In [1]:

from __future__ import print_function, division
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import vcfnp
vcfnp.__version__

Out[1]:

'2.0.0'

In [2]:

# VCF file name
filename = 'fixture/sample.vcf'

In [3]:

# load data from fixed fields (including INFO)
v = vcfnp.variants(filename, cache=True).view(np.recarray)

[vcfnp] 2015-01-23 11:22:59.232507 :: caching is enabled
[vcfnp] 2015-01-23 11:22:59.232909 :: cache file available
[vcfnp] 2015-01-23 11:22:59.233095 :: loading from cache file fixture/sample.vcf.vcfnp_cache/variants.npy

In [4]:

v.dtype

Out[4]:

dtype([('CHROM', 'S12'), ('POS', '<i4'), ('ID', 'S12'), ('REF', 'S12'), ('ALT', 'S12'), ('QUAL', '<f4'), ('FILTER_q10', '?'), ('FILTER_s50', '?'), ('FILTER_PASS', '?'), ('num_alleles', 'u1'), ('is_snp', '?'), ('svlen', '<i4'), ('AA', 'S12'), ('AC', '<u2'), ('AF', '<f2'), ('AN', '<u2'), ('DB', '?'), ('DP', '<i4'), ('H2', '?'), ('NS', '<i4')])

In [5]:

v.CHROM

Out[5]:

chararray([b'19', b'19', b'20', b'20', b'20', b'20', b'20', b'20', b'X'], 
      dtype='|S12')

In [6]:

v.POS

Out[6]:

array([    111,     112,   14370,   17330, 1110696, 1230237, 1234567,
       1235237,      10], dtype=int32)

In [7]:

v.DP

Out[7]:

array([ 0,  0, 14, 11, 10, 13,  9,  0,  0], dtype=int32)

In [8]:

# print some simple variant metrics
print('found %s variants (%s SNPs)' % (v.size, np.count_nonzero(v.is_snp)))
print('QUAL mean (std): %s (%s)' % (np.mean(v.QUAL), np.std(v.QUAL)))

found 9 variants (5 SNPs)
QUAL mean (std): 25.0667 (22.816)

In [9]:

# plot a histogram of variant depth
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.hist(v.DP)
ax.set_title('DP histogram')
ax.set_xlabel('DP');

In [10]:

# load data from sample columns
c = vcfnp.calldata_2d(filename, cache=True).view(np.recarray)

[vcfnp] 2015-01-23 11:22:59.494181 :: caching is enabled
[vcfnp] 2015-01-23 11:22:59.494571 :: cache file available
[vcfnp] 2015-01-23 11:22:59.494840 :: loading from cache file fixture/sample.vcf.vcfnp_cache/calldata_2d.npy

In [11]:

c.dtype

Out[11]:

dtype([('is_called', '?'), ('is_phased', '?'), ('genotype', 'i1', (2,)), ('DP', '<u2'), ('GQ', 'u1'), ('GT', 'S3'), ('HQ', '<i4', (2,))])

In [12]:

c.genotype

Out[12]:

array([[[ 0,  0],
        [ 0,  0],
        [ 0,  1]],

       [[ 0,  0],
        [ 0,  0],
        [ 0,  1]],

       [[ 0,  0],
        [ 1,  0],
        [ 1,  1]],

       [[ 0,  0],
        [ 0,  1],
        [ 0,  0]],

       [[ 1,  2],
        [ 2,  1],
        [ 2,  2]],

       [[ 0,  0],
        [ 0,  0],
        [ 0,  0]],

       [[ 0,  1],
        [ 0,  2],
        [-1, -1]],

       [[ 0,  0],
        [ 0,  0],
        [-1, -1]],

       [[ 0, -1],
        [ 0,  1],
        [ 0,  2]]], dtype=int8)

In [13]:

# print some simple genotype metrics
count_phased = np.count_nonzero(c.is_phased)
count_variant = np.count_nonzero(np.any(c.genotype > 0, axis=2))
count_missing = np.count_nonzero(~c.is_called)
print('calls (phased, variant, missing): %s (%s, %s, %s)'
    % (c.flatten().size, count_phased, count_variant, count_missing))

calls (phased, variant, missing): 27 (14, 12, 2)

In [14]:

# plot a histogram of genotype quality
fig = plt.figure(2)
ax = fig.add_subplot(111)
ax.hist(c.GQ.flatten())
ax.set_title('GQ histogram')
ax.set_xlabel('GQ');