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Merge pull request #1106 from nf-core/kallisto_quant
Kallisto quantification
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,160 @@ | ||
| #!/usr/bin/env python | ||
| import logging | ||
| import argparse | ||
| import glob | ||
| import os | ||
| from collections import Counter, defaultdict, OrderedDict | ||
| from collections.abc import Set | ||
| from typing import Dict | ||
|
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| # Configure logging | ||
| logging.basicConfig(format="%(name)s - %(asctime)s %(levelname)s: %(message)s") | ||
| logger = logging.getLogger(__name__) | ||
| logger.setLevel(logging.INFO) | ||
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| def read_top_transcripts(quant_dir: str, file_pattern: str) -> Set[str]: | ||
| """ | ||
| Read the top 100 transcripts from the quantification file. | ||
| Parameters: | ||
| quant_dir (str): Directory where quantification files are located. | ||
| file_pattern (str): Pattern to match quantification files. | ||
| Returns: | ||
| set: A set containing the top 100 transcripts. | ||
| """ | ||
| try: | ||
| # Find the quantification file within the directory | ||
| quant_file_path = glob.glob(os.path.join(quant_dir, "*", file_pattern))[0] | ||
| with open(quant_file_path, "r") as file_handle: | ||
| # Read the file and extract the top 100 transcripts | ||
| return {line.split()[0] for i, line in enumerate(file_handle) if i > 0 and i <= 100} | ||
| except IndexError: | ||
| # Log an error and raise a FileNotFoundError if the quant file does not exist | ||
| logger.error("No quantification files found.") | ||
| raise FileNotFoundError("Quantification file not found.") | ||
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| def discover_transcript_attribute(gtf_file: str, transcripts: Set[str]) -> str: | ||
| """ | ||
| Discover the attribute in the GTF that corresponds to transcripts, prioritizing 'transcript_id'. | ||
| Parameters: | ||
| gtf_file (str): Path to the GTF file. | ||
| transcripts (Set[str]): A set of transcripts to match in the GTF file. | ||
| Returns: | ||
| str: The attribute name that corresponds to transcripts in the GTF file. | ||
| """ | ||
| votes = Counter() | ||
| with open(gtf_file) as inh: | ||
| # Read GTF file, skipping header lines | ||
| for line in filter(lambda x: not x.startswith("#"), inh): | ||
| cols = line.split("\t") | ||
| # Parse attribute column and update votes for each attribute found | ||
| attributes = dict(item.strip().split(" ", 1) for item in cols[8].split(";") if item.strip()) | ||
| votes.update(key for key, value in attributes.items() if value.strip('"') in transcripts) | ||
|
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||
| if not votes: | ||
| # Log a warning if no matching attribute is found | ||
| logger.warning("No attribute in GTF matching transcripts") | ||
| return "" | ||
|
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| # Check if 'transcript_id' is among the attributes with the highest votes | ||
| if "transcript_id" in votes and votes["transcript_id"] == max(votes.values()): | ||
| logger.info("Attribute 'transcript_id' corresponds to transcripts.") | ||
| return "transcript_id" | ||
|
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||
| # If 'transcript_id' isn't the highest, determine the most common attribute that matches the transcripts | ||
| attribute, _ = votes.most_common(1)[0] | ||
| logger.info(f"Attribute '{attribute}' corresponds to transcripts.") | ||
| return attribute | ||
|
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| def parse_attributes(attributes_text: str) -> Dict[str, str]: | ||
| """ | ||
| Parse the attributes column of a GTF file. | ||
| :param attributes_text: The attributes column as a string. | ||
| :return: A dictionary of the attributes. | ||
| """ | ||
| # Split the attributes string by semicolon and strip whitespace | ||
| attributes = attributes_text.strip().split(";") | ||
| attr_dict = OrderedDict() | ||
|
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| # Iterate over each attribute pair | ||
| for attribute in attributes: | ||
| # Split the attribute into key and value, ensuring there are two parts | ||
| parts = attribute.strip().split(" ", 1) | ||
| if len(parts) == 2: | ||
| key, value = parts | ||
| # Remove any double quotes from the value | ||
| value = value.replace('"', "") | ||
| attr_dict[key] = value | ||
|
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| return attr_dict | ||
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| def map_transcripts_to_gene( | ||
| quant_type: str, gtf_file: str, quant_dir: str, gene_id: str, extra_id_field: str, output_file: str | ||
| ) -> bool: | ||
| """ | ||
| Map transcripts to gene names and write the output to a file. | ||
| Parameters: | ||
| quant_type (str): The quantification method used (e.g., 'salmon'). | ||
| gtf_file (str): Path to the GTF file. | ||
| quant_dir (str): Directory where quantification files are located. | ||
| gene_id (str): The gene ID attribute in the GTF file. | ||
| extra_id_field (str): Additional ID field in the GTF file. | ||
| output_file (str): The output file path. | ||
| Returns: | ||
| bool: True if the operation was successful, False otherwise. | ||
| """ | ||
| # Read the top transcripts based on quantification type | ||
| transcripts = read_top_transcripts(quant_dir, "quant.sf" if quant_type == "salmon" else "abundance.tsv") | ||
| # Discover the attribute that corresponds to transcripts in the GTF | ||
| transcript_attribute = discover_transcript_attribute(gtf_file, transcripts) | ||
|
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| if not transcript_attribute: | ||
| # If no attribute is found, return False | ||
| return False | ||
|
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| # Open GTF and output file to write the mappings | ||
| # Initialize the set to track seen combinations | ||
| seen = set() | ||
|
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| with open(gtf_file) as inh, open(output_file, "w") as output_handle: | ||
| # Parse each line of the GTF, mapping transcripts to genes | ||
| for line in filter(lambda x: not x.startswith("#"), inh): | ||
| cols = line.split("\t") | ||
| attr_dict = parse_attributes(cols[8]) | ||
| if gene_id in attr_dict and transcript_attribute in attr_dict: | ||
| # Create a unique identifier for the transcript-gene combination | ||
| transcript_gene_pair = (attr_dict[transcript_attribute], attr_dict[gene_id]) | ||
|
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| # Check if the combination has already been seen | ||
| if transcript_gene_pair not in seen: | ||
| # If it's a new combination, write it to the output and add to the seen set | ||
| extra_id = attr_dict.get(extra_id_field, attr_dict[gene_id]) | ||
| output_handle.write(f"{attr_dict[transcript_attribute]}\t{attr_dict[gene_id]}\t{extra_id}\n") | ||
| seen.add(transcript_gene_pair) | ||
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| return True | ||
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| # Main function to parse arguments and call the mapping function | ||
| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser(description="Map transcripts to gene names for tximport.") | ||
| parser.add_argument("--quant_type", type=str, help="Quantification type", default="salmon") | ||
| parser.add_argument("--gtf", type=str, help="GTF file", required=True) | ||
| parser.add_argument("--quants", type=str, help="Output of quantification", required=True) | ||
| parser.add_argument("--id", type=str, help="Gene ID in the GTF file", required=True) | ||
| parser.add_argument("--extra", type=str, help="Extra ID in the GTF file") | ||
| parser.add_argument("-o", "--output", dest="output", default="tx2gene.tsv", type=str, help="File with output") | ||
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| args = parser.parse_args() | ||
| if not map_transcripts_to_gene(args.quant_type, args.gtf, args.quants, args.id, args.extra, args.output): | ||
| logger.error("Failed to map transcripts to genes.") |
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