diff --git a/transcript_sampler/cli.py b/transcript_sampler/cli.py
index f4d278805d2f8277dc9d2e71813aeaf85cf9c01a..13446a4524f90fb98ce3f1ff16114f8110b19a91 100644
--- a/transcript_sampler/cli.py
+++ b/transcript_sampler/cli.py
@@ -52,8 +52,8 @@ def main():
log.info("Started transcript sampler.")
dict_repr_trans = find_rep_trans.get_rep_trans(args.input_gtf)
df_repr = match_reptrs_explvl.match_repr_transcript_expression_level(
- dict_reprTrans=dict_repr_trans,
- exprTrans=args.input_csv,
+ dict_repr_trans=dict_repr_trans,
+ expr_trans=args.input_csv,
gtf_file=args.input_gtf
)
log.info(
diff --git a/transcript_sampler/find_reptrans.py b/transcript_sampler/find_reptrans.py
index 46af0ba6d35fde24fea74d108e80c18dfd66f85c..9c2511f5e42c9d32c1adc9ecfd4ad41f535c4cce 100644
--- a/transcript_sampler/find_reptrans.py
+++ b/transcript_sampler/find_reptrans.py
@@ -52,10 +52,9 @@ class FindRepTrans:
if look_for in attributes:
index = attributes.index(look_for) + 1
return attributes[index]
- else:
- LOG.warning('No %s in the entry, the return was set to NA',
- look_for)
- return "NA"
+ LOG.warning('No %s in the entry, the return was set to NA',
+ look_for)
+ return "NA"
@staticmethod
def reformat_reptrans(rep_trans_dict: dict) -> dict:
@@ -99,6 +98,9 @@ class FindRepTrans:
# setting default variables
rep_transcripts: dict = {}
cur_g_id = ""
+ cur_t_id = ""
+ pot_best_trans: list = []
+ cur_best_trans: list = []
# [transcript_id, transcript_support_level, transcript_length]
cur_best_trans = ["", 100, 0]
@@ -126,7 +128,7 @@ class FindRepTrans:
if (
self.find_in_attributes(
attributes, "transcript_id"
- ) != cur_t_ID
+ ) != cur_t_id
):
LOG.error("Exon from an unexpected transcript")
raise ValueError("Exon from an unexpected transcript")
@@ -137,7 +139,6 @@ class FindRepTrans:
pot_best_trans[2] += int(entry[4]) - int(entry[3])
if pot_best_trans[2] > cur_best_trans[2]:
cur_best_trans = pot_best_trans
- pot_best_trans = False
else:
cur_best_trans[2] += int(entry[4]) - int(entry[3])
@@ -149,7 +150,7 @@ class FindRepTrans:
raise ValueError("Transcript from an unexpected gene")
# finding the transcript id and the support level
- cur_t_ID = self.find_in_attributes(
+ cur_t_id = self.find_in_attributes(
attributes, "transcript_id"
)
t_supp_lvl: Union[int, str] = self.find_in_attributes(
@@ -162,21 +163,22 @@ class FindRepTrans:
if t_supp_lvl == "NA":
t_supp_lvl = 100
else:
- if isinstance(t_supp_lvl, str) and 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
# 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_t_ID, t_supp_lvl, 0]
- pot_best_trans = False
- ignor_trans = False
+ if (
+ t_supp_lvl < cur_best_trans[1] or
+ cur_best_trans[0] == ""
+ ):
+ cur_best_trans = [cur_t_id, t_supp_lvl, 0]
elif t_supp_lvl == cur_best_trans[1]:
- pot_best_trans = [cur_t_ID, t_supp_lvl, 0]
- else:
- ignor_trans = True
+ pot_best_trans = [cur_t_id, t_supp_lvl, 0]
# looking for and processing gene entries
elif entry[2] == "gene":
@@ -221,8 +223,8 @@ class FindRepTrans:
"""
output = []
- with open(original_file, "r", encoding="utf-8") as f:
- for line in f:
+ with open(original_file, "r", encoding="utf-8") as file:
+ for line in file:
if line.startswith("#"):
continue
@@ -243,51 +245,3 @@ class FindRepTrans:
with open(output_file, "w", encoding="utf-8") as last_file:
last_file.writelines(output)
-
-
-# def _test():
-# """
-# This funtion is meant to be run for test
-# Output:
-# file with the dictionary generated based on the test file
-# """
-# file_name = "test.gtf"
-# rt = get_rep_trans(file_name)
-# expected_result = {"ENSG00000160072": "ENST00000472194",
-# "ENSG00000234396": "ENST00000442483",
-# "ENSG00000225972": "ENST00000416931",
-# "ENSG00000224315": "ENST00000428803",
-# "ENSG00000198744": "ENST00000416718",
-# "ENSG00000279928": "ENST00000624431",
-# "ENSG00000228037": "ENST00000424215",
-# 'ENSG00000142611': 'ENST00000378391'}
-# if rt != expected_result:
-# print("The test failed due to not yielding the same results")
-# print("The results the program got\n", rt)
-# print("The expected results\n", expected_result)
-# else:
-# print("The test was successful")
-
-
-# # Execution part #
-# if __name__ == "__main__":
-# parser = argparse.ArgumentParser(
-# description="find_representativ_transcripts",
-# formatter_class=argparse.ArgumentDefaultsHelpFormatter
-# )
-# parser.add_argument("-file_name", required=True,
-# help="gtf file with genome annotation")
-# parser.add_argument("-t", required=False, default=False,
-# help="to run the test input -t True")
-# args = parser.parse_args()
-
-# # standadize the file_name inlude .gtf#
-# file_name = args.file_name
-# i_gtf = file_name.find(".gtf")
-# if i_gtf == -1:
-# file_name += ".gtf"
-
-# if args.t:
-# _test()
-# else:
-# get_rep_trans(file_name)
diff --git a/transcript_sampler/match_reptrans_explvl.py b/transcript_sampler/match_reptrans_explvl.py
index 654f8dc95c04ac41822508b82e04bbe85277e114..a914d8ca744c19e8c59b43ce7b526f1d9233cd72 100644
--- a/transcript_sampler/match_reptrans_explvl.py
+++ b/transcript_sampler/match_reptrans_explvl.py
@@ -2,8 +2,8 @@
# Made by Hugo Gillet #
import logging
-import pandas as pd
-from gtfparse import read_gtf
+import pandas as pd # type: ignore
+from gtfparse import read_gtf # type: ignore
LOG = logging.getLogger(__name__)
@@ -43,8 +43,7 @@ class MatchReptransExplvl:
def dict_repr_trans_to_df(
dict_repr_trans: "dict[str, str]"
) -> pd.DataFrame:
- """
- Convert a dict of genes and their representative transcript into a df.
+ """Convert a dict of genes and representative transcript into a df.
Args:
dict_repr_trans (dict):
@@ -87,8 +86,7 @@ class MatchReptransExplvl:
@staticmethod
def tsv_or_csv_to_df(input_txt: str) -> pd.DataFrame:
- """
- Convert a TSV or CSV file into a pandas DataFrame.
+ """Convert a TSV or CSV file into a pandas DataFrame.
Args:
input_txt (str): TSV or CSV file containing transcript expression
@@ -148,8 +146,7 @@ class MatchReptransExplvl:
df_repr_transcript: pd.DataFrame,
df_expression_level_by_gene: pd.DataFrame
) -> pd.DataFrame:
- """
- Find matching genes between the two DataFrames.
+ """Find matching genes between the two DataFrames.
Args:
df_repr_transcript (pd.DataFrame): Pandas DataFrame
@@ -207,184 +204,3 @@ class MatchReptransExplvl:
inplace=True
)
return df_match
-
-
-# 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_repr_trans (dict):
-# {'Gene':['transcriptA', 'transcriptB'], ...}
-
-# Returns:
-# Pandas dataframe having Gene and transcript as columns
-
-# Raises:
-# Only dict are allowed
-# Key should be strings
-# Value should be strings
-
-# """
-# pass
-# if not type(dict_repr_trans) is dict:
-# raise TypeError("Only dict are allowed")
-# if type(list(dict_repr_trans.keys())[0]) is not str:
-# raise TypeError("Key should be strings")
-# if type(list(dict_repr_trans.values())[0]) is not str:
-# raise TypeError("Values should be strings")
-
-# df_repr_trans = pd.DataFrame.from_dict(
-# dict_repr_trans, orient="index", columns=["reprTranscript"]
-# )
-# 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_repr_trans
-
-
-# def gene_and_transcript(gtf_file: str) -> pd.DataFrame:
-# """
-# This function take a .gtf file and convert it into a
-# dataframe containing gene_id and their transcripts_id.
-# Args:
-# gtf_file(str) : path to the .gtf file
-
-# Returns:
-# df_gtf(pd.DataFrame): pandas df containing having has columns
-# gene_id and their transcripts_id.
-# Raises:
-# None
-# """
-# df_gtf = read_gtf(gtf_file)
-# df_gtf = df_gtf.loc[df_gtf["feature"] == "transcript"]
-# df_gtf = df_gtf[["gene_id", "transcript_id"]]
-# df_gtf = df_gtf.rename(columns={"gene_id": "Gene",
-# "transcript_id": "Transcript"})
-# return df_gtf
-
-
-# def tsv_or_csv_to_df(input_txt: str) -> pd.DataFrame:
-# """Convert tsv or csv file into a pandas dataframe
-
-# Args:
-# input_txt (str): csv or tsv file containing transcript exp level
-
-# Returns:
-# df_gene (str): Pandas dataframe having transcript and exp level
-# as columns
-
-# Raises:
-# None
-# """
-# pass
-# df_input = pd.read_csv(
-# input_txt,
-# sep=r"[\t,]",
-# lineterminator="\n",
-# names=["Transcript", "Expression_level"],
-# engine="python",
-# )
-# return df_input
-
-
-# def expr_level_by_gene(
-# df_exprTrasncript: pd.DataFrame, df_output_gtf_selection: pd.DataFrame
-# ) -> pd.DataFrame:
-# """find the gene of each transcipt given by the expression level csv/tsv
-# file, and summ expression level of all transcipts from the same gene.
-
-# Args:
-# 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
-
-# Returns:
-# Pandas dataframe having gene and sum of its transcript exp level
-
-# Raises:
-# None
-# """
-# pass
-# df_merged = pd.merge(
-# df_output_gtf_selection, df_exprTrasncript,
-# how="inner", on="Transcript"
-# )
-# df_sum = df_merged.groupby("Gene").sum(
-# "Expression_level"
-# )
-# return df_sum
-
-
-# def match_by_gene(
-# df_repr_transcript: pd.DataFrame,
-# df_expression_level_by_gene: pd.DataFrame
-# ) -> pd.DataFrame:
-# """Find matching genes bewteen the 2 args
-
-# Args:
-# df_repr_transcript : pandas Dataframe containing genes
-# and their representative transcript, generated by
-# "dict_repr_trans_to_df()"
-# df_expression_level_by_gene : pandas Dataframe containing
-# genes and their expression level generated by
-# "transcript_by_gene_inDf()"
-
-# Returns:
-# Pandas dataframe having representative trasncripts
-# and their expression level
-
-# Raises:
-# None
-# """
-# pass
-# df_merged = pd.merge(
-# 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"]]
-# return df_clean
-
-
-# # functions to run this part of the programm
-# def match_repr_transcript_expression_level(
-# expr_trans: str, dict_repr_trans: dict, gtf_file: str,
-# ):
-# """Combine functions to replace transcripts from exp level csv/tsv file
-# with representative transcripts
-
-# Args:
-# expr_trans (str): csv or tsv file containing transcripts
-# and their expression level
-# 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
-# output_path : path indicating were the tsv file should be written
-
-# Returns:
-# tsv file of representative trasncripts and their expression level
-
-# Raises:
-# None
-# """
-# df_gene_transcript = gene_and_transcript(gtf_file)
-# 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_expr_trans, df_gene_transcript
-# ) # error here
-# 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
-
-
-# # run the program
-# if __name__ == "__main__":
-# match_repr_transcript_expression_level()
diff --git a/transcript_sampler/poisson_sampling.py b/transcript_sampler/poisson_sampling.py
index f86e2bb22cf0c290c61d4b28a4340f195d7f51a1..0df129c1fbc688507858970bc8ca2657e8552aca 100644
--- a/transcript_sampler/poisson_sampling.py
+++ b/transcript_sampler/poisson_sampling.py
@@ -1,6 +1,6 @@
"""Sample transcripts by Poisson-sampling."""
-import pandas as pd
+import pandas as pd # type: ignore
import numpy as np
@@ -30,47 +30,3 @@ class SampleTranscript:
"id": df_repr["id"], "count": levels
})
transcript_numbers.to_csv(output_csv, index=False, header=False)
-
-
-# python_version = "3.7.13"
-# module_list = [pd, np, argparse]
-# modul_name_list = ["pd", "np", "argparse"]
-
-# def transcript_sampling(total_transcript_number, df_repr, output_csv):
-# # df = pd.read_csv(
-# # csv_file, sep="\t", lineterminator="\n", names=["id", "level"])
-# # the function match_reprTranscript_expressionLevel() now outputs a df
-# df = df_repr
-# levels = []
-# sums = df['level'].tolist()
-# total = sum(sums)
-# # I added this because writting a number in the terminal inputed a string
-# total_transcript_number = int(total_transcript_number)
-# normalized = total_transcript_number/total
-# for expression_level in df['level']:
-# poisson_sampled = np.random.poisson(expression_level*normalized)
-# levels.append(poisson_sampled)
-
-# transcript_numbers = pd.DataFrame({'id': df['id'], 'count': levels})
-# pd.DataFrame.to_csv(transcript_numbers, output_csv)
-
-
-# if __name__ == '__main__':
-# # te.version_control(module_list,modul_name_list,python_version)
-# parser = argparse.ArgumentParser(
-# description="Transcript Poisson sampler, csv output",
-# formatter_class=argparse.ArgumentDefaultsHelpFormatter
-# )
-
-# parser.add_argument("--expression_level", required=True,
-# help="csv file with expression level")
-# parser.add_argument("--output_csv", required=True,
-# help="output csv file")
-# parser.add_argument("--input_csv", required=True,
-# help="input csv file")
-# parser.add_argument("--transcript_number", required=True,
-# help="total number of transcripts to sample")
-# args = parser.parse_args()
-
-# transcript_sampling(args.transcript_number, args.input_csv,
-# args.output_csv, args.transcript_number)