Commit 28da6bc9 authored by Robin Erich Muench's avatar Robin Erich Muench

Merge branch 'master' of

parents 4c47bd61 1dae596f
......@@ -40,7 +40,9 @@ def get_arguments():
parser.add_argument('-m',metavar='INT', type=int, help="Minimum number of samples per species", default=2)
parser.add_argument('-c',metavar='FLOAT', type=float, help="FILTERING STEP II: minimum coverage per position per sample per species", default=5.0)
parser.add_argument('-p',metavar='FLOAT', type=float, help="FILTERING STEP II: required proportion of informative samples (coverage non-zero) per position", default=0.50)
parser.add_argument('-div',action='store_true',help="Compute diversity measures - note that this is a slow computation")
# REQUIRED arguments:
parser.add_argument('projdir', help='project name', metavar='Proj')
......@@ -316,6 +318,55 @@ def computeAllDist(args):
dist.to_csv(args.projdir+'/distances/'+'%s.allele.dist' % f.split('/')[-1].replace('.freq',''), sep='\t')
# Compute the diversity per position
def compute_diversity(ind, sample1, sample2):
# Get per position nucleotide frequency
s1 = sample1.loc[ind].values
s2 = sample2.loc[ind].values
# Add the reference nucleotide frequency
s1 = np.append(s1, [1-np.sum(s1)])
s2 = np.append(s2, [1-np.sum(s2)])
# Compute all the combinaison
out = np.outer(s1, s2)
# Initialize the per position diversity
sum_i = np.nan
# If not all values are nan, then sum without taking the diagonal
if ~np.isnan(out).all():
np.fill_diagonal(out, 0)
sum_i = np.nansum(out)
return sum_i
# Apply the compute_diversity to all positions for a specific pairwise comparison
def genetic_distance(sample1, sample2):
temp_res = [compute_diversity(ind, sample1, sample2) for ind in list(sample1.index.levels[0])]
return np.nansum(temp_res)
def computeAllDiv(args):
print "Computing diversities"
allFreq = glob.glob(args.projdir + '/filtered/pop/*.freq')
for f in allFreq:
# Read the freq table as data.frame
data = pd.read_table(f, index_col=0, na_values=['-1'])
pre_index = [i.split(':') for i in list(data.index)]
# Setting index for each position
index1 = [item[0] + ':' + item[1] + ':' + item[2] for item in pre_index]
# Setting index for Non-synonymous vs Synonymous
index2 = [item[4] for item in pre_index]
indexes = [index1, index2]
index = pd.MultiIndex.from_arrays(indexes, names=['position', 'significance'])
data = data.set_index(index)
dist = [[genetic_distance(data.iloc[:, [i]], data.iloc[:, [j]]) for i in range(j+1)] for j in range(3)]
dist.to_csv(args.projdir+'/distances/'+'%s.diversity' % f.split('/')[-1].replace('.freq',''), sep='\t')
# To do : divide by the minimal horizontal coverage of each pairwise comparison.
# dist = dist / min(sample_i.1X_horizontal_coverage, sample_j.1X_horizontal_coverage)
if __name__ == "__main__":
# print("globals: {}".format(globals()))
......@@ -347,3 +398,6 @@ if __name__ == "__main__":
if args.div:
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