initial commit

1 files changed, 213 insertions, 0 deletions

diff --git a/select_mers.py b/select_mers.py
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+++ b/select_mers.py
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+import sys
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot
+import os
+
+from multiprocessing import Pool
+from subprocess import *
+import numpy as np
+import pdb
+
+fg_mers = {}
+bg_mers = {}
+# empty class to fill up mer information with
+class Mer:
+	pass
+
+class Score:
+	pass
+
+# import our variables
+min_mer_range = int(os.getenv("min_mer_range"));
+max_mer_range = int(os.getenv("max_mer_range"));
+min_mer_count = int(os.getenv("min_mer_count"));
+max_select    = int(os.getenv("max_select"));
+
+def populate_locations(input_fn, mers, selected):
+
+	mer_str = ' '.join(selected)
+	cmd = './strstream ' + mer_str + " < " + input_fn
+	
+	strstream = Popen(cmd, stdout=PIPE, shell=True)
+	for line in strstream.stdout:
+		(index, pos) = line.split(' ')
+		mers[selected[int(index)]].pts.append(int(pos))
+
+
+	return None
+
+def select_mers(selected):
+	from itertools  import combinations
+
+	scores = []
+
+	p = Pool()
+
+	for select_n in range(1, max_select):
+		print "scoring size ", select_n
+		scores_it = []
+	 	scores_it = p.map(score, combinations(selected, select_n))
+	 	scores = scores + scores_it
+
+
+	p.close()
+
+
+	scores = sorted(scores, key=lambda score: score.score)
+	return scores
+
+def score(combination):
+	
+  #	 print "scoring", combination
+
+	fg_pts_arr = []
+	fg_dist_arr = []
+
+	bg_pts_arr = []
+	bg_dist_arr = []
+
+	for mer in combination:
+		fg_pts_arr = fg_pts_arr + fg_mers[mer].pts
+		bg_pts_arr = bg_pts_arr + bg_mers[mer].pts
+
+	fg_pts_arr.sort()
+	bg_pts_arr.sort()
+
+	# remove any exact duplicates
+	fg_pts_arr = list(set(fg_pts_arr))
+	bg_pts_arr = list(set(bg_pts_arr))
+
+	#	distances
+	for i in range(1, len(fg_pts_arr)):
+		fg_dist_arr.append(abs(fg_pts_arr[i-1] - fg_pts_arr[i]));
+
+	for i in range(1, len(bg_pts_arr)):
+		bg_dist_arr.append(abs(bg_pts_arr[i-1] - bg_pts_arr[i]));
+
+	fg_dist_arr = np.array(fg_dist_arr)
+	bg_dist_arr = np.array(bg_dist_arr)
+
+	nb_primers = len(combination)
+	fg_mean_dist =  np.mean(fg_dist_arr)
+	fg_variance_dist = np.var(fg_dist_arr)
+	bg_mean_dist = np.mean(bg_dist_arr)
+	bg_variance_dist = np.var(bg_dist_arr)
+
+	# this is our equation
+	
+	score = Score()
+	score.mers = combination
+	score.score = (nb_primers * (fg_mean_dist * fg_variance_dist)) / (bg_mean_dist * bg_variance_dist)
+	# score.bg_dist_arr = bg_dist_arr
+	# score.fg_dist_arr = fg_dist_arr
+	return score
+				
+
+
+def most_frequent(mers, select_nb):
+		print "selecting the top ", select_nb, " mers"
+		selected = []
+		for mer in mers.keys():
+			selected.append([mer, mers[mer].count])
+			
+		selected = sorted(selected, key=lambda mer: mer[1])
+		selected = selected[-select_nb:]
+		selected = list(row[0] for row in selected)
+
+		return selected
+
+
+def main():
+	fg_count_fn =  sys.argv[1]
+	fg_fasta_fn =  sys.argv[2]
+
+	bg_count_fn =  sys.argv[3]
+	bg_fasta_fn =  sys.argv[4]
+
+	fg_count_fh = open(fg_count_fn, "r")
+	bg_count_fh = open(bg_count_fn, "r")
+	
+	# get our genome length
+	fg_genome_length = os.path.getsize(fg_fasta_fn)
+	bg_genome_length = os.path.getsize(bg_fasta_fn)
+
+	# copy in our fg_mers and counts
+	for mers,fh in [(fg_mers, fg_count_fh), (bg_mers, bg_count_fh)]:
+
+		for line in fh:
+			(mer, count) = line.split()
+			count = int(count)
+			mers[mer] = Mer() 
+			mers[mer].count = count
+			mers[mer].pts = []
+	
+	# if min_mer_count is less than one, then we are looking at a bottom cutoff threshold
+	if(min_mer_count < 1 and min_mer_count > 0):
+		pass
+		#	counts = []
+		#	for mer in fg_mers.keys():
+		#		counts.append(fg_mers[mer]['count'])
+		#
+		#	counts = counts.sort()
+		#	
+		#	count_set = set(counts):
+		#	len(count_set)
+		#	count_set(
+	# if it is 1 or great then use it as a numerical cutoff
+	print "removing mers that don't meet our minimum count"
+	if min_mer_count >= 1:
+		for mer in fg_mers.keys():
+			if(fg_mers[mer].count < min_mer_count):
+				del fg_mers[mer]
+				if mer in bg_mers:
+					del bg_mers[mer]
+	
+	print "removing useless mers from the background"
+	for mer in bg_mers.keys():
+		if mer not in fg_mers:
+			del bg_mers[mer]
+
+	print "adding empty mers to the background"
+	for mer in fg_mers:
+		if mer not in bg_mers:
+			bg_mers[mer] = Mer()
+			bg_mers[mer].count = 2
+			bg_mers[mer].pts = [0, bg_genome_length]
+
+		
+	exhaustive = True
+
+	if exhaustive:
+		selected = fg_mers.keys()
+	if not exhaustive:
+		selected = most_frequent(fg_mers, max_select)
+
+	print "selected the top ", max_select, " mers"
+	print "selected:", ", ".join(selected)
+
+	print "Populating foreground locations"
+	populate_locations(fg_fasta_fn, fg_mers, selected)
+	print "Populating background locations"
+	populate_locations(bg_fasta_fn, bg_mers, selected)
+
+	scores = select_mers(selected)
+
+	print "fg_genome_length", fg_genome_length
+	print "bg_genome_length", bg_genome_length
+	
+
+	for score in scores:
+		sys.stdout.write(str(score.score) + "\t")
+		sys.stdout.write(", ".join(list(score.mers)) + "\t")
+		# sys.stdout.write("\t".join(score.dist_arr));
+		sys.stdout.write("\n");
+
+		#matplotlib.pyplot.hist(score.fg_dist_arr)	
+		#matplotlib.pyplot.savefig('graph/' + str(x) + ".png")
+		#matplotlib.pyplot.clf()
+	import pdb
+	pdb.set_trace()
+
+if __name__ == "__main__":
+	sys.exit(main())