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Exploration of GABA Receptors





In [1]:



    


cd ..



    


















    






/cellar/users/agross/TCGA_Code/DX/Notebooks

















In [2]:



    


import NotebookImport
from metaPCNA import *



    


















    






importing IPython notebook from metaPCNA









    






importing IPython notebook from Imports









    






/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas/rpy/__init__.py:11: FutureWarning: The pandas.rpy module is deprecated and will be removed in a future version. We refer to external packages like rpy2. 
See here for a guide on how to port your code to rpy2: http://pandas.pydata.org/pandas-docs/stable/r_interface.html
  FutureWarning)










    






importing IPython notebook from Global_Parameters
















In [4]:



    


switch_plot('FOXM1')



    


















    






---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-6b879808788c> in <module>()
----> 1 switch_plot('FOXM1')

/cellar/users/agross/TCGA_Code/DX/Notebooks/metaPCNA.ipynb in switch_plot(g)

/cellar/users/agross/TCGA_Code/DX/Notebooks/metaPCNA.ipynb in <lambda>(s)

/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas/core/generic.pyc in stat_func(self, axis, skipna, level, numeric_only, **kwargs)
   4253                                               skipna=skipna)
   4254                 return self._reduce(f, name, axis=axis,
-> 4255                                     skipna=skipna, numeric_only=numeric_only)
   4256             stat_func.__name__ = name
   4257             return stat_func

/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas/core/series.pyc in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
   2078         if isinstance(delegate, np.ndarray):
   2079             # Validate that 'axis' is consistent with Series's single axis.
-> 2080             self._get_axis_number(axis)
   2081             if numeric_only:
   2082                 raise NotImplementedError(

/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_axis_number(self, axis)
    301                 pass
    302         raise ValueError('No axis named {0} for object type {1}'
--> 303                          .format(axis, type(self)))
    304 
    305     def _get_axis_name(self, axis):

ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>

GABRD is the top hit for tumor-associated, proliferation independent genes.

  • Kidney cancer associated with hypertension link
  • GABA could have anti-hypertensive effects link 




In [5]:



    


f_win.order().tail()



    


















    
Out[5]:








LOC283914    0.41
CST2         0.42
C16orf13     0.43
GABRD        0.47
SEMA5B       0.48
Name: fraction overexpressed\n(detrended), dtype: float64

















In [6]:



    


switch_plot('GABRD')



    


















    






---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-6-96ef4c1c07d5> in <module>()
----> 1 switch_plot('GABRD')

/cellar/users/agross/TCGA_Code/DX/Notebooks/metaPCNA.ipynb in switch_plot(g)

/cellar/users/agross/TCGA_Code/DX/Notebooks/metaPCNA.ipynb in <lambda>(s)

/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas/core/generic.pyc in stat_func(self, axis, skipna, level, numeric_only, **kwargs)
   4253                                               skipna=skipna)
   4254                 return self._reduce(f, name, axis=axis,
-> 4255                                     skipna=skipna, numeric_only=numeric_only)
   4256             stat_func.__name__ = name
   4257             return stat_func

/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas/core/series.pyc in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
   2078         if isinstance(delegate, np.ndarray):
   2079             # Validate that 'axis' is consistent with Series's single axis.
-> 2080             self._get_axis_number(axis)
   2081             if numeric_only:
   2082                 raise NotImplementedError(

/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_axis_number(self, axis)
    301                 pass
    302         raise ValueError('No axis named {0} for object type {1}'
--> 303                          .format(axis, type(self)))
    304 
    305     def _get_axis_name(self, axis):

ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>









    
























In [7]:



    


gabra = [g for g in rna_df.index if g.startswith('GABRA')]



    











In [8]:



    


gab_s = rna_df.ix[gabra].sum()



    











In [9]:



    


cc = codes[codes.isin(ti(codes.value_counts() > 50))]



    











In [10]:



    


series_scatter(rna_df.ix['GABRD'][:,'11'].groupby(cc).mean(), 
                gab_s[:,'11'].groupby(cc).mean())



    


















    
























In [11]:



    


series_scatter(rna_df.ix['GABRD'][:,'01'].groupby(cc).mean(), 
                gab_s[:,'01'].groupby(cc).mean())



    


















    
























In [12]:



    


gabr = [g for g in rna_df.index if g.startswith('GABR')]
f = dx_rna.ix[gabr].dropna()
f.join(f_win).sort(f_win.name)



    


















    
Out[12]:










  
    

      
      num_ox
      num_dx
      frac
      p
      fraction overexpressed
(detrended)
    

  
  
    

      GABRA2
      141
      654
      0.22
      1.59e-50
      -0.31
    

    

      GABRA4
      145
      645
      0.22
      1.39e-46
      -0.27
    

    

      GABRB1
      201
      629
      0.32
      8.11e-20
      -0.16
    

    

      GABRP
      248
      684
      0.36
      6.25e-13
      -0.15
    

    

      GABRB3
      218
      684
      0.32
      1.29e-21
      -0.12
    

    

      GABRG1
      189
      635
      0.30
      6.76e-25
      -0.12
    

    

      GABRG3
      273
      632
      0.43
      7.10e-04
      -0.12
    

    

      GABRA3
      333
      602
      0.55
      1.02e-02
      -0.10
    

    

      GABRA5
      281
      617
      0.46
      2.96e-02
      -0.09
    

    

      GABRB2
      245
      678
      0.36
      4.92e-13
      -0.06
    

    

      GABRR3
      161
      339
      0.47
      3.85e-01
      -0.05
    

    

      GABRR2
      319
      678
      0.47
      1.34e-01
      -0.04
    

    

      GABRG2
      271
      504
      0.54
      9.92e-02
      -0.02
    

    

      GABRR1
      358
      631
      0.57
      8.12e-04
      0.03
    

    

      GABRE
      343
      684
      0.50
      9.70e-01
      0.04
    

    

      GABRA1
      272
      546
      0.50
      9.66e-01
      0.05
    

    

      GABRQ
      435
      637
      0.68
      1.43e-20
      0.10
    

    

      GABRD
      607
      684
      0.89
      4.59e-103
      0.47
    

  




















In [13]:



    


paired_boxplot_tumor_normal(matched_rna.ix[gabr].clip(-9,10).T, 
                            order=list(f.frac.order().index))
prettify_ax(plt.gca())



    


















    
























In [14]:



    


paired_boxplot_tumor_normal(matched_rna.ix[gabr, ti(codes.str.startswith('KIRC'))].clip(-9,10).T, 
                            order=list(f.frac.order().index), sig=False)
prettify_ax(plt.gca())



    


















    
























In [15]:



    


o = ['GABRA1','GABRA2', 'GABRA3', 'GABRA4', 'GABRA5',
     'GABRB1', 'GABRB2', 'GABRB3',
     'GABRR1', 'GABRR2', 'GABRR3',
     'GABRD','GABRE','GABRP','GABRQ']



    











In [16]:



    


fig = plt.figure(figsize=(15, 7.5))
ax1 = plt.subplot2grid((5, 7), (0, 0), colspan=2, rowspan=3)
ax2 = plt.subplot2grid((5, 7), (0, 2), colspan=5, rowspan=3)
ax3 = plt.subplot2grid((5, 7), (3, 0), colspan=7, rowspan=2)

ax = ax1
cc = pd.Series(matched_tn.columns.get_level_values(1), 
               matched_tn.columns).map({'01': colors[0],
                                        '11': colors[1]})
series_scatter(meta_pcna_all, matched_tn.ix['GABRD'], zorder=1,
               ax=ax, ann=None, s=20, edgecolor='grey', linewidths=1,
               alpha=.6, color=cc)

line_args = {'lw':5, 'solid_capstyle':'round'}
l1, l2 = process_line_args(line_args)
x,y = meta_pcna_all[:,'01'], matched_tn.ix['GABRD'][:,'01']
reg = linear_regression(x,y)
line_me(reg['slope'], reg['intercept'], start=x.min(), end=x.max(),
            ax=ax, **l1)

x,y = meta_pcna_all[:,'11'], matched_tn.ix['GABRD'][:,'11']
reg = linear_regression(x,y)
line_me(reg['slope'], reg['intercept'], start=x.min(), end=x.max(),
            ax=ax, **l1)

ax1.legend(['Tumor','Normal'], loc='upper left', scatterpoints=1, markerscale=1.5,
          fancybox=True)
ax1.set_xlabel('Proliferation Score')
prettify_ax(ax1)

paired_bp_tn_split(matched_rna.ix['GABRD'], codes, ax=ax2)
ax2.set_ylabel('')
ax1.set_ylabel('GABRD mRNA Expression')

paired_boxplot_tumor_normal(matched_rna.ix[gabr, ti(codes.str.startswith('KIRC'))].clip(-9,10).T, 
                            order=o, sig=False, ax=ax3)
prettify_ax(ax3)
ax3.get_legend().set_visible(False)
ax3.set_ylabel('mRNA Expression')
fig.tight_layout()
fig.savefig(FIGDIR + 'GABA_F4.pdf')



    


















    
























In [17]:



    


fig = plt.figure(figsize=(15, 7.5))
ax1 = plt.subplot2grid((5, 7), (0, 0), colspan=2, rowspan=3)
ax2 = plt.subplot2grid((5, 7), (0, 2), colspan=5, rowspan=3)
ax3 = plt.subplot2grid((5, 7), (3, 0), colspan=7, rowspan=2)

series_scatter(meta_pcna_all[:,'01'], matched_tn.ix['GABRD'][:,'01'], ax=ax1,
               ann=None, s=20, edgecolor='grey', linewidths=1, 
               alpha=.8, color=colors[0])
series_scatter(meta_pcna_all[:,'11'], matched_tn.ix['GABRD'][:,'11'], 
               ax=ax1, ann=None, s=20, edgecolor='grey', linewidths=1,
               alpha=.8, color=colors[1])
ax1.legend(['Tumor','Normal'], loc='upper left', scatterpoints=1, markerscale=1.5,
          fancybox=True)
ax1.set_xlabel('Proliferation Score')
prettify_ax(ax1)

paired_bp_tn_split(matched_rna.ix['GABRD'], codes, ax=ax2)
ax2.set_ylabel('')
ax1.set_ylabel('GABRD mRNA Expression')

paired_boxplot_tumor_normal(matched_rna.ix[gabr, ti(codes.str.startswith('KIRC'))].clip(-9,10).T, 
                            order=o, sig=False, ax=ax3)
prettify_ax(ax3)
ax3.get_legend().set_visible(False)
ax3.set_ylabel('mRNA Expression')
fig.tight_layout()
fig.savefig(FIGDIR + 'GABA_F4.pdf')



    


















    
























In [18]:



    


cc = codes.ix[matched_tn.columns.get_level_values(0)].value_counts()
cc



    


















    
Out[18]:








BRCA    224
KIRC    144
THCA    118
LUAD    116
PRAD    104
LUSC    102
LIHC    100
HNSC     86
COAD     82
KIRP     64
KICH     50
UCEC     46
BLCA     38
READ     18
CESC      6
dtype: int64

















In [21]:



    


fig, axs = subplots(7,1, figsize=(12,15), sharex=True)
letters = list(map(chr, range(97, 123)))
for i,c in enumerate(ti(cc > 99)):
    paired_boxplot_tumor_normal(matched_rna.ix[gabr, ti(codes == c)].clip(-9,10).T, 
                                order=o, sig=False, ax=axs[i])
    axs[i].set_ylabel(c + '\nmRNA expression')
for ax in axs:
    prettify_ax(ax)
    ax.get_legend().set_visible(False)
    prettify_ax(ax)
fig.tight_layout()
fig.savefig(FIGDIR + 'S5_Fig.pdf')



    


















    
























In [ ]:

Content source: theandygross/TCGA_differential_expression

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