5 files changed, 370 insertions, 0 deletions
diff --git a/src/generate_kmers b/src/generate_kmers
new file mode 100755
index 0000000..38fdf0a
--- /dev/null
+++ b/src/generate_kmers
@@ -0,0 +1,5 @@
+#!/usr/bin/python
+import quikr
+import sys
+
+print quikr.generate_kmers(int(sys.argv[1]))
diff --git a/src/multifasta_to_otu.py b/src/multifasta_to_otu.py
new file mode 100755
index 0000000..c6fb562
--- /dev/null
+++ b/src/multifasta_to_otu.py
@@ -0,0 +1,154 @@
+#!/usr/bin/python
+
+from multiprocessing import Pool
+from Bio import SeqIO
+import multiprocessing
+from subprocess import *
+import os
+import gzip
+import quikr as q
+import sys
+import numpy as np
+import argparse
+import platform
+import csv
+
+# our defaults
+kmer = 6
+lamb = 10000
+output_directory = ""
+input_directory = ""
+
+
+def main():
+
+  global kmer
+  global input_directory 
+  global output_directory 
+  global lamb
+  global trained_matrix 
+
+  parser = argparse.ArgumentParser(description="MultifastaOTU")
+
+  parser.add_argument("-i", "--input-directory", help="directory containing fasta files", required=True)
+  parser.add_argument("-o", "--otu-table", help="otu_table", required=True)
+  parser.add_argument("-t", "--trained-matrix", help="your trained matrix ", required=True)
+  parser.add_argument("-f", "--trained-fasta", help="the fasta file used to train your matrix", required=True)
+  parser.add_argument("-d", "--output-directory", help="quikr output directory", required=True)
+  parser.add_argument("-l", "--lamb", type=int, help="the default lambda value is 10,000")
+  parser.add_argument("-k", "--kmer", type=int, help="specifies which kmer to use, default=6")
+  parser.add_argument("-j", "--jobs", type=int, help="specifies how many jobs to run at once, default=number of CPUs")
+  args = parser.parse_args()
+    
+  # our defaults
+  jobs=multiprocessing.cpu_count()
+  trained_matrix = args.trained_matrix
+  input_directory = args.input_directory
+  output_directory = args.output_directory
+
+  # Make sure our input exist
+  if not os.path.isdir(args.input_directory):
+    parser.error("Input directory not found")
+
+  if not os.path.isdir(args.output_directory):
+    print "Output directory not found, creating directory"
+    os.path.mkdir(args, output_directory)
+
+  if not os.path.isfile(args.trained_matrix):
+    parser.error("Custom trained matrix not found")
+    
+  if not os.path.isfile(args.trained_fasta):
+    parser.error("Fasta file of trained matrix not found")
+
+  # use alternative lambda
+  if args.lamb is not None:
+    lamb = args.lamb
+    
+  if args.jobs is not None:
+    jobs = args.jobs
+
+  if args.kmer is not None:
+    kmer = args.kmer
+
+  # Load trained matrix
+  if qu.isCompressed(args.trained_matrix):
+    trained_matrix_file = gzip.open(args.trained_matrix, "rb")
+  else:
+    trained_matrix_file = open(args.trained_matrix, "rb")
+  
+  trained_matrix = np.load(trained_matrix_file)
+
+  # Return a list of the input directory
+  fasta_list = os.listdir(args.input_directory)
+
+  # Queue up and run our quikr functions.
+  pool = Pool(processes=jobs)
+  results = pool.map(quikr_call, fasta_list)
+
+  # Create an array of headers
+  headers = []
+  trained_matrix_headers = open(args.trained_fasta, "rU")
+  for header in SeqIO.parse(trained_matrix_headers, "fasta"):
+    headers.append(header.id)
+  trained_matrix_headers.close()
+
+  # create our number of reads matrix
+  number_of_reads = np.zeros((len(headers), len(fasta_list)))
+
+  # load the keys with values from each fasta result
+  for fasta, fasta_it in map(None, fasta_list, range(len(fasta_list))):
+
+    count_sequences = Popen(["grep", "-c" , "^>", args.input_directory + fasta], stdout=PIPE) 
+    number_of_sequences = int(count_sequences.stdout.readline())
+
+    proportions = np.loadtxt(output_directory + fasta);
+    
+    for proportion, proportion_it in map(None, proportions, range(len(proportions))):
+      number_of_reads[proportion_it, fasta_it] = round(proportion * number_of_sequences)
+
+  # remove empty rows from our matrix
+  final_headers = list()
+  final_data = list()
+  for row, header in map(None, number_of_reads, headers):
+    if row.sum() != 0:
+      final_headers.append(header)
+      final_data.append(row)
+
+  # convert from a list back into a numpy array
+  final_data = np.array(final_data, dtype=int)
+
+  # stack our final header and our number_of_reads matrix
+  number_of_reads = np.column_stack((final_headers, final_data))
+
+  # write our OTU table
+  output_file = open(args.otu_table, "wb") 
+  writer = csv.writer(output_file, delimiter="\t")
+
+  #write out our fasta file row
+  writer.writerow(['# QIIME vGail OTU table'])
+
+  fasta_row = ['#OTU_ID']
+  fasta_row.append(' '.join(fasta_list))
+  fasta_row = [' '.join(fasta_row)]
+  writer.writerow(fasta_row)
+
+  # write out our results
+  for i in range(0, np.shape(number_of_reads)[0]):
+      writer.writerow(list(number_of_reads[i]))
+
+  output_file.close()
+
+  return 0
+
+
+def quikr_call(fasta_file):
+  input_location = input_directory + fasta_file
+  output_location = output_directory + os.path.basename(fasta_file)
+
+  xstar = q.quikr(input_location, trained_matrix, kmer, lamb)
+  np.savetxt(output_location, xstar, delimiter=",", fmt="%f")
+  return xstar
+
+if __name__ == "__main__":
+  sys.exit(main())
+ 
diff --git a/src/quikr b/src/quikr
new file mode 100755
index 0000000..bac01ca
--- /dev/null
+++ b/src/quikr
@@ -0,0 +1,48 @@
+#!/usr/bin/python
+import sys
+import os
+import argparse
+import quikr
+import numpy as np
+
+def main():
+
+    parser = argparse.ArgumentParser(description=
+    "Quikr returns the estimated frequencies of batcteria present when given a \
+    input FASTA file. \n \
+    A default trained matrix will be used if none is supplied \n \
+    You must supply a kmer and default lambda if using a custom trained \
+    matrix.")
+
+    parser.add_argument("-f", "--fasta", help="fasta file", required=True)
+    parser.add_argument("-o", "--output", help="output path (csv output)", required=True)
+    parser.add_argument("-t", "--trained-matrix", help="trained matrix", required=True)
+    parser.add_argument("-l", "--lamb", type=int, help="the default lambda value is 10,000")
+    parser.add_argument("-k", "--kmer", type=int, required=True,
+        help="specifies which kmer to use, must be used with a custom trained database")
+
+
+    args = parser.parse_args()
+    
+    # our default lambda is 10,000
+    lamb = 10000
+
+    # Make sure our input exist
+    if not os.path.isfile(args.fasta):
+        parser.error( "Input fasta file not found")
+
+    if not os.path.isfile(args.trained_matrix):
+        parser.error("Custom trained matrix not found")
+    
+    # use alternative lambda
+    if args.lamb is not None:
+        lamb = args.lamb
+    
+    trained_matrix = quikr.load_trained_matrix_from_file(args.trained_matrix)
+    xstar = quikr.calculate_estimated_frequencies(args.fasta, trained_matrix, args.kmer, lamb)
+
+    np.savetxt(args.output, xstar, delimiter=",", fmt="%f")
+    return 0
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/quikr.py b/src/quikr.py
new file mode 100755
index 0000000..3f8221e
--- /dev/null
+++ b/src/quikr.py
@@ -0,0 +1,115 @@
+#!/usr/bin/python
+import os
+import sys
+import scipy.optimize.nnls
+import scipy.sparse
+import numpy as np
+from subprocess import *
+import argparse
+import platform
+import gzip
+import itertools
+
+def generate_kmers(kmer):
+  """ This will return a list of kmers seperated by newlines """
+  return '\n'.join(''.join(x) for x in itertools.product('acgt', repeat=kmer))
+
+def isCompressed(filename):
+  """ This function checks to see if the file is gzipped """ 
+
+  f = open(filename, "rb")
+
+  # The first two bytes of a gzipped file are always '1f 8b'
+  if f.read(2) == '\x1f\x8b':
+    f.close()
+    return True
+  else:
+    f.close()
+    return False
+
+  
+def train_matrix(input_file_location, kmer):
+  """
+  Takes a input fasta file, and kmer, returns a custom trained matrix
+  """
+
+  kmer_file_name = str(kmer) + "mers.txt"
+
+  if not os.path.isfile(kmer_file_name):
+    print "could not find kmer file"
+    exit()
+  
+  uname = platform.uname()[0]
+
+  if uname == "Linux": 
+    input_file = Popen(["./probabilities-by-read-linux", str(kmer), input_file_location, kmer_file_name], stdout=PIPE) 
+  elif uname == "Darwin":
+    input_file = Popen(["./probabilities-by-read-osx", str(kmer), input_file_location, kmer_file_name]) 
+
+  # load and  normalize the matrix by dividing each element by the sum of it's column.
+  # also do some fancy rotations so that it works properly with quikr
+  matrix  = np.loadtxt(input_file.stdout)
+  
+  matrix = np.rot90(matrix)
+  matrix = matrix / matrix.sum(0)
+  matrix = np.flipud(matrix);
+  return matrix
+
+
+def load_trained_matrix_from_file(trained_matrix_location):
+  """ This is a helper function to load our trained matrix and run quikr """
+  
+  if isCompressed(trained_matrix_location):
+    trained_matrix_file = gzip.open(trained_matrix_location, "rb")
+  else:
+    trained_matrix_file = open(trained_matrix_location, "rb")
+  
+  trained_matrix = np.load(trained_matrix_file)
+
+  return trained_matrix
+
+
+def calculate_estimated_frequencies(input_fasta_location, trained_matrix, kmer, default_lambda):
+  """
+  input_fasta is the input fasta file to find the estimated frequencies of
+  trained_matrix is the trained matrix we are using to estimate the species
+  kmer is the desired k-mer to use
+  default_lambda is inp 
+  
+  returns the estimated requencies of bacteria present when given an input
+  FASTA file of amplicon (454) reads. A k-mer based, L1 regularized, sparsity
+  promoting algorthim is utilized. 
+
+  In practice reconstruction is accurate only down to the genus level (not 
+  species or strain).
+
+  """
+
+  uname = platform.uname()[0]
+
+  # We use the count program to count ____
+  if uname == "Linux" and os.path.isfile("./count-linux"):
+    count_input = Popen(["./count-linux", "-r", str(kmer), "-1", "-u", input_fasta_location], stdout=PIPE) 
+  elif uname == "Darwin" and os.path.isfile("./count-osx"):
+    count_input = Popen(["count-osx", "-r", str(kmer), "-1", "-u", input_fasta_location], stdout=PIPE) 
+
+
+  # load the output of our count program and form a probability vector from the counts  
+  counts = np.loadtxt(count_input.stdout) 
+  counts = counts / counts.sum(0) 
+  counts = default_lambda * counts
+  counts = np.concatenate([np.zeros(1), counts])
+
+  #form the k-mer sensing matrix
+  trained_matrix = trained_matrix * default_lambda;
+  trained_matrix = np.vstack((np.ones(trained_matrix.shape[1]), trained_matrix))
+
+
+  xstar, rnorm = scipy.optimize.nnls(trained_matrix, counts) 
+  xstar = xstar / xstar.sum(0) 
+
+  return xstar
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/quikr_train b/src/quikr_train
new file mode 100755
index 0000000..6e599c9
--- /dev/null
+++ b/src/quikr_train
@@ -0,0 +1,48 @@
+#!/usr/bin/python
+import numpy as np
+import quikr
+import os
+import sys
+import gzip
+from subprocess import *
+import platform
+import argparse
+
+def main():
+  """
+  You can call this script independently, and will save the 
+  trained matrix as a numpy file.
+
+  example: python quikr-train.py -i input.fasta -k 6 -o trained_matrix.npy
+ 
+  """
+  parser = argparse.ArgumentParser(description=
+  " quikr_train returns a custom trained matrix that can be used with \
+    the quikr function. \n You must supply a kmer. \n ")
+
+  parser.add_argument("-i", "--input", help="training database of sequences (fasta format)", required=True)
+  parser.add_argument("-o", "--output", help="sensing matrix (text file)", required=True)
+  parser.add_argument("-k", "--kmer", help="kmer size (integer)", 
+                      type=int, required=False )
+  parser.add_argument("-z", "--compress", help="compress output (integer)", 
+                      action='store_true', required=False)
+
+  args = parser.parse_args()
+
+  if not os.path.isfile(args.input):
+    parser.error( "Input database not found")
+
+  # call the quikr train function, save the output with np.save
+  matrix = quikr.train_matrix(args.input, args.kmer)
+
+  if args.compress: 
+    output_file = gzip.open(args.output, "wb")
+  else:
+    output_file = open(args.output, "wb")
+
+  np.save(output_file, matrix)
+
+  return 0
+  
+if __name__ == "__main__":
+    sys.exit(main())