# HG changeset patch # User charles # Date 1546602539 18000 # Node ID efcc0e22daa4e8bbd57a6f0f51daddb77eaa2603 Uploaded diff -r 000000000000 -r efcc0e22daa4 alfa/ALFA.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/alfa/ALFA.py Fri Jan 04 06:48:59 2019 -0500 @@ -0,0 +1,1678 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +__author__ = "noel & bahin" +""" ALFA provides a global overview of features distribution composing NGS dataset(s). """ + +import os +import sys +import re +import numpy as np +import collections +import copy +import argparse +import pysam +import pybedtools +pybedtools.set_tempdir("/localtmp/") +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.patheffects as PathEffects +from multiprocessing import Pool +#import progressbar + +# To correctly embed the texts when saving plots in svg format +matplotlib.rcParams["svg.fonttype"] = "none" + + +########################################################################## +# FUNCTIONS # +########################################################################## + +def init_dict(d, key, init): + if key not in d: + d[key] = init + + +def tryint(s): + """ Function called by "alphanum_key" function to sort the chromosome names. """ + try: + return int(s) + except ValueError: + return s + + +def alphanum_key(s): + """ Turn a string into a list of string and number chunks. + "z23a" -> ["z", 23, "a"] + """ + return [tryint(c) for c in re.split("([0-9]+)", s)] + + +def existing_file(filename): + """ Checks if filename already exists and exit if so. """ + if os.path.isfile(filename): + sys.exit("Error: The file '" + filename + "' is about to be produced but already exists in the directory. \n### End of program") + + +def get_chromosome_names_in_GTF(): + """ Function to get the list of chromosome names present in the provided GTF file. """ + chr_list = [] + with open(options.annotation, "r") as GTF_file: + for line in GTF_file: + if not line.startswith("#"): + chr = line.split("\t")[0] + if chr not in chr_list: + chr_list.append(chr) + return sorted(chr_list) + + +def get_chromosome_names_in_index(genome_index): + """ Returns the chromosome names in a genome index file. """ + with open(genome_index, "r") as index_fh: + for line in index_fh: + if not line.startswith("#") and (line.split("\t")[0] not in index_chrom_list): + index_chrom_list.append(line.split("\t")[0]) + return index_chrom_list + + +def GTF_splitter(GTF_file, size=10000): + """ Function to split a GTF file into chunks of one chromosome or several chromosomes/scaffolds up to N (default=10k) lines. """ + if os.path.isfile(chunk_basename + "1.gtf"): + sys.exit("Error: There is already a file called '" + chunk_basename + "1.gtf' in the directory. Running the command would crush this file. Aborting") + prev_chr = "" # Chr/scaffold previously processed + prev_cpt = 0 # Currently building chunk file line counter + cpt = 0 # Processed chr/scaffold line counter + cpt_chunk = 1 # Chunk counter + current_file = open("current.gtf", "w") # New piece to add to the building chunk file (one chromosome/scaffold) + # Processing the input GTF file + with open(GTF_file, "r") as input_file: + for line in input_file: + # Burning header lines + if line.startswith("#"): + continue + # Getting the chromosome/scaffold + chr = line.split("\t")[0] # Processed chr/scaffold + # Reaching another chromosome/scaffold + if (chr != prev_chr) and (prev_chr != ""): + if cpt > size: + # Packing up the processed chr/scaffold + current_file.close() + os.rename("current.gtf", chunk_basename + str(cpt_chunk) + ".gtf") + current_file = open("current.gtf", "w") + # Updating counters + cpt_chunk += 1 + else: + if cpt + prev_cpt > size: + # Packing up the currently building chunk file without the last chr/scaffold + os.rename("old.gtf", chunk_basename + str(cpt_chunk) + ".gtf") + # Updating counters + cpt_chunk += 1 + prev_cpt = 0 + # Moving the new piece to the currently building chunk file + current_file.close() + with open("current.gtf", "r") as input_file, open("old.gtf", "a") as output_file: + for line in input_file: + output_file.write(line) + current_file = open("current.gtf", "w") + # Updating counters + prev_cpt += cpt + # Updating the processed chr/scaffold line counter and the previous chromosome + cpt = 0 + prev_chr = chr + current_file.write(line) + else: # First content line or another line for the processed chr/scaffold + current_file.write(line) + prev_chr = chr + cpt += 1 + # Processing the last chromosome/scaffold + current_file.close() + if prev_cpt == 0: # There was only one chromosome/scaffold in the annotation file + # Packing up the processed chr/scaffold + os.rename("current.gtf", chunk_basename + str(cpt_chunk) + ".gtf") + else: + if prev_cpt + cpt > size: + # Packing up the processed chr/scaffold + os.rename("current.gtf", chunk_basename + str(cpt_chunk) + ".gtf") + cpt_chunk += 1 + else: + # Moving the new piece to the currently building chunk file + with open("current.gtf", "r") as input_file, open("old.gtf", "a") as output_file: + for line in input_file: + output_file.write(line) + os.remove("current.gtf") + # Packing up the currently building chunk file without the last chr/scaffold + os.rename("old.gtf", chunk_basename + str(cpt_chunk) + ".gtf") + + +def get_chromosome_lengths(): + """ + Parse the file containing the chromosome lengths. + If no length file is provided, browse the annotation file (GTF) to estimate the chromosome sizes. + """ + lengths = {} + gtf_chrom_names = set() + # If the user provided a chromosome length file + if options.chr_len: + # Getting the chromosome lengths from the chromosome lengths file + with open(options.chr_len, "r") as chr_len_fh: + for line in chr_len_fh: + try: + lengths[line.split("\t")[0]] = int(line.rstrip().split("\t")[1]) + except IndexError: + sys.exit("Error: The chromosome lengths file is not correctly formed. It is supposed to be tabulated file with two fields per line.") + # Getting the chromosome lengths from the GTF file + with open(options.annotation, "r") as gtf_fh: + for line in gtf_fh: + if not line.startswith("#"): + gtf_chrom_names.add(line.split("\t")[0]) + # Checking if the chromosomes from the chromosome lengths file are present in the GTF file + for chrom in lengths: + if chrom not in gtf_chrom_names: + print >> sys.stderr, "Warning: chromosome '" + chrom + "' of the chromosome lengths file does not match any chromosome name in the GTF file provided and was ignored." + # Checking if the chromosomes from the GTF file are present in the lengths file + for chrom in gtf_chrom_names: + if chrom not in lengths: + print >> sys.stderr, "Warning: at least one chromosome ('" + chrom + "') was found in the GTF file and does not match any chromosome provided in the lengths file." + print >> sys.stderr, "\t=> All the chromosome lengths will be approximated using annotations in the GTF file." + break + else: + return lengths + # If no chromosome lengths file was provided or if at least one chromosome was missing in the file, the end of the last annotation of the chromosome in the GTF file is considered as the chromosome length + with open(options.annotation, "r") as gtf_fh: + for line in gtf_fh: + if not line.startswith("#"): + chrom = line.split("\t")[0] + end = int(line.split("\t")[4]) + init_dict(lengths, chrom, 0) + lengths[chrom] = max(lengths[chrom], end) + print "The chromosome lengths have been approximated using the GTF file annotations (the stop position of the last annotation of each chromosome is considered as its length)." + return lengths + + +def write_index_line(feat, chrom, start, stop, sign, fh): + """ Write a new line in an index file. """ + # Formatting the features info + feat_by_biotype = [] + for biot, cat in feat.iteritems(): + feat_by_biotype.append(":".join((str(biot), ",".join(sorted(cat))))) + # Writing the features info in the index file + fh.write("\t".join((chrom, start, stop, sign)) + "\t" + "\t".join(feat_by_biotype) + "\n") + + +def write_index(feat_values, chrom, start, stop, stranded_genome_index, unstranded_genome_index): + """ Writing the features info in the proper index files. """ + # Writing info to the stranded indexes + if feat_values[0] != {}: + write_index_line(feat_values[0], chrom, start, stop, "+", stranded_genome_index) + else: + stranded_genome_index.write("\t".join((chrom, start, stop, "+", "antisense\n"))) + if feat_values[1] != {}: + write_index_line(feat_values[1], chrom, start, stop, "-", stranded_genome_index) + else: + stranded_genome_index.write("\t".join((chrom, start, stop, "-", "antisense\n"))) + # Writing info to the unstranded index + unstranded_feat = dict(feat_values[0], **feat_values[1]) + for name in set(feat_values[0]) & set(feat_values[1]): + unstranded_feat[name] += feat_values[0][name] + write_index_line(unstranded_feat, chrom, start, stop, ".", unstranded_genome_index) + + +def register_interval(features_dict, chrom, stranded_index_fh, unstranded_index_fh): + """ Write the interval features info into the genome index files. """ + # Writing the interval in the index file + with open(unstranded_index_fh, "a") as unstranded_index_fh, open(stranded_index_fh, "a") as stranded_index_fh: + # Initializing the first interval start and features + sorted_pos = sorted(features_dict["+"].keys()) + interval_start = sorted_pos[0] + features_plus = features_dict["+"][interval_start] + features_minus = features_dict["-"][interval_start] + # Browsing the interval boundaries + for interval_stop in sorted_pos[1:]: + # Writing the current interval features to the indexes + write_index([features_plus, features_minus], chrom, str(interval_start), str(interval_stop), stranded_index_fh, unstranded_index_fh) + # Initializing the new interval start and features + interval_start = interval_stop + # Store current features + prev_features_plus = features_plus + prev_features_minus = features_minus + # Update features + features_plus = features_dict["+"][interval_start] + features_minus = features_dict["-"][interval_start] + # If feature == transcript and prev interval's feature is exon => add intron feature + for biotype, categ in features_plus.iteritems(): + if categ == ["gene", "transcript"]: + if "exon" in prev_features_plus[biotype] or "intron" in prev_features_plus[biotype]: + categ.append("intron") + else: + continue + for biotype, categ in features_minus.iteritems(): + if categ == ["gene", "transcript"]: + if "exon" in prev_features_minus[biotype]: + categ.append("intron") + else: + continue + + +def generate_genome_index_1chr(annotation): + # Setting the annotation file basename + annotation_basename = re.sub(".gtf$", "", annotation) + # Processing the annotation file + with open(annotation, "r") as gtf_fh: + max_value = -1 # Maximum value of the currently processed interval + intervals_dict = {} + prev_chrom = "" + for line in gtf_fh: + # Processing lines except comment ones + if not line.startswith("#"): + # Getting the line info + line_split = line.rstrip().split("\t") + chrom = line_split[0] + cat = line_split[2] + start = int(line_split[3]) - 1 + stop = int(line_split[4]) + strand = line_split[6] + antisense = reverse_strand[strand] + biotype = line_split[8].split("gene_biotype")[1].split(";")[0].strip('" ') + # Registering stored features info in the genome index file(s) if the new line concerns a new chromosome or the new line concerns an annotation not overlapping previously recorded ones + if (start > max_value) or (chrom != prev_chrom): + # Write the previous features + if intervals_dict: + register_interval(intervals_dict, prev_chrom, annotation_basename + ".stranded.ALFA_index", annotation_basename + ".unstranded.ALFA_index") + if chrom != prev_chrom: + with open(chunk_basename + "txt", "a") as input_file: + input_file.write(chrom + "\n") + prev_chrom = chrom + # (Re)Initializing the intervals info dict + intervals_dict = {strand: {start: {biotype: [cat]}, stop: {}}, antisense: {start: {}, stop: {}}} + max_value = stop + + # Update the dictionary which represents intervals for every distinct annotation + else: + # Storing the intervals on the strand of the current feature + stranded_intervals = intervals_dict[strand] + added_info = False # Variable to know if the features info were already added + # Browsing the existing boundaries + for boundary in sorted(stranded_intervals): + # While the GTF line start is after the browsed boundary: keep the browsed boundary features info in case the GTF line start is before the next boundary + if boundary < start: + stored_feat_strand = copy.deepcopy(dict(stranded_intervals[boundary])) + stored_feat_antisense = copy.deepcopy(dict(intervals_dict[antisense][boundary])) + continue + + # The GTF line start is already an existing boundary: store the existing features info (to manage after the GTF line stop) and update it with the GTF line features info + elif boundary == start: + stored_feat_strand = copy.deepcopy(dict(stranded_intervals[boundary])) + stored_feat_antisense = copy.deepcopy(dict(intervals_dict[antisense][boundary])) + # Adding the GTF line features info to the interval + try: + if cat not in stranded_intervals[boundary][biotype]: + stranded_intervals[boundary][biotype].append(cat) + except KeyError: # If the GTF line features info regards a new biotype + stranded_intervals[boundary][biotype] = [cat] + added_info = True # The features info were added + continue + + # The browsed boundary is after the GTF line start: add the GTF line features info + elif boundary > start: + # Create a new boundary for the GTF line start if necessary (if it is between 2 existing boundaries, it was not created before) + if not added_info: + stranded_intervals[start] = copy.deepcopy(stored_feat_strand) + try: + if cat not in stranded_intervals[start][biotype]: + stranded_intervals[start][biotype].append(cat) + except KeyError: + stranded_intervals[start][biotype] = [cat] + intervals_dict[antisense][start] = copy.deepcopy(stored_feat_antisense) + added_info = True # The features info were added + # While the browsed boundary is before the GTF line stop: store the existing features info (to manage after the GTF line stop) and update it with the GTF line features info + if boundary < stop: + stored_feat_strand = copy.deepcopy(dict(stranded_intervals[boundary])) + stored_feat_antisense = copy.deepcopy(dict(intervals_dict[antisense][boundary])) + try: + if cat not in stranded_intervals[boundary][biotype]: + stranded_intervals[boundary][biotype].append(cat) + except KeyError: + stranded_intervals[boundary][biotype] = [cat] + # The GTF line stop is already exists, nothing more to do, the GTF line features info are integrated + elif boundary == stop: + break + # The browsed boundary is after the GTF line stop: create a new boundary for the GTF line stop (with the stored features info) + else: # boundary > stop + stranded_intervals[stop] = copy.deepcopy(stored_feat_strand) + intervals_dict[antisense][stop] = copy.deepcopy(stored_feat_antisense) + break # The GTF line features info are integrated + # If the GTF line stop is after the last boundary, extend the dictionary + if stop > max_value: + max_value = stop + stranded_intervals[stop] = {} + intervals_dict[antisense][stop] = {} + + # Store the categories of the last chromosome + register_interval(intervals_dict, chrom, annotation_basename + ".stranded.ALFA_index", annotation_basename + ".unstranded.ALFA_index") + if chrom != prev_chrom: + with open(chunk_basename + "txt", "a") as input_file: + input_file.write(chrom + "\n") + return None + + +def generate_genome_index(chrom_sizes): + """ Create an index of the genome annotations and save it in a file. """ + # Write the chromosome lengths as comment lines before the genome index + with open(unstranded_genome_index, "w") as unstranded_index_fh, open(stranded_genome_index, "w") as stranded_index_fh: + for key, value in chrom_sizes.items(): + unstranded_index_fh.write("#%s\t%s\n" % (key, value)) + stranded_index_fh.write("#%s\t%s\n" % (key, value)) + # Chunk file list creation + chunks = np.array([f for f in os.listdir(".") if f.startswith(chunk_basename) and f.endswith(".gtf")]) + # Sorting the chunks by file size + file_sizes = np.array([os.stat(f).st_size for f in chunks]) + chunks = chunks[file_sizes.argsort()] + # Progress bar to track the genome indexes creation + #pbar = progressbar.ProgressBar(widgets=["Indexing the genome ", progressbar.Percentage(), " ", progressbar.Bar(), progressbar.Timer()], maxval=len(chunks)).start() + pool = Pool(options.nb_processors) + #list(pbar(pool.imap_unordered(generate_genome_index_1chr, chunks))) + list(pool.imap_unordered(generate_genome_index_1chr, chunks)) + """ + # Non-parallel version for debugging + for f in chunks: + generate_genome_index_1chr(f) + """ + + +def merge_index_chunks(): + """ Merges the genome index chunks into a single file. """ + for fh, strandness in zip([unstranded_genome_index, stranded_genome_index], ["unstranded", "stranded"]): + files = [f for f in os.listdir(".") if f.startswith(chunk_basename) and f.endswith("." + strandness + ".ALFA_index")] + with open(fh, "a") as output_file: + for file in sorted(files): + with open(file, "r") as input_file: + for line in input_file: + output_file.write(line) + + +def chunks_cleaner(): + """ Cleans the chunks created to index the genome. """ + for f in os.listdir("."): + if f.startswith(chunk_basename): + os.remove(f) + + +def count_genome_features(cpt, features, start, stop, coverage=1): + """ Reads genome index and registers feature counts. """ + # If no biotype priority: category with the highest priority for each found biotype has the same weight (1/n_biotypes) + if not biotype_prios: + nb_biot = len(features) + if options.keep_ambiguous and nb_biot != 1: + # Increment "ambiguous" counter if more than 1 biotype + try: + cpt[("ambiguous", "ambiguous")] += (int(stop) - int(start)) * coverage + except: + cpt[("ambiguous", "ambiguous")] = (int(stop) - int(start)) * coverage + else: + # For each categ(s)/biotype pairs + for feat in features: + cur_prio = 0 + # Separate categorie(s) and biotype + try: + biot, cats = feat.split(":") + # Error if the feature is "antisense": update the "antisense/antisense" counts + except ValueError: + try: + cpt[("opposite_strand", "opposite_strand")] += (int(stop) - int(start)) * coverage / float(nb_biot) + except KeyError: + cpt[("opposite_strand", "opposite_strand")] = (int(stop) - int(start)) * coverage / float(nb_biot) + return None + # Browse the categories and get only the one(s) with highest priority + for cat in cats.split(","): + try: + prio = prios[cat] + except KeyError: + if cat not in unknown_cat: + print >> sys.stderr, "Warning: Unknown categorie '%s' found and ignored.\n" % cat, + unknown_cat.add(cat) + continue + # Check if the category has a highest priority than the current one + if prio > cur_prio: + cur_prio = prio + cur_cat = {cat} + if prio == cur_prio: + cur_cat.add(cat) + + nb_cat = len(cur_cat) + if options.keep_ambiguous and nb_cat != 1: + # Increment "ambiguous" counter if more than 1 category + try: + cpt[("ambiguous", "ambiguous")] += (int(stop) - int(start)) * coverage / (float(nb_biot)) + except KeyError: + cpt[("ambiguous", "ambiguous")] = (int(stop) - int(start)) * coverage / (float(nb_biot)) + else: + # Increase each counts by the coverage divided by the number of categories and biotypes + for cat in cur_cat: + try: + cpt[(cat, biot)] += (int(stop) - int(start)) * coverage / (float(nb_biot * nb_cat)) + except KeyError: + cpt[(cat, biot)] = (int(stop) - int(start)) * coverage / (float(nb_biot * nb_cat)) + else: + # TODO: Add an option to provide biotype priorities and handle it + pass + + +def read_index(): + """ Parse index files info (chromosomes list, lengths and genome features). """ + with open(genome_index, "r") as index_fh: + for line in index_fh: + if line.startswith("#"): + lengths[line.split("\t")[0][1:]] = int(line.split("\t")[1]) + else: + chrom = line.split("\t")[0] + if chrom not in index_chrom_list: + index_chrom_list.append(chrom) + count_genome_features(cpt_genome, line.rstrip().split("\t")[4:], line.split("\t")[1], line.split("\t")[2]) + + +def run_genomecov((strand, bam_file, sample_label, name)): + """ Run genomecov (from Bedtools through pybedtools lib) for a set of parameters to produce a BedGraph file. """ + # Load the BAM file + input_file = pybedtools.BedTool(bam_file) + # Run genomecov + if strand == "": + input_file.genome_coverage(bg=True, split=True).saveas(sample_label + name + bedgraph_extension) + else: + input_file.genome_coverage(bg=True, split=True, strand=strand).saveas(sample_label + name + bedgraph_extension) + return None + + +def generate_bedgraph_files_parallel(sample_labels, bam_files): + """ Creates, through multi-processors, BedGraph files from BAM ones. """ + # Sorting the BAM file on size to process the biggest first + files = zip(sample_labels, bam_files, [os.stat(i).st_size for i in bam_files]) + files.sort(key=lambda p: p[2], reverse=True) + # Defining parameters sets to provide to the genomecov instances to run + parameter_sets = [] + for l, b, s in files: + # If the dataset is stranded, one BedGraph file for each strand is created + if options.strandness in ["forward", "fr-firststrand"]: + parameter_sets.append(["+", b, l, ".plus"]) + parameter_sets.append(["-", b, l, ".minus"]) + elif options.strandness in ["reverse", "fr-secondstrand"]: + parameter_sets.append(["-", b, l, ".plus"]) + parameter_sets.append(["+", b, l, ".minus"]) + else: + parameter_sets.append(["", b, l, ""]) + # Setting the progressbar + #pbar = progressbar.ProgressBar(widgets=["Generating the BedGraph files ", progressbar.Percentage(), progressbar.Bar(), progressbar.SimpleProgress(), "|", progressbar.Timer()], maxval=len(parameter_sets)).start() + # Setting the processors number + pool = Pool(options.nb_processors) + # Running the instances + #list(pbar(pool.imap_unordered(run_genomecov, parameter_sets))) + list(pool.imap_unordered(run_genomecov, parameter_sets)) + pybedtools.cleanup() # If pybedtools can't finish properly but the program is not stopped, the /tmp will be cleaned anyway from pybedtools temp files + return None + + +def read_gtf(gtf_index_file, sign): + global gtf_line, gtf_chrom, gtf_start, gtf_stop, gtf_features, endGTF + strand = "" + while strand != sign: + gtf_line = gtf_index_file.readline() + # If the GTF file is finished + if not gtf_line: + endGTF = True + return endGTF + splitline = gtf_line.rstrip().split("\t") + try: + strand = splitline[3] + # strand information can not be found in the file header + except IndexError: + pass + gtf_chrom = splitline[0] + gtf_features = splitline[4:] + gtf_start, gtf_stop = map(int, splitline[1:3]) + return endGTF + + +def intersect_bedgraphs_and_index_to_count_categories_1_file((sample_labels, bedgraph_files, biotype_prios, strand, sign)): + global gtf_line, gtf_chrom, gtf_start, gtf_stop, gtf_cat, endGTF + unknown_chrom = [] + cpt = {} # Counter for the nucleotides in the BAM input file(s) + prev_chrom = "" + endGTF = False # Reaching the next chr or the end of the GTF index + intergenic_adds = 0.0 + # Checking if the BedGraph file is empty + if os.stat(bedgraph_files + strand + bedgraph_extension).st_size == 0: + return sample_labels, sign, {}, [] + with open(bedgraph_files + strand + bedgraph_extension, "r") as bedgraph_fh: + # Running through the BedGraph file + for bam_line in bedgraph_fh: + # Getting the BAM line info + bam_chrom = bam_line.split("\t")[0] + bam_start, bam_stop, bam_cpt = map(float, bam_line.split("\t")[1:4]) + # Skip the line if the chromosome is not in the index + if bam_chrom not in index_chrom_list: + if bam_chrom not in unknown_chrom: + unknown_chrom.append(bam_chrom) + continue + # If this is a new chromosome (or the first one) + if bam_chrom != prev_chrom: + intergenic_adds = 0.0 + # Closing the GTF file if it was open (exception caught only for the first chr) + try: + gtf_index_file.close() + except UnboundLocalError: + pass + # (Re)opening the GTF index and looking for the first line of the matching chr + gtf_index_file = open(genome_index, "r") + endGTF = False + read_gtf(gtf_index_file, sign) + while bam_chrom != gtf_chrom: + read_gtf(gtf_index_file, sign) + if endGTF: + break + prev_chrom = bam_chrom + + # Looking for the first matching annotation in the GTF index + while (not endGTF) and (gtf_chrom == bam_chrom) and (bam_start >= gtf_stop): + read_gtf(gtf_index_file, sign) + if gtf_chrom != bam_chrom: + endGTF = True + # Processing BAM lines before the first GTF annotation if there are + if bam_start < gtf_start: + # Increase the "intergenic" category counter with all or part of the BAM interval + try: + intergenic_adds += min(bam_stop, gtf_start) - bam_start + cpt[("intergenic", "intergenic")] += (min(bam_stop, gtf_start) - bam_start) * bam_cpt + except KeyError: + cpt[("intergenic", "intergenic")] = (min(bam_stop, gtf_start) - bam_start) * bam_cpt + # Go to next line if the BAM interval was totally upstream the first GTF annotation, carry on with the remaining part otherwise + if endGTF or (bam_stop <= gtf_start): + continue + else: + bam_start = gtf_start + + # We can start the crossover + while not endGTF: + # Update category counter + count_genome_features(cpt, gtf_features, bam_start, min(bam_stop, gtf_stop), coverage=bam_cpt) + # Read the next GTF file line if the BAM line is not entirely covered + if bam_stop > gtf_stop: + # Update the BAM start pointer + bam_start = gtf_stop + endGTF = read_gtf(gtf_index_file, sign) + # If we read a new chromosome in the GTF file then it is considered finished + if bam_chrom != gtf_chrom: + endGTF = True + if endGTF: + break + else: + # Next if the BAM line is entirely covered + bam_start = bam_stop + break + + # Processing the end of the BAM line if necessary + if endGTF and (bam_stop > bam_start): + try: + cpt[("intergenic", "intergenic")] += (bam_stop - bam_start) * bam_cpt + except KeyError: + cpt[("intergenic", "intergenic")] = (bam_stop - bam_start) * bam_cpt + # In stranded mode, if one of the BedGraph files doesn't have any of the chromosomes from the reference file, the error is not detected during the preprocessing (a chromosome is found within the other BedGraph file) + try: + gtf_index_file.close() + except UnboundLocalError: + # Then the file is not opened and can't be closed + pass + return sample_labels, sign, cpt, unknown_chrom + + +def intersect_bedgraphs_and_index_to_count_categories(sample_labels, bedgraph_files, biotype_prios=None): ## MB: To review + # Initializing variables + unknown_chrom = [] + cpt = {} # Counter for the nucleotides in the BAM input file(s) + if bedgraph_files == []: + bedgraph_files = sample_labels + if options.strandness == "unstranded": + strands = [("", ".")] + else: + strands = [(".plus", "+"), (".minus", "-")] + + # Initializing the progress bar + #pbar = progressbar.ProgressBar(widgets=["Intersecting BAM and genome ", progressbar.Percentage(), " ", progressbar.Bar(), progressbar.SimpleProgress(), "|", progressbar.Timer()], maxval=len(sample_labels) * len(strands)).start() + pool = Pool(options.nb_processors) + inputs = [sample + strand for sample in zip(sample_labels, bedgraph_files, [biotype_prios] * len(sample_labels)) for strand in strands] + + # Running the intersection in parallel + #results = list(pbar(pool.imap_unordered(intersect_bedgraphs_and_index_to_count_categories_1_file, inputs))) + results = list(pool.imap_unordered(intersect_bedgraphs_and_index_to_count_categories_1_file, inputs)) + """ + # Non-parallel version for debugging + results = [] + for i in inputs: + results.append(intersect_bedgraphs_and_index_to_count_categories_1_file(i)) + """ + + # Reformatting the results + for res in results: + init_dict(cpt, res[0], {}) + if res[1] != {}: + init_dict(cpt[res[0]], res[1], res[2]) + unknown_chrom.append(res[3]) + # Merging strands counts for the same samples + final_cpt = {} + for sample in cpt: + final_cpt[sample] = {} + for strand in strands: + for feat in cpt[sample][strand[1]]: + try: + final_cpt[sample][feat] += cpt[sample][strand[1]][feat] + except KeyError: + final_cpt[sample][feat] = cpt[sample][strand[1]][feat] + + print "Unknown chromosomes: " + str(set([i for u in unknown_chrom for i in u])) + "." + return final_cpt + + +def write_counts_in_files(cpt, genome_counts): + """ Writes the biotype/category counts in an output file. """ + for sample_label, counters in cpt.items(): + sample_label = "_".join(re.findall(r"[\w\-']+", sample_label)) + with open(sample_label + ".ALFA_feature_counts.tsv", "w") as output_fh: + output_fh.write("#Category,biotype\tCounts_in_BAM/BedGraph\tSize_in_genome\n") + for features_pair, counts in counters.items(): + output_fh.write("%s\t%s\t%s\n" % (",".join(features_pair), counts, genome_counts[features_pair])) + + +def read_counts(sample_labels, counts_files): + """ Reads the counts from an input file. """ + cpt = {} + cpt_genome = {} + for sample_label, filename in zip(sample_labels, counts_files): + cpt[sample_label] = {} + with open(filename, "r") as counts_fh: + for line in counts_fh: + if not line.startswith("#"): + feature = tuple(line.split("\t")[0].split(",")) + cpt[sample_label][feature] = float(line.split("\t")[1]) + cpt_genome[feature] = float(line.rstrip().split("\t")[2]) + return cpt, cpt_genome + + +def group_counts_by_categ(cpt, cpt_genome, final, selected_biotype): + final_cat_cpt = {} + final_genome_cpt = {} + filtered_cat_cpt = {} + for f in cpt: + final_cat_cpt[f] = {} + filtered_cat_cpt[f] = {} + for final_cat in final: + tot = 0 + tot_filter = 0 + tot_genome = 0 + for cat in final[final_cat]: + for key, value in cpt[f].items(): + if key[0] == cat: + tot += value + tot_genome += cpt_genome[key] + if key[1] == selected_biotype: + tot_filter += value + # output_file.write('\t'.join((final_cat, str(tot))) + '\n') + # print '\t'.join((final_cat, str(tot))) + final_cat_cpt[f][final_cat] = tot + if tot_genome == 0: + final_genome_cpt[final_cat] = 1e-100 + else: + final_genome_cpt[final_cat] = tot_genome + filtered_cat_cpt[f][final_cat] = tot_filter + # if "antisense" in final_genome_cpt: final_genome_cpt["antisense"] = 0 + return final_cat_cpt, final_genome_cpt, filtered_cat_cpt + + +def group_counts_by_biotype(cpt, cpt_genome, biotypes): + final_cpt = {} + final_genome_cpt = {} + for f in cpt: + final_cpt[f] = {} + for biot in biotypes: + tot = 0 + tot_genome = 0 + try: + for final_biot in biotypes[biot]: + for key, value in cpt[f].items(): + if key[1] == final_biot: + tot += value + # if key[1] != 'antisense': + tot_genome += cpt_genome[key] + except: + for key, value in cpt[f].items(): + if key[1] == biot: + tot += value + tot_genome += cpt_genome[key] + if tot != 0: + final_cpt[f][biot] = tot + final_genome_cpt[biot] = tot_genome + return final_cpt, final_genome_cpt + + +def display_percentage_of_ambiguous(cpt, count_files_option=False): + if count_files_option: + print "INFO: Ambiguous counts were discarded in at least one sample\n" \ + " (see --ambiguous option for more information)" + else: + print "INFO: Reads matching ambiguous annotation have been discarded.\n" \ + " To change this option, please see \"--ambiguous\" help." + print "INFO: Percentage of ambiguous counts:" + + # Loop for each sample + for sample, counts in cpt.iteritems(): + # Compute and display the percentage of ambiguous counts + try: + ambiguous = counts[('ambiguous', 'ambiguous')] + total = sum([count for feat, count in counts.iteritems() if 'intergenic' not in feat]) + print " {!s:25.25} {:5.2f}% of ambiguous".format(sample, float(ambiguous / total) * 100) + # If ambiguous is not in the count file + except KeyError: + if count_files_option: + print " {!s:25.25} {:3.2f}% of ambiguous (this sample may have been processed with --ambiguous option)".format(sample, 0) + else: + print " {!s:25.25} {:3.2f}% of ambiguous".format(sample, 0) + + +def recategorize_the_counts(cpt, cpt_genome, final): + final_cat_cpt = {} + final_genome_cpt = {} + for f in cpt: + # print "\nFinal categories for",f,"sample" + final_cat_cpt[f] = {} + for final_cat in final: + tot = 0 + tot_genome = 0 + for cat in final[final_cat]: + tot += cpt[f][cat] + tot_genome += cpt_genome[cat] + # output_file.write('\t'.join((final_cat, str(tot))) + '\n') + # print '\t'.join((final_cat, str(tot))) + final_cat_cpt[f][final_cat] = tot + final_genome_cpt[final_cat] = tot_genome + return final_cat_cpt, final_genome_cpt + + +def one_sample_plot(ordered_categs, percentages, enrichment, n_cat, index, index_enrichment, bar_width, counts_type, + title, sample_labels): + ### Initialization + fig = plt.figure(figsize=(13, 9)) + ax1 = plt.subplot2grid((2, 4), (0, 0), colspan=2) + ax2 = plt.subplot2grid((2, 4), (1, 0), colspan=2) + cmap = plt.get_cmap("Spectral") + cols = [cmap(x) for x in xrange(0, 256, 256 / n_cat)] + if title: + ax1.set_title(title + "in: %s" % sample_labels[0]) + else: + ax1.set_title(counts_type + " distribution in mapped reads in: %s" % sample_labels[0]) + ax2.set_title("Normalized counts of " + counts_type) + + ### Barplots + # First barplot: percentage of reads in each categorie + ax1.bar(index, percentages, bar_width, + color=cols) + # Second barplot: enrichment relative to the genome for each categ + # (the reads count in a categ is divided by the categ size in the genome) + ax2.bar(index_enrichment, enrichment, bar_width, + color=cols, ) + ### Piecharts + pielabels = [ordered_categs[i] if percentages[i] > 0.025 else "" for i in xrange(n_cat)] + sum_enrichment = np.sum(enrichment) + pielabels_enrichment = [ordered_categs[i] if enrichment[i] / sum_enrichment > 0.025 else "" for i in xrange(n_cat)] + # Categories piechart + ax3 = plt.subplot2grid((2, 4), (0, 2)) + pie_wedge_collection, texts = ax3.pie(percentages, labels=pielabels, shadow=True, colors=cols) + # Enrichment piechart + ax4 = plt.subplot2grid((2, 4), (1, 2)) + pie_wedge_collection, texts = ax4.pie(enrichment, labels=pielabels_enrichment, shadow=True, colors=cols) + # Add legends (append percentages to labels) + labels = [" ".join((ordered_categs[i], "({:.1%})".format(percentages[i]))) for i in range(len(ordered_categs))] + ax3.legend(pie_wedge_collection, labels, loc="center", fancybox=True, shadow=True, prop={"size": "medium"}, + bbox_to_anchor=(1.7, 0.5)) + labels = [" ".join((ordered_categs[i], "({:.1%})".format(enrichment[i] / sum_enrichment))) for i in + range(len(ordered_categs))] # if ordered_categs[i] != "antisense"] + ax4.legend(pie_wedge_collection, labels, loc="center", fancybox=True, shadow=True, prop={"size": "medium"}, + bbox_to_anchor=(1.7, 0.5)) + # Set aspect ratio to be equal so that pie is drawn as a circle + ax3.set_aspect("equal") + ax4.set_aspect("equal") + return fig, ax1, ax2 + + +def make_plot(sample_labels, ordered_categs, categ_counts, genome_counts, counts_type, title=None, categ_groups=[]): + + #Test matplotlib version. If __version__ >= 2, use a shift value to correct the positions of bars and xticks + if int(matplotlib.__version__[0]) == 2: + shift_mpl = 0.5 + else: + shift_mpl = 0 + # From ordered_categs, keep only the features (categs or biotypes) that we can find in at least one sample. + existing_categs = set() + for sample in categ_counts.values(): + existing_categs |= set(sample.keys()) + ordered_categs = filter(existing_categs.__contains__, ordered_categs) + xlabels = [cat if len(cat.split("_")) == 1 else "\n".join(cat.split("_")) if cat.split("_")[0] != 'undescribed' else "\n".join(["und.",cat.split("_")[1]]) for cat in ordered_categs] + n_cat = len(ordered_categs) + #n_exp = len(sample_names) + nb_samples = len(categ_counts) + ##Initialization of the matrix of counts (nrow=nb_experiements, ncol=nb_categorie) + #counts = np.matrix(np.zeros(shape=(n_exp, n_cat))) + counts = np.matrix(np.zeros(shape=(nb_samples, n_cat))) + ''' + for exp in xrange(len(sample_names)): + for cat in xrange(len(ordered_categs)): + try: + counts[exp, cat] = categ_counts[sample_names[exp]][ordered_categs[cat]] + except: + pass + ''' + for sample_label in sample_labels: + for cat in xrange(len(ordered_categs)): + try: + counts[sample_labels.index(sample_label), cat] = categ_counts[sample_label][ordered_categs[cat]] + except: + pass + + ##Normalize the categorie sizes by the total size to get percentages + sizes = [] + sizes_sum = 0 + for cat in ordered_categs: + sizes.append(genome_counts[cat]) + sizes_sum += genome_counts[cat] + if "opposite_strand" in ordered_categs: + antisense_pos = ordered_categs.index("opposite_strand") + sizes[antisense_pos] = 1e-100 + for cpt in xrange(len(sizes)): + sizes[cpt] /= float(sizes_sum) + + ## Create array which contains the percentage of reads in each categ for every sample + percentages = np.array(counts / np.sum(counts, axis=1)) + ## Create the enrichment array (counts divided by the categorie sizes in the genome) + enrichment = np.array(percentages / sizes) + if "antisense_pos" in locals(): + ''' + for i in xrange(len(sample_names)): + enrichment[i][antisense_pos] = 0 + ''' + for n in xrange(nb_samples): + enrichment[n][antisense_pos] = 0 + + # enrichment=np.log(np.array(percentages/sizes)) + #for exp in xrange(n_exp): + for n in xrange(nb_samples): + for i in xrange(n_cat): + val = enrichment[n][i] + if val > 1: + enrichment[n][i] = val - 1 + elif val == 1 or val == 0: + enrichment[n][i] = 0 + else: + enrichment[n][i] = -1 / val + 1 + + #### Finally, produce the plot + + ##Get the colors from the colormap + ncolor = 16 + cmap = ["#e47878", "#68b4e5", "#a3ea9b", "#ea9cf3", "#e5c957", "#a3ecd1", "#e97ca0", "#66d985", "#8e7ae5", + "#b3e04b", "#b884e4", "#e4e758", "#738ee3", "#e76688", "#70dddd", "#e49261"] + ''' + if n_exp > ncolor: + cmap = plt.get_cmap("Set3", n_exp) + cmap = [cmap(i) for i in xrange(n_exp)] + ''' + if nb_samples > ncolor: + cmap = plt.get_cmap("tab20", nb_samples) + cmap = [cmap(i) for i in xrange(nb_samples)] + + ## Parameters for the plot + opacity = 1 + # Create a vector which contains the position of each bar + index = np.arange(n_cat) + # Size of the bars (depends on the categs number) + #bar_width = 0.9 / n_exp + bar_width = 0.9 / nb_samples + + ##Initialise the subplot + # if there is only one sample, also plot piecharts + # if n_exp == 1 and counts_type.lower() == 'categories': + # fig, ax1, ax2 = one_sample_plot(ordered_categs, percentages[0], enrichment[0], n_cat, index, bar_width, counts_type, title) + ## If more than one sample + # else: + if counts_type.lower() != "categories": + #fig, (ax1, ax2) = plt.subplots(2, figsize=(5 + (n_cat + 2 * n_exp) / 3, 10)) + fig, (ax1, ax2) = plt.subplots(2, figsize=(5 + (n_cat + 2 * nb_samples) / 3, 10)) + else: + #fig, (ax1, ax2) = plt.subplots(2, figsize=(5 + (n_cat + 2 * n_exp) / 3, 10)) + fig, (ax1, ax2) = plt.subplots(2, figsize=(5 + (n_cat + 2 * nb_samples) / 3, 10)) + # Store the bars objects for percentages plot + rects = [] + # Store the bars objects for enrichment plot + rects_enrichment = [] + # For each sample/experiment + #for i in range(n_exp): + for sample_label in sample_labels: + # First barplot: percentage of reads in each categorie + n = sample_labels.index(sample_label) + #ax1.bar(index + i * bar_width, percentages[i], bar_width, + rects.append(ax1.bar(index + n * bar_width + shift_mpl/nb_samples, percentages[n], bar_width, + alpha=opacity, + #color=cmap[i], + color=cmap[n], + #label=sample_names[i], edgecolor="#FFFFFF", lw=0) + label=sample_label, edgecolor="#FFFFFF", lw=0)) + # Second barplot: enrichment relative to the genome for each categ + # (the reads count in a categ is divided by the categ size in the genome) + rects_enrichment.append(ax2.bar(index + n * bar_width + shift_mpl/nb_samples, enrichment[n], bar_width, + alpha=opacity, + #color=cmap[i], + color=cmap[n], + #label=sample_names[i], edgecolor=cmap[i], lw=0)) + label=sample_label, edgecolor=cmap[n], lw=0)) + + ## Graphical options for the plot + # Adding of the legend + #if n_exp < 10: + if nb_samples < 10: + ax1.legend(loc="best", frameon=False) + legend_ncol = 1 + #elif n_exp < 19: + elif nb_samples < 19: + legend_ncol = 2 + else: + legend_ncol = 3 + ax1.legend(loc="best", frameon=False, ncol=legend_ncol) + ax2.legend(loc="best", frameon=False, ncol=legend_ncol) + # ax2.legend(loc='upper center',bbox_to_anchor=(0.5,-0.1), fancybox=True, shadow=True) + # Main titles + if title: + ax1.set_title(title) + else: + ax1.set_title(counts_type + " counts") + ax2.set_title(counts_type + " normalized counts") + + # Adding enrichment baseline + # ax2.axhline(y=0,color='black',linestyle='dashed',linewidth='1.5') + # Axes limits + ax1.set_xlim(-0.1, len(ordered_categs) + 0.1) + if len(sizes) == 1: ax1.set_xlim(-2, 3) + ax2.set_xlim(ax1.get_xlim()) + # Set axis limits (max/min values + 5% margin) + ax2_ymin = [] + ax2_ymax = [] + for sample_values in enrichment: + ax2_ymin.append(min(sample_values)) + ax2_ymax.append(max(sample_values)) + ax2_ymax = max(ax2_ymax) + ax2_ymin = min(ax2_ymin) + margin_top, margin_bottom = (abs(0.05 * ax2_ymax), abs(0.05 * ax2_ymin)) + ax1.set_ylim(0, ax1.get_ylim()[1] * 1.05) + if options.threshold: + threshold_bottom = -abs(float(options.threshold[0])) + 1 + threshold_top = abs(float(options.threshold[1]) - 1) + + #for i in xrange(n_exp): + for n in xrange(nb_samples): + for y in xrange(n_cat): + #val = enrichment[i][y] + val = enrichment[n][y] + if not np.isnan(val) and not (threshold_bottom < val < threshold_top): + #rect = rects_enrichment[i][y] + rect = rects_enrichment[n][y] + rect_height = rect.get_height() + if rect.get_y() < 0: + diff = rect_height + threshold_bottom + rect.set_y(threshold_bottom) + ax2_ymin = threshold_bottom + margin_bottom = 0 + else: + diff = rect_height - threshold_top + ax2_ymax = threshold_top + margin_top = 0 + rect.set_height(rect.get_height() - diff) + if margin_top != 0 and margin_bottom != 0: + margin_top, margin_bottom = [max(margin_top, margin_bottom) for i in xrange(2)] + ax2.set_ylim(ax2_ymin - margin_bottom, ax2_ymax + margin_top) + # Y axis title + ax1.set_ylabel("Proportion of reads (%)") + ax2.set_ylabel("Enrichment relative to genome") + + # Add the categories on the x-axis + #ax1.set_xticks(index + bar_width * n_exp / 2) + ax1.set_xticks(index + bar_width * nb_samples / 2) + #ax2.set_xticks(index + bar_width * n_exp / 2) + ax2.set_xticks(index + bar_width * nb_samples / 2) + if counts_type.lower() != "categories": + ax1.set_xticklabels(ordered_categs, rotation="30", ha="right") + ax2.set_xticklabels(ordered_categs, rotation="30", ha="right") + else: + ax1.set_xticklabels(xlabels) + ax2.set_xticklabels(xlabels) + + # Display fractions values in percentages + ax1.set_yticklabels([str(int(i * 100)) for i in ax1.get_yticks()]) + # Correct y-axis ticks labels for enrichment subplot + # ax2.set_yticklabels([str(i+1)+"$^{+1}$" if i>0 else 1 if i==0 else str(-(i-1))+"$^{-1}$" for i in ax2.get_yticks()]) + yticks = list(ax2.get_yticks()) + yticks = [yticks[i] - 1 if yticks[i] > 9 else yticks[i] + 1 if yticks[i] < -9 else yticks[i] for i in + xrange(len(yticks))] + ax2.set_yticks(yticks) + ax2.set_yticklabels([str(int(i + 1)) if i > 0 and i % 1 == 0 else str( + i + 1) if i > 0 else 1 if i == 0 else str( + int(-(i - 1))) + "$^{-1}$" if i < 0 and i % 1 == 0 else str(-(i - 1)) + "$^{-1}$" for i in ax2.get_yticks()]) + # ax2.set_yticklabels([i+1 if i>0 else 1 if i==0 else "$\\frac{1}{%s}$" %-(i-1) for i in ax2.get_yticks()]) + # Change appearance of 'antisense' bars on enrichment plot since we cannot calculate an enrichment for this artificial category + if "antisense_pos" in locals(): # ax2.text(antisense_pos+bar_width/2,ax2.get_ylim()[1]/10,'NA') + #for i in xrange(n_exp): + for n in xrange(nb_samples): + #rect = rects_enrichment[i][antisense_pos] + rect = rects_enrichment[n][antisense_pos] + rect.set_y(ax2.get_ylim()[0]) + rect.set_height(ax2.get_ylim()[1] - ax2.get_ylim()[0]) + rect.set_hatch("/") + rect.set_fill(False) + rect.set_linewidth(0) + # rect.set_color('lightgrey') + # rect.set_edgecolor('#EDEDED') + rect.set_color("#EDEDED") + #ax2.text(index[antisense_pos] + bar_width * n_exp / 2 - 0.1, (ax2_ymax + ax2_ymin) / 2, "NA") + ax2.text(index[antisense_pos] + bar_width * nb_samples / 2 - 0.1, (ax2_ymax + ax2_ymin) / 2, "NA") + + # Add text for features absent in sample & correct for bars too small to be seen + for n in xrange(nb_samples): + for y in xrange(n_cat): + # if no counts in sample for this feature, display "Abs. in sample" + if percentages[n][y] == 0: + txt = ax1.text(y + bar_width * (n + 0.5), 0.02, "Abs.", rotation="vertical", color=cmap[n], + horizontalalignment="center", verticalalignment="bottom") + txt.set_path_effects([PathEffects.Stroke(linewidth=0.5), PathEffects.Normal()]) + else: + # if percentage value is lower than 1% of the plot height, modify it to fit this minimum value + if rects[n][y].get_height() < 5e-3 * (ax1.get_ylim()[1] - ax1.get_ylim()[0]): + rects[n][y].set_height(5e-3 * (ax1.get_ylim()[1] - ax1.get_ylim()[0])) + # if enrichment value equal to 0, increase the line width to see the bar on the plot + if enrichment[n][y] == 0: + #rects_enrichment[i][y].set_linewidth(1) + rects_enrichment[n][y].set_linewidth(1) + # if enrichment value is too small to be seen, increase the bar height to 1% of the plot height + elif abs(rects_enrichment[n][y].get_height()) < 1e-2 * (ax2_ymax - ax2_ymin): + # Correction for negative value in Matplotlib v1 + if rects_enrichment[n][y].get_y() < 0: + rects_enrichment[n][y].set_height(1e-2 * (ax2_ymax - ax2_ymin)) + rects_enrichment[n][y].set_y(-1e-2 * (ax2_ymax - ax2_ymin)) + # Correction for negative value in Matplotlib v2 + elif rects_enrichment[n][y].get_height() < 0: + rects_enrichment[n][y].set_height(-1e-2 * (ax2_ymax - ax2_ymin)) + # Correction for positive value + else: + rects_enrichment[n][y].set_height(1e-2 * (ax2_ymax - ax2_ymin)) + # Remove top/right/bottom axes + for ax in [ax1, ax2]: + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + ax.spines["bottom"].set_visible(False) + ax.tick_params(axis="x", which="both", bottom="on", top="off", labelbottom="on") + ax.tick_params(axis="y", which="both", left="on", right="off", labelleft="on") + + ### Add second axis with categ groups + annotate_group(categ_groups, label=None, ax=ax1) + annotate_group(categ_groups, label=None, ax=ax2) + + ### Adjust figure margins to + if counts_type.lower() == "categories": + plt.tight_layout(h_pad=5.0) + fig.subplots_adjust(bottom=0.1) + else: + plt.tight_layout() + + ## Displaying or saving the plot + if not options.pdf and not options.svg and not options.png: + plt.show() + else: # If any of the 3 plot output format is set + for output_basename, output_format in [(options.pdf, "pdf"), (options.svg, "svg"), (options.png, "png")]: + if output_basename: + # Checking if the file extension have been specified and removing it if so + if output_basename.endswith("." + output_format): + output_basename = output_basename[:-4] + # Saving the plot + plt.savefig(".".join((output_basename.rstrip("." + output_format), counts_type, output_format))) + plt.close() + + +def annotate_group(groups, ax=None, label=None, labeloffset=30): + """Annotates the categories with their parent group and add x-axis label""" + + def annotate(ax, name, left, right, y, pad): + """Draw the group annotation""" + arrow = ax.annotate(name, xy=(left, y), xycoords="data", + xytext=(right, y - pad), textcoords="data", + annotation_clip=False, verticalalignment="top", + horizontalalignment="center", linespacing=2.0, + arrowprops={'arrowstyle': "-", 'shrinkA': 0, 'shrinkB': 0, + 'connectionstyle': "angle,angleB=90,angleA=0,rad=5"} + ) + return arrow + + if ax is None: + ax = plt.gca() + level = 0 + for level in range(len(groups)): + grp = groups[level] + for name, coord in grp.items(): + ymin = ax.get_ylim()[0] - np.ptp(ax.get_ylim()) * 0.12 - np.ptp(ax.get_ylim()) * 0.05 * (level) + ypad = 0.01 * np.ptp(ax.get_ylim()) + xcenter = np.mean(coord) + annotate(ax, name, coord[0], xcenter, ymin, ypad) + annotate(ax, name, coord[1], xcenter, ymin, ypad) + + if label is not None: + # Define xlabel and position it according to the number of group levels + ax.annotate(label, + xy=(0.5, 0), xycoords="axes fraction", + xytext=(0, -labeloffset - (level + 1) * 15), textcoords="offset points", + verticalalignment="top", horizontalalignment="center") + + return + + +def filter_categs_on_biotype(selected_biotype, cpt): + filtered_cpt = {} + for sample in cpt: + filtered_cpt[sample] = {} + for feature, count in cpt[sample].items(): + if feature[1] == selected_biotype: + filtered_cpt[sample][feature[0]] = count + return filtered_cpt + +def usage_message(): + return """ + README on GitHub: https://github.com/biocompibens/ALFA/blob/master/README.md + + Generate ALFA genome indexes: + python ALFA.py -a GTF [-g GENOME_INDEX_BASENAME] + [--chr_len CHR_LENGTHS_FILE] + [-p NB_PROCESSORS] + Process BAM file(s): + python ALFA.py -g GENOME_INDEX_BASENAME --bam BAM1 LABEL1 [BAM2 LABEL2 ...] + [--bedgraph] [-s STRAND] + [-d {1,2,3,4}] [-t YMIN YMAX] + [--keep_ambiguous] + [-n] [--pdf output.pdf] [--svg output.svg] [--png output.png] + [-p NB_PROCESSORS] + Index genome + process BAM files(s): + python ALFA.py -a GTF [-g GENOME_INDEX_BASENAME] [--chr_len CHR_LENGTHS_FILE] + --bam BAM1 LABEL1 [BAM2 LABEL2 ...] + [--bedgraph][-s STRAND] + [-d {1,2,3,4}] [-t YMIN YMAX] + [--keep_ambiguous] + [-n] [--pdf output.pdf] [--svg output.svg] [--png output.png] + [-p NB_PROCESSORS] + + Process previously created ALFA count file(s): + python ALFA.py -c COUNTS1 [COUNTS2 ...] + [-s STRAND] + [-d {1,2,3,4}] [-t YMIN YMAX] + [-n] [--pdf output.pdf] [--svg output.svg] [--png output.png] + + """ + + +########################################################################## +# MAIN # +########################################################################## + + +if __name__ == "__main__": + + #### Parse command line arguments and store them in the variable options + parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, usage=usage_message()) + parser.add_argument("--version", action="version", version="version 0.25", + help="Show ALFA version number and exit.\n\n-----------\n\n") + # Options regarding the index + parser.add_argument("-g", "--genome_index", metavar="GENOME_INDEX_BASENAME", + help="Genome index files path and basename for existing index, or path and basename for new index creation.\n\n") + parser.add_argument("-a", "--annotation", metavar="GTF_FILE", help="Genomic annotations file (GTF format).\n\n") + parser.add_argument("--chr_len", help="Tabulated file containing chromosome names and lengths.\n\n-----------\n\n") + + # Options regarding the intersection step + parser.add_argument("--bam", metavar=("BAM1 LABEL1", ""), nargs="+", + help="Input BAM file(s) and label(s). The BAM files must be sorted by position.\n\n") + parser.add_argument("--bedgraph", metavar=("BEDGRAPH1 LABEL1", ""), nargs="+", help="Use this options if your input(s) is/are BedGraph file(s). If stranded, provide the BedGraph files\nfor each strand for all samples (e.g. '--bedgraph file.plus.bedgraph file.minus.bedgraph LABEL').\n\n") + parser.add_argument("-c", "--counts", metavar=("COUNTS1", ""), nargs="+", + help="Use this options instead of '--bam/--bedgraph' to provide ALFA counts files as input \ninstead of bam/bedgraph files.\n\n") + parser.add_argument("-s", "--strandness", default="unstranded", + choices=["unstranded", "forward", "reverse", "fr-firststrand", "fr-secondstrand"], metavar="", + help="Library orientation. Choose within: 'unstranded', " + "'forward'/'fr-firststrand' \nor 'reverse'/'fr-secondstrand'. " + "(Default: 'unstranded')\n\n-----------\n\n") + + # Options regarding the plot + #parser.add_argument("--biotype_filter", help=argparse.SUPPRESS) # "Make an extra plot of categories distribution using only counts of the specified biotype." + parser.add_argument("-d", "--categories_depth", type=int, default=3, choices=range(1, 5), + help="Use this option to set the hierarchical level that will be considered in the GTF file (default=3): \n(1) gene,intergenic; \n(2) intron,exon,intergenic; \n(3) 5'UTR,CDS,3'UTR,intron,intergenic; \n(4) start_codon,5'UTR,CDS,3'UTR,stop_codon,intron,intergenic. \n\n") + parser.add_argument("--pdf", nargs="?", const="ALFA_plots.pdf", + help="Save produced plots in PDF format at the specified path ('categories_plots.pdf' if no argument provided).\n\n") + parser.add_argument("--png", nargs="?", const="ALFA_plots.png", + help="Save produced plots in PNG format with the provided argument as basename \n('categories.png' and 'biotypes.png' if no argument provided).\n\n") + parser.add_argument("--svg", nargs="?", const="ALFA_plots.svg", + help="Save produced plots in SVG format with the provided argument as basename \nor 'categories.svg' and 'biotypes.svg' if no argument provided.\n\n") + parser.add_argument("-n", "--no_display", action="store_const", const=True, default=False, help="Do not display plots.\n\n") # We have to add "const=None" to avoid a bug in argparse + parser.add_argument("-t", "--threshold", dest="threshold", nargs=2, metavar=("YMIN", "YMAX"), type=float, + help="Set ordinate axis limits for enrichment plots.\n\n") + parser.add_argument("-p", "--processors", dest="nb_processors", type=int, default=1, help="Set the number of processors used for multi-processing operations.\n\n") + parser.add_argument("--keep_ambiguous", action="store_const", const=False, default=True, help="Keep reads mapping to different features (discarded by default).\n\n") + parser.add_argument("--temp_dir", dest="temp_dir", help="Temp directory to store pybedtools files ('/tmp/' by default).\n\n") + + if len(sys.argv) == 1: + parser.print_usage() + sys.exit(1) + + options = parser.parse_args() + print "### ALFA ###" + + # Sample labels and file paths + labels = [] + bams = [] + bedgraphs = [] + bedgraph_extension = ".bedgraph" + count_files = [] + lengths = {} + index_chrom_list = [] # Not a set because we need to sort it according to the chromosome names later + + # Booleans for steps to be executed + generate_index = False + generate_BedGraph = False + intersect_indexes_BedGraph = False + generate_plot = False + + # Checking whether the script will be able to write in the current directory + if not os.access(".", os.W_OK): + # The only exception would be if the user already has the count files and wants to display the plots without saving them + if not options.counts or options.pdf or options.svg or options.png: + sys.exit("Error: write permission denied in the directory, ALFA will not be able to run correctly.\n### End of program") + + #### Check arguments conformity and define which steps have to be performed + print "### Checking parameters" + # Checking whether there is at least one parameter among "-a", "--bam/bedgraph" and "-c" + if not (options.counts or options.bam or options.bedgraph or options.annotation): + sys.exit("Error: argument(s) are missing. At least '-a', '--bam', '--bedgraph' or '-c' is required. Please refer to help (-h/--help) and usage cases for more details.\n### End of program") + # Checking the parameters related to the counts file(s) + if options.counts: + # Checking whether the counts file(s) exist + for filename in options.counts: + if not os.path.isfile(filename): + sys.exit("Error: the file '" + filename + "' doesn't exist.\n### End of program") + # Checking whether the counts file(s) have the correct "ALFA_feature_counts.tsv" extension + if not filename.endswith(".ALFA_feature_counts.tsv"): + sys.exit("Error: the counts file '" + filename + "' doesn't have the extension 'ALFA_feature_counts.tsv'.\n### End of program") + # Registering the sample labels + label = os.path.basename(filename) + label = re.sub(".ALFA_feature_counts.tsv", "", label) + label = "_".join(re.findall(r"[\w\-']+", label)) + labels.append(label) + # Registering the counts filename + count_files.append(filename) + else: + # If the counts are not provided, then at least '-a' or '-g' arguments are mandatory + if not options.annotation and not options.genome_index: + sys.exit("Error: at least '-a' or '-g' argument is missing.\n###End of program") + # Declare genome_index variables (either from the parameter "-g" of from the annotation filename) + if options.genome_index: + genome_index_basename = options.genome_index + else: + # Otherwise the GTF filename without extension will be used as the basename + genome_index_basename = options.annotation.split("/")[-1].split(".gtf")[0] + # Setting the stranded and unstranded ALFA index files and choosing the one to use + stranded_genome_index = genome_index_basename + ".stranded.ALFA_index" + unstranded_genome_index = genome_index_basename + ".unstranded.ALFA_index" + if options.strandness == "unstranded": + genome_index = unstranded_genome_index + else: + genome_index = stranded_genome_index + # Checking the parameters related to genome annotation and ALFA index files + if options.annotation: + # Checking whether the annotation file exists + if not os.path.isfile(options.annotation): + sys.exit("Error: the file '" + options.annotation + "' doesn't exist.\n### End of program") + # Checking whether the chromosomes lengths file exists + if options.chr_len and not os.path.isfile(options.chr_len): + sys.exit("Error: the file '" + options.chr_len + "' doesn't exist.\n### End of program") + # Checking if the annotation file is correctly formatted (a biotype associated to each line) + with open (options.annotation, 'r') as input_file: + for line in input_file: + if not line.startswith("#"): + try: + biot_split = line.split("gene_biotype")[1] + except IndexError: + sys.exit("Error: at least one feature in the annotation file doesn't have a biotype description. ALFA won't be able to work robustly.\n=>" + line.rstrip() + "\n### End of program") + # If "--genome_index" parameter is present, setting the future ALFA index basename + if options.genome_index: + genome_index_basename = options.genome_index + else: + # Otherwise the GTF filename without extension will be used as the basename + genome_index_basename = options.annotation.split("/")[-1].split(".gtf")[0] + # Set this step as a task to process + generate_index = True + # Check if the ALFA indexes already exist and warning if BAM/BedGrap(s) is/are provided, raise an error otherwise + if os.path.isfile(genome_index_basename + ".stranded.ALFA_index") or os.path.isfile( + genome_index_basename + ".unstranded.ALFA_index"): + if options.bam or options.bedgraph: + print >> sys.stderr, "Warning: an ALFA index file named '%s' already exists and will be used. If you want to create a new index, please delete this file or specify another path." % (genome_index_basename + ".(un)stranded.ALFA_index") + generate_index = False + else: + sys.exit("Error: an ALFA index file named '%s' already exists. If you want to create a new index, please delete this file or specify an other path.\n### End of program" % ( + genome_index_basename + ".(un)stranded.ALFA_index")) + elif options.genome_index: # Checking whether the ALFA index files exist if no annotation file was provided + if not os.path.isfile(options.genome_index + ".stranded.ALFA_index") or not os.path.isfile( + options.genome_index + ".unstranded.ALFA_index"): + sys.exit("Error: the file '" + options.genome_index + ".stranded.ALFA_index" + "' and/or the file '" + options.genome_index + ".unstranded.ALFA_index" + "' doesn't exist.\n### End of program") + genome_index_basename = options.genome_index + + # Getting the reference genome chromosome list to check whether there is at least one in common with each BAM/BedGraph file + if options.bam or options.bedgraph: + if options.annotation: + # Checking the chromosomes list from GTF file + reference_chr_list = get_chromosome_names_in_GTF() + else: + # Checking chromosome list from genome index + reference_chr_list = get_chromosome_names_in_index(genome_index) + + # Checking parameters related to the BAM file(s) + if options.bam: + # Checking the input BAM file(s) + if len(options.bam) % 2 != 0: + sys.exit("Error: Make sure to follow the expected format: --bam BAM_file1 Label1 [BAM_file2 Label2 ...].\n### End of program ###") + for sample_package_nb in xrange(0, len(options.bam), 2): + # Check whether the BAM file exists + if not os.path.isfile(options.bam[sample_package_nb]): + sys.exit("Error: the file '" + options.bam[sample_package_nb] + "' doesn't exist.\n### End of program") + # Check whether the BAM file has a correct extension + if not options.bam[sample_package_nb].endswith(".bam"): + sys.exit("Error: at least one of the BAM file(s) doesn't have a '.bam' extension.\n" + "Make sure to follow the expected format: --bam BAM_file1 Label1 [BAM_file2 Label2 ...].\n### End of program ###") + # Registering the BAM filename + bams.append(options.bam[sample_package_nb]) + # Registering the label(s) (all that is not a character or a "minus" will be transformer into a "_") + label = "_".join(re.findall(r"[\w\-']+", options.bam[sample_package_nb + 1])) + labels.append(label) + # Checking whether the BedGraph file(s) don't already exist + if options.strandness == "unstranded": + existing_file(label + bedgraph_extension) + else: + existing_file(label + ".plus" + bedgraph_extension) + existing_file(label + ".minus" + bedgraph_extension) + # Checking whether the counts file(s) that will be created already exist + if os.path.isfile(label + ".ALFA_feature_counts.tsv"): + sys.exit("Error: The file '" + label + ".ALFA_feature_counts.tsv' is about to be produced but already exists in the directory. \n### End of program") + # Listing the BAM chromosome(s) to check whether there is at least one common with the reference genome + BAM_chr_list = pysam.AlignmentFile(options.bam[sample_package_nb], "r").references + # Checking if there is at least one common chromosome name between the reference genome and the processed BAM file + if not any(i in reference_chr_list for i in BAM_chr_list): + print ("Reference genome chromosome(s): " + str(reference_chr_list)) + print ("BAM file chromosome(s): " + str(list(BAM_chr_list))) + sys.exit("Error: no matching chromosome between the BAM file '" + options.bam[sample_package_nb] + "' and the reference genome.\n### End of program") + # Set these steps as a tasks to process + generate_BedGraph = True + intersect_indexes_BedGraph = True + + # Checking parameters related to the BedGraph file(s) + if options.bedgraph: + # Determining the number of files (BedGraph + label(s)) expected + if options.strandness == "unstranded": + sample_file_nb = 2 + else: + sample_file_nb = 3 + # Setting and checking the BedGraph extension + bedgraph_extension = "." + options.bedgraph[0].split(".")[-1] + # Checking the input BedGraph file(s) + for sample_package_nb in xrange(0, len(options.bedgraph), sample_file_nb): + for sample_file in xrange(0, sample_file_nb - 1): + # Check whether the BedGraph file exists + if not os.path.isfile(options.bedgraph[sample_package_nb + sample_file]): + sys.exit("Error: the file '" + options.bedgraph[ + sample_package_nb + sample_file] + "' doesn't exist.\n### End of program") + # Check whether the BedGraph file has a correct extension + if options.bedgraph[sample_package_nb + sample_file].split(".")[-1] not in ("bedgraph", "bg"): + sys.exit("Error: at least one of the BedGraph files doesn't have a '.bedgraph'/'.bg' extension." + "Make sure to follow the expected format: --bedgraph BedGraph_file1 Label1 [BedGraph_file2 Label2 ...].\n" + "Or for stranded samples: --bedgraph BedGraph_file1_plus BedGraph_file2_minus Label1 [...].\n### End of program ###") + # Listing the BedGraph chromosome(s) to check whether there is at least one common with the reference genome + bedgraph_chr_list = [] + with open(options.bedgraph[sample_package_nb + sample_file], "r") as bedgraph_file: + for line in bedgraph_file: + chr = line.split("\t")[0] + if chr not in bedgraph_chr_list: + bedgraph_chr_list.append(chr) + # Checking if there is at least one common chromosome name between the reference genome and the processed BedGraph file + if not any(i in reference_chr_list for i in bedgraph_chr_list): + print ("Reference genome chromosome(s): " + str(reference_chr_list)) + print ("BedGraph file chromosome(s): " + str(list(bedgraph_chr_list))) + sys.exit("Error: no matching chromosome between the BedGraph file '" + options.bedgraph[ + sample_package_nb + sample_file] + "' and the reference genome.\n### End of program") + # Register the BedGraph filename(s) + bedgraphs.append(re.sub("(.(plus|minus))?" + bedgraph_extension, "", options.bedgraph[sample_package_nb + sample_file])) + # Registering the label(s) (all that is not a character or a "minus" will be transformer into a "_") + label = "_".join(re.findall(r"[\w\-']+", options.bedgraph[sample_package_nb + sample_file_nb - 1])) + labels.append(label) + # Checking whether the count file(s) that will be created already exist + existing_file(label + ".ALFA_feature_counts.tsv") + # Set this step as a task to process + intersect_indexes_BedGraph = True + + # Checking that there is no duplicated labels + if len(labels) != len(set(labels)): + sys.exit("Error: at least one label is duplicated.\n### End of program") + + # Setting whether plots should be generated + try: + # Checking if a X server environment variable is set + x_server = os.environ['DISPLAY'] + # Plots are generated except if the flag "no_display" was used or if there is only the "-a" and eventually the "-g" argument + if not options.no_display and (options.counts or options.svg or options.pdf or options.png or options.bam or options.bedgraph): + generate_plot = True + # Checking the sample number for the colors + if len(labels) > 20: + print >> sys.stderr, "Warning: there are more than 20 samples, some colors on the plot will be duplicated." + except KeyError: + print >> sys.stderr, "Warning: your current configuration does not allow graphical interface ('$DISPLAY' variable is not set). Plotting step will not be performed." + + # Setting the temp directory if specified + if options.temp_dir: + pybedtools.set_tempdir(options.temp_dir) + + + #### Initialization of some variables + + # Miscellaneous variables + reverse_strand = {"+": "-", "-": "+"} + samples = collections.OrderedDict() # Structure: {