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Figure 3D/E/F, NCO recombination parameters

Setup





In [1]:



    


%run ../../shared_setup.ipynb



    


















    






docker image cggh/biipy:v1.6.0










    























In [2]:



    


# load variation data
sample_exclusions = dup_samples.copy()
for cross in excessive_recomb_samples:
    sample_exclusions[cross] += excessive_recomb_samples[cross]

callsets = load_callsets(COMBINED_CALLSET_FN_TEMPLATE, 
                         sample_exclusions=sample_exclusions, 
                         variant_filter='FILTER_PASS',
                         call_filter=combined_conf_calls)

samples = {cross: callsets[cross]['calldata'].dtype.names
           for cross in CROSSES}
progeny = {cross: samples[cross][2:] for cross in CROSSES}
n_progeny = {cross: len(progeny[cross]) for cross in CROSSES}
print(n_progeny)
print(np.sum(list(n_progeny.values())))



    


















    






2016-03-08 18:07:59.109120 :: loading /data/plasmodium/pfalciparum/pf-crosses/data/public/20141022/3d7_hb3.combined.final.npz
2016-03-08 18:07:59.402186 :: filter variants: excluding 157 (0.4%) retaining 42087 (99.6%) of 42244 variants
2016-03-08 18:07:59.420460 :: filter samples: excluding ['C01/PG0062-C/ERR019070', 'C02/PG0053-C/ERR019067', 'C02/PG0055-C/ERR019066', 'C02/PG0056-C/ERR019068'] including ['3D7/PG0051-C/ERR019061', 'HB3/PG0052-C/ERR019054', 'C01/PG0065-C/ERR019064', 'C02/PG0067-C/ERR019073', 'C03/PG0066-C/ERR019072', 'C04/PG0061-C/ERR019059', 'C05/PG0068-C/ERR019065', 'C06/PG0069-C/ERR019055', 'C07/PG0070-C/ERR019056', 'C08/PG0071-C/ERR019074', 'C09/PG0072-C/ERR019057', 'C10/PG0063-C/ERR019060', 'C11/PG0064-C/ERR019071', 'C12/PG0058-C/ERR019063', 'C13/PG0054-C/ERR019062', 'C14/PG0060-C/ERR019058', 'C15/PG0057-C/ERR019069']
2016-03-08 18:07:59.468446 :: filter calls: excluding 2216 (0.3%) retaining 713263 (99.7%) of 715479 calls
2016-03-08 18:07:59.469582 :: loading /data/plasmodium/pfalciparum/pf-crosses/data/public/20141022/hb3_dd2.combined.final.npz
2016-03-08 18:07:59.866487 :: filter variants: excluding 450 (1.2%) retaining 36461 (98.8%) of 36911 variants
2016-03-08 18:07:59.889371 :: filter samples: excluding ['SC01/PG0025-C/ERR019045'] including ['HB3/PG0004-CW/ERR012788', 'DD2/PG0008-CW/ERR012840', '1BB5/PG0023-C/ERR015449', '3BA6/PG0022-Cx/ERR126027', '3BD5/PG0024-C/ERR019053', '7C101/PG0074-C/ERR019048', '7C111/PG0038-C/ERR015457', '7C12/PG0035-Cx/ERR037704', '7C126/PG0047-C/ERR015452', '7C140/PG0039-C/ERR015454', '7C159/PG0040-Cx/ERR107475', '7C16/PG0036-C/ERR015455', '7C170/PG0041-C/ERR015446', '7C183/PG0042-C/ERR015448', '7C188/PG0030-C/ERR019046', '7C20/PG0037-C/ERR015451', '7C3/PG0034-C/ERR019047', '7C408/PG0031-C/ERR015458', '7C421/PG0043-C/ERR015459', '7C424/PG0044-C/ERR019043', '7C46/PG0046-Cx/ERR107476', '7C7/PG0048-C/ERR019049', 'B1SD/PG0015-C/ERR019044', 'B4R3/PG0018-C/ERR019042', 'CH3_116/PG0032-Cx/ERR037703', 'CH3_61/PG0033-Cx/ERR175544', 'D43/PG0029-Cx/ERR107474', 'GC03/PG0021-C/ERR015447', 'GC06/PG0028-C/ERR015456', 'QC01/PG0017-C/ERR019050', 'QC13/PG0016-C/ERR012895', 'QC23/PG0045-C/ERR012892', 'QC34/PG0026-C/ERR015453', 'SC05/PG0019-C/ERR019051', 'TC05/PG0027-C/ERR015450', 'TC08/PG0020-C/ERR019052']
2016-03-08 18:07:59.978640 :: filter calls: excluding 28934 (2.2%) retaining 1283662 (97.8%) of 1312596 calls
2016-03-08 18:07:59.980795 :: loading /data/plasmodium/pfalciparum/pf-crosses/data/public/20141022/7g8_gb4.combined.final.npz
2016-03-08 18:08:00.367046 :: filter variants: excluding 304 (0.9%) retaining 34471 (99.1%) of 34775 variants
2016-03-08 18:08:00.386717 :: filter samples: excluding ['AUD/PG0112-CW/ERR045639', 'JC9/PG0111-C/ERR029409', 'JE11/PG0100-CW/ERR045630', 'JF6/PG0079-CW/ERR045637', 'KB8/PG0104-CW/ERR045642', 'LA10/PG0086-CW/ERR045629', 'NIC/PG0095-C/ERR027107', 'QF5/PG0078-CW/ERR045638', 'XD8/PG0105-CW/ERR045628', 'XF12/PG0102-CW/ERR045635', 'D2/PG0094-CW/ERR045632'] including ['7G8/PG0083-C/ERR027099', 'GB4/PG0084-C/ERR027100', 'AL2/PG0103-CW/ERR045627', 'AUD/PG0112-C/ERR029406', 'DAN/PG0098-C/ERR027110', 'DEV/PG0081-CW/ERR045633', 'JB12/PG0099-C/ERR029146', 'JB8/PG0087-C/ERR029091', 'JC3/PG0077-CW/ERR045636', 'JC9/PG0111-CW/ERR045634', 'JE11/PG0100-C/ERR029404', 'JF6/PG0079-C/ERR027102', 'JON/PG0107-C/ERR029408', 'KA6/PG0091-C/ERR027117', 'KB8/PG0104-C/ERR029148', 'KH7/PG0088-C/ERR027111', 'LA10/PG0086-C/ERR029090', 'NF10/PG0096-C/ERR027108', 'NIC/PG0095-CW/ERR045631', 'QF5/PG0078-C/ERR029092', 'TF1/PG0080-C/ERR027103', 'WC4/PG0082-C/ERR029093', 'WE2/PG0085-C/ERR027101', 'WF12/PG0097-C/ERR027109', 'XB3/PG0093-C/ERR029105', 'XD8/PG0105-C/ERR029144', 'XE7/PG0106-C/ERR029407', 'XF12/PG0102-C/ERR029143', 'XG10/PG0109-C/ERR029405']
2016-03-08 18:08:00.457166 :: filter calls: excluding 12209 (1.2%) retaining 987450 (98.8%) of 999659 calls










    






{'7g8_gb4': 27, 'hb3_dd2': 34, '3d7_hb3': 15}
76

















In [3]:



    


tbl_nco = (etl
    .frompickle(os.path.join(PUBLIC_DIR, 'tbl_conversion_tracts.pickle'))
    .eq('tract_type', 'NCO')
    # exclude the really long outliers
    .lt('tract_length_min', 18000)
)
display_with_nrows(tbl_nco, caption='NCO tracts')
df_nco = tbl_nco.todataframe()



    


















    








NCO tracts (230 rows)



0|sample
1|cross
2|chrom
3|tract_start_min
4|tract_start_mid
5|tract_start_max
6|tract_stop_min
7|tract_stop_mid
8|tract_stop_max
9|tract_length_min
10|tract_length_mid
11|tract_length_max
12|tract_support
13|tract_is_complex
14|tract_blocks
15|co_pos_mid
16|co_pos_min
17|co_pos_max
18|co_pos_range
19|tract_type






1BB5/PG0023-C/ERR015449
hb3_dd2
b'Pf3D7_02_v3'
551191
551710
552230
553769
554132
554496
1539
2422
3305
5
False
1
None
None
None
None
NCO




1BB5/PG0023-C/ERR015449
hb3_dd2
b'Pf3D7_08_v3'
1293542
1294381
1295221
1296788
1297035
1297283
1567
2654
3741
4
False
1
None
None
None
None
NCO




1BB5/PG0023-C/ERR015449
hb3_dd2
b'Pf3D7_09_v3'
950476
951138
951801
954256
954735
955215
2455
3597
4739
4
True
3
None
None
None
None
NCO




1BB5/PG0023-C/ERR015449
hb3_dd2
b'Pf3D7_12_v3'
331487
331820
332154
336886
337497
338108
4732
5677
6621
7
False
1
None
None
None
None
NCO




1BB5/PG0023-C/ERR015449
hb3_dd2
b'Pf3D7_13_v3'
756601
756957
757313
760183
761269
762355
2870
4312
5754
9
False
1
None
None
None
None
NCO






...

Simulate random uniform/geometric NCOs





In [4]:



    


def simulate_nco(phi, sim_factor=10):
    tbls_nco_sim = [simulate_random_uniform_non_crossovers(len(progeny[cross])*sim_factor, callsets[cross], phi).addfield('cross', cross)
                    for cross in CROSSES]
    tbl_nco_sim = etl.cat(*tbls_nco_sim)
    return tbl_nco_sim.todataframe()



    











In [5]:



    


# simulation based on uniform location and geometric length
# N.B., don't want to have to repeat this if possible
nco_sim_fn = os.path.join(PUBLIC_DIR, 'nco_sim.dataframe.pickle')
if not os.path.exists(nco_sim_fn):
    log('simulating')
    df_nco_sim = simulate_nco(phi=.9993, sim_factor=5000)
    log('saving', nco_sim_fn)
    df_nco_sim.to_pickle(nco_sim_fn)
else:
    log('loading', nco_sim_fn)
    df_nco_sim = pandas.read_pickle(nco_sim_fn)



    


















    






2016-03-08 18:08:04.059834 :: loading /data/plasmodium/pfalciparum/pf-crosses/data/public/20141022/nco_sim.dataframe.pickle

















In [6]:



    


# discovery rates
nco_discovery_rate = dict()
for cross in CROSSES:
    n = np.count_nonzero(df_nco_sim.cross == cross)
    n_discovered = np.count_nonzero((df_nco_sim.cross == cross) & (df_nco_sim.tract_support > 1) & (df_nco_sim.tract_length_min > 100))
    discovery_rate = n_discovered / n
    print(cross, n, n_discovered, discovery_rate)
    nco_discovery_rate[cross] = discovery_rate



    


















    






3d7_hb3 75000 34244 0.45658666666666664
hb3_dd2 170000 68450 0.4026470588235294
7g8_gb4 135000 53131 0.39356296296296295

Minimal tract length distribution





In [7]:



    


colors = sns.color_palette()
sns.palplot(colors)



    


















    
























In [8]:



    


def plot_tract_length_distribution(ax):

    sns.despine(ax=ax, offset=5)
    data = [
        df_nco.tract_length_min.loc[df_nco.tract_is_complex].values,
        df_nco.tract_length_min.loc[~df_nco.tract_is_complex].values,
    ]
    bin_edges = np.logspace(2, 4.5, 15)
    ax.hist(data, bins=bin_edges, stacked=True, color=[colors[2], colors[0]], rwidth=1, label=['complex', 'simple'])
    ax.set_xscale('log')
    ax.set_xlabel('minimal tract length (bp)')
    ax.set_ylabel('frequency')
    ax.autoscale(axis='y', tight=True)

    ax2 = ax.twinx()
    sns.despine(ax=ax2, offset=5)
    bin_edges = np.logspace(2, 4.5, 40)
    Z = df_nco_sim.tract_length_min[(df_nco_sim.tract_support > 1) & (df_nco_sim.tract_length_min > 100)].values
    Y, _ = np.histogram(Z, bins=bin_edges)
    X = (bin_edges[1:] + bin_edges[:-1])/2
    ax2.plot(X, Y, color='k', linewidth=1, linestyle='--')
    ax2.autoscale(axis='y', tight=True)
    ax2.set_yticks([])



    











In [9]:



    


width = 8/3
height = width
fig, ax = plt.subplots(figsize=(width, height))
plot_tract_length_distribution(ax)
ax.set_xlim(100, 15000)
ax.set_xlabel('NCO minimal tract length (bp)')
ax.set_title('D', fontweight='bold')
fig.tight_layout()
fig.savefig('../../artwork/main/fig3D.jpg', dpi=900, jpeg_quality=100)



    


















    
























In [10]:



    


from matplotlib.patches import Patch
fig, ax = plt.subplots(figsize=(2, 2))
ax.set_axis_off()
handles = []
handles.append(Patch(color=colors[0], label='simple'))
handles.append(Patch(color=colors[2], label='complex'))
handles.append(plt.Line2D((0, 0), (1, 1), linestyle='--', marker=' ', color='k', label='geometric'))
ax.legend(handles=handles, loc='upper left', bbox_to_anchor=(0, 1), numpoints=1, fontsize=8, frameon=False, framealpha=.5);
fig.tight_layout()
fig.savefig('../../artwork/main/fig3D_legend.jpg', dpi=900, jpeg_quality=100)



    


















    
























In [11]:



    


def plot_tract_length_pp(ax, percentiles=range(0, 100, 4), vmax=15000, scatter_kws=dict()):

    sns.despine(ax=ax, offset=5)
    X = df_nco.tract_length_min.values
    Y = df_nco_sim.tract_length_min[(df_nco_sim.tract_support > 1) & (df_nco_sim.tract_length_min > 100)].values
    px = np.array([np.percentile(X, p) for p in percentiles])
    py = np.array([np.percentile(Y, p) for p in percentiles])
    ax.set_xscale('log')
    ax.set_yscale('log')
    ax.plot([1, vmax], [1, vmax], color='k', linestyle=':', marker=' ')
    ax.scatter(px, py, **scatter_kws)
    ax.set_xlim(0, vmax)
    ax.set_ylim(0, vmax)
    ax.set_xlim(100, vmax)
    ax.set_ylim(100, vmax)
#    ax.grid(axis='both', which='both')



    











In [12]:



    


width = 8/3
height = width
fig, ax = plt.subplots(figsize=(width, height))
plot_tract_length_pp(ax, scatter_kws=dict(s=12, marker='o', color=colors[0], zorder=10))
ax.set_xlabel('NCO minimal tract length (bp)')
ax.set_ylabel('geometric model, $\phi = 0.9993$')
ax.set_title('E', fontweight='bold')
fig.tight_layout()
fig.savefig('../../artwork/main/fig3E.jpg', dpi=900, jpeg_quality=100)



    


















    
























In [13]:



    


X = df_nco.tract_length_min.values
print(stats.percentileofscore(X, 1000))
print(stats.percentileofscore(X, 2000))



    


















    






50.0
72.6086956522

NCO recombination rate





In [14]:



    


tbl_nco_adjusted = (
    tbl_nco
    .valuecounts('cross')
    .cutout('frequency')
    .rename('count', 'n_obs')
    .addfield('n_progeny', lambda row: len(progeny[row.cross]), index=1)
    .addfield('rate_obs', lambda row: row.n_obs / row.n_progeny)
    .addfield('n_adj', lambda row: row.n_obs / nco_discovery_rate[row.cross])
    .addfield('rate_adj', lambda row: row.n_adj / row.n_progeny)
    .addfield('rate_adj_stderr', lambda row: np.sqrt(row.rate_adj / row.n_progeny))
    .addfield('rate_adj_95ci', lambda row: row.rate_adj_stderr * 1.96)
)
tbl_nco_adjusted.display(caption='NCO events by cross')



    


















    








NCO events by cross



0|cross
1|n_progeny
2|n_obs
3|rate_obs
4|n_adj
5|rate_adj
6|rate_adj_stderr
7|rate_adj_95ci






hb3_dd2
34
111
3.264705882352941
275.6756756756757
8.108108108108109
0.488337760045
0.957142009688




7g8_gb4
27
77
2.8518518518518516
195.64849146449342
7.246240424610868
0.518053351486
1.01538456891




3d7_hb3
15
42
2.8
91.9869174161897
6.132461161079314
0.63939873637
1.25322152329






















In [15]:



    


n_nco_adj = tbl_nco_adjusted.values('n_adj').sum()
print('#NCOs:', n_nco_adj)

n = np.sum(len(s) for s in progeny.values())
# N.B., some samples have 0 NCOs
print('meioses:', n)

mu_adj_hat = n_nco_adj / n
print('recombination rate: %.2f' % mu_adj_hat, 'NCO/meiosis')

mu_adj_stderr = np.sqrt(mu_adj_hat / n)
print('recombination rate stderr:', mu_adj_stderr)

mu_adj_95ci = 1.96 * mu_adj_stderr
print('recombination rate 95%% CI: %.2f - %.2f' % (mu_adj_hat - mu_adj_95ci, mu_adj_hat + mu_adj_95ci))



    


















    






#NCOs: 563.3110845563588
meioses: 76
recombination rate: 7.41 NCO/meiosis
recombination rate stderr: 0.31229178231
recombination rate 95% CI: 6.80 - 8.02

















In [16]:



    


# calculate marker span using accessibility regions, it's fairer and simpler
# N.B., here we **exclude** internal hypervariable regions from the total marker span, because NCOs within them will be hidden.
tbl_marker_span = (
    tbl_regions_1b
    .eq('region_type', 'Core')
    .aggregate('region_chrom', sum, 'region_size')
    .rename(dict(region_chrom='chrom', value='span'))
)

total_marker_span = tbl_marker_span.values('span').sum()
lkp_marker_span = tbl_marker_span.lookupone('chrom', 'span')
tbl_marker_span.displayall(caption='Core genome span (total = %.2f)' % (total_marker_span/1e6))



    


















    








Core genome span (total = 20.78)



0|chrom
1|span






Pf3D7_01_v3
480620




Pf3D7_02_v3
753550




Pf3D7_03_v3
929971




Pf3D7_04_v3
932894




Pf3D7_05_v3
1281978




Pf3D7_06_v3
1200478




Pf3D7_07_v3
1204739




Pf3D7_08_v3
1249936




Pf3D7_09_v3
1392114




Pf3D7_10_v3
1502845




Pf3D7_11_v3
1891043




Pf3D7_12_v3
2030380




Pf3D7_13_v3
2715189




Pf3D7_14_v3
3216370






















In [17]:



    


nco_adj_hat = (mu_adj_hat / (total_marker_span / 1e6))
print('NCO rate (adjusted): %.4f NCO / meiosis / Mb' % nco_adj_hat)
nco_adj_95ci = (mu_adj_95ci / (total_marker_span / 1e6))
print('NCO rate (adjusted) 95%% CI: %.4f - %.4f' % (nco_adj_hat - nco_adj_95ci, nco_adj_hat + nco_adj_95ci))



    


















    






NCO rate (adjusted): 0.3567 NCO / meiosis / Mb
NCO rate (adjusted) 95% CI: 0.3272 - 0.3861

NCO rate by chromosome





In [18]:



    


lkp_nco_sample_chrom = (
    tbl_nco
    .valuecounter('sample', 'chrom')
#    .lookupone('value', 'count')
)
lkp_nco_sample_chrom



    


















    
Out[18]:








Counter({('1BB5/PG0023-C/ERR015449', b'Pf3D7_02_v3'): 1,
         ('1BB5/PG0023-C/ERR015449', b'Pf3D7_08_v3'): 1,
         ('1BB5/PG0023-C/ERR015449', b'Pf3D7_09_v3'): 1,
         ('1BB5/PG0023-C/ERR015449', b'Pf3D7_12_v3'): 1,
         ('1BB5/PG0023-C/ERR015449', b'Pf3D7_13_v3'): 1,
         ('1BB5/PG0023-C/ERR015449', b'Pf3D7_14_v3'): 1,
         ('3BA6/PG0022-Cx/ERR126027', b'Pf3D7_02_v3'): 1,
         ('3BA6/PG0022-Cx/ERR126027', b'Pf3D7_06_v3'): 1,
         ('3BA6/PG0022-Cx/ERR126027', b'Pf3D7_07_v3'): 2,
         ('3BA6/PG0022-Cx/ERR126027', b'Pf3D7_12_v3'): 1,
         ('3BA6/PG0022-Cx/ERR126027', b'Pf3D7_14_v3'): 1,
         ('3BD5/PG0024-C/ERR019053', b'Pf3D7_10_v3'): 1,
         ('3BD5/PG0024-C/ERR019053', b'Pf3D7_12_v3'): 1,
         ('7C101/PG0074-C/ERR019048', b'Pf3D7_06_v3'): 1,
         ('7C101/PG0074-C/ERR019048', b'Pf3D7_13_v3'): 1,
         ('7C101/PG0074-C/ERR019048', b'Pf3D7_14_v3'): 1,
         ('7C111/PG0038-C/ERR015457', b'Pf3D7_03_v3'): 1,
         ('7C111/PG0038-C/ERR015457', b'Pf3D7_07_v3'): 1,
         ('7C111/PG0038-C/ERR015457', b'Pf3D7_10_v3'): 1,
         ('7C111/PG0038-C/ERR015457', b'Pf3D7_11_v3'): 2,
         ('7C111/PG0038-C/ERR015457', b'Pf3D7_12_v3'): 2,
         ('7C111/PG0038-C/ERR015457', b'Pf3D7_14_v3'): 1,
         ('7C12/PG0035-Cx/ERR037704', b'Pf3D7_13_v3'): 1,
         ('7C126/PG0047-C/ERR015452', b'Pf3D7_11_v3'): 1,
         ('7C140/PG0039-C/ERR015454', b'Pf3D7_13_v3'): 1,
         ('7C159/PG0040-Cx/ERR107475', b'Pf3D7_02_v3'): 1,
         ('7C159/PG0040-Cx/ERR107475', b'Pf3D7_08_v3'): 1,
         ('7C159/PG0040-Cx/ERR107475', b'Pf3D7_09_v3'): 1,
         ('7C159/PG0040-Cx/ERR107475', b'Pf3D7_13_v3'): 1,
         ('7C170/PG0041-C/ERR015446', b'Pf3D7_09_v3'): 1,
         ('7C170/PG0041-C/ERR015446', b'Pf3D7_14_v3'): 1,
         ('7C183/PG0042-C/ERR015448', b'Pf3D7_01_v3'): 1,
         ('7C183/PG0042-C/ERR015448', b'Pf3D7_02_v3'): 1,
         ('7C183/PG0042-C/ERR015448', b'Pf3D7_04_v3'): 1,
         ('7C183/PG0042-C/ERR015448', b'Pf3D7_10_v3'): 1,
         ('7C183/PG0042-C/ERR015448', b'Pf3D7_11_v3'): 2,
         ('7C188/PG0030-C/ERR019046', b'Pf3D7_02_v3'): 1,
         ('7C188/PG0030-C/ERR019046', b'Pf3D7_08_v3'): 1,
         ('7C188/PG0030-C/ERR019046', b'Pf3D7_10_v3'): 1,
         ('7C188/PG0030-C/ERR019046', b'Pf3D7_12_v3'): 1,
         ('7C20/PG0037-C/ERR015451', b'Pf3D7_02_v3'): 1,
         ('7C20/PG0037-C/ERR015451', b'Pf3D7_10_v3'): 1,
         ('7C20/PG0037-C/ERR015451', b'Pf3D7_11_v3'): 2,
         ('7C20/PG0037-C/ERR015451', b'Pf3D7_12_v3'): 1,
         ('7C3/PG0034-C/ERR019047', b'Pf3D7_03_v3'): 1,
         ('7C3/PG0034-C/ERR019047', b'Pf3D7_07_v3'): 1,
         ('7C3/PG0034-C/ERR019047', b'Pf3D7_08_v3'): 1,
         ('7C3/PG0034-C/ERR019047', b'Pf3D7_13_v3'): 1,
         ('7C421/PG0043-C/ERR015459', b'Pf3D7_06_v3'): 1,
         ('7C421/PG0043-C/ERR015459', b'Pf3D7_07_v3'): 2,
         ('7C424/PG0044-C/ERR019043', b'Pf3D7_06_v3'): 1,
         ('7C424/PG0044-C/ERR019043', b'Pf3D7_07_v3'): 1,
         ('7C424/PG0044-C/ERR019043', b'Pf3D7_09_v3'): 1,
         ('7C424/PG0044-C/ERR019043', b'Pf3D7_14_v3'): 1,
         ('7C46/PG0046-Cx/ERR107476', b'Pf3D7_09_v3'): 1,
         ('7C46/PG0046-Cx/ERR107476', b'Pf3D7_14_v3'): 3,
         ('7C7/PG0048-C/ERR019049', b'Pf3D7_09_v3'): 1,
         ('7C7/PG0048-C/ERR019049', b'Pf3D7_11_v3'): 1,
         ('7C7/PG0048-C/ERR019049', b'Pf3D7_12_v3'): 1,
         ('AUD/PG0112-C/ERR029406', b'Pf3D7_03_v3'): 1,
         ('AUD/PG0112-C/ERR029406', b'Pf3D7_04_v3'): 1,
         ('AUD/PG0112-C/ERR029406', b'Pf3D7_13_v3'): 1,
         ('AUD/PG0112-C/ERR029406', b'Pf3D7_14_v3'): 1,
         ('B1SD/PG0015-C/ERR019044', b'Pf3D7_09_v3'): 1,
         ('B1SD/PG0015-C/ERR019044', b'Pf3D7_10_v3'): 1,
         ('B1SD/PG0015-C/ERR019044', b'Pf3D7_13_v3'): 2,
         ('B4R3/PG0018-C/ERR019042', b'Pf3D7_04_v3'): 1,
         ('B4R3/PG0018-C/ERR019042', b'Pf3D7_05_v3'): 1,
         ('B4R3/PG0018-C/ERR019042', b'Pf3D7_09_v3'): 1,
         ('C01/PG0065-C/ERR019064', b'Pf3D7_06_v3'): 1,
         ('C01/PG0065-C/ERR019064', b'Pf3D7_10_v3'): 1,
         ('C01/PG0065-C/ERR019064', b'Pf3D7_11_v3'): 1,
         ('C02/PG0067-C/ERR019073', b'Pf3D7_08_v3'): 1,
         ('C02/PG0067-C/ERR019073', b'Pf3D7_09_v3'): 1,
         ('C03/PG0066-C/ERR019072', b'Pf3D7_04_v3'): 1,
         ('C03/PG0066-C/ERR019072', b'Pf3D7_09_v3'): 1,
         ('C03/PG0066-C/ERR019072', b'Pf3D7_12_v3'): 1,
         ('C04/PG0061-C/ERR019059', b'Pf3D7_09_v3'): 1,
         ('C04/PG0061-C/ERR019059', b'Pf3D7_13_v3'): 1,
         ('C05/PG0068-C/ERR019065', b'Pf3D7_11_v3'): 1,
         ('C06/PG0069-C/ERR019055', b'Pf3D7_06_v3'): 1,
         ('C06/PG0069-C/ERR019055', b'Pf3D7_10_v3'): 1,
         ('C06/PG0069-C/ERR019055', b'Pf3D7_12_v3'): 2,
         ('C07/PG0070-C/ERR019056', b'Pf3D7_05_v3'): 1,
         ('C07/PG0070-C/ERR019056', b'Pf3D7_06_v3'): 1,
         ('C08/PG0071-C/ERR019074', b'Pf3D7_03_v3'): 1,
         ('C08/PG0071-C/ERR019074', b'Pf3D7_12_v3'): 2,
         ('C09/PG0072-C/ERR019057', b'Pf3D7_06_v3'): 1,
         ('C09/PG0072-C/ERR019057', b'Pf3D7_09_v3'): 1,
         ('C09/PG0072-C/ERR019057', b'Pf3D7_10_v3'): 1,
         ('C09/PG0072-C/ERR019057', b'Pf3D7_13_v3'): 2,
         ('C10/PG0063-C/ERR019060', b'Pf3D7_03_v3'): 1,
         ('C10/PG0063-C/ERR019060', b'Pf3D7_05_v3'): 1,
         ('C10/PG0063-C/ERR019060', b'Pf3D7_14_v3'): 1,
         ('C11/PG0064-C/ERR019071', b'Pf3D7_05_v3'): 1,
         ('C11/PG0064-C/ERR019071', b'Pf3D7_11_v3'): 1,
         ('C11/PG0064-C/ERR019071', b'Pf3D7_12_v3'): 1,
         ('C11/PG0064-C/ERR019071', b'Pf3D7_14_v3'): 1,
         ('C12/PG0058-C/ERR019063', b'Pf3D7_09_v3'): 1,
         ('C12/PG0058-C/ERR019063', b'Pf3D7_10_v3'): 1,
         ('C12/PG0058-C/ERR019063', b'Pf3D7_14_v3'): 1,
         ('C13/PG0054-C/ERR019062', b'Pf3D7_03_v3'): 1,
         ('C13/PG0054-C/ERR019062', b'Pf3D7_13_v3'): 2,
         ('C13/PG0054-C/ERR019062', b'Pf3D7_14_v3'): 1,
         ('C14/PG0060-C/ERR019058', b'Pf3D7_10_v3'): 1,
         ('C14/PG0060-C/ERR019058', b'Pf3D7_13_v3'): 1,
         ('C15/PG0057-C/ERR019069', b'Pf3D7_05_v3'): 1,
         ('CH3_116/PG0032-Cx/ERR037703', b'Pf3D7_08_v3'): 1,
         ('CH3_116/PG0032-Cx/ERR037703', b'Pf3D7_13_v3'): 1,
         ('CH3_61/PG0033-Cx/ERR175544', b'Pf3D7_01_v3'): 2,
         ('CH3_61/PG0033-Cx/ERR175544', b'Pf3D7_02_v3'): 1,
         ('CH3_61/PG0033-Cx/ERR175544', b'Pf3D7_12_v3'): 1,
         ('CH3_61/PG0033-Cx/ERR175544', b'Pf3D7_13_v3'): 1,
         ('CH3_61/PG0033-Cx/ERR175544', b'Pf3D7_14_v3'): 1,
         ('D43/PG0029-Cx/ERR107474', b'Pf3D7_08_v3'): 1,
         ('D43/PG0029-Cx/ERR107474', b'Pf3D7_09_v3'): 1,
         ('D43/PG0029-Cx/ERR107474', b'Pf3D7_10_v3'): 1,
         ('D43/PG0029-Cx/ERR107474', b'Pf3D7_11_v3'): 1,
         ('D43/PG0029-Cx/ERR107474', b'Pf3D7_13_v3'): 1,
         ('DAN/PG0098-C/ERR027110', b'Pf3D7_01_v3'): 1,
         ('DAN/PG0098-C/ERR027110', b'Pf3D7_09_v3'): 1,
         ('DEV/PG0081-CW/ERR045633', b'Pf3D7_01_v3'): 1,
         ('DEV/PG0081-CW/ERR045633', b'Pf3D7_06_v3'): 1,
         ('DEV/PG0081-CW/ERR045633', b'Pf3D7_08_v3'): 1,
         ('DEV/PG0081-CW/ERR045633', b'Pf3D7_10_v3'): 1,
         ('GC03/PG0021-C/ERR015447', b'Pf3D7_06_v3'): 1,
         ('GC03/PG0021-C/ERR015447', b'Pf3D7_09_v3'): 1,
         ('GC03/PG0021-C/ERR015447', b'Pf3D7_12_v3'): 1,
         ('GC03/PG0021-C/ERR015447', b'Pf3D7_13_v3'): 1,
         ('GC03/PG0021-C/ERR015447', b'Pf3D7_14_v3'): 1,
         ('GC06/PG0028-C/ERR015456', b'Pf3D7_01_v3'): 1,
         ('GC06/PG0028-C/ERR015456', b'Pf3D7_07_v3'): 1,
         ('GC06/PG0028-C/ERR015456', b'Pf3D7_10_v3'): 1,
         ('GC06/PG0028-C/ERR015456', b'Pf3D7_12_v3'): 1,
         ('GC06/PG0028-C/ERR015456', b'Pf3D7_14_v3'): 1,
         ('JB12/PG0099-C/ERR029146', b'Pf3D7_12_v3'): 1,
         ('JB12/PG0099-C/ERR029146', b'Pf3D7_14_v3'): 2,
         ('JB8/PG0087-C/ERR029091', b'Pf3D7_04_v3'): 1,
         ('JB8/PG0087-C/ERR029091', b'Pf3D7_09_v3'): 1,
         ('JC3/PG0077-CW/ERR045636', b'Pf3D7_06_v3'): 1,
         ('JC3/PG0077-CW/ERR045636', b'Pf3D7_12_v3'): 1,
         ('JC3/PG0077-CW/ERR045636', b'Pf3D7_13_v3'): 1,
         ('JC9/PG0111-CW/ERR045634', b'Pf3D7_07_v3'): 1,
         ('JC9/PG0111-CW/ERR045634', b'Pf3D7_09_v3'): 1,
         ('JC9/PG0111-CW/ERR045634', b'Pf3D7_10_v3'): 1,
         ('JC9/PG0111-CW/ERR045634', b'Pf3D7_11_v3'): 1,
         ('JC9/PG0111-CW/ERR045634', b'Pf3D7_14_v3'): 1,
         ('JE11/PG0100-C/ERR029404', b'Pf3D7_02_v3'): 1,
         ('JE11/PG0100-C/ERR029404', b'Pf3D7_03_v3'): 1,
         ('JE11/PG0100-C/ERR029404', b'Pf3D7_04_v3'): 1,
         ('JF6/PG0079-C/ERR027102', b'Pf3D7_10_v3'): 2,
         ('JF6/PG0079-C/ERR027102', b'Pf3D7_12_v3'): 2,
         ('JF6/PG0079-C/ERR027102', b'Pf3D7_13_v3'): 2,
         ('JON/PG0107-C/ERR029408', b'Pf3D7_09_v3'): 2,
         ('KA6/PG0091-C/ERR027117', b'Pf3D7_11_v3'): 1,
         ('KA6/PG0091-C/ERR027117', b'Pf3D7_12_v3'): 2,
         ('KA6/PG0091-C/ERR027117', b'Pf3D7_14_v3'): 2,
         ('KB8/PG0104-C/ERR029148', b'Pf3D7_04_v3'): 1,
         ('KB8/PG0104-C/ERR029148', b'Pf3D7_07_v3'): 1,
         ('KB8/PG0104-C/ERR029148', b'Pf3D7_12_v3'): 1,
         ('KH7/PG0088-C/ERR027111', b'Pf3D7_05_v3'): 1,
         ('KH7/PG0088-C/ERR027111', b'Pf3D7_08_v3'): 1,
         ('KH7/PG0088-C/ERR027111', b'Pf3D7_09_v3'): 1,
         ('LA10/PG0086-C/ERR029090', b'Pf3D7_08_v3'): 3,
         ('LA10/PG0086-C/ERR029090', b'Pf3D7_13_v3'): 2,
         ('NF10/PG0096-C/ERR027108', b'Pf3D7_01_v3'): 2,
         ('NF10/PG0096-C/ERR027108', b'Pf3D7_04_v3'): 1,
         ('NIC/PG0095-CW/ERR045631', b'Pf3D7_03_v3'): 1,
         ('NIC/PG0095-CW/ERR045631', b'Pf3D7_06_v3'): 1,
         ('NIC/PG0095-CW/ERR045631', b'Pf3D7_07_v3'): 1,
         ('NIC/PG0095-CW/ERR045631', b'Pf3D7_08_v3'): 1,
         ('NIC/PG0095-CW/ERR045631', b'Pf3D7_09_v3'): 1,
         ('QC01/PG0017-C/ERR019050', b'Pf3D7_04_v3'): 1,
         ('QC01/PG0017-C/ERR019050', b'Pf3D7_07_v3'): 1,
         ('QC01/PG0017-C/ERR019050', b'Pf3D7_14_v3'): 1,
         ('QC13/PG0016-C/ERR012895', b'Pf3D7_01_v3'): 1,
         ('QC13/PG0016-C/ERR012895', b'Pf3D7_09_v3'): 1,
         ('QC23/PG0045-C/ERR012892', b'Pf3D7_04_v3'): 1,
         ('QC23/PG0045-C/ERR012892', b'Pf3D7_11_v3'): 2,
         ('QC23/PG0045-C/ERR012892', b'Pf3D7_12_v3'): 1,
         ('QC34/PG0026-C/ERR015453', b'Pf3D7_13_v3'): 1,
         ('QF5/PG0078-C/ERR029092', b'Pf3D7_13_v3'): 1,
         ('SC05/PG0019-C/ERR019051', b'Pf3D7_07_v3'): 1,
         ('TC05/PG0027-C/ERR015450', b'Pf3D7_05_v3'): 1,
         ('TC05/PG0027-C/ERR015450', b'Pf3D7_14_v3'): 1,
         ('TC08/PG0020-C/ERR019052', b'Pf3D7_14_v3'): 1,
         ('TF1/PG0080-C/ERR027103', b'Pf3D7_10_v3'): 1,
         ('TF1/PG0080-C/ERR027103', b'Pf3D7_13_v3'): 1,
         ('WC4/PG0082-C/ERR029093', b'Pf3D7_04_v3'): 1,
         ('WC4/PG0082-C/ERR029093', b'Pf3D7_05_v3'): 1,
         ('WF12/PG0097-C/ERR027109', b'Pf3D7_02_v3'): 1,
         ('WF12/PG0097-C/ERR027109', b'Pf3D7_07_v3'): 1,
         ('WF12/PG0097-C/ERR027109', b'Pf3D7_09_v3'): 1,
         ('WF12/PG0097-C/ERR027109', b'Pf3D7_14_v3'): 1,
         ('XB3/PG0093-C/ERR029105', b'Pf3D7_07_v3'): 1,
         ('XB3/PG0093-C/ERR029105', b'Pf3D7_14_v3'): 1,
         ('XD8/PG0105-C/ERR029144', b'Pf3D7_10_v3'): 1,
         ('XD8/PG0105-C/ERR029144', b'Pf3D7_11_v3'): 1,
         ('XE7/PG0106-C/ERR029407', b'Pf3D7_06_v3'): 2,
         ('XE7/PG0106-C/ERR029407', b'Pf3D7_08_v3'): 1,
         ('XE7/PG0106-C/ERR029407', b'Pf3D7_11_v3'): 1,
         ('XF12/PG0102-C/ERR029143', b'Pf3D7_12_v3'): 1,
         ('XG10/PG0109-C/ERR029405', b'Pf3D7_14_v3'): 1})

















In [19]:



    


tbl_nco_chrom = [['cross', 'sample', 'chrom', 'n_obs']]
for cross in CROSSES:
    for sample in progeny[cross]:
        for chrom in CHROMOSOMES:
            tbl_nco_chrom.append([cross, sample, chrom, lkp_nco_sample_chrom.get((sample, chrom), 0)])
tbl_nco_chrom = (etl
    .wrap(tbl_nco_chrom)
    .addfield('n_adj', lambda row: row.n_obs / nco_discovery_rate[row.cross])
    .addfield('span', lambda row: lkp_marker_span[str(row.chrom, 'ascii')])
)
tbl_nco_chrom.display(20)



    


















    











0|cross
1|sample
2|chrom
3|n_obs
4|n_adj
5|span






3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_01_v3'
0
0.0
480620




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_02_v3'
0
0.0
753550




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_03_v3'
0
0.0
929971




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_04_v3'
0
0.0
932894




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_05_v3'
0
0.0
1281978




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_06_v3'
1
2.190164700385469
1200478




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_07_v3'
0
0.0
1204739




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_08_v3'
0
0.0
1249936




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_09_v3'
0
0.0
1392114




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_10_v3'
1
2.190164700385469
1502845




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_11_v3'
1
2.190164700385469
1891043




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_12_v3'
0
0.0
2030380




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_13_v3'
0
0.0
2715189




3d7_hb3
C01/PG0065-C/ERR019064
b'Pf3D7_14_v3'
0
0.0
3216370




3d7_hb3
C02/PG0067-C/ERR019073
b'Pf3D7_01_v3'
0
0.0
480620




3d7_hb3
C02/PG0067-C/ERR019073
b'Pf3D7_02_v3'
0
0.0
753550




3d7_hb3
C02/PG0067-C/ERR019073
b'Pf3D7_03_v3'
0
0.0
929971




3d7_hb3
C02/PG0067-C/ERR019073
b'Pf3D7_04_v3'
0
0.0
932894




3d7_hb3
C02/PG0067-C/ERR019073
b'Pf3D7_05_v3'
0
0.0
1281978




3d7_hb3
C02/PG0067-C/ERR019073
b'Pf3D7_06_v3'
0
0.0
1200478






...

















In [20]:



    


def plot_nco_rate_by_chrom(ax, scatter_kws=dict()):
    df_nco_chrom = tbl_nco_chrom.todataframe()
    sns.despine(ax=ax)
    sns.offset_spines(ax=ax)
    sns.regplot('span', 'n_adj', df_nco_chrom, x_estimator=np.mean, ax=ax, scatter_kws=scatter_kws)



    











In [21]:



    


width = 8/3
height = width
fig, ax = plt.subplots(figsize=(width, height))
plot_nco_rate_by_chrom(ax, scatter_kws=dict(s=12))
ax.set_title('F', fontweight='bold')
ax.set_xticks(range(0, 3500000, 1000000))
ax.set_xticklabels(range(0, 4, 1))
ax.set_xlabel('chromosome marker span (Mbp)')
ax.set_ylim(0, 1.4)
ax.set_ylabel('NCO/meiosis')
fig.tight_layout()
fig.savefig('../../artwork/main/fig3F.jpg', dpi=900, jpeg_quality=100)



    


















    






/usr/local/lib/python3.5/dist-packages/seaborn/utils.py:260: UserWarning: `offset_spines` is deprecated and will be removed in v0.5
  warnings.warn(warn_msg, UserWarning)

Long tracts





In [22]:



    


tbl_nco_long = (etl
    .frompickle(os.path.join(PUBLIC_DIR, 'tbl_conversion_tracts.pickle'))
    .eq('tract_type', 'NCO')
    .ge('tract_length_min', 18000)
)
tbl_nco_long.displayall()



    


















    











0|sample
1|cross
2|chrom
3|tract_start_min
4|tract_start_mid
5|tract_start_max
6|tract_stop_min
7|tract_stop_mid
8|tract_stop_max
9|tract_length_min
10|tract_length_mid
11|tract_length_max
12|tract_support
13|tract_is_complex
14|tract_blocks
15|co_pos_mid
16|co_pos_min
17|co_pos_max
18|co_pos_range
19|tract_type






3BD5/PG0024-C/ERR019053
hb3_dd2
b'Pf3D7_10_v3'
1308754
1309392
1310031
1330511
1330807
1331104
20480
21415
22350
39
True
3
None
None
None
None
NCO




7C3/PG0034-C/ERR019047
hb3_dd2
b'Pf3D7_08_v3'
1202631
1203122
1203613
1227264
1227421
1227578
23651
24299
24947
44
True
5
None
None
None
None
NCO




C04/PG0061-C/ERR019059
3d7_hb3
b'Pf3D7_14_v3'
2891817
2892879
2893941
2922794
2923016
2923239
28853
30137
31422
77
True
9
None
None
None
None
NCO




JF6/PG0079-C/ERR027102
7g8_gb4
b'Pf3D7_11_v3'
1685640
1686586
1687533
1713449
1713727
1714005
25916
27141
28365
40
True
7
None
None
None
None
NCO




JF6/PG0079-C/ERR027102
7g8_gb4
b'Pf3D7_11_v3'
1726074
1726125
1726177
1746099
1747010
1747922
19922
20885
21848
42
True
11
None
None
None
None
NCO






















In [ ]:

Content source: wtchg-kwiatkowski/pfx-paper-2015

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