_expr_trans = self.tsv_or_csv_to_df(expr_trans)
+ df_repr_trans = self.dict_repr_trans_to_df(dict_repr_trans)
+ df_expr_level_by_gene = self.expr_level_by_gene(
+ df_expr_trans, df_gene_transcript
+ )
+ df_match = self.match_by_gene(df_repr_trans, df_expr_level_by_gene)
+ df_match.rename(
+ columns={"reprTrans": "id", "Expression_level": "level"},
+ inplace=True
+ )
return df_match
-
-# def dict_repr_trans_to_df(dict_reprTrans: "dict[str, str]") -> pd.DataFrame:
+# def dict_repr_trans_to_df(dict_repr_trans: "dict[str, str]") -> pd.DataFrame:
# """Convert a dictionary of genes and their representative
# transcript into a dataframe
# Args:
-# dict_reprTrans (dict): {'Gene':['transcriptA', 'transcriptB'], ...}
+# dict_repr_trans (dict):
+# {'Gene':['transcriptA', 'transcriptB'], ...}
# Returns:
# Pandas dataframe having Gene and transcript as columns
@@ -189,22 +228,22 @@ class MatchReptransExplvl:
# """
# pass
-# if not type(dict_reprTrans) is dict:
+# if not type(dict_repr_trans) is dict:
# raise TypeError("Only dict are allowed")
-# if type(list(dict_reprTrans.keys())[0]) is not str:
+# if type(list(dict_repr_trans.keys())[0]) is not str:
# raise TypeError("Key should be strings")
-# if type(list(dict_reprTrans.values())[0]) is not str:
+# if type(list(dict_repr_trans.values())[0]) is not str:
# raise TypeError("Values should be strings")
-# df_reprTrans = pd.DataFrame.from_dict(
-# dict_reprTrans, orient="index", columns=["reprTranscript"]
+# df_repr_trans = pd.DataFrame.from_dict(
+# dict_repr_trans, orient="index", columns=["reprTranscript"]
# )
-# df_reprTrans = df_reprTrans.reset_index(level=0)
-# df_reprTrans.columns = ["Gene", "reprTrans"]
-# df_reprTrans["reprTrans"] = df_reprTrans["reprTrans"].str.replace(
+# df_repr_trans = df_repr_trans.reset_index(level=0)
+# df_repr_trans.columns = ["Gene", "reprTrans"]
+# df_repr_trans["reprTrans"] = df_repr_trans["reprTrans"].str.replace(
# r"\.[1-9]", "", regex=True
# )
-# return df_reprTrans
+# return df_repr_trans
# def gene_and_transcript(gtf_file: str) -> pd.DataFrame:
@@ -259,7 +298,7 @@ class MatchReptransExplvl:
# file, and summ expression level of all transcipts from the same gene.
# Args:
-# df_exprTranscript: pandas df containing transcript and
+# df_expr_transcript: pandas df containing transcript and
# their exp level generated by "tsv_or_csv_to_df" function
# df_output_gtf_selection : pandas df containing genes and
# transcripts, generated by "transcripts_by_gene_inDf" function
@@ -282,15 +321,16 @@ class MatchReptransExplvl:
# def match_by_gene(
-# df_reprTranscript: pd.DataFrame, df_expressionLevel_byGene: pd.DataFrame
+# df_repr_transcript: pd.DataFrame,
+# df_expression_level_by_gene: pd.DataFrame
# ) -> pd.DataFrame:
# """Find matching genes bewteen the 2 args
# Args:
-# df_reprTranscript : pandas Dataframe containing genes
+# df_repr_transcript : pandas Dataframe containing genes
# and their representative transcript, generated by
# "dict_repr_trans_to_df()"
-# df_expressionLevel_byGene : pandas Dataframe containing
+# df_expression_level_by_gene : pandas Dataframe containing
# genes and their expression level generated by
# "transcript_by_gene_inDf()"
@@ -303,7 +343,8 @@ class MatchReptransExplvl:
# """
# pass
# df_merged = pd.merge(
-# df_reprTranscript, df_expressionLevel_byGene, how="outer", on="Gene"
+# df_repr_transcript, df_expression_level_by_gene,
+# how="outer", on="Gene"
# )
# df_clean = df_merged.dropna(axis=0)
# df_clean = df_clean.loc[:, ["reprTrans", "Expression_level"]]
@@ -312,15 +353,15 @@ class MatchReptransExplvl:
# # functions to run this part of the programm
# def match_repr_transcript_expression_level(
-# exprTrans: str, dict_reprTrans: dict, gtf_file: str,
+# expr_trans: str, dict_repr_trans: dict, gtf_file: str,
# ):
-# """Combine functions to replace transcripts from an exp level csv/tsv file
+# """Combine functions to replace transcripts from exp level csv/tsv file
# with representative transcripts
# Args:
-# exprTrans (str): csv or tsv file containing transcripts
+# expr_trans (str): csv or tsv file containing transcripts
# and their expression level
-# dict_reprTrans (dict) : dict of genes and their
+# dict_repr_trans (dict) : dict of genes and their
# representative transcipt
# intemediate_file (str) : txt file containing genes, transcript
# and their expression level from the transkript_extractor function
@@ -333,12 +374,12 @@ class MatchReptransExplvl:
# None
# """
# df_gene_transcript = gene_and_transcript(gtf_file)
-# df_exprTrans = tsv_or_csv_to_df(exprTrans)
-# df_reprTrans = dict_repr_trans_to_df(dict_reprTrans)
+# df_expr_trans = tsv_or_csv_to_df(expr_trans)
+# df_repr_trans = dict_repr_trans_to_df(dict_repr_trans)
# df_expr_level_by_gene = expr_level_by_gene(
-# df_exprTrans, df_gene_transcript
+# df_expr_trans, df_gene_transcript
# ) # error here
-# df_match = match_by_gene(df_reprTrans, df_expr_level_by_gene)
+# df_match = match_by_gene(df_repr_trans, df_expr_level_by_gene)
# df_match.rename(columns={'reprTrans': 'id', 'Expression_level': 'level'},
# inplace=True)
# return df_match