Figure 1

In [1]:

    

cd ..


    

    




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

In [2]:

    

import NotebookImport
from DX_screen import *


    

    




/cellar/users/agross/anaconda2/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead.
  "You should import from nbformat instead.", ShimWarning)






    





importing IPython notebook from DX_screen






    





importing IPython notebook from Imports






    





importing IPython notebook from /cellar/users/agross/anaconda2/lib/python2.7/site-packages/MethylTools/Probe_Annotations

In [3]:

    

microarray = pd.read_hdf(MICROARRAY_STORE, 'data')
tissue = pd.read_hdf(MICROARRAY_STORE, 'tissue')

dx = microarray.xs('01',1,1) - microarray.xs('11',1,1)
tt = tissue[:,'01'].replace('COAD','COADREAD')
pos = (dx>0).groupby(tt, axis=1).sum() 
count = dx.groupby(tt, axis=1).count().replace(0, np.nan)
count = count[count.sum(1) > 500]
frac_df = 1.*pos / count
frac_microarray = frac_df.mean(1)


    

In [4]:

    

pts = ti(microarray.groupby(level=0, axis=1).size() > 1)


    

In [5]:

    

cc = microarray.columns
pts = list({p[0] for p in cc if (p[0], '01') in cc and (p[0], '11') in cc})


    

In [6]:

    

microarray.shape


    

    
Out[6]:





(20383, 2904)

In [7]:

    

frac_microarray.name = 'GEO mean'
df = frac_df
df = df.join(frac_microarray).ix[dx_rna.frac.index].dropna(0, how='all')
df.to_csv(FIGDIR + 'geo.csv')


    

In [8]:

    

tt = tissue.groupby(lambda s: s[0].split('_')[0]).first()
cc = pd.DataFrame([(c.split('_')[0], c) for c in pts])[0].value_counts()
pd.concat([tt, cc], 1)


    

    
Out[8]:






  

    

      

      
0
      
1
    
  
  

    

      
GSE14520
      
     LIHC
      
 213
    
    

      
GSE25097
      
     LIHC
      
 243
    
    

      
GSE33532
      
    NSCLC
      
  20
    
    

      
GSE39004
      
     BRCA
      
  45
    
    

      
GSE39791
      
     LIHC
      
  72
    
    

      
GSE41258
      
 COADREAD
      
  46
    
    

      
GSE44076
      
     COAD
      
  98
    
    

      
GSE5364
      
   PANCAN
      
  71
    
    

      
GSE53757
      
     KIRC
      
  72
    
    

      
GSE62872
      
     PRAD
      
 160
    
    

      
GSE68468
      
     COAD
      
  44
    
  

In [9]:

    

match_series(frac_microarray.dropna(), dx_rna.frac.dropna())[0].shape


    

    
Out[9]:





(16825,)

In [10]:

    

fig, ax = subplots(figsize=(4,4))
s1, s2 = match_series(dx_rna.frac, frac_microarray)
plot_regression(s1, s2, density=True, rad=.02, ax=ax, rasterized=True,
                line_args={'lw':0})
ax.set_ylabel("GEO microarray")
ax.set_xlabel("TCGA mRNASeq")
ann = ax.get_children()[4]
ann.set_text(ann.get_text().split()[0])
ax.set_xticks([0, .5, 1])
ax.set_yticks([0, .5, 1])
fig.tight_layout()
fig.savefig(FIGDIR + 'geo_fup.png', dpi=300)


    

In [11]:

    

fig = plt.figure(figsize=(7, 3))
ax1 = plt.subplot2grid((1, 7), (0, 0), colspan=4)
ax2 = plt.subplot2grid((1, 7), (0, 4), colspan=3)
fig_1e(ax1)


ax=ax2
s1, s2 = match_series(dx_rna.frac, frac_microarray)
plot_regression(s1, s2, density=True, rad=.02, ax=ax, rasterized=True,
                line_args={'lw':0})
ax.set_ylabel("GEO microarray")
ax.set_xlabel("TCGA mRNASeq")
ann = ax.get_children()[4]
ann.set_text(ann.get_text().split()[0])
ax.set_xticks([0, .5, 1])
ax.set_yticks([0, .5, 1])
fig.tight_layout()
fig.savefig(FIGDIR + 'dx_fig1.pdf', transparent=True)