Maren Equations

Sergey and Lauren developed a set of equtions found here:

Nuzhdin, S. V, Friesen, M. L., & McIntyre, L. M. (2012). Genotype-phenotype mapping in a post-GWAS world. Trends in Genetics : TIG, 28(9), 421–6. doi:10.1016/j.tig.2012.06.003

which potentially allow for the identificqtion of cis- and trans-effects. Here I try using these qeustions and test if they give reasonable results.

Basics: For a given gene the expression level of $E_{ii}$ of allele i in F1 genotype i.

$E_{ii} = \mu + C_i + (T_i + T_t)/2$

$E_{ti} = \mu + C_t + (T_i + T_t)/2$

For each allele the cis- and trans-effects are deviations from the population means, we expect that they will sum to zero:

$\sum^n_{i=1}C_i = 0$

$\sum^n_{i=1}T_i = 0$

Then the expected difference in expression between the Line and Tester allele over the entire population is:

$\sum^n_{i=1} \frac{E_{ti} - E_{ii}}{n}$

Which can be re-written as

$\sum^n_{i=1} \frac{C_{t} - C_{i}}{n} = C_t$

The cis-effect of allele i can be estimated by:

$\hat C_i = \hat E_{ii} - \hat E_{ti} + \hat C_t$


In [93]:
# Set-up default environment
%run '../ipython_startup.py'

# Import additional libraries
import sas7bdat as sas
import cPickle as pickle

from ase_cisEq import marenEq
from ase_cisEq import marenPrintTable

from ase_normalization import meanCenter
from ase_normalization import q3Norm
from ase_normalization import meanStd


Importing commonly used libraries: os, sys, numpy as np, scipy as sp, pandas as pd, matplotlib as mp, matplotlib.pyplot as plt, datetime as dt, mclib_Python/flagging as fg
Creating project level variables: MCLAB = /home/jfear/mclab, PROJ = /home/jfear/mclab/cegs_ase_paper, TODAY = 20150909
Adding ['scripts/mclib_Python', 'scripts/ase_Python'] to PYTHONPATH
The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

In [94]:
def denPlt(df, values='cis_line', columns='line', index='fusion_id', layout=(7, 7)):
    axes = df.pivot_table(values=values, columns=columns, index=index).plot(kind='kde', subplots=True, layout=layout, figsize=(10,10), rot=90, xlim=(-800, 800))

    for ax in axes.ravel():
        ax.get_yaxis().set_visible(False)
        h, lbl = ax.get_legend_handles_labels()
        ax.set_title(lbl[0])
        ax.get_legend().remove()

    plt.tight_layout()
    return plt.gcf()

In [95]:
def scatPlt(df, line='sum_line', tester='sum_tester', title=None):
    # Plot the cis-line effects x proportion by fusion
    df['prop'] = df[tester] / (df[line] + df[tester])
    # Create 5x5 panel plot
    fig, axes = plt.subplots(5, 5, figsize=(20, 20))
    fig.suptitle(title)
    axes = axes.ravel()

    # Group by fusion_id
    grp = df.groupby('fusion_id')
    for i, (n, gdf) in enumerate(grp):
        gdf.plot(kind='scatter', x='cis_line', y='prop', ax=axes[i], title=n)
        if i == 24:
            break
    
    return fig

Import clean data set

This data set was created by: ase_summarize_ase_filters.sas

The data has had the following droped:

  • regions that were always bias in 100 genome simulation
  • regions with APN $\le 25$
  • regions not in at least 10% of genotypes
  • regions not in mated and virgin
  • genotypes with extreme bias in median(q5_mean_theta)
  • genotypes with $\le500$ regions

In [96]:
# Import clean dataset
with sas.SAS7BDAT(os.path.join(PROJ, 'sas_data/clean_ase_stack.sas7bdat')) as FH:
    df = FH.to_data_frame()
    
dfClean = df[['line', 'mating_status', 'fusion_id', 'flag_AI_combined', 'q5_mean_theta', 'sum_both', 'sum_line', 'sum_tester', 'sum_total', 'mean_apn']]


[clean_ase_stack.sas7bdat] header length 65536 != 8192
[clean_ase_stack.sas7bdat] [clean_ase_stack.sas7bdat] header length 65536 != 8192
WARNING:/home/jfear/mclab/cegs_ase_paper/sas_data/clean_ase_stack.sas7bdat:[clean_ase_stack.sas7bdat] [clean_ase_stack.sas7bdat] header length 65536 != 8192

Additional cleaning

For the maren equations, I am also going to drop exonic regions with less than 10 genotypes. The maren equations make some assumptions about the population level sums. Obvisouly the more genotypes that are present for each fusions the better, but I am comfortable with as few as 10 genotypes.


In [97]:
# Drop groups with less than 10 lines per fusion
grp = dfClean.groupby(['mating_status', 'fusion_id'])
dfGt10 = grp.filter(lambda x: x['line'].count() >= 10).copy()
print 'Rows ' + str(dfGt10.shape[0])
print 'Columns ' + str(dfGt10.shape[1])


Rows 131700
Columns 10

Raw Counts

Raw counts seem to have some issues. The magnitude of line cis and tester trans effects are very different, while tester cis and trans are the same number just different signs. Also of concern is that the estimated cis and trans effects for the line are not centered at 0, which is a major assumption of the equations.


In [98]:
# Calculate Maren TIG equations by mating status and exonic region
marenRawCounts = marenEq(dfGt10, Eii='sum_line', Eti='sum_tester', group=['mating_status', 'fusion_id'])
marenRawCounts['mag_cis'] = abs(marenRawCounts['cis_line'])
marenPrintTable(marenRawCounts)


flag_AI_combined sum_both sum_line sum_tester cis_line cis_tester mean_apn
line mating_status fusion_id
r101 M F10005_SI 0 1274 155 133 28.666667 6.666667 29.523922
r280 M F10005_SI 0 1208 126 143 -10.333333 6.666667 27.917307
r315 M F10005_SI 0 1231 237 218 25.666667 6.666667 31.867690
r324 M F10005_SI 1 2554 349 276 79.666667 6.666667 60.087419
r335 M F10005_SI 0 1339 215 217 4.666667 6.666667 33.474306
r340 M F10005_SI 1 1897 332 102 236.666667 6.666667 44.059067
r357 M F10005_SI 1 2797 358 408 -43.333333 6.666667 67.345540
r358 M F10005_SI 0 1494 185 162 29.666667 6.666667 34.797401
r365 M F10005_SI 0 2313 385 386 5.666667 6.666667 58.291790
r373 M F10005_SI 0 1546 146 113 39.666667 6.666667 34.116952
r374 M F10005_SI 0 1703 231 155 82.666667 6.666667 39.484938
r380 M F10005_SI 0 1584 295 254 47.666667 6.666667 40.316598
r427 M F10005_SI 0 4246 522 510 18.666667 6.666667 99.761370
r491 M F10005_SI 0 3827 657 599 64.666667 6.666667 96.075605
r517 M F10005_SI 0 2591 580 520 66.666667 6.666667 69.764914
r732 M F10005_SI 0 1281 276 237 45.666667 6.666667 33.909037
r737 M F10005_SI 0 1576 126 156 -23.333333 6.666667 35.118724
r799 M F10005_SI 0 2798 520 480 46.666667 6.666667 71.787360
r820 M F10005_SI 0 1880 236 180 62.666667 6.666667 43.397519
r85 M F10005_SI 0 1086 131 191 -53.333333 6.666667 26.613113
w114 M F10005_SI 0 1042 230 243 -6.333333 6.666667 28.635558
w38 M F10005_SI 1 2046 444 501 -50.333333 6.666667 56.533963
w47 M F10005_SI 0 5492 1425 1417 14.666667 6.666667 157.523922
w52 M F10005_SI 0 3848 407 397 16.666667 6.666667 87.929120
w55 M F10005_SI 1 4191 252 882 -623.333333 6.666667 100.649734
w59 M F10005_SI 0 1429 367 329 44.666667 6.666667 40.165387
w64 M F10005_SI 0 2153 284 239 51.666667 6.666667 50.580035
w68 M F10005_SI 0 1915 371 305 72.666667 6.666667 48.973420
w76 M F10005_SI 0 1757 286 376 -83.333333 6.666667 45.722386
w79 M F10005_SI 0 2596 384 583 -192.333333 6.666667 67.345540

In [99]:
# Plot the cis-line effects x proportion by fusion
fig = scatPlt(marenRawCounts, line='sum_line', tester='sum_tester', title='Raw Counts')



In [100]:
# Plot the distribution of cis-line effects by genotype.
fig = denPlt(marenRawCounts)



In [101]:
# Plot the distribution of cis-line effects by fusion
# Group by fusion_id
fusions = marenRawCounts.groupby('fusion_id').groups.keys()

# Get a randome set of fusions
sample = np.random.choice(fusions, size=25, replace=False)

# Plot density of 25 random fusions
fig = denPlt(marenRawCounts[marenRawCounts['fusion_id'].isin(sample)], columns='fusion_id', index='line', layout=(5, 5))
#fig.savefig(os.path.join(PROJ, 'pipeline_output/cis_effects/exonic_region_distributions_of_cis_line.png'))


Coverage Centered

Center counts based on some fudge factor.

$line\_adj = \frac{sum\_line}{sum\_line + sum\_tester} * 1000$

$tester\_adj = \frac{sum\_line}{sum\_line + sum\_tester} * 1000$


In [102]:
# Center allele specific counts by taking the proportion and multiplying by 1000
dfGt10['tot_allele_specific'] = dfGt10[['sum_line', 'sum_tester']].sum(axis=1)
dfGt10['line_adj'] = dfGt10['sum_line'] / dfGt10['tot_allele_specific'] * 1000
dfGt10['tester_adj'] = dfGt10['sum_tester'] / dfGt10['tot_allele_specific'] * 1000

In [103]:
# Calculate Maren TIG equations by mating status and exonic region
marenRawCountsAdj = marenEq(dfGt10, Eii='line_adj', Eti='tester_adj', group=['mating_status', 'fusion_id'])
marenRawCountsAdj['mag_cis'] = abs(marenRawCountsAdj['cis_line'])
marenPrintTable(marenRawCountsAdj, line='line_adj', tester='tester_adj')


flag_AI_combined sum_both line_adj tester_adj cis_line cis_tester mean_apn
line mating_status fusion_id
r101 M F10005_SI 0 1274 538.194444 461.805556 61.906589 -14.4823 29.523922
r280 M F10005_SI 0 1208 468.401487 531.598513 -77.679326 -14.4823 27.917307
r315 M F10005_SI 0 1231 520.879121 479.120879 27.275942 -14.4823 31.867690
r324 M F10005_SI 1 2554 558.400000 441.600000 102.317700 -14.4823 60.087419
r335 M F10005_SI 0 1339 497.685185 502.314815 -19.111930 -14.4823 33.474306
r340 M F10005_SI 1 1897 764.976959 235.023041 515.471617 -14.4823 44.059067
r357 M F10005_SI 1 2797 467.362924 532.637076 -79.756452 -14.4823 67.345540
r358 M F10005_SI 0 1494 533.141210 466.858790 51.800121 -14.4823 34.797401
r365 M F10005_SI 0 2313 499.351492 500.648508 -15.779317 -14.4823 58.291790
r373 M F10005_SI 0 1546 563.706564 436.293436 112.930827 -14.4823 34.116952
r374 M F10005_SI 0 1703 598.445596 401.554404 182.408892 -14.4823 39.484938
r380 M F10005_SI 0 1584 537.340619 462.659381 60.198938 -14.4823 40.316598
r427 M F10005_SI 0 4246 505.813953 494.186047 -2.854393 -14.4823 99.761370
r491 M F10005_SI 0 3827 523.089172 476.910828 31.696044 -14.4823 96.075605
r517 M F10005_SI 0 2591 527.272727 472.727273 40.063154 -14.4823 69.764914
r732 M F10005_SI 0 1281 538.011696 461.988304 61.541092 -14.4823 33.909037
r737 M F10005_SI 0 1576 446.808511 553.191489 -120.865279 -14.4823 35.118724
r799 M F10005_SI 0 2798 520.000000 480.000000 25.517700 -14.4823 71.787360
r820 M F10005_SI 0 1880 567.307692 432.692308 120.133084 -14.4823 43.397519
r85 M F10005_SI 0 1086 406.832298 593.167702 -200.817704 -14.4823 26.613113
w114 M F10005_SI 0 1042 486.257928 513.742072 -41.966444 -14.4823 28.635558
w38 M F10005_SI 1 2046 469.841270 530.158730 -74.799761 -14.4823 56.533963
w47 M F10005_SI 0 5492 501.407460 498.592540 -11.667381 -14.4823 157.523922
w52 M F10005_SI 0 3848 506.218905 493.781095 -2.044489 -14.4823 87.929120
w55 M F10005_SI 1 4191 222.222222 777.777778 -570.037856 -14.4823 100.649734
w59 M F10005_SI 0 1429 527.298851 472.701149 40.115401 -14.4823 40.165387
w64 M F10005_SI 0 2153 543.021033 456.978967 71.559765 -14.4823 50.580035
w68 M F10005_SI 0 1915 548.816568 451.183432 83.150836 -14.4823 48.973420
w76 M F10005_SI 0 1757 432.024169 567.975831 -150.433962 -14.4823 45.722386
w79 M F10005_SI 0 2596 397.104447 602.895553 -220.273407 -14.4823 67.345540

In [104]:
# Plot the cis-line effects x proportion by fusion
fig = scatPlt(marenRawCountsAdj, line='line_adj', tester='tester_adj', title='Coverage Centered')



In [105]:
# Plot the distribution of cis-line effects by genotype
fig = denPlt(marenRawCountsAdj)
fig.savefig(os.path.join(PROJ, 'pipeline_output/cis_effects/genotype_distributions_of_cis_line.png'))



In [106]:
# Plot the distribution of cis-tester effects by genotype
fig = denPlt(marenRawCountsAdj, values='cis_tester')
fig.savefig(os.path.join(PROJ, 'pipeline_output/cis_effects/genotype_distributions_of_cis_tester.png'))



In [107]:
# Plot the distribution of cis-line effects by fusion
fusions = marenRawCountsAdj.groupby('fusion_id').groups.keys()
sample = np.random.choice(fusions, size=25, replace=False)
fig = denPlt(marenRawCountsAdj[marenRawCountsAdj['fusion_id'].isin(sample)], columns='fusion_id', index='line', layout=(5, 5))
fig.savefig(os.path.join(PROJ, 'pipeline_output/cis_effects/exonic_region_distributions_of_cis_line.png'))


Mean Centered Counts

I am concerned about the calculation of $\mu$. Mean centering the raw counts will allow $\mu = 0$ and I can effectively ignore it. For each fusion_id I take the mean of all the raw counts (line and tester), then subtract this mean value from each count.

This had no affect on the results.


In [108]:
# Mean center
dfMeanCenter = meanCenter(dfGt10, columns=['sum_line', 'sum_tester'], group=['mating_status', 'fusion_id'])

In [109]:
# Calculate Maren TIG equations by mating status and exonic region for mean centered data.
marenMeanCenter = marenEq(dfMeanCenter, Eii='mean_center_sum_line', Eti='mean_center_sum_tester', group=['mating_status', 'fusion_id'])
marenMeanCenter['mag_cis'] = abs(marenMeanCenter['cis_line'])
marenPrintTable(marenMeanCenter, line='mean_center_sum_line', tester='mean_center_sum_tester')


flag_AI_combined sum_both mean_center_sum_line mean_center_sum_tester cis_line cis_tester mean_apn
line mating_status fusion_id
r101 M F10005_SI 0 1274 -198.733333 -220.733333 28.666667 6.666667 29.523922
r280 M F10005_SI 0 1208 -227.733333 -210.733333 -10.333333 6.666667 27.917307
r315 M F10005_SI 0 1231 -116.733333 -135.733333 25.666667 6.666667 31.867690
r324 M F10005_SI 1 2554 -4.733333 -77.733333 79.666667 6.666667 60.087419
r335 M F10005_SI 0 1339 -138.733333 -136.733333 4.666667 6.666667 33.474306
r340 M F10005_SI 1 1897 -21.733333 -251.733333 236.666667 6.666667 44.059067
r357 M F10005_SI 1 2797 4.266667 54.266667 -43.333333 6.666667 67.345540
r358 M F10005_SI 0 1494 -168.733333 -191.733333 29.666667 6.666667 34.797401
r365 M F10005_SI 0 2313 31.266667 32.266667 5.666667 6.666667 58.291790
r373 M F10005_SI 0 1546 -207.733333 -240.733333 39.666667 6.666667 34.116952
r374 M F10005_SI 0 1703 -122.733333 -198.733333 82.666667 6.666667 39.484938
r380 M F10005_SI 0 1584 -58.733333 -99.733333 47.666667 6.666667 40.316598
r427 M F10005_SI 0 4246 168.266667 156.266667 18.666667 6.666667 99.761370
r491 M F10005_SI 0 3827 303.266667 245.266667 64.666667 6.666667 96.075605
r517 M F10005_SI 0 2591 226.266667 166.266667 66.666667 6.666667 69.764914
r732 M F10005_SI 0 1281 -77.733333 -116.733333 45.666667 6.666667 33.909037
r737 M F10005_SI 0 1576 -227.733333 -197.733333 -23.333333 6.666667 35.118724
r799 M F10005_SI 0 2798 166.266667 126.266667 46.666667 6.666667 71.787360
r820 M F10005_SI 0 1880 -117.733333 -173.733333 62.666667 6.666667 43.397519
r85 M F10005_SI 0 1086 -222.733333 -162.733333 -53.333333 6.666667 26.613113
w114 M F10005_SI 0 1042 -123.733333 -110.733333 -6.333333 6.666667 28.635558
w38 M F10005_SI 1 2046 90.266667 147.266667 -50.333333 6.666667 56.533963
w47 M F10005_SI 0 5492 1071.266667 1063.266667 14.666667 6.666667 157.523922
w52 M F10005_SI 0 3848 53.266667 43.266667 16.666667 6.666667 87.929120
w55 M F10005_SI 1 4191 -101.733333 528.266667 -623.333333 6.666667 100.649734
w59 M F10005_SI 0 1429 13.266667 -24.733333 44.666667 6.666667 40.165387
w64 M F10005_SI 0 2153 -69.733333 -114.733333 51.666667 6.666667 50.580035
w68 M F10005_SI 0 1915 17.266667 -48.733333 72.666667 6.666667 48.973420
w76 M F10005_SI 0 1757 -67.733333 22.266667 -83.333333 6.666667 45.722386
w79 M F10005_SI 0 2596 30.266667 229.266667 -192.333333 6.666667 67.345540

In [110]:
# Plot the cis-line effects x proportion by fusion
fig = scatPlt(marenMeanCenter, line='mean_center_sum_line', tester='mean_center_sum_tester', title='Mean Centered')



In [111]:
fig = denPlt(marenMeanCenter)


Mean Standardized Counts

For mean standardization, for each exonic regions, I subtract the mean value of the exonic regions across genotypes and divide by the standard deviation. Note I am doing environments separately (mated and virgin).


In [112]:
# Mean standardization
dfMeanStd = dfGt10.copy()
meanStd(dfMeanStd, column='sum_line', group=['mating_status', 'fusion_id'])
meanStd(dfMeanStd, column='sum_tester', group=['mating_status', 'fusion_id'])

In [113]:
# Calculate Maren TIG equations by mating status and exonic region
marenMeanStd = marenEq(dfMeanStd, Eii='mean_std_sum_line', Eti='mean_std_sum_tester', group=['mating_status', 'fusion_id'])
marenMeanStd['mag_cis'] = abs(marenMeanStd['cis_line'])
marenPrintTable(marenMeanStd, line='mean_std_sum_line', tester='mean_std_sum_tester')


flag_AI_combined sum_both mean_std_sum_line mean_std_sum_tester cis_line cis_tester mean_apn
line mating_status fusion_id
r101 M F10005_SI 0 1274 -0.799759 -0.832726 0.032966 -4.857226e-17 29.523922
r280 M F10005_SI 0 1208 -0.918454 -0.795562 -0.122893 -4.857226e-17 27.917307
r315 M F10005_SI 0 1231 -0.464139 -0.516830 0.052691 -4.857226e-17 31.867690
r324 M F10005_SI 1 2554 -0.005730 -0.301278 0.295548 -4.857226e-17 60.087419
r335 M F10005_SI 0 1339 -0.554183 -0.520546 -0.033637 -4.857226e-17 33.474306
r340 M F10005_SI 1 1897 -0.075310 -0.947935 0.872625 -4.857226e-17 44.059067
r357 M F10005_SI 1 2797 0.031106 0.189290 -0.158183 -4.857226e-17 67.345540
r358 M F10005_SI 0 1494 -0.676971 -0.724950 0.047978 -4.857226e-17 34.797401
r365 M F10005_SI 0 2313 0.141616 0.107528 0.034087 -4.857226e-17 58.291790
r373 M F10005_SI 0 1546 -0.836596 -0.907054 0.070458 -4.857226e-17 34.116952
r374 M F10005_SI 0 1703 -0.488696 -0.750965 0.262268 -4.857226e-17 39.484938
r380 M F10005_SI 0 1584 -0.226749 -0.383039 0.156290 -4.857226e-17 40.316598
r427 M F10005_SI 0 4246 0.702348 0.568364 0.133983 -4.857226e-17 99.761370
r491 M F10005_SI 0 3827 1.254894 0.899126 0.355768 -4.857226e-17 96.075605
r517 M F10005_SI 0 2591 0.939738 0.605529 0.334209 -4.857226e-17 69.764914
r732 M F10005_SI 0 1281 -0.304514 -0.446218 0.141704 -4.857226e-17 33.909037
r737 M F10005_SI 0 1576 -0.918454 -0.747248 -0.171206 -4.857226e-17 35.118724
r799 M F10005_SI 0 2798 0.694162 0.456872 0.237290 -4.857226e-17 71.787360
r820 M F10005_SI 0 1880 -0.468232 -0.658054 0.189822 -4.857226e-17 43.397519
r85 M F10005_SI 0 1086 -0.897990 -0.617173 -0.280816 -4.857226e-17 26.613113
w114 M F10005_SI 0 1042 -0.492789 -0.423920 -0.068870 -4.857226e-17 28.635558
w38 M F10005_SI 1 2046 0.383099 0.534917 -0.151818 -4.857226e-17 56.533963
w47 M F10005_SI 0 5492 4.398267 3.939157 0.459110 -4.857226e-17 157.523922
w52 M F10005_SI 0 3848 0.231660 0.148409 0.083251 -4.857226e-17 87.929120
w55 M F10005_SI 1 4191 -0.402745 1.950872 -2.353617 -4.857226e-17 100.649734
w59 M F10005_SI 0 1429 0.067943 -0.104308 0.172250 -4.857226e-17 40.165387
w64 M F10005_SI 0 2153 -0.271771 -0.438785 0.167014 -4.857226e-17 50.580035
w68 M F10005_SI 0 1915 0.084314 -0.193502 0.277816 -4.857226e-17 48.973420
w76 M F10005_SI 0 1757 -0.263585 0.070364 -0.333949 -4.857226e-17 45.722386
w79 M F10005_SI 0 2596 0.137523 0.839663 -0.702140 -4.857226e-17 67.345540

In [114]:
# Plot the cis-line effects x proportion by fusion
fig = scatPlt(marenMeanStd, line='mean_std_sum_line', tester='mean_std_sum_tester', title='Mean Standardized')



In [115]:
fig = denPlt(marenMeanStd)


Upper Quartile Normalization

For most of the CEGS projects, we have used a q3 normalization. Here I am taking the count value / the upper quartile for the line * median of the overall upper quartile.

$\frac{\text{sum_line}_{mgf}}{q3_mg} * \widetilde {q3_m}$

Where m is the mating status ('M' or 'V'), g is genotypes 1...G and f is exonic region 1...F.


In [116]:
dfQ3 = dfGt10.set_index(['mating_status', 'line', 'fusion_id'])
dfQ3['q3_norm_sum_line'] = q3Norm(dfQ3['sum_line'])
dfQ3['q3_norm_sum_tester'] = q3Norm(dfQ3['sum_tester'])
dfQ3.reset_index(inplace=True)

In [117]:
# Calculate Maren TIG equations by mating status and exonic region
marenQ3Norm = marenEq(dfQ3, Eii='q3_norm_sum_line', Eti='q3_norm_sum_tester', group=['mating_status', 'fusion_id'])
marenQ3Norm['mag_cis'] = abs(marenQ3Norm['cis_line'])
marenPrintTable(marenQ3Norm, line='q3_norm_sum_line', tester='q3_norm_sum_tester')


flag_AI_combined sum_both q3_norm_sum_line q3_norm_sum_tester cis_line cis_tester mean_apn
line mating_status fusion_id
r101 M F10005_SI 0 1274 213.641667 176.772152 22.519099 -14.350415 29.523922
r280 M F10005_SI 0 1208 126.000000 147.749077 -36.099493 -14.350415 27.917307
r315 M F10005_SI 0 1231 337.929310 251.538462 72.040433 -14.350415 31.867690
r324 M F10005_SI 1 2554 309.681330 182.839117 112.491798 -14.350415 60.087419
r335 M F10005_SI 0 1339 244.237637 221.213592 8.673630 -14.350415 33.474306
r340 M F10005_SI 1 1897 234.670085 110.984456 109.335214 -14.350415 44.059067
r357 M F10005_SI 1 2797 254.352234 242.891566 -2.889748 -14.350415 67.345540
r358 M F10005_SI 0 1494 207.873641 179.052632 14.470594 -14.350415 34.797401
r365 M F10005_SI 0 2313 261.837993 269.750416 -22.262838 -14.350415 58.291790
r373 M F10005_SI 0 1546 89.306213 74.214230 0.741568 -14.350415 34.116952
r374 M F10005_SI 0 1703 236.431931 156.490385 65.591131 -14.350415 39.484938
r380 M F10005_SI 0 1584 297.881563 261.791411 21.739736 -14.350415 40.316598
r427 M F10005_SI 0 4246 253.788360 249.504950 -10.067006 -14.350415 99.761370
r491 M F10005_SI 0 3827 342.368620 339.056604 -11.038399 -14.350415 96.075605
r517 M F10005_SI 0 2591 304.352792 302.283737 -12.281361 -14.350415 69.764914
r732 M F10005_SI 0 1281 267.587339 239.855422 13.381502 -14.350415 33.909037
r737 M F10005_SI 0 1576 114.381998 157.500000 -57.468418 -14.350415 35.118724
r799 M F10005_SI 0 2798 259.685990 245.255474 0.080100 -14.350415 71.787360
r820 M F10005_SI 0 1880 242.751244 175.000000 53.400828 -14.350415 43.397519
r85 M F10005_SI 0 1086 134.747512 231.849711 -111.452614 -14.350415 26.613113
w114 M F10005_SI 0 1042 299.543307 282.323651 2.869240 -14.350415 28.635558
w38 M F10005_SI 1 2046 481.242464 449.615385 17.276664 -14.350415 56.533963
w47 M F10005_SI 0 5492 686.756993 520.227273 152.179305 -14.350415 157.523922
w52 M F10005_SI 0 3848 247.128488 198.500000 34.278072 -14.350415 87.929120
w55 M F10005_SI 1 4191 160.805556 441.000000 -294.544860 -14.350415 100.649734
w59 M F10005_SI 0 1429 267.173415 241.573427 11.249573 -14.350415 40.165387
w64 M F10005_SI 0 2153 258.665198 237.867299 6.447484 -14.350415 50.580035
w68 M F10005_SI 0 1915 381.139130 302.122642 64.666073 -14.350415 48.973420
w76 M F10005_SI 0 1757 369.565625 417.777778 -62.562568 -14.350415 45.722386
w79 M F10005_SI 0 2596 310.125000 458.539326 -162.764741 -14.350415 67.345540

In [118]:
# Plot the cis-line effects x proportion by fusion
fig = scatPlt(marenQ3Norm, line='q3_norm_sum_line', tester='q3_norm_sum_tester', title='Q3 Normalized')


---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-118-2c705f185589> in <module>()
      1 # Plot the cis-line effects x proportion by fusion
----> 2 fig = scatPlt(marenQ3Norm, line='q3_norm_sum_line', tester='q3_norm_sum_tester', title='Q3 Normalized')

<ipython-input-95-71f3413f7ce8> in scatPlt(df, line, tester, title)
      3     df['prop'] = df[tester] / (df[line] + df[tester])
      4     # Create 5x5 panel plot
----> 5     fig, axes = plt.subplots(5, 5, figsize=(20, 20))
      6     fig.suptitle(title)
      7     axes = axes.ravel()

/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.pyc in subplots(nrows, ncols, sharex, sharey, squeeze, subplot_kw, gridspec_kw, **fig_kw)
   1115         else:
   1116             subplot_kw['sharey'] = axarr[sys[i]]
-> 1117         axarr[i] = fig.add_subplot(gs[i // ncols, i % ncols], **subplot_kw)
   1118 
   1119     # returned axis array will be always 2-d, even if nrows=ncols=1

/usr/local/lib/python2.7/dist-packages/matplotlib/figure.pyc in add_subplot(self, *args, **kwargs)
    962                     self._axstack.remove(ax)
    963 
--> 964             a = subplot_class_factory(projection_class)(self, *args, **kwargs)
    965 
    966         self._axstack.add(key, a)

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_subplots.pyc in __init__(self, fig, *args, **kwargs)
     76 
     77         # _axes_class is set in the subplot_class_factory
---> 78         self._axes_class.__init__(self, fig, self.figbox, **kwargs)
     79 
     80     def __reduce__(self):

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc in __init__(self, fig, rect, axisbg, frameon, sharex, sharey, label, xscale, yscale, **kwargs)
    435         self._hold = rcParams['axes.hold']
    436         self._connected = {}  # a dict from events to (id, func)
--> 437         self.cla()
    438         # funcs used to format x and y - fall back on major formatters
    439         self.fmt_xdata = None

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc in cla(self)
    889         self._ymargin = rcParams['axes.ymargin']
    890         self._tight = False
--> 891         self._update_transScale()  # needed?
    892 
    893         self._get_lines = _process_plot_var_args(self)

/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc in _update_transScale(self)
    703         self.transScale.set(
    704             mtransforms.blended_transform_factory(
--> 705                 self.xaxis.get_transform(), self.yaxis.get_transform()))
    706         if hasattr(self, "lines"):
    707             for line in self.lines:

/usr/local/lib/python2.7/dist-packages/matplotlib/transforms.pyc in set(self, child)
   1568 
   1569         self._invalid = 0
-> 1570         self.invalidate()
   1571         self._invalid = 0
   1572 

/usr/local/lib/python2.7/dist-packages/matplotlib/transforms.pyc in invalidate(self)
    133         if self.is_affine:
    134             value = self.INVALID_AFFINE
--> 135         return self._invalidate_internal(value, invalidating_node=self)
    136 
    137     def _invalidate_internal(self, value, invalidating_node):

/usr/local/lib/python2.7/dist-packages/matplotlib/transforms.pyc in _invalidate_internal(self, value, invalidating_node)
    157             for parent in list(six.itervalues(self._parents)):
    158                 parent._invalidate_internal(value=value,
--> 159                                             invalidating_node=self)
    160 
    161     def set_children(self, *children):

/usr/local/lib/python2.7/dist-packages/matplotlib/transforms.pyc in _invalidate_internal(self, value, invalidating_node)
   2255 
   2256         Transform._invalidate_internal(self, value=value,
-> 2257                                        invalidating_node=invalidating_node)
   2258 
   2259     def __eq__(self, other):

/usr/local/lib/python2.7/dist-packages/matplotlib/transforms.pyc in _invalidate_internal(self, value, invalidating_node)
    157             for parent in list(six.itervalues(self._parents)):
    158                 parent._invalidate_internal(value=value,
--> 159                                             invalidating_node=self)
    160 
    161     def set_children(self, *children):

/usr/local/lib/python2.7/dist-packages/matplotlib/transforms.pyc in _invalidate_internal(self, value, invalidating_node)
    157             for parent in list(six.itervalues(self._parents)):
    158                 parent._invalidate_internal(value=value,
--> 159                                             invalidating_node=self)
    160 
    161     def set_children(self, *children):

/usr/local/lib/python2.7/dist-packages/matplotlib/transforms.pyc in _invalidate_internal(self, value, invalidating_node)
   2255 
   2256         Transform._invalidate_internal(self, value=value,
-> 2257                                        invalidating_node=invalidating_node)
   2258 
   2259     def __eq__(self, other):

/usr/local/lib/python2.7/dist-packages/matplotlib/transforms.pyc in _invalidate_internal(self, value, invalidating_node)
    155             self._invalid = value
    156 
--> 157             for parent in list(six.itervalues(self._parents)):
    158                 parent._invalidate_internal(value=value,
    159                                             invalidating_node=self)

/usr/lib/python2.7/weakref.pyc in itervalues(self)
    176     def itervalues(self):
    177         with _IterationGuard(self):
--> 178             for wr in self.data.itervalues():
    179                 obj = wr()
    180                 if obj is not None:

KeyboardInterrupt: 

In [ ]:
fig = denPlt(marenQ3Norm)

Plots looking at cis effects


In [208]:
fus = 'F10005_SI'
shapes = {'M': '+', 'V': '^'}

cnt = 0
colors = {}
for x in df['line']:
    colors[x]= cnt
    cnt+=1

In [216]:
df = marenRawCounts[marenRawCounts['fusion_id'] == fus]

x = df['cis_line'].values
xlab = 'cis_line'

y = (1 - df['prop']).values
ylab = 'prop'

s = df['mating_status'].map(shapes)
c = df['line'].map(colors)


fig, ax = plt.subplots(1, 1, figsize=(8, 8))
ax.scatter(x=x, y=y, s=40, c=c, cmap='Set1')
ax.plot([0, 1], [0, 1], transform=ax.transAxes)
ax.set_title(fus)
ax.set_xlabel(xlab)
ax.set_ylabel(ylab)


Out[216]:
<matplotlib.text.Text at 0x7fd7d1f8c910>

In [206]:
df['trans_tester1'] = df['sum_line'].sum() / df.shape[0]
df['trans_tester2'] = df['sum_tester'].sum() / df.shape[0] - df['cis_tester']
df


/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
Out[206]:
line mating_status fusion_id flag_AI_combined q5_mean_theta sum_both sum_line sum_tester sum_total mean_apn cis_line cis_tester mag_cis prop trans_tester1 trans_tester2 trans_line1 trans_line2
10 r101 M F10005_SI 0 0.475 1274 155 133 1562 29.523922 28.666667 6.666667 28.666667 0.461806 361.25 356.633333 -234.916667 -234.916667
11 r280 M F10005_SI 0 0.545 1208 126 143 1477 27.917307 -10.333333 6.666667 10.333333 0.531599 361.25 356.633333 -224.916667 -224.916667
12 r315 M F10005_SI 0 0.490 1231 237 218 1686 31.867690 25.666667 6.666667 25.666667 0.479121 361.25 356.633333 -149.916667 -149.916667
13 r324 M F10005_SI 1 0.450 2554 349 276 3179 60.087419 79.666667 6.666667 79.666667 0.441600 361.25 356.633333 -91.916667 -91.916667
14 r335 M F10005_SI 0 0.514 1339 215 217 1771 33.474306 4.666667 6.666667 4.666667 0.502315 361.25 356.633333 -150.916667 -150.916667
15 r340 M F10005_SI 1 0.241 1897 332 102 2331 44.059067 236.666667 6.666667 236.666667 0.235023 361.25 356.633333 -265.916667 -265.916667
16 r357 M F10005_SI 1 0.542 2797 358 408 3563 67.345540 -43.333333 6.666667 43.333333 0.532637 361.25 356.633333 40.083333 40.083333
17 r358 M F10005_SI 0 0.480 1494 185 162 1841 34.797401 29.666667 6.666667 29.666667 0.466859 361.25 356.633333 -205.916667 -205.916667
18 r365 M F10005_SI 0 0.508 2313 385 386 3084 58.291790 5.666667 6.666667 5.666667 0.500649 361.25 356.633333 18.083333 18.083333
19 r373 M F10005_SI 0 0.450 1546 146 113 1805 34.116952 39.666667 6.666667 39.666667 0.436293 361.25 356.633333 -254.916667 -254.916667
20 r374 M F10005_SI 0 0.412 1703 231 155 2089 39.484938 82.666667 6.666667 82.666667 0.401554 361.25 356.633333 -212.916667 -212.916667
21 r380 M F10005_SI 0 0.472 1584 295 254 2133 40.316598 47.666667 6.666667 47.666667 0.462659 361.25 356.633333 -113.916667 -113.916667
22 r427 M F10005_SI 0 0.501 4246 522 510 5278 99.761370 18.666667 6.666667 18.666667 0.494186 361.25 356.633333 142.083333 142.083333
23 r491 M F10005_SI 0 0.484 3827 657 599 5083 96.075605 64.666667 6.666667 64.666667 0.476911 361.25 356.633333 231.083333 231.083333
24 r517 M F10005_SI 0 0.480 2591 580 520 3691 69.764914 66.666667 6.666667 66.666667 0.472727 361.25 356.633333 152.083333 152.083333
25 r732 M F10005_SI 0 0.471 1281 276 237 1794 33.909037 45.666667 6.666667 45.666667 0.461988 361.25 356.633333 -130.916667 -130.916667
26 r737 M F10005_SI 0 0.566 1576 126 156 1858 35.118724 -23.333333 6.666667 23.333333 0.553191 361.25 356.633333 -211.916667 -211.916667
27 r799 M F10005_SI 0 0.487 2798 520 480 3798 71.787360 46.666667 6.666667 46.666667 0.480000 361.25 356.633333 112.083333 112.083333
28 r820 M F10005_SI 0 0.442 1880 236 180 2296 43.397519 62.666667 6.666667 62.666667 0.432692 361.25 356.633333 -187.916667 -187.916667
29 r85 M F10005_SI 0 0.606 1086 131 191 1408 26.613113 -53.333333 6.666667 53.333333 0.593168 361.25 356.633333 -176.916667 -176.916667
30 w114 M F10005_SI 0 0.524 1042 230 243 1515 28.635558 -6.333333 6.666667 6.333333 0.513742 361.25 356.633333 -124.916667 -124.916667
31 w38 M F10005_SI 1 0.537 2046 444 501 2991 56.533963 -50.333333 6.666667 50.333333 0.530159 361.25 356.633333 133.083333 133.083333
32 w47 M F10005_SI 0 0.504 5492 1425 1417 8334 157.523922 14.666667 6.666667 14.666667 0.498593 361.25 356.633333 1049.083333 1049.083333
33 w52 M F10005_SI 0 0.501 3848 407 397 4652 87.929120 16.666667 6.666667 16.666667 0.493781 361.25 356.633333 29.083333 29.083333
34 w55 M F10005_SI 1 0.784 4191 252 882 5325 100.649734 -623.333333 6.666667 623.333333 0.777778 361.25 356.633333 514.083333 514.083333
35 w59 M F10005_SI 0 0.481 1429 367 329 2125 40.165387 44.666667 6.666667 44.666667 0.472701 361.25 356.633333 -38.916667 -38.916667
36 w64 M F10005_SI 0 0.467 2153 284 239 2676 50.580035 51.666667 6.666667 51.666667 0.456979 361.25 356.633333 -128.916667 -128.916667
37 w68 M F10005_SI 0 0.460 1915 371 305 2591 48.973420 72.666667 6.666667 72.666667 0.451183 361.25 356.633333 -62.916667 -62.916667
38 w76 M F10005_SI 0 0.578 1757 286 376 2419 45.722386 -83.333333 6.666667 83.333333 0.567976 361.25 356.633333 8.083333 8.083333
39 w79 M F10005_SI 0 0.611 2596 384 583 3563 67.345540 -192.333333 6.666667 192.333333 0.602896 361.25 356.633333 215.083333 215.083333
65860 r101 V F10005_SI 1 0.461 4444 454 377 5275 99.704666 74.433333 -2.566667 74.433333 0.453670 361.25 365.866667 18.316667 18.316667
65861 r280 V F10005_SI 0 0.481 1472 236 210 1918 36.252806 23.433333 -2.566667 23.433333 0.470852 361.25 365.866667 -148.683333 -148.683333
65862 r315 V F10005_SI 1 0.436 2754 674 508 3936 74.395747 163.433333 -2.566667 163.433333 0.429780 361.25 365.866667 149.316667 149.316667
65863 r324 V F10005_SI 0 0.441 2473 331 252 3056 57.762552 76.433333 -2.566667 76.433333 0.432247 361.25 365.866667 -106.683333 -106.683333
65864 r335 V F10005_SI 0 0.498 1359 185 175 1719 32.491435 7.433333 -2.566667 7.433333 0.486111 361.25 365.866667 -183.683333 -183.683333
65865 r340 V F10005_SI 0 0.511 1661 166 165 1992 37.651506 -1.566667 -2.566667 1.566667 0.498489 361.25 365.866667 -193.683333 -193.683333
65866 r357 V F10005_SI 0 0.520 2373 322 335 3030 57.271116 -15.566667 -2.566667 15.566667 0.509893 361.25 365.866667 -23.683333 -23.683333
65867 r358 V F10005_SI 0 0.526 2075 238 252 2565 48.481985 -16.566667 -2.566667 16.566667 0.514286 361.25 365.866667 -106.683333 -106.683333
65868 r365 V F10005_SI 0 0.500 3119 489 474 4082 77.155346 12.433333 -2.566667 12.433333 0.492212 361.25 365.866667 115.316667 115.316667
65869 r373 V F10005_SI 0 0.426 3498 371 266 4135 78.157118 102.433333 -2.566667 102.433333 0.417582 361.25 365.866667 -92.683333 -92.683333
65870 r374 V F10005_SI 0 0.527 2930 401 433 3764 71.144714 -34.566667 -2.566667 34.566667 0.519185 361.25 365.866667 74.316667 74.316667
65871 r380 V F10005_SI 0 0.477 1724 371 327 2422 45.779090 41.433333 -2.566667 41.433333 0.468481 361.25 365.866667 -31.683333 -31.683333
65872 r427 V F10005_SI 0 0.479 3023 367 326 3716 70.237448 38.433333 -2.566667 38.433333 0.470418 361.25 365.866667 -32.683333 -32.683333
65873 r491 V F10005_SI 0 0.517 2550 448 465 3463 65.455405 -19.566667 -2.566667 19.566667 0.509310 361.25 365.866667 106.316667 106.316667
65874 r517 V F10005_SI 0 0.425 1393 363 258 2014 38.067336 102.433333 -2.566667 102.433333 0.415459 361.25 365.866667 -100.683333 -100.683333
65875 r732 V F10005_SI 0 0.532 1126 186 202 1514 28.616657 -18.566667 -2.566667 18.566667 0.520619 361.25 365.866667 -156.683333 -156.683333
65876 r737 V F10005_SI 0 0.457 1353 148 118 1619 30.601299 27.433333 -2.566667 27.433333 0.443609 361.25 365.866667 -240.683333 -240.683333
65877 r799 V F10005_SI 0 0.488 1609 272 250 2131 40.278795 19.433333 -2.566667 19.433333 0.478927 361.25 365.866667 -108.683333 -108.683333
65878 r820 V F10005_SI 0 0.506 2337 248 245 2830 53.490845 0.433333 -2.566667 0.433333 0.496957 361.25 365.866667 -113.683333 -113.683333
65879 r85 V F10005_SI 0 0.562 1859 234 289 2382 45.023036 -57.566667 -2.566667 57.566667 0.552581 361.25 365.866667 -69.683333 -69.683333
65880 w114 V F10005_SI 0 0.397 1095 472 300 1867 35.288836 169.433333 -2.566667 169.433333 0.388601 361.25 365.866667 -58.683333 -58.683333
65881 w38 V F10005_SI 0 0.514 3793 704 727 5224 98.740697 -25.566667 -2.566667 25.566667 0.508036 361.25 365.866667 368.316667 368.316667
65882 w47 V F10005_SI 0 0.495 2971 772 737 4480 84.678086 32.433333 -2.566667 32.433333 0.488403 361.25 365.866667 378.316667 378.316667
65883 w52 V F10005_SI 0 0.495 2136 225 212 2573 48.633196 10.433333 -2.566667 10.433333 0.485126 361.25 365.866667 -146.683333 -146.683333
65884 w55 V F10005_SI 1 0.773 3809 228 750 4787 90.480803 -524.566667 -2.566667 524.566667 0.766871 361.25 365.866667 391.316667 391.316667
65885 w59 V F10005_SI 0 0.567 1531 329 417 2277 43.038393 -90.566667 -2.566667 90.566667 0.558981 361.25 365.866667 58.316667 58.316667
65886 w64 V F10005_SI 0 0.486 3494 423 387 4304 81.351447 33.433333 -2.566667 33.433333 0.477778 361.25 365.866667 28.316667 28.316667
65887 w68 V F10005_SI 1 0.438 2826 639 487 3952 74.698169 149.433333 -2.566667 149.433333 0.432504 361.25 365.866667 128.316667 128.316667
65888 w76 V F10005_SI 0 0.514 2561 475 488 3524 66.608387 -15.566667 -2.566667 15.566667 0.506750 361.25 365.866667 129.316667 129.316667
65889 w79 V F10005_SI 1 0.633 2754 392 654 3800 71.825162 -264.566667 -2.566667 264.566667 0.625239 361.25 365.866667 295.316667 295.316667

In [207]:
df['trans_line1'] = df['sum_line'] - df['cis_line'] - df['trans_tester1']
df['trans_line2'] = df['sum_tester'] - df['cis_tester'] - df['trans_tester1']

df['trans_line3'] = 2 * (df['sum_tester'] - df['cis_tester'] - df['trans_tester1'] / 2)
df


/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()
Out[207]:
line mating_status fusion_id flag_AI_combined q5_mean_theta sum_both sum_line sum_tester sum_total mean_apn cis_line cis_tester mag_cis prop trans_tester1 trans_tester2 trans_line1 trans_line2 trans_line3
10 r101 M F10005_SI 0 0.475 1274 155 133 1562 29.523922 28.666667 6.666667 28.666667 0.461806 361.25 356.633333 -234.916667 -234.916667 -108.583333
11 r280 M F10005_SI 0 0.545 1208 126 143 1477 27.917307 -10.333333 6.666667 10.333333 0.531599 361.25 356.633333 -224.916667 -224.916667 -88.583333
12 r315 M F10005_SI 0 0.490 1231 237 218 1686 31.867690 25.666667 6.666667 25.666667 0.479121 361.25 356.633333 -149.916667 -149.916667 61.416667
13 r324 M F10005_SI 1 0.450 2554 349 276 3179 60.087419 79.666667 6.666667 79.666667 0.441600 361.25 356.633333 -91.916667 -91.916667 177.416667
14 r335 M F10005_SI 0 0.514 1339 215 217 1771 33.474306 4.666667 6.666667 4.666667 0.502315 361.25 356.633333 -150.916667 -150.916667 59.416667
15 r340 M F10005_SI 1 0.241 1897 332 102 2331 44.059067 236.666667 6.666667 236.666667 0.235023 361.25 356.633333 -265.916667 -265.916667 -170.583333
16 r357 M F10005_SI 1 0.542 2797 358 408 3563 67.345540 -43.333333 6.666667 43.333333 0.532637 361.25 356.633333 40.083333 40.083333 441.416667
17 r358 M F10005_SI 0 0.480 1494 185 162 1841 34.797401 29.666667 6.666667 29.666667 0.466859 361.25 356.633333 -205.916667 -205.916667 -50.583333
18 r365 M F10005_SI 0 0.508 2313 385 386 3084 58.291790 5.666667 6.666667 5.666667 0.500649 361.25 356.633333 18.083333 18.083333 397.416667
19 r373 M F10005_SI 0 0.450 1546 146 113 1805 34.116952 39.666667 6.666667 39.666667 0.436293 361.25 356.633333 -254.916667 -254.916667 -148.583333
20 r374 M F10005_SI 0 0.412 1703 231 155 2089 39.484938 82.666667 6.666667 82.666667 0.401554 361.25 356.633333 -212.916667 -212.916667 -64.583333
21 r380 M F10005_SI 0 0.472 1584 295 254 2133 40.316598 47.666667 6.666667 47.666667 0.462659 361.25 356.633333 -113.916667 -113.916667 133.416667
22 r427 M F10005_SI 0 0.501 4246 522 510 5278 99.761370 18.666667 6.666667 18.666667 0.494186 361.25 356.633333 142.083333 142.083333 645.416667
23 r491 M F10005_SI 0 0.484 3827 657 599 5083 96.075605 64.666667 6.666667 64.666667 0.476911 361.25 356.633333 231.083333 231.083333 823.416667
24 r517 M F10005_SI 0 0.480 2591 580 520 3691 69.764914 66.666667 6.666667 66.666667 0.472727 361.25 356.633333 152.083333 152.083333 665.416667
25 r732 M F10005_SI 0 0.471 1281 276 237 1794 33.909037 45.666667 6.666667 45.666667 0.461988 361.25 356.633333 -130.916667 -130.916667 99.416667
26 r737 M F10005_SI 0 0.566 1576 126 156 1858 35.118724 -23.333333 6.666667 23.333333 0.553191 361.25 356.633333 -211.916667 -211.916667 -62.583333
27 r799 M F10005_SI 0 0.487 2798 520 480 3798 71.787360 46.666667 6.666667 46.666667 0.480000 361.25 356.633333 112.083333 112.083333 585.416667
28 r820 M F10005_SI 0 0.442 1880 236 180 2296 43.397519 62.666667 6.666667 62.666667 0.432692 361.25 356.633333 -187.916667 -187.916667 -14.583333
29 r85 M F10005_SI 0 0.606 1086 131 191 1408 26.613113 -53.333333 6.666667 53.333333 0.593168 361.25 356.633333 -176.916667 -176.916667 7.416667
30 w114 M F10005_SI 0 0.524 1042 230 243 1515 28.635558 -6.333333 6.666667 6.333333 0.513742 361.25 356.633333 -124.916667 -124.916667 111.416667
31 w38 M F10005_SI 1 0.537 2046 444 501 2991 56.533963 -50.333333 6.666667 50.333333 0.530159 361.25 356.633333 133.083333 133.083333 627.416667
32 w47 M F10005_SI 0 0.504 5492 1425 1417 8334 157.523922 14.666667 6.666667 14.666667 0.498593 361.25 356.633333 1049.083333 1049.083333 2459.416667
33 w52 M F10005_SI 0 0.501 3848 407 397 4652 87.929120 16.666667 6.666667 16.666667 0.493781 361.25 356.633333 29.083333 29.083333 419.416667
34 w55 M F10005_SI 1 0.784 4191 252 882 5325 100.649734 -623.333333 6.666667 623.333333 0.777778 361.25 356.633333 514.083333 514.083333 1389.416667
35 w59 M F10005_SI 0 0.481 1429 367 329 2125 40.165387 44.666667 6.666667 44.666667 0.472701 361.25 356.633333 -38.916667 -38.916667 283.416667
36 w64 M F10005_SI 0 0.467 2153 284 239 2676 50.580035 51.666667 6.666667 51.666667 0.456979 361.25 356.633333 -128.916667 -128.916667 103.416667
37 w68 M F10005_SI 0 0.460 1915 371 305 2591 48.973420 72.666667 6.666667 72.666667 0.451183 361.25 356.633333 -62.916667 -62.916667 235.416667
38 w76 M F10005_SI 0 0.578 1757 286 376 2419 45.722386 -83.333333 6.666667 83.333333 0.567976 361.25 356.633333 8.083333 8.083333 377.416667
39 w79 M F10005_SI 0 0.611 2596 384 583 3563 67.345540 -192.333333 6.666667 192.333333 0.602896 361.25 356.633333 215.083333 215.083333 791.416667
65860 r101 V F10005_SI 1 0.461 4444 454 377 5275 99.704666 74.433333 -2.566667 74.433333 0.453670 361.25 365.866667 18.316667 18.316667 397.883333
65861 r280 V F10005_SI 0 0.481 1472 236 210 1918 36.252806 23.433333 -2.566667 23.433333 0.470852 361.25 365.866667 -148.683333 -148.683333 63.883333
65862 r315 V F10005_SI 1 0.436 2754 674 508 3936 74.395747 163.433333 -2.566667 163.433333 0.429780 361.25 365.866667 149.316667 149.316667 659.883333
65863 r324 V F10005_SI 0 0.441 2473 331 252 3056 57.762552 76.433333 -2.566667 76.433333 0.432247 361.25 365.866667 -106.683333 -106.683333 147.883333
65864 r335 V F10005_SI 0 0.498 1359 185 175 1719 32.491435 7.433333 -2.566667 7.433333 0.486111 361.25 365.866667 -183.683333 -183.683333 -6.116667
65865 r340 V F10005_SI 0 0.511 1661 166 165 1992 37.651506 -1.566667 -2.566667 1.566667 0.498489 361.25 365.866667 -193.683333 -193.683333 -26.116667
65866 r357 V F10005_SI 0 0.520 2373 322 335 3030 57.271116 -15.566667 -2.566667 15.566667 0.509893 361.25 365.866667 -23.683333 -23.683333 313.883333
65867 r358 V F10005_SI 0 0.526 2075 238 252 2565 48.481985 -16.566667 -2.566667 16.566667 0.514286 361.25 365.866667 -106.683333 -106.683333 147.883333
65868 r365 V F10005_SI 0 0.500 3119 489 474 4082 77.155346 12.433333 -2.566667 12.433333 0.492212 361.25 365.866667 115.316667 115.316667 591.883333
65869 r373 V F10005_SI 0 0.426 3498 371 266 4135 78.157118 102.433333 -2.566667 102.433333 0.417582 361.25 365.866667 -92.683333 -92.683333 175.883333
65870 r374 V F10005_SI 0 0.527 2930 401 433 3764 71.144714 -34.566667 -2.566667 34.566667 0.519185 361.25 365.866667 74.316667 74.316667 509.883333
65871 r380 V F10005_SI 0 0.477 1724 371 327 2422 45.779090 41.433333 -2.566667 41.433333 0.468481 361.25 365.866667 -31.683333 -31.683333 297.883333
65872 r427 V F10005_SI 0 0.479 3023 367 326 3716 70.237448 38.433333 -2.566667 38.433333 0.470418 361.25 365.866667 -32.683333 -32.683333 295.883333
65873 r491 V F10005_SI 0 0.517 2550 448 465 3463 65.455405 -19.566667 -2.566667 19.566667 0.509310 361.25 365.866667 106.316667 106.316667 573.883333
65874 r517 V F10005_SI 0 0.425 1393 363 258 2014 38.067336 102.433333 -2.566667 102.433333 0.415459 361.25 365.866667 -100.683333 -100.683333 159.883333
65875 r732 V F10005_SI 0 0.532 1126 186 202 1514 28.616657 -18.566667 -2.566667 18.566667 0.520619 361.25 365.866667 -156.683333 -156.683333 47.883333
65876 r737 V F10005_SI 0 0.457 1353 148 118 1619 30.601299 27.433333 -2.566667 27.433333 0.443609 361.25 365.866667 -240.683333 -240.683333 -120.116667
65877 r799 V F10005_SI 0 0.488 1609 272 250 2131 40.278795 19.433333 -2.566667 19.433333 0.478927 361.25 365.866667 -108.683333 -108.683333 143.883333
65878 r820 V F10005_SI 0 0.506 2337 248 245 2830 53.490845 0.433333 -2.566667 0.433333 0.496957 361.25 365.866667 -113.683333 -113.683333 133.883333
65879 r85 V F10005_SI 0 0.562 1859 234 289 2382 45.023036 -57.566667 -2.566667 57.566667 0.552581 361.25 365.866667 -69.683333 -69.683333 221.883333
65880 w114 V F10005_SI 0 0.397 1095 472 300 1867 35.288836 169.433333 -2.566667 169.433333 0.388601 361.25 365.866667 -58.683333 -58.683333 243.883333
65881 w38 V F10005_SI 0 0.514 3793 704 727 5224 98.740697 -25.566667 -2.566667 25.566667 0.508036 361.25 365.866667 368.316667 368.316667 1097.883333
65882 w47 V F10005_SI 0 0.495 2971 772 737 4480 84.678086 32.433333 -2.566667 32.433333 0.488403 361.25 365.866667 378.316667 378.316667 1117.883333
65883 w52 V F10005_SI 0 0.495 2136 225 212 2573 48.633196 10.433333 -2.566667 10.433333 0.485126 361.25 365.866667 -146.683333 -146.683333 67.883333
65884 w55 V F10005_SI 1 0.773 3809 228 750 4787 90.480803 -524.566667 -2.566667 524.566667 0.766871 361.25 365.866667 391.316667 391.316667 1143.883333
65885 w59 V F10005_SI 0 0.567 1531 329 417 2277 43.038393 -90.566667 -2.566667 90.566667 0.558981 361.25 365.866667 58.316667 58.316667 477.883333
65886 w64 V F10005_SI 0 0.486 3494 423 387 4304 81.351447 33.433333 -2.566667 33.433333 0.477778 361.25 365.866667 28.316667 28.316667 417.883333
65887 w68 V F10005_SI 1 0.438 2826 639 487 3952 74.698169 149.433333 -2.566667 149.433333 0.432504 361.25 365.866667 128.316667 128.316667 617.883333
65888 w76 V F10005_SI 0 0.514 2561 475 488 3524 66.608387 -15.566667 -2.566667 15.566667 0.506750 361.25 365.866667 129.316667 129.316667 619.883333
65889 w79 V F10005_SI 1 0.633 2754 392 654 3800 71.825162 -264.566667 -2.566667 264.566667 0.625239 361.25 365.866667 295.316667 295.316667 951.883333

In [ ]: